Miracle-dz/UniFAR-FTUs huggingface dataset:
The Generation of a 17 kDa Neurotoxic Fragment: An Alternative Mechanism by which Tau Mediates β-Amyloid-Induced Neurodegeneration

Recently, we have shown that the microtubule-associated protein tau is essential for β-amyloid (Aβ)-induced neurotoxicity in hippocampal neurons. However, the mechanisms by which tau mediates Aβ-induced neurite degeneration remain poorly understood. In the present study, we analyzed whether tau cleavage played a role in these events. Our results showed that pre-aggregated Aβ induced the generation of a 17 kDa tau fragment in cultured hippocampal neurons. The generation of this fragment was preceded by the activation of calpain-1. Conversely, inhibitors of this protease, but not of caspases, completely prevented tau proteolysis leading to the generation of the 17 kDa fragment and significantly reduced Aβ-induced neuronal death. Furthermore, the expression of this fragment in cultured hippocampal neurons induced the formation of numerous varicosity-bearing tortuous processes, as well as the complete degeneration of some of those neurite processes. These results suggest that Aβ-induced neurotoxicity may be mediated, at least in part, through the calpain-mediated generation of a toxic 17 kDa tau fragment. Collectively, these results provide insight into a novel mechanism by which tau could mediate Aβ-induced neurotoxicity.
What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm

The K-means algorithm is one of the most popular clustering algorithms in current use as it is relatively fast yet simple to understand and deploy in practice. Nevertheless, its use entails certain restrictive assumptions about the data, the negative consequences of which are not always immediately apparent, as we demonstrate. While more flexible algorithms have been developed, their widespread use has been hindered by their computational and technical complexity. Motivated by these considerations, we present a flexible alternative to K-means that relaxes most of the assumptions, whilst remaining almost as fast and simple. This novel algorithm which we call MAP-DP (maximum a-posteriori Dirichlet process mixtures), is statistically rigorous as it is based on nonparametric Bayesian Dirichlet process mixture modeling. This approach allows us to overcome most of the limitations imposed by K-means. The number of clusters K is estimated from the data instead of being fixed a-priori as in K-means. In addition, while K-means is restricted to continuous data, the MAP-DP framework can be applied to many kinds of data, for example, binary, count or ordinal data. Also, it can efficiently separate outliers from the data. This additional flexibility does not incur a significant computational overhead compared to K-means with MAP-DP convergence typically achieved in the order of seconds for many practical problems. Finally, in contrast to K-means, since the algorithm is based on an underlying statistical model, the MAP-DP framework can deal with missing data and enables model testing such as cross validation in a principled way. We demonstrate the simplicity and effectiveness of this algorithm on the health informatics problem of clinical sub-typing in a cluster of diseases known as parkinsonism.
Rapid prototyping of pattern mining problems isomorphic to boolean lattices

Interesting pattern mining is an important family of data mining problems with applications in many domains. In this paper, we focus on the special class of pattern mining problems known to be dasiarepresentable as setspsila. The main contribution of this paper is to take advantage of the common theoretical background of these problems from an implementation point of view by providing efficient data structures for boolean lattice representation and several implementations of well known algorithms. By the way, these problems can be implemented with only minimal effort, i.e. programmers do not have to be aware of low level code, customized data structures and algorithms being available for free. A toolkit, called iZi, has been devised and applied to several problems such as itemset mining, constraint mining in relational databases and query rewriting in data integration systems. According to our first results, the programs obtained using our toolkit offer a very good tradeoff between performances and development simplicity. Some methodological guidelines are also provided to guide the programmers both at the theoretical level and at the code level.
Automated citation sentiment analysis: What can we learn from biomedical researchers

Automated citation sentiment analysis is a newly emerged research topic inspired by traditional citation context analysis in scientometrics and applied linguistics. The main goals of current citation sentiment analysis are to develop new tools to model scientific literature and provide authoring support for researchers in tasks like literature review. In terms of developing authoring support tools, however, current studies have not taken into consideration the behavioral patterns of researchers' literature review practice, leaving user need assessment as a missing piece in current interdisciplinary research effort. This paper analyzed biomedical researchers' need by reviewing their publications on using manual citation sentiment analysis for detecting citation bias, and discussed the differences between biomedical researchers' approach and current automated citation sentiment analysis model. These differences are expected to inform the modeling of automated citation sentiment analysis.
Extracting Emotions from

Determining emotion in a piece of text is a difficult subset of sentiment analysis, a field which has largely focused on binary classification of text as positive or negative. Classifying sentences in one of six major emotion categories (anger, disgust, fear, joy, sadness, and surprise) can make significant contributions to political polling, marketing, and stock market predictions. In this study, we focus on the first of these six emotions, anger, and develop a model to predict whether or not a reader will feel anger after reading a headline. Our model involves NLP tools stemming, named entity recognition, and part of speech tagging and a bag of words model run on a SVM with a linear kernel and a Naive Bayes Classifier with Laplace smoothing. We developed experimental models to test on smaller subsets of data including: PCA, to reduce dimensionality of data, a synonym algorithm, to expand each headline allowing our model to classify with respect to more words, and a co-occurrence model, to score each headline based on co-occurrences of words with the word ‘anger’ in the New York Times archives. We also made an emotion headline generation model with Markov chains to see if we can automatically create angry headlines. With these models, we hope to gain greater insight into methods that one can use to detect emotion within text.
A new look at calretinin‐immunoreactive amacrine cell types in the monkey retina

We have examined amacrine cells that are calretinin‐immunoreactive (‐IR) in the macaque monkey retina with the aim of classifying them into morphological and functional subtypes. There are calretinin‐IR cells in the fovea and throughout the retina. Their highest density is reached at 1.0 mm from the foveal pit (10,500 cells/mm2) and falls to 2,600/mm2 by 10 mm of eccentricity. Nearest‐neighbor statistics for the calretinin‐IR cell body distribution indicate a nonregular pattern, with a regularity index of 1.4–1.6. There is an increase or “bump” of cell density 3.5–4.0 mm from the foveal pit, corresponding to the rod photoreceptor density peak. Based on morphological differences, there appear to be three types of amacrine cell that are calretinin‐IR. To determine the types, we doubly immunolabeled retinas, from fovea to periphery, for calretinin‐IR in combination with other calcium binding proteins and inhibitory amino acid neurotransmitters. Labeling with parvalbumin and calretinin antibodies indicated that 70% of the amacrine cells were solely calretinin‐IR, and 30% contained parvalbumin‐IR as well. In the same way, 70% of the calretinin‐IR amacrine cells colocalized calbindin, but 30% were only calretinin‐IR. Among the calretinin/calbindin‐colocalized cells, there were small‐field and wide‐field types. Double labeling with antibodies to calretinin and γ‐aminobutyric acid (GABA) and to calretinin and glycine revealed the majority to be glycine‐IR, but some were GABA‐IR. The glycine‐IR population consists mainly of AII amacrine cell types, but clearly another non‐AII type is involved. The non‐AII glycine‐IR population resembles a small‐ to medium‐field diffuse type. The calretinin‐IR wide‐field type is GABAergic and corresponds to an A19 type. The central, rod‐free, fovea contains the calretinin‐IR, non‐AII glycine‐IR type and the calretinin‐IR, GABAergic type only. To learn more concerning the circuitry of the calretinin/glycine‐IR, non‐AII amacrine cell type in isolation from AII amacrine cells, we concentrated on the rod‐free fovea, where AII amacrine cells are absent. We performed a serial section electron microscopy (EM) study on four calretinin‐IR cells. They were involved with cone pathway circuitry. They got input from ON and OFF midget bipolar cells, reciprocated synapses to these bipolar cells, and provided synapses to ON‐center ganglion cells. Thus we have obtained new information on a cone pathway amacrine cell of the central monkey fovea that is involved in the midget system. J. Comp. Neurol. 453:168–184, 2002. © 2002 Wiley‐Liss, Inc.
Questioning evidence for recombination in human mitochondrial DNA.

The possibility of recombination in human mitochondrial DNA (mtDNA), raised recently by Awadalla et al . ([1][1]), holds crucial implications for many evolutionary studies ([2][2]). Here, we reexamine the data analyzed in ([1][1]), show that some of those data are likely unreliable, and suggest that
Reproducibility of Assessment of Left-Ventricular Function using Intraoperative Transesophageal Echocardiography

Reproducibility of results is an important point in assessing the utility of intraoperative transesophageal echocardiography for evaluating changes in left-ventricular function. The purpose of the present study was to define the intra- and interobserver reproducibility of the qualitative assessment of left-ventricular regional wall motion and the quantitative assessment of global left-ventricular function. In addition, the interstudy reproducibility of two examinations was tested when the probe was displaced and replaced in the esophagus. A transesophageal short-axis view at the level of the papillary muscles was obtained in 86 patients undergoing cardiac surgery. In the 80 patients with adequate images, regional wall motion was visually graded and area ejection fraction was calculated by two observers and assessment was repeated by the same observer one day later. The same observer graded wall motion differently in only 5% (24/480) of segments. Grading by two observers differed in 9% (43/480) of segments. Assessment differed by one grade at the most and in not more than 2 out of 6 segments per patient. Repeated measurements of area ejection fraction (AEF) by the same observer correlated well (r = 0.97 before and r = 0.97 after cardiopulmonary bypass) with a mean percent difference of 6%. A similarly close correlation was found for measurements of two observers (r = 0.90 and r = 0.93, respectively) with a mean percent difference of 10% for area ejection fraction. The correlation for the first and second examination in the same patient by one observer was acceptable (r = 0.78 and r = 0.80, respectively) with a mean percent difference of 15% for area ejection fraction. (ABSTRACT TRUNCATED AT 250 WORDS)
Entwicklung von Messmethoden hydraulischer Kraftstoffeigenschaften unter Hochdruck

For the design and development of fuel injection systems knowledge concerning the hydraulic properties of the fuels is important. Currently, new biofuels are being developed with unknown hydraulic and tribological properties. To aid the design process it is essential to know the viscosity and the bulk modulus depending on pressure and temperature. These properties are especially influenced by the high injection pressures. Therefore, this thesis deals with the development of a test rig to measure viscosity and bulk modulus depending on pressures up to 3000 bar. Additionally, mathematic equations with parameters are developed to describe the fuel properties. The influence of measurement accuracy and accuracy of the equation-based description are estimated and determined using reference measurements. Finally, the influence of different fuels on the injection system is approximated. Inhaltsverzeichnis i
GlycoDigest: a tool for the targeted use of exoglycosidase digestions in glycan structure determination

UNLABELLED Sequencing oligosaccharides by exoglycosidases, either sequentially or in an array format, is a powerful tool to unambiguously determine the structure of complex N- and O-link glycans. Here, we introduce GlycoDigest, a tool that simulates exoglycosidase digestion, based on controlled rules acquired from expert knowledge and experimental evidence available in GlycoBase. The tool allows the targeted design of glycosidase enzyme mixtures by allowing researchers to model the action of exoglycosidases, thereby validating and improving the efficiency and accuracy of glycan analysis. AVAILABILITY AND IMPLEMENTATION http://www.glycodigest. org.
Explicit reduced-order integral formulations of state and parameter estimation problems for a class of nonlinear systems

We propose a technique for reformulation of state and parameter estimation problems as that of matching explicitly computable definite integrals with known kernels to data. The technique applies for a class of systems of nonlinear ordinary differential equations and is aimed to exploit parallel computational streams in order to increase speed of calculations. The idea is based on the classical adaptive observers design. It has been shown that in case the data is periodic it may be possible to reduce dimensionality of the inference problem to that of the dimension of the vector of parameters entering the right-hand side of the model nonlinearly. Performance and practical implications of the method are illustrated on a benchmark model governing dynamics of voltage in generated in barnacle giant muscle.
Construction of Recommender System based on Cognitive Model for "Self-Reflection"

Every human processes a set of mental schemas for problem solving. We develop and improve these schemas by reflecting on our experiences with errors, which is a type of metacognition (Kayashima, 2008). In this study, we proposed a cognitive model of this "self-reflection" process based on Kayashima's two-layer working memory model, and developed a food recommender system using our cognitive model. In the test simulation, the users were satisfied with the foods that the system recommended, although the recommendation results were unexpected to the users. This implied the system practically worked to satisfy the user's expectation. On the other hand, the candidate recommendations which the system selected as its final output were different from those provided by the users. This suggests that the cognitive model needs improvement in terms of psychological reality.
Relevance feedback using generalized Bayesian framework with region-based optimization learning

This paper presents a generalized Bayesian framework for relevance feedback in content-based image retrieval. The proposed feedback technique is based on the Bayesian learning method and incorporates a time-varying user model into the formulation. We define the user model with two terms: a target query and a user conception. The target query is aimed to learn the common features from relevant images so as to specify the user's ideal query. The user conception is aimed to learn a parameter set to determine the time-varying matching criterion. Therefore, at each feedback step, the learning process updates not only the target distribution, but also the target query and the matching criterion. In addition, another objective of this paper is to conduct the relevance feedback on images represented in region level. We formulate the matching criterion using a weighting scheme and proposed a region clustering technique to determine the region correspondence between relevant images. With the proposed region clustering technique, we derive a representation in region level to characterize the target query. Experiments demonstrate that the proposed method combined with time-varying user model indeed achieves satisfactory results and our proposed region-based techniques further improve the retrieval accuracy.
Thermal conductivity and electrical properties of hybrid SiO2-graphene naphthenic mineral oil nanofluid as potential transformer oil

Hybrid SiO2-graphene nanoparticles were synthesised by sol gel centrifugation technique under four different pH levels ranging from 9 to 12. Stability, thermal conductivity, viscosity and electrical conductivity of hybrid SiO2-graphene and pure graphene dispersed in naphthenic oil were investigated. Nanofluids were synthesied at three different nanoparticle concentrations (0.01, 0.04 and 0.08 wt%) while the temperature was varied from 20 °C to 100 °C. Field emission scanning electron microscopy (FESEM), x-ray spectroscopy and Fourier transform infrared (FTIR) spectrometer show successful coating of SiO2 on graphene surface. The growth units and size distribution of SiO2 nanoparticles increased with pH level. Moreover, the presence of SiO2 improved the dispersion behaviour of the nanofluid as confirmed by visual observation and UV–Vis studies. Zeta potential measurements show the hybrid nanofluids at pH 11 are most stable due to its optimum amount and size of SiO2 coated on graphene surface while at pH 12 shows least stability due to precipitation. The presence of SiO2 on graphene further enhanced the thermal conductivity by 80% at pH 9. Also, the viscosity of hybrid nanofluids was higher than pure graphene based nanofluids due to increase in density and particle size. Moreover, the addition of hybrid SiO2-graphene nanoparticles significantly reduced the electrical conductivity enhancement of base fluid from 557% to 97%.
What Anaesthesia is doing to combat scientific misconduct and investigate data fabrication and falsification

Scientific misconduct is not new and neither is there any evidence that it is on the increase, despite an apparent increase in the number of retractions over the last ten years [1]. However, a recent spate of high-profile cases involving anaesthetists has firmly turned the spotlight onto our profession [2]. Anaesthesia has been and remains at the forefront of this fight against individuals and groups who bring our profession in particular and science in general into disrepute [3]. This issue of the journal contains an analysis of 32 randomised controlled trials published from 1993 to 2012 in nine anaesthetic journals [4]. This demonstrates clearly that there is a high probability that the data in these trials are not consistent with random sampling; in other words, that the studies warrant further urgent investigation.
A Review of Hough Transform and Line Segment Detection Approaches

In a wide range of image processing and computer vision problems, line segment detection is one of the most critical challenges. For more than three decades researchers have contributed to build more robust and accurate algorithms with faster performance. In this paper we review the main approaches and in particular the Hough transform and its extensions, which are among the most well-known techniques for the detection of straight lines in a digital image. This paper is based on extensive practical research and is organised into two main parts. In the first part, the HT and its major research directions and limitations are discussed. In the second part of the paper, state-of-the-art line segmentation techniques are reviewed and categorized into three main groups with fundamentally distinctive characteristics. Their relative advantages and disadvantages are compared and summarised in a table.
Realizing Greater Business Value of Contemporary RFID Systems

RFID has been gaining momentum in the last few years. To-date many enterprises, in particular suppliers, have found themselves in a position that they must implement RFID to comply with well-publicized high-profile mandates (e.g. Wal-Mart and US DoD). Therefore, many early RFID solutions just did enough to satisfy the commercial and contractual requirements. The solutions mainly focused on technical features like tags, readers, basic tag-reader communications, etc. Future RFID implementations such as in supply chain management and businesses need to include a more comprehensive and inter-relating set of capabilities and to embrace a systematic and integrated architectural approach in order to take advantage of the long-term benefits of RFID technology. This paper proposes a holistic architectural approach that can be used to gain an understanding of the "next big thing" technology, the architectural considerations for designing and implementing successful RFID systems to fully realize greater business value. It is grounded in industry observations and real-world enterprise architecture practice.
Fractal response of physiological signals to stress conditions, environmental changes, and neurodegenerative diseases

In the past two decades the biomedical community has witnessed several applications of nonlinear system theory to the analysis of biomedical time series and the development of nonlinear dynamic models. The development of this area of medicine can best be described as nonlinear and fractal physiology. These studies have been intended to develop more reliable methodologies for understanding how biological systems respond to peculiar altered conditions induced by internal stress, environment stress, and/or disease. Herein, we summarize the theory and some of our results showing the fractal dependency on different conditions of physiological signals such as inter-breath intervals, heart inter-beat
A new pseudorandom bit generator using compounded chaotic tent maps

In this paper, we propose a new chaotic scheme for generating a pseudorandom bit sequence (PRBG) based on the composition of a couple of tent maps with different parameters, which can be further used for the development of some chaotic streamciphers. The viability of the method proposed for generating pseudorandom bit was argued theoretical using specific tools of chaotic theory. Finally, we present the detailed results of the statistical testing on generated bit sequences, done by the most stringent tests of randomness, the NIST suite, to detect the specific characteristics expected from truly random sequences.
Optimal Opportunistic Rate Allocation in Cognitive Radio Ad Hoc Networks

Cognitive Radios (CR) technology gives network operators more opportunities to create new communication services by sharing under-utilized spectrum owned by the licensed users. Despite of the advantages from CR, since secondary users should protect the primary service during their communication, the channel access of the secondary user is very restricted and opportunistic. This restriction occasionally makes the secondary users hard to meet the required QoS of them in some situations. As a resolution of this performance restriction, in this paper, we propose a scheme to create additional transmission opportunity for the secondary users and efficiently allocate the opportunity to maximize the network throughput considering two overlaid wireless ad hoc networks. Using the acquirable geometric and statistical information about the primary system, additional transmission opportunity is created by means of a power control and estimated as the opportunistic rate. The opportunistic rate is optimally assigned to the flows among the secondary users to maximize the throughput with an optimization method. Simulation results show that the proposed opportunity estimation and allocation scheme considerably improve the secondary network performance without degrading the primary service quality.