Miracle-dz/UniFAR-FTUs huggingface dataset:
Pooling and Analysis of Published in Vitro Data: A Proof of Concept Study for the Grouping of Nanoparticles

The study aim was to test the applicability of pooling of nanomaterials-induced in vitro data for identifying the toxic capacity of specific (SiO2, TiO2, ZnO, CuO, CeO2 and carbon nanotubes, [CNT]) nanoparticles (NP) and to test the usefulness for grouping purposes. Publication selection was based on specific criteria regarding experimental conditions. Two relevant biological endpoints were selected; generation of intracellular reactive oxygen species (ROS) and viability above 90%. The correlations of the ROS ratios with the NP parameters’ size, concentration, and exposure time were analysed. The obtained data sets were then analysed with multiple regression analysis of variance (ANOVA) and the Tukey post-hoc test. The results show that this method is applicable for the selected metal oxide NP, but might need reconsideration and a larger data set for CNT. Several statistically significant correlations and results were obtained, thus validating the method. Furthermore, the relevance of the combination of ROS release with a cell viability test was shown. The data also show that it is advisable to compare ROS production of professional phagocytic with non-phagocytic cells. In conclusion, this is the first systematic analysis showing that pooling of available data into groups is a useful method for evaluation of data regarding NP induced toxicity in vitro.
EVOLUTIONARY OPTIMISATION OF KERNEL FUNCTIONS FOR SVMS

The kernel-based classiflers use one of the classical kernels, but the real-world applications have emphasized the need to consider a new kernel function in order to boost the classiflcation accuracy by a bet- ter adaptation of the kernel function to the characteristics of the data. Our purpose is to automatically design a complex kernel by evolutionary means. In order to achieve this purpose we develop a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vec- tor Machine (SVM) classifler. Each GP chromosome is a tree encoding the mathematical expression of the kernel function. The evolved kernel is compared to several human-designed kernels and to a previous genetic kernel on several datasets. Numerical experiments show that the SVM embedding our evolved kernel performs statistically better than standard kernels, but also than previous genetic kernel for the considered classiflca- tion problems.
On the Design of a Document Protection Scheme Based on Steganography and Secret Sharing

In this paper, we integrate secret sharing and steganography to propose a document protection scheme. The secret message is split into n shadows according to a k-1 degree polynomial and is recovered by Lagrange's interpolation. The sharing of the secret message is tied in with a cover message. Without the appropriate cover message, the probability of guessing the secret message right is very small. Hence the protection of our scheme comes from two sides: secret sharing and steganography. To fulfill the requirement of the Shamir's (k, n)-threshold secret sharing scheme, we introduce a hash function with collision handling into our scheme. The security of our scheme is based on Sharmir's scheme. The experimental result shows that our scheme is secure and practicable
The impact of HIV-associated neuropsychological impairment on everyday functioning

HIV-1 infection can be associated with neuropsychological (NP) deficits ranging from subtle to severe. The purpose of this study was to evaluate the functional, or “real-world” impact of HIV-associated NP impairment in a group of 267 HIV-infected participants. All participants received comprehensive NP, neuromedical, and standardized functional evaluations that included laboratory measures of shopping, cooking, financial management, medication management and vocational abilities. Compared to NP-normal participants, those with NP impairment performed significantly worse on all laboratory measures of everyday functioning. Multivariate analyses revealed that the NP ability domains of Abstraction/Executive Function, Learning, Attention/Working Memory and Verbal abilities most strongly and consistently predicted failures on the functional battery. Both NP impairment and impairment on the functional battery were significantly associated with subjective experiences of cognitive difficulties, as well as unemployment and increased dependence in activities of daily living; multivariate prediction models that also considered depressed mood and biological measures of disease progression revealed that impairment on the functional battery and depression were the only unique predictors of all three indicators of “real-world” functioning. The current results add to growing evidence concerning the clinical significance of HIV-associated NP impairment. Objective, laboratory based functional measures, such as those used here, may compliment NP testing in future studies directed at understanding the impact on life quality of central nervous system disorders and their treatments. Finally, there is a need for additional research investigating the apparently independent effect of depression on level of everyday functioning in HIV infected persons.NOTE: Dr. Erin D. Bigler served as action editor during the course of this review. (JINS, 2004, 10, 317–331.)
Mitochondrial DNA Toxicity in Forebrain Neurons Causes Apoptosis, Neurodegeneration, and Impaired Behavior

ABSTRACT Mitochondrial dysfunction underlying changes in neurodegenerative diseases is often associated with apoptosis and a progressive loss of neurons, and damage to the mitochondrial genome is proposed to be involved in such pathologies. In the present study we designed a mouse model that allows us to specifically induce mitochondrial DNA toxicity in the forebrain neurons of adult mice. This is achieved by CaMKIIα-regulated inducible expression of a mutated version of the mitochondrial UNG DNA repair enzyme (mutUNG1). This enzyme is capable of removing thymine from the mitochondrial genome. We demonstrate that a continual generation of apyrimidinic sites causes apoptosis and neuronal death. These defects are associated with behavioral alterations characterized by increased locomotor activity, impaired cognitive abilities, and lack of anxietylike responses. In summary, whereas mitochondrial base substitution and deletions previously have been shown to correlate with premature and natural aging, respectively, we show that a high level of apyrimidinic sites lead to mitochondrial DNA cytotoxicity, which causes apoptosis, followed by neurodegeneration.
Automatic birdsong recognition based on autoregressive time-delay neural networks

A template-based technique for automatic recognition of birdsong syllables is presented. This technique combines time delay neural networks (TDNNs) with an autoregressive (AR) version of the backpropagation algorithm in order to improve the accuracy of bird species identification. The proposed neural network structure (AR-TDNN) has the advantage of dealing with a pattern classification of syllable alphabet and also of capturing the temporal structure of birdsong. We choose to carry out trials on song patterns obtained from sixteen species living in New Brunswick province of Canada. The results show that the proposed AR-TDNN system achieves a highly recognition rate compared to the baseline backpropagation-based system
Dynamic threshold-based call admission framework for prioritized multimedia traffic in wireless cellular networks

Next generation wireless cellular networks aim at supporting wireless multimedia services with different classes of traffic that are characterized by diverse quality of service (QoS) and bandwidth requirements. They will use micro/picocellular architectures in order to provide higher capacity. However, small-size cells increase the handoff rate drastically. As a result, it is a challenge to provide stable QoS in these networks. In this paper, we present a novel dynamic call admission control (DyCAC) framework for next generation wireless cellular networks. The framework consists of the following components: (i) a threshold-based bandwidth reservation policy; (ii) a threshold update processing module; and (iii) an admission controller module. In this work, each base station locally, independently of other base stations in the network, differentiates between new and handoff calls for each class of traffic by assigning a threshold to each class according to its QoS requirements. The threshold values change dynamically and periodically in order to respond to the varying traffic conditions. The main feature of the proposed framework is its ability to simultaneously achieve several design goals. Numerical results show that our proposed DyCAC framework can guarantee the connection-level quality of service of individual traffic classes while maximizing resource utilization. As well, DyCAC requires low communication overhead, and is highly scalable.
The Robust Principal Component Using Minimum Vector Variance

Component Analysis (PCA) is a technique to transform the original set of variables into a smaller set of linear combinations that account for most of the original set variance. The data reduction based on the classical PCA is fruitless if outlier is present in the data. The decomposed classical covariance matrix is very sensitive to outlying observations. ROBPCA is an effective PCA method combining two advantages of both projection pursuit and robust covariance estimation. The estimation is computed with the idea of minimum covariance determinant (MCD) of covariance matrix. The limitation of MCD is when covariance determinant almost equal zero. This paper discusses PCA using the minimum vector variance (MVV) to enhance the result. The usefulness of MVV is not limited to small or low dimension data set and to non-singular or singular covariance matrix. The MVV algorithm, compared with FMCD algorithm, has a lower computational complexity; the complexity of VV is of order 0(p 2).
Multipiple Face Detection and Tracking using Adaboost and Camshift Algorithm

Recent heightened security concerns have made the need for robust face detection and tracking more imperative, especially in the areas of security and surveillance. In addition, face detection and tracking are also crucial in applications such as teleconferencing, face and facial gesture recognition, telecommunications, robotics as well as human-computer interactions (HCI). But face detection and tracking is a very challenging and interesting problem in real time. This proposed work describes an application for automatic face detection and tracking on video streams through surveillance cameras in public or commercial places. Some situations may required to track the people those who are moving from one place to another in restricted or high alert areas. So here prototype system is designed to work with cameras for the face detection and tracking, based on the platforms CCS and OpenCV. The system adopted with Adaboost algorithm and cam-shift algorithm. This system can be used for security purpose to record the visitor face as well as to detect and track the face within the camera range. A algorithm is developed using OpenCV that can detect people's face and track them by taking input from the camera.
Data-Driven Computer Go Based on Hadoop

Although the research for computer-based Go AI engines started at about 1980's, Go is currently considered as the last board game which need to be implemented using computer systems. Most current Go implementations are using tree-search technique to find the best moves, however it is nearly impossible to review all possible candidate moves in Go games. In this paper, we propose a totally different approach: a data-driven Go engine. Hugh amount of game log database is stored in Hadoop servers which support parallel pattern retrieval. In each game status, Go engine transfers the board configuration to Hadoop server to find the best move from database. We anticipate that this simple data-driven approach could be expandable to the one having learning capability and could be easily merged with other traditional approaches for better performance.
Realistic Threats to Self-Enforcing Privacy

A recent privacy protocol for secure e-polls aims at ensuring the submitting individuals that the pollster will preserve the privacy of their submitted preferences. Otherwise the individuals can indict the pollster, provided that the pollster participates actively in this phase. The analysis of the protocol in a realistic threat model denounces that a malicious pollster that abuses the private preferences by disclosure will arguably not help out during its own indictment. Therefore, the protocol ensures insufficient fairness among their participants because it gives the pollster some advantage over the individuals. Two variant protocols are introduced and analysed in the same threat model - one is found to move the advantage over the individuals, the other is found to achieve a satisfactory level of fairness.
Exploring potentials of perpendicular magnetic anisotropy STT-MRAM for cache design

Traditional CMOS integrated circuits suffer from elevated power consumption as technology node advances. A few emerging technologies are proposed to deal with this issue. Among them, STT-MRAM is one of the most important candidates for future on-chip cache design. However, most STT-MRAM based architecture level evaluations focus on in-plane magnetic anisotropy effect. In the paper, we evaluate the most advanced perpendicular magnetic anisotropy (PMA) STT-MRAM for on-chip cache design in terms of performance, area and power consumption perspectively. The experimental results show that PMA STT-MRAM has higher power efficiency compared to SRAM as well as desirable scalability with technology node shrinking.
Pearl's Causality in a Logical Setting

We provide a logical representation of Pearl's structural causal models in the causal calculus of McCain and Turner (1997) and its first-order generalization by Lifschitz. It will be shown that, under this representation, the nonmonotonic semantics of the causal calculus describes precisely the solutions of the structural equations (the causal worlds of the causal model), while the causal logic from Bochman (2004) is adequate for describing the behavior of causal models under interventions (forming submodels).
A Study on the Significance of Software Metrics in Defect Prediction

In the case of metrics-based software defect prediction, an intelligent selection of metrics plays an important role in improving the model performance. In this paper, we use different ways for feature selection and dimensionality reduction to determine the most important software metrics. Three different classifiers are utilized, namely Naïve Bayes, support vector machine and decision tree. On the publicly NASA data, a comparative experiment results show that instead of 22 or more metrics, less than 10 metrics can get better performance.
Phylogenetic analysis of AGAMOUS sequences reveals the origin of the diploid and tetraploid forms of self-pollinating wild buckwheat, Fagopyrum homotropicum Ohnishi

Fagopyrum homotropicum Ohnishi is a self-pollinating wild buckwheat species indigenous to eastern Tibet and the Yunnan and Sichuan Provinces of China. It is useful breeding material for shifting cultivated buckwheat (F. esculentum ssp. esculentum Moench) from out-crossing to self-pollinating. Despite its importance as a genetic resource in buckwheat breeding, the genetic variation of F. homotropicum is poorly understood. In this study, we investigated the genetic variation and phylogenetic relationships of the diploid and tetraploid forms of F. homotropicum based on the nucleotide sequences of a nuclear gene, AGAMOUS (AG). Neighbor-joining analysis revealed that representative individuals clustered into three large groups (Group I, II and III). Each group contained diploid and tetraploid forms of F. homotropicum. We identified tetraploid plants that had two diverged AG sequences; one belonging to Group I and the other belonging to Group II, or one belonging to Group II and the other belonging to Group III. These results suggest that the tetraploid form originated from at least two hybridization events between deeply differentiated diploids. The results also imply that the genetic diversity contributed by tetraploidization of differentiated diploids may have allowed the distribution range of F. homotropicum to expand to the northern areas of China.
Meta-heuristic for no-wait job shops with makespan minimization

In the paper, the no-wait job shop problem with makespan minimization is considered, which is decomposed into the sequencing problem and the timetabling problem. Based on the non-delay timetabling procedure and the inverse timetabling procedure, an enhanced timetabling procedure is constructed by shifting jobs leftwards or rightwards to obtain better timetables. The two sub-problems are solved independently by traditional methods. However, a meta-heuristic algorithm MCLM (modified complete local search with memory) is presented to solve the sub-problems integrally in this paper. Experimental results show that MCLM outperforms all the existing effective algorithms for the considered problem with little more computation time.
Transgenic approaches to the study of neural development in mice

Transgenic mice were used as an assay system for the isolation and characterization of the promoter for cerebroglycan, a neural-specific cell surface proteoglycan expressed during the period of axon extension and guidance. A nuclear-localized P-galactosidase reporter gene was fused to a segment of upstream cerebroglycan genomic DNA and used to create several transgenic mice through pronuclear injection. These mice were stained for transgene expression patterns using X-gal, a chromogenic substrate for Pgalactosidase. Nine out of eleven transgenic mice possessed nearly identical staining patterns which were neural-specific and similar, but not identical to the pattern of cerebroglycan expression. The promoter fragment was sequenced and was found to have significant homologies to the promoter for another neural-specific gene, Ta 1. The isolated promoter may be useful for the study of neural-specific gene regulation and for the study of neural development by providing the means of expressing exogenous genes in a unique pattern during axon growth and guidance. Several independent strains of transgenic mice were created which greatly facilitate the rapid visualization of axon pathways during neural development. The promoter for Ta 1, an a-tubulin gene expressed specifically in neurons during axon growth, was fused to the gene for tau-f3-gal, an axonally-targeted P-galactosidase reporter. This DNA construct was then used to create four independent strains of transgenic mice through pronuclear injection. Embryonic and early postnatal mice from these strains were stained for transgene expression using X-gal and were shown to possess several clearlyvisible axon pathways. The transgene from one of the strains was placed on the genetic background of a mutant mouse null for the cerebroglycan gene in order to analyze the null mutants for possible axon guidance errors. Embryonic and early postnatal mice from this new strain, which possessed both the reporter transgene and cerebroglycan null alleles, were stained with X-gal and their axon patterns analyzed. No deviations in axon patterns were visible in cerebroglycan null animals, showing that the absence of cerebroglycan expression does not affect the establishment of major axon pathways. Thesis Supervisor: Arthur Lander
Network modules help the identification of key transport routes, signaling pathways in cellular and other networks

Complex systems are successfully reduced to interacting elements via the network concept. Transport plays a key role in the survival of networks – for example the specialized signaling cascades of cellular networks filter noise and efficiently adapt the network structure to new stimuli. However, our general understanding of transport mechanisms and signaling pathways in complex systems is yet limited. Here we summarize the key network structures involved in transport, list the solutions available to overloaded systems for relaxing their load and outline a possible method for the computational determination of signaling pathways. We highlight that in addition to hubs, bridges and the network skeleton, the overlapping modular structure is also essential in network transport. Path‐lenghts in the module‐space of the yeast protein‐protein interaction network indicated that module‐based paths may cross fewer modular boundaries than shortest paths. Moreover, by locating network elements in the space of overlapping network modules and evaluating their distance in this ‘module space’, it may be possible to approximate signaling pathways computationally, which, in turn could serve the identification of signaling pathways of complex systems. Our model may be applicable in a wide range of fields including traffic control or drug design.
Digital receivers and transmitters using polyphase filter banks for wireless communications

Provides a tutorial overview of multichannel wireless digital receivers and the relationships between channel bandwidth, channel separation, and channel sample rate. The overview makes liberal use of figures to support the underlying mathematics. A multichannel digital receiver simultaneously down-converts a set of frequency-division-multiplexed (FDM) channels residing in a single sampled data signal stream. In a similar way, a multichannel digital transmitter simultaneously up-converts a number of baseband signals to assemble a set of FDM channels in a single sampled data signal stream. The polyphase filter bank has become the architecture of choice to efficiently accomplish these tasks. This architecture uses three interacting processes to assemble or to disassemble the channelized signal set. In a receiver, these processes are an input commutator to effect spectral folding or aliasing due to a reduction in sample rate, a polyphase M-path filter to time align the partitioned and resampled time series in each path, and a discrete Fourier transform to phase align and separate the multiple baseband aliases. In a transmitter, these same processes operate in a related manner to alias baseband signals to high order Nyquist zones while increasing the sample rate with the output commutator.
Metrics for Measuring the Quality of Modularization of Large-Scale Object-Oriented Software

The metrics formulated to date for characterizing the modularization quality of object-oriented software have considered module and class to be synonymous concepts. But a typical class in object oriented programming exists at too low a level of granularity in large object-oriented software consisting of millions of lines of code. A typical module (sometimes referred to as a superpackage) in a large object-oriented software system will typically consist of a large number of classes. Even when the access discipline encoded in each class makes for "clean" class-level partitioning of the code, the intermodule dependencies created by associational, inheritance-based, and method invocations may still make it difficult to maintain and extend the software. The goal of this paper is to provide a set of metrics that characterize large object-oriented software systems with regard to such dependencies. Our metrics characterize the quality of modularization with respect to the APIs of the modules, on the one hand, and, on the other, with respect to such object-oriented inter-module dependencies as caused by inheritance, associational relationships, state access violations, fragile base-class design, etc. Using a two-pronged approach, we validate the metrics by applying them to popular open-source software systems.