Miracle-dz/UniFAR-FTUs huggingface dataset:
Using the zebrafish model for Alzheimer’s disease research

Rodent models have been extensively used to investigate the cause and mechanisms behind Alzheimer’s disease. Despite many years of intensive research using these models we still lack a detailed understanding of the molecular events that lead to neurodegeneration. Although zebrafish lack the complexity of advanced cognitive behaviors evident in rodent models they have proven to be a very informative model for the study of human diseases. In this review we give an overview of how the zebrafish has been used to study Alzheimer’s disease. Zebrafish possess genes orthologous to those mutated in familial Alzheimer’s disease and research using zebrafish has revealed unique characteristics of these genes that have been difficult to observe in rodent models. The zebrafish is becoming an increasingly popular model for the investigation of Alzheimer’s disease and will complement studies using other models to help complete our understanding of this disease.
A rapid posttranslational myristylation of a 68-kD protein in D. discoideum

Cells incubated with [3H]myristate were shown to rapidly and specifically acylate a 68-kD protein, p68, in a developmentally- regulated manner. The fatty acid incorporated into p68 was identified as myristate, and is linked to the protein via an amide bond, apparently to an NH2-terminal glycine. The acylation of p68 in D. discoideum displays some unusual properties. Unexpectedly, myristylation of p68 is a posttranslational event and occurs in the presence of inhibitors of protein synthesis. Another unusual finding was that although p68 is a stable protein, the acyl moiety is removed with a half time of approximately 15 min.
Microbial Successions Are Associated with Changes in Chemical Profiles of a Model Refrigerated Fresh Pork Sausage during an 80-Day Shelf Life Study

ABSTRACT Fresh pork sausage is produced without a microbial kill step and therefore chilled or frozen to control microbial growth. In this report, the microbiota in a chilled fresh pork sausage model produced with or without an antimicrobial combination of sodium lactate and sodium diacetate was studied using a combination of traditional microbiological methods and deep pyrosequencing of 16S rRNA gene amplicons. In the untreated system, microbial populations rose from 102 to 106 CFU/g within 15 days of storage at 4°C, peaking at nearly 108 CFU/g by day 30. Pyrosequencing revealed a complex community at day 0, with taxa belonging to the Bacilli, Gammaproteobacteria, Betaproteobacteria, Actinobacteria, Bacteroidetes, and Clostridia. During storage at 4°C, the untreated system displayed a complex succession, with species of Weissella and Leuconostoc that dominate the product at day 0 being displaced by species of Pseudomonas (P. lini and P. psychrophila) within 15 days. By day 30, a second wave of taxa (Lactobacillus graminis, Carnobacterium divergens, Buttiauxella brennerae, Yersinia mollaretti, and a taxon of Serratia) dominated the population, and this succession coincided with significant chemical changes in the matrix. Treatment with lactate-diacetate altered the dynamics dramatically, yielding a monophasic growth curve of a single species of Lactobacillus (L. graminis), followed by a uniform selective die-off of the majority of species in the population. Of the six species of Lactobacillus that were routinely detected, L. graminis became the dominant member in all samples, and its origins were traced to the spice blend used in the formulation.
An Error Tolerant, Scalable Video Stream Encoding and Compression for Mobile Computing

We present a prototype implementation of a video coding and decoding algorithm suitable for wireless networks. Typical bursty errors of wireless networks are handled by a coding scheme which allows for graceful service degradation when the video data is received incompletely. This property is achieved by applying a coding scheme based on the wavelet transformation (WT) which yields a nonredundant multi-resolution representation. In addition, an interleaved segmentation and the introduction of redundancy for critical low frequency components of the coded video may be activated to reduce the visible loss to a minimum. The algorithms provide scalability over a wide range of video QoS parameters, namely image quality, frame rate, compression ratio and computational complexity. The tagged representation of the stream makes it possible to extract and discard particular information, like high resolution or color, from the video by just identifying and dropping tagged stream segments. There is no need to decompress these segments for this identification, which allows efficient, low delay video filtering in the network. The implementations goal was low computational complexity to be able to use off-the-shelf hardware. While the presented system has many advantages for wireless transmission systems it is not limited to this field. Particularly, scalability makes it a valid candidate for Internet applications .
Profile-guided floating- to fixed-point conversion for hybrid FPGA-processor applications

The key to enabling widespread use of FPGAs for algorithm acceleration is to allow programmers to create efficient designs without the time-consuming hardware design process. Programmers are used to developing scientific and mathematical algorithms in high-level languages (C/C++) using floating point data types. Although easy to implement, the dynamic range provided by floating point is not necessary in many applications; more efficient implementations can be realized using fixed point arithmetic. While this topic has been studied previously [Han et al. 2006; Olson et al. 1999; Gaffar et al. 2004; Aamodt and Chow 1999], the degree of full automation has always been lacking. We present a novel design flow for cases where FPGAs are used to offload computations from a microprocessor. Our LLVM-based algorithm inserts value profiling code into an unmodified C/C++ application to guide its automatic conversion to fixed point. This allows for fast and accurate design space exploration on a host microprocessor before any accelerators are mapped to the FPGA. Through experimental results, we demonstrate that fixed-point conversion can yield resource savings of up to 2x--3x reductions. Embedded RAM usage is minimized, and 13%--22% higher Fmax than the original floating-point implementation is observed. In a case study, we show that 17% reduction in logic and 24% reduction in register usage can be realized by using our algorithm in conjunction with a High-Level Synthesis (HLS) tool.
Effect of nicotine on the structure of cochlea of guinea pigs

Smoking has been positively associated with hearing loss in human. However, its effect on the cochlea has not been previously evaluated. Aim of work is to investigate the effect of nicotine, which is the primary pharmacological component of tobacco, on the structure of the cochlea of adult male guinea pigs. Fifteen male guinea pigs were classified into two groups: group I (control) and group II (nicotine treated group). Group II was further subdivided into two subgroups; IIA and IIB according to the dose of nicotine (3 mg/kg and 6 mg/kg, respectively). The cochlea was harvested and processed for light microscopy, transmission electron microscopy and scanning electron microscopy. Nicotine administration induced damage of outer hair cells which were distorted in shape with vacuolated cytoplasm and heterochromatic nuclei. Topography revealed damage of the stereocilia which included disorganization, bent and limp or complete loss and expansion of the surrounding supporting cells. These changes were more pronounced in the basal turn of the cochlea and mainly involved the outer hair cells. High dose induced more damage and resulted in protrusion of the apical poles of hair cells (blebing), particularly the outer two rows. Nicotine is proved to be harmful to the cells of the cochlea, particularly the outer hair cells of the basal turn. High doses induce blebing of hair cells.
On-line hand-printing recognition with neural networks

The need for fast and accurate text entry on small handheld computers has led to a resurgence of interest in on-line word recognition using artificial neural networks. Classical methods have been combined and improved to produce robust recognition of hand-printed English text. The central concept of a neural net as a character classifier provides a good base for a recognition system; long-standing issues relative to training generalization, segmentation, probabilistic formalisms, etc., need to resolved, however, to get adequate performance. A number of innovations in how to use a neural net as a classifier in a word recognizer are presented: negative training, stroke warping, balancing, normalized output error, error emphasis, multiple representations, quantized weights, and integrated word segmentation all contribute to efficient and robust performance.
Naming Games in Two-Dimensional and Small-World-Connected Random Geometric Networks

We investigate a prototypical agent-based model, the naming game, on two-dimensional random geometric networks. The naming game [Baronchelli, J. Stat. Mech. : Theory Exp. (2006) P06014] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the naming games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long-range communication links are added on top of the random geometric graph, resulting in a "small-world"-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case.
Research on ellipsoidal intersection fusion method with unknown correlation

This paper reviews the advantages and shortages of the covariance intersection (CI) and ellipsoidal intersection (EI) methods for decentralized state fusion with unknown correlation, and makes some progress on both of them. New results are: a). For CI method, the convexity property is proved for the two classical cost functions (i.e., trace and natural logarithm of determinant of the fused covariance), and a simple form of the optimization conditions is derived for the latter one. Furthermore, a fast 2-sensor CI algorithm is proposed by expressing the cost function in scalar form. b). For the 2-sensor EI algorithm which minimized the natural logarithm of determinant of the mutual covariance, a new proof for its optimality is presented, which partly makes up the gap in [17]. Simulation results show the efficiency for both the 2-sensor CI and EI algorithms.
Notice of RetractionImproved Decaying Bloom Filter for duplicate detection in data streams over sliding windows

Approximate duplicate detection based on the Decaying Bloom Filter (DBF) for data streams over sliding windows (DDMDBF) is an effective technique, but may have a large false positive rate. Because it simply takes a querying element to be duplicated when the counters that this element is hashed to are non-zero, while neglects the actual values of the counters. In this paper, we propose a new data structure, Flag Decaying Bloom Filter (FDBF), which can maintain duplicate information more accurately by extending DBF with one additional flag bit for each integer counter. Then we propose an efficient approximate duplicate detection method (DDMFDBF) based on FDBF that reduces the false positive rate (FPR) p (0 <; p <; 1)of DDMDBF by a factor of p1-√(2) for approximately same bit space. Experimental results on synthetic data validate the analytical results on the efficiency and accuracy of our method.
An analysis of behavior change in Gestalt clients and non-clients in terms of the goals of Gestalt therapy.

There have been many new therapies since the beginning of the Human Potential Movement in 1954. Peterson (1973) has listed one hundred new therapeutic methods for healing. One of these has been gestalt therapy. The increasing interest in this form of therapy has stimulated approximately 225 publications (Kogan, 1972) , and 15 gestalt training institutes have sprung up since 1969 (Fagan, 1974) . In 1951 the founder of the gestalt movement, Frederick Peris, in his first publication, Gestalt Therapy: Excitement and Growth in the Human Personality, met with small numbers of interested people. Now in the 1970's there is an emerging and rapidly growing client population who have experienced intensive gestalt therapy in groups or as individuals. The increase in people who are "graduates" of gestalt groups and individual gestalt therapy, and the popularization of literature on gestalt therapy and practice, arouse one's curiosity to investigate the effectiveness of gestalt therapy on behavior change. This work is part of the dissertation submitted by Peller Marion for the Ed.D. degree under the direction of Dr. Donald K. Carew at the University of Massachusetts, Amherst. It represents an attemp to present a publishable article as an outcome of the research project.
Dependent Fluents

We discuss the persistence of the indirect effects of an action--the question when such effects are subject to the commonsense law of inertia, and how to describe their evolution in the cases when inertia does not apply. Our model of nonpersistent effects involves the assumption that the value of the fluent in question is determined by the values of other fluents, although the dependency may be partially or completely unknown. This view leads us to a new highlevel action language ARD (for Actions, Ramifications and Dependencies) that is capable of describing both persistent and nonpersistent effects. Unlike the action languages introduced in the past, ARD is "non-Markovian," in the sense that the evolution of the fluents described in this language may depend on their history, and not only on their current values.
Initialisation of Radial Basis Function Networks UsingClassi cation

Learning in radial basis function (RBF) networks is the topic of this paper. Particularly we address the problem of intialisation the centers and scaling parameters in RBF networks utilizing classiication tree algorithms. This method was introduced by Kubat in 1998. Algorithms for the calculation of the centers and scaling parameters in an RBF network are presented and numerical results for these algorithms are shown for two diierent data sets.
Strapdown sensors and seeker based guidance filter design

Precision guidance filter design for a tactical missile with a strapdown seeker aided by low-cost strapdown sensors has been addressed in this paper. The low-cost strapdown sensors consist of an IMU with 3-axis accelerometers and gyroscopes, two 3-axis magnetometers, and a barometer. Missilepsilas position, velocity, attitude, and bias error of the barometer are considered as state variables. Since the state and measurement equations are highly nonlinear, we adopt UKF(Unscented Kalman Filter). The proposed guidance filter has a function of a navigation filter if target position error is not considered. In the case that the target position error is introduced, the proposed filter can effectively estimate the relative states of the missile to the true target. For specific engagement scenarios, we can observe that observability problems occur. Estimation performance of the proposed filter is affected by the missilepsilas maneuver pattern.
Adaptive Low-Rank Methods for Problems on Sobolev Spaces with Error Control in $L_2$

Low-rank tensor methods for the approximate solution of second-order elliptic partial differential equations in high dimensions have recently attracted significant attention. A critical issue is to rigorously bound the error of such approximations, not with respect to a fixed finite dimensional discrete background problem, but with respect to the exact solution of the continuous problem. While the energy norm offers a natural error measure corresponding to the underlying operator considered as an isomorphism from the energy space onto its dual, this norm requires a careful treatment in its interplay with the tensor structure of the problem. In this paper we build on our previous work on energy norm-convergent subspace-based tensor schemes contriving, however, a modified formulation which now enforces convergence only in $L_2$. In order to still be able to exploit the mapping properties of elliptic operators, a crucial ingredient of our approach is the development and analysis of a suitable asymmetric preconditioning scheme. We provide estimates for the computational complexity of the resulting method in terms of the solution error and study the practical performance of the scheme in numerical experiments. In both regards, we find that controlling solution errors in this weaker norm leads to substantial simplifications and to a reduction of the actual numerical work required for a certain error tolerance.
Throughput optimization in OFDMA cognitive radio networks based on node selection and power allocation

Node selection for sensing and transmission processes in cognitive radio networks, is an important problem. Due to channel impairments in wireless networks, cooperative spectrum sensing is used in orthogonal frequency division multiple access (OFDMA) cognitive radio networks. To exploit the benefits of OFDMA in cognitive radio networks, efficient managements of resources such as power and subcarrier for selected transmission nodes should be done. We have to maximize the throughput of secondary users under total power budget along with the detection performance constraints. Based on the standard optimization techniques, the optimal conditions are obtained and a closed-form equation is expressed to determine the priority of PUs for assigning the sensing nodes to them. Simulation results show that our proposed algorithms can improve the sensing and transmission performance.
STRUCTURAL FEATURES OF MESOSOMES (CHONDRIOIDS) OF BACILLUS SUBTILIS AFTER FREEZE-ETCHING

Freeze-etched cells of Bacillus subtilis have been studied with the electron microscope. The outer surface of the plasma membrane, i.e. the side facing the cell wall, is covered with numerous granules and short strands, each measuring approximately 50 A in diameter. These strands are occasionally seen to enter the cell wall. The inner surface of the plasma membrane, i.e. the side facing the cytoplasm, appears to be sparsely dotted with small particles measuring about 50 A. The envelope of mesosomes differs from the plasma membrane. Blunt protrusions arise from its outer surface; the inner surface appears smooth. Stalked particles, as described by other investigators after negative staining with phosphotungstic acid, were not observed on any membrane surface in our material. Preparations were also made of specimens prefixed in osmium tetroxide prior to freeze-etching. Under these conditions the bacterial membranes appeared to be surprisingly well preserved. In contrast to directly frozen, unfixed cells, some osmium tetroxide-fixed preparations showed a differentiation in cytoplasm and nucleoplasm, which made it possible to observe the close association of the mesosome with the latter.
Gold standard datasets for evaluating word sense disambiguation programs

Abstract There are now many computer programs for automatically determining the sense in which a word is being used. One would like to be able to say which are better, which worse, and also which words, or varieties of language, present particular problems to which algorithms. An evaluation exercise is required, and such an exercise requires a “gold standard” dataset of correct answers. Producing this proves to be a difficult and challenging task. In this paper I discuss the background, challenges and strategies, and present a detailed methodology for ensuring that the gold standard is not fool's gold.
Chinese Text Categorization via Bottom-Up Weighted Word Clustering

Most of the researches on text categorization are focus on using bag of words. Some researches provided other methods for classification such as term phrase, Latent Semantic Indexing, and term clustering. Term clustering is an effective way for classification, and had been proved as a good method for decreasing the dimensions in term vectors. The authors used hierarchical term clustering and aggregating similar terms. In order to enhance the performance, they present a modify indexing with terms in cluster. Their test collection extracted from Chinese NETNEWS, and used the Centroid-Based classifier to deal with the problems of categorization. The results had shown that term clustering is not only reducing the dimensions but also outperform than bag of words. Thus, term clustering can be applied to text classification by using any large corpus, its objective is to save times and increase the efficiency and effectiveness. In addition to performance, these clusters can be considered as conceptual knowledge base, and kept related terms of real world.
Recognizing gym exercises using acceleration data from wearable sensors

The activity recognition approaches can be used for entertainment, to give people information about their own behavior, and to monitor and supervise people through their actions. Thus, it is a natural consequence of that fact that the amount of wearable sensors based studies has increased as well, and new applications of activity recognition are being invented in the process. In this study, gym data, including 36 different exercise classes, is used aiming in the future to create automatic activity diaries showing reliably to end users how many sets of given exercise have been performed. The actual recognition is divided into two different steps. In the first step, activity recognition of certain time intervals is performed and in the second step the state-machine approach is used to decide when actual events (sets in gym data) were performed. The results showed that when recognizing different exercise sets from the same occasion (sequential exercise sets), on average, over 96 percent window-wise true positive rate can be achieved, and moreover, all the exercise events can be discovered using the state-machine approach. When using a separate validation test set, the accuracies decreased significantly for some classes, but even in this case, all the different sets were discovered for 26 different classes.
Anomaly detection by clustering ellipsoids in wireless sensor networks

A major challenge for the management of low-cost sensor networks is how to ensure the integrity of the data collected, and how to detect unusual events. In this paper, we present a distributed algorithm for anomaly detection in wireless sensor networks, which reduces the amount of data that needs to be communicated through the network. Our approach learns an ellipsoidal boundary for normal data at each sensor, and introduces a method to cluster these ellipsoids at a global level in order to model normal behaviour in the network. We demonstrate that our approach can achieve greater accuracy in non-homogeneous sensing environments than existing methods, while achieving low communication and computational overhead in the network.