Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm

Summary. This paper discusses the analysis of an extended finite mixture model where the latent classes corresponding to the mixture components for one set of observed variables influence a second set of observed variables. The research is motivated by a repeated measurement study using a random coefficient model to assess the influence of latent growth trajectory class membership on the probability of a binary disease outcome. More generally, this model can be seen as a combination of latent class modeling and conventional mixture modeling. The EM algorithm is used for estimation. As an illustration, a random‐coefficient growth model for the prediction of alcohol dependence from three latent classes of heavy alcohol use trajectories among young adults is analyzed.
SEPARATION OF EVENT AND CONSTRAINT RULES IN UML&OCL MODELS OF SERVICE ORIENTED INFORMATION SYSTEMS

In this paper, possibilities of advancing Business Process Modelling when joining it with Business Rules approach are analysed. The problem currently under discussion in business rule and business process management communities is that business process (or event) rules are changing independently from structural, or constraint-oriented business rules, and coupling them together requires changing business processes when rules are changing, and vice versa. The paper addresses the problem of modelling – separating business constraint rules from event rules in UML&OCL models. The proposed principles of separation are based on UML 2 state machines applied in the context of Extended Model Driven Approach for Service Oriented Information Systems. Representation of event and constraint rules in SBVR, Template Based Language and OCL is analysed.
Full Room Equalization at Low Frequencies with Asymmetric Loudspeaker Arrangements

For rectangular rooms with symmetric loudspeaker arrangements, full room equalization can be achieved at low frequencies, as demonstrated by previous research. The method is based on generating a plane wave that propagates along the room. However, often the room is not rectangular, and/or a symmetric loudspeaker setup cannot be assured, leading to a deteriorated equalization performance. In addition, the performance of the method drops significantly above a cutoff frequency where a plane wave cannot be generated. These problems are addressed by the proposed method by prescribing only the magnitude in the control points, while the phase is determined by an iterative optimization process starting from the plane wave solution. A true “magnitude-only” variant of the method is also presented. Comparison is given to the plane-wave based methods by introducing asymmetries to the loudspeaker setup in a simulated environment, showing that the new methods result in smaller average magnitude deviations compared to the previous plane-wave based approach.
Physiological and Transcriptional Responses of Saccharomyces cerevisiae to Zinc Limitation in Chemostat Cultures

ABSTRACT Transcriptional responses of the yeast Saccharomyces cerevisiae to Zn availability were investigated at a fixed specific growth rate under limiting and abundant Zn concentrations in chemostat culture. To investigate the context dependency of this transcriptional response and eliminate growth rate-dependent variations in transcription, yeast was grown under several chemostat regimens, resulting in various carbon (glucose), nitrogen (ammonium), zinc, and oxygen supplies. A robust set of genes that responded consistently to Zn limitation was identified, and the set enabled the definition of the Zn-specific Zap1p regulon, comprised of 26 genes and characterized by a broader zinc-responsive element consensus (MHHAACCBYNMRGGT) than so far described. Most surprising was the Zn-dependent regulation of genes involved in storage carbohydrate metabolism. Their concerted down-regulation was physiologically relevant as revealed by a substantial decrease in glycogen and trehalose cellular content under Zn limitation. An unexpectedly large number of genes were synergistically or antagonistically regulated by oxygen and Zn availability. This combinatorial regulation suggested a more prominent involvement of Zn in mitochondrial biogenesis and function than hitherto identified.
Research on Immune Based Adaptive Intrusion Detection System Model

Intrusion Detection Systems (IDSs) are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Recently applying Artificial Intelligence, machine learning and data mining techniques to IDS are increasing. Artificial Intelligence plays a driving role in security services. This paper proposes an Immune based Adaptive Intrusion Detection System Model (IAIDSM). Analyzing the training data obtaining from internet, the self behavior set and nonself behavior set can be obtained by the partitional clustering algorithm, then it extracts Self and nonself pattern sets from these two behavior sets by association rules and sequential patterns mining. The Self and nonself sets can update automatically and constantly online. So IAIDSM improves the ability of detecting new type intrusions and the adaptability of the system.
Dynamic modeling and learning user profile in personalized news agent

Finding relevant information eeectively on the Internet is a challenging task. Although the information is widely available, exploring Web sites and nding information relevant to a user's interest can be a time-consuming and tedious task. As a result, many software agents have been employed to perform autonomous information gathering and ltering on behalf of the user. One of the critical issues in such an agent is the capability of the agent to model its users and adapt itself over time to changing user interests. In this thesis, a novel scheme is proposed to learn user proole. The proposed scheme is designed to handle multiple domains of long-term and short-term users' interests simultaneously, which are learned through positive and negative user feedback. A 3-descriptor interest category representation approach is developed to achieve this objective. Using such a representation, the learning algorithm is derived by imitating human personal assistants doing the same task. Based on experimental evaluation, the scheme performs very well and adapts quickly to signiicant changes in user interest. iv To my wife Lina Handayani and my daughter Risma Cahyani Widyantoro. v ACKNOWLEDGMENTS
Characterization of Plasma Membrane Proteins from Ovarian Cancer Cells Using Mass Spectrometry

To determine how the repertoire of plasma membrane proteins change with disease state, specifically related to cancer, several methods for preparation of plasma membrane proteins were evaluated. Cultured cells derived from stage IV ovarian tumors were grown to 90% confluence and harvested in buffer containing CHAPS detergent. This preparation was centrifuged at low speed to remove insoluble cellular debris resulting in a crude homogenate. Glycosylated proteins in the crude homogenate were selectively enriched using lectin affinity chromatography. The crude homogenate and the lectin purified sample were prepared for mass spectrometric evaluation. The general procedure for protein identification began with trypsin digestion of protein fractions followed by separation by reversed phase liquid chromatography that was coupled directly to a conventional tandem mass spectrometer (i.e. LCQ ion trap). Mass and fragmentation data for the peptides were searched against a human proteome data base using the informatics program SEQUEST. Using this procedure 398 proteins were identified with high confidence, including receptors, membrane-associated ligands, proteases, phosphatases, as well as structural and adhesion proteins. Results indicate that lectin chromatography provides a select subset of proteins and that the number and quality of the identifications improve as does the confidence of the protein identifications for this subset. These results represent the first step in development of methods to separate and successfully identify plasma membrane proteins from advanced ovarian cancer cells. Further characterization of plasma membrane proteins will contribute to our understanding of the mechanisms underlying progression of this deadly disease and may lead to new targeted interventions as well as new biomarkers for diagnosis.
Single Rate Multicast Congestion Control Protocols : A Performance Comparison

Multicast Congestion Control (MCC) at the transport layer is a very difficult problem that has been the target of the research community for nearly the last ten years. There exist two broad categories of Multicast Congestion Control (MCC) solutions, Single-Rate and Multi-Rate. For the single Rate solutions, the source multicasts data packets according to the rate of the slowest receiver, whereas for multi-rate solutions the source transmits data with a rate varying with the grade of service of each of the receivers. Single rate MCC is much simpler and easier to deploy in practice. In this paper we evaluate and compare two recently proposed single rate MCC protocols, TCP-Friendly Multicast Congestion Control (TFMCC) [2, 8] and One Rate Multicast Congestion Control (ORMCC) [1]. TFMCC is an equation based congestion control protocol and ORMCC adapts the additive increase and the multiplicative decrease (AIMD) approach. Comparison is carried with respect to throughput, TCP-friendliness and responsiveness to congestion as the number of receivers grows within a session. Simulation results show that, under homogeneous and heterogeneous scenarios, ORMCC consistently exhibits superior performance.
Synchronization of a class of non-identical chaotic systems via DSC approach

This paper aims to propose a generalized scheme for the synchronization of a class of nonlinear non-identical chaotic systems. The proposed procedure is based on well-established backstepping control technique which guarantees global stability of the system, but due to the problem of `explosion of terms', its use is restricted to lower order systems. Here, dynamical surface control (DSC) approach which is much less complex is combined with backstepping control leading to a better approach for synchronization of two non-identical chaotic systems. Although DSC offers semiglobal stability but still it is worth employing as it makes the analysis and design very simple. Numerical simulations are given to prove the validity of the technique.
Design, Synthesis, and Analysis of Minor Groove Binder Pyrrolepolyamide-2′-Deoxyguanosine Hybrids

Pyrrolepolyamide-2′-deoxyguanosine hybrids (Hybrid 2 and Hybrid 3) incorporating the 3-aminopropionyl or 3-aminopropyl linker were designed and synthesized on the basis of previously reported results of a pyrrolepolyamide-adenosine hybrid (Hybrid 1). Evaluation of the DNA binding sequence selectivity of pyrrolepolyamide-2′-deoxyguanosine hybrids was performed by CD spectral and T m analyses. It was shown that Hybrid 3 possessed greater binding specificity than distamycin A, Hybrid 1 and Hybrid 2.
Particle Swarm Optimization of Hidden Markov Models : a comparative study

In recent years, Hidden Markov Models (HMM) have been increasingly applied in data mining applications. However, most authors have used classical optimization ExpectationMaximization (EM) scheme. A new method of HMM learning based on Particle Swarm Optimization (PSO) has been developed. Along with others global approaches as Simulating Annealing (SIM) and Genetic Algorithms (GA) the following local gradient methods have been also compared: classical Expectation-Maximization algorithm, Maximum A Posteriory approach (MAP) and Bayes Variational learning (VAR). The methods are evaluated on a synthetic data set using different evaluation criteria including classification problem. The most reliable optimization approach in terms of performance, numerical stability and speed is VAR learning followed by PSO approach.
The lightweight domain entailment recognition system

The inference ability aids to build a consolidated, consistent corpus or to search related text in a corpus. In this paper, we proposed a lightweight domain entailment recognition system which utilizes domain knowledge to recognize the atomic relations between constituents and join the atomic relations along the dependency structure. By telling the user not only the related texts existing in the corpus but also the entailment relations, redundancy or inconsistency can be prevented in advance.
Active Expiration Induced by Excitation of Ventral Medulla in Adult Anesthetized Rats

Data from perinatal and juvenile rodents support our hypothesis that the preBötzinger complex generates inspiratory rhythm and the retrotrapezoid nucleus–parafacial respiratory group (RTN/pFRG) generates active expiration (AE). Although the role of the RTN/pFRG in adulthood is disputed, we hypothesized that its rhythmogenicity persists but is typically silenced by synaptic inhibition. We show in adult anesthetized rats that local pharmacological disinhibition or optogenetic excitation of the RTN/pFRG can generate AE and transforms previously silent RTN/pFRG neurons into rhythmically active cells whose firing is correlated with late-phase active expiration. Brief excitatory stimuli also reset the respiratory rhythm, indicating strong coupling of AE to inspiration. The AE network location in adult rats overlaps with the perinatal pFRG and appears lateral to the chemosensitive region of adult RTN. We suggest that (1) the RTN/pFRG contains a conditional oscillator that generates AE, and (2) at rest and in anesthesia, synaptic inhibition of RTN/pFRG suppresses AE.
Defective Axonal Transport of Rab7 GTPase Results in Dysregulated Trophic Signaling

Retrograde trophic signaling of nerve growth factor (NGF) supports neuronal survival and differentiation. Dysregulated trophic signaling could lead to various neurological disorders. Charcot-Marie-Tooth type 2B (CMT2B) is one of the most common inherited peripheral neuropathies characterized by severe terminal axonal loss. Genetic analysis of human CMT2B patients has revealed four missense point mutations in Rab7, a small GTPase that regulates late endosomal/lysosomal pathways, but the exact pathological mechanism remains poorly understood. Here, we show that these Rab7 mutants dysregulated axonal transport and diminished the retrograde signaling of NGF and its TrkA receptor. We found that all CMT2B Rab7 mutants were transported significantly faster than Rab7wt in the anterograde direction, accompanied with an increased percentile of anterograde Rab7-vesicles within axons of rat E15.5 dorsal root ganglion (DRG) neurons. In PC12M cells, the CMT2B Rab7 mutants drastically reduced the level of surface TrkA and NGF binding, presumably by premature degradation of TrkA. On the other hand, siRNA knock-down of endogenous Rab7 led to the appearance of large TrkA puncta in enlarged Rab5-early endosomes within the cytoplasm, suggesting delayed TrkA degradation. We also show that CMT2B Rab7 mutants markedly impaired NGF-induced Erk1/2 activation and differentiation in PC12M cells. Further analysis revealed that CMT2B Rab7 mutants caused axonal degeneration in rat E15.5 DRG neurons. We propose that Rab7 mutants induce premature degradation of retrograde NGF-TrkA trophic signaling, which may potentially contribute to the CMT2B disease.
Kernel Bayes' Rule

A nonparametric kernel-based method for realizing Bayes' rule is proposed, based on kernel representations of probabilities in reproducing kernel Hilbert spaces. The prior and conditional probabilities are expressed as empirical kernel mean and covariance operators, respectively, and the kernel mean of the posterior distribution is computed in the form of a weighted sample. The kernel Bayes' rule can be applied to a wide variety of Bayesian inference problems: we demonstrate Bayesian computation without likelihood, and filtering with a nonparametric state-space model. A consistency rate for the posterior estimate is established.
Mechanisms of Maggot-Induced Wound Healing: What Do We Know, and Where Do We Go from Here?

Medicinal maggots are believed to have three major mechanisms of action on wounds, brought about chemically and through physical contact: debridement (cleaning of debris), disinfection, and hastened wound healing. Until recently, most of the evidence for these claims was anecdotal; but the past 25 years have seen an increase in the use and study of maggot therapy. Controlled clinical studies are now available, along with laboratory investigations that examine the interaction of maggot and host on a cellular and molecular level. This review was undertaken to extract the salient data, make sense, where possible, of seemingly conflicting evidence, and reexamine our paradigm for maggot-induced wound healing. Clinical and laboratory data strongly support claims of effective and efficient debridement. Clinical evidence for hastened wound healing is meager, but laboratory studies and some small, replicated clinical studies strongly suggest that maggots do promote tissue growth and wound healing, though it is likely only during and shortly after the period when they are present on the wound. The best way to evaluate—and indeed realize—maggot-induced wound healing may be to use medicinal maggots as a “maintenance debridement” modality, applying them beyond the point of gross debridement.
Weighted Majorization Algorithms for Weighted Least Squares Decomposition Models

For many least-squares decomposition models efficient algorithms are well known. A more difficult problem arises in decomposition models where each residual is weighted by a nonnegative value. A special case is principal components analysis with missing data. Kiers (1997) discusses an algorithm for minimizing weighted decomposition models by iterative majorization. In this paper, we for computing a solution. We will show that the algorithm by Kiers is a special case of our algorithm. Here, we will apply weighted majorization to weighted principal components analysis, robust Procrustes analysis, and logistic bi-additive models of which the two parameter logistic model in item response theory is a special case. Simulation studies show that weighted majorization is generally faster than the method by Kiers by a factor one to four and obtains the same or better quality solutions. For logistic bi-additive models, we propose a new iterative majorization algorithm called logistic majorization.
Path finding and Collision Avoidance in Crowd Simulation

Motion planning for multiple entities or a crowd is a challenging problem in today’s virtual environments. We describe in this paper a system designed to simulate pedestrian behaviour in crowds in real time, concentrating particularity on collision avoidance. On-line planning is also referred as the navigation problem. Additional difficulties in approaching navigation problem are that some environments are dynamic. In our model we adopted a popular methodology in computer games, namely A* algorithm. The idea behind A* is to look for the shortest possible routes to the destination not through exploring exhaustively all the possible combination but utilizing all the possible directions at any given point. The environment is formed in regions and the algorithm is used to find a path only in visual region. In order to deal with collision avoidance, priority rules are given to some entities as well as some social behaviour.
C++CSP2: A Many-to-Many Threading Model for Multicore Architectures

The advent of mass-market multicore processors provides exciting new op- portunities for parallelism on the desktop. The original C++CSP - a library providing concurrency in C++ - used only user-threads, which would have prevented it taking advantage of this parallelism. This paper details the devel opment of C++CSP2, which has been built around a many-to-many threading model that mixes user-threads and kernel-threads, providing maximum flexibility in taking ad vantage of multicore and multi-processor machines. New and existing algorithms are described for dealing with the run-queue and implementing channels, barriers and mutexes. The latter two are benchmarked to motivate the choice of algorithm. Most of these algorithms are based on the use of atomic instructions, to gain maximal speed and efficiency. Other issues related to the new design and related to implementing concurrency in a language like C++ that has no direct support for it, are also described. The C++CSP2 library will be publicly released under the LGPL before CPA 2007.
Aluminum Nanoparticles Induce ERK and p38MAPK Activation in Rat Brain

Aluminum nanoparticles (Al-NPs) are one of the most widely used nanomaterial in cosmetics and medical materials. For this reason, Al-NP exposure is very likely to occur via inhalation in the environment and the workplace. Nevertheless, little is known about the mechanism of Al-NP neurotoxicity via inhalation exposure. In this study, we investigated the effect AL-NPs on the brain. Rats were exposed to Al-NPs by nasal instillation at 1 mg/kg body weight (low exposure group), 20 mg/kg body weight (moderate exposure group), and 40 mg/kg body weight (high exposure group), for a total of 3 times, with a 24-hr interval after each exposure. Inductively coupled plasma mass spectrometry (ICP-MS) analysis indicated that the presence of aluminum was increased in a dose-dependent manner in the olfactory bulb (OFB) and the brain. In microarray analysis, the regulation of mitogen-activated protein kinases (MAPK) activity (GO: 0043405), including Ptprc, P2rx7, Map2k4, Trib3, Trib1, and Fgd4 was significantly over-expressed in the treated mice than in the controls ( p = 0.0027). Moreover, Al-NPs induced the activation of ERK1 and p38 MAPK protein expression in the brain, but did not alter the protein expression of JNK, when compared to the control. These data demonstrate that the nasal exposure of Al-NPs can permeate the brain via the olfactory bulb and modulate the gene and protein expression of MAPK and its activity.
Optimizing Computational Performance for Real-Time Mapping with Airborne Laser Scanning

This paper focuses on evaluating and optimizing the computation procedures for achieving real-time mapping with modern Airborne Laser Scanning (ALS) instrumentation. The computation steps under consideration include real-time point cloud generation at full resolution, strip-wise analyses of scanning density and coverage, error propagation, ground classification and digital terrain and surface model production and DTM quality map. Different approaches for each mentioned step are implemented within a custom system and evaluated under real mapping scenarios for speed and correctness. The investigation reveals that real-time generation of laser point cloud (georeferencing) is feasible with conventional laptop for scanning rates up to 200 kHz. Further it is shown that the analyses of point cloud density and coverage are less computationally demanding, so is the calculation of digital surface model (DSM) and generation of corresponding hillshade image. Therefore, these tasks can be completed shortly after each flight line. On the other hand, the most computationally demanding tasks are identified as point-cloud classification required for DTM production as well as the error propagation. The latter tasks are therefore examined for optimizing their execution speed while maintaining correctness. The retained solutions are presented in the paper together with recommendation for further development.