Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding and Harnessing Self-Organization

A nonlinear dynamical system that exhibits limit cycle behaviour is investigated through numerical simulations of cardiac cells. This system's self-organizing behaviour is characterized aiming at harnessing it in an artificial system. Results of circuit simulations for different configurations of an architecture based on simple electronic elements are shown, characterizing self-organization in an artificial electronic circuit
Analytical Expressions for the Power Spectral Density of CP-OFDM and ZP-OFDM Signals

In this letter, analytical expressions are derived for the power spectral density (PSD) of orthogonal frequency division multiplex (OFDM) signals employing a cyclic prefix (CP-OFDM) or zero padding (ZP-OFDM) time guard interval. Under the relatively weak assumptions that (i) the data are independent and identically distributed on all OFDM subcarriers and (ii) the OFDM pulse shape is sufficiently localized in time, simple closed-form PSD expressions can be obtained. These expressions are then compared to existing OFDM PSD expressions and validated by inspecting the power spectra of some standardized OFDM signals.
A Novel Approach to Characterize Clonality and Differentiation of Human Melanoma-Specific T Cell Responses: Spontaneous Priming and Efficient Boosting by Vaccination1

Despite major progress in T lymphocyte analysis in melanoma patients, TCR repertoire selection and kinetics in response to tumor Ags remain largely unexplored. In this study, using a novel ex vivo molecular-based approach at the single-cell level, we identified a single, naturally primed T cell clone that dominated the human CD8+ T cell response to the Melan-A/MART-1 Ag. The dominant clone expressed a high-avidity TCR to cognate tumor Ag, efficiently killed tumor cells, and prevailed in the differentiated effector-memory T lymphocyte compartment. TCR sequencing also revealed that this particular clone arose at least 1 year before vaccination, displayed long-term persistence, and efficient homing to metastases. Remarkably, during concomitant vaccination over 3.5 years, the frequency of the pre-existing clone progressively increased, reaching up to 2.5% of the circulating CD8 pool while its effector functions were enhanced. In parallel, the disease stabilized, but subsequently progressed with loss of Melan-A expression by melanoma cells. Collectively, combined ex vivo analysis of T cell differentiation and clonality revealed for the first time a strong expansion of a tumor Ag-specific human T cell clone, comparable to protective virus-specific T cells. The observed successful boosting by peptide vaccination support further development of immunotherapy by including strategies to overcome immune escape.
Predicting Dialogue Acts for Intelligent Virtual Agents with Multimodal Student Interaction Data

Recent years have seen a growing interest in intelligent game-based learning environments featuring virtual agents. A key challenge posed by incorporating virtual agents in game-based learning environments is dynamically determining the dialogue moves they should make in order to best support students’ problem solving. This paper presents a data-driven modeling approach that uses a Wizard-of-Oz framework to predict human wizards’ dialogue acts based on a sequence of multimodal data streams of student interactions with a game-based learning environment. To effectively deal with multiple, parallel sequential data streams, this paper investigates two sequence-labeling techniques: long short-term memory networks (LSTMs) and conditional random fields. We train predictive models utilizing data corpora collected from two Wizard-of-Oz experiments in which a human wizard played the role of the virtual agent unbeknownst to the student. Empirical results suggest that LSTMs that utilize game trace logs and facial action units achieve the highest predictive accuracy. This work can inform the design of intelligent virtual agents that leverage rich multimodal student interaction data in game-based learning environments.
AER Auditory Filtering and CPG for Robot Control

Address-event-representation (AER) is a communication protocol for transferring asynchronous events between VLSI chips, originally developed for bio-inspired processing systems (for example, image processing). The event information in an AER system is transferred using a high-speed digital parallel bus. This paper presents an experiment using AER for sensing, processing and finally actuating a robot. The AER output of a silicon cochlea is processed by an AER filter implemented on a FPGA to produce rhythmic walking in a humanoid robot (Redbot). We have implemented both the AER rhythm detector and the central pattern generator (CPG) on a Spartan II FPGA which is part of a USB-AER platform developed by some of the authors
Recognition of human motion through a fuzzy hidden Markov model

A new type of Hidden Markov Model (HMM) developed based on the fuzzy clustering result is proposed for identification of human motion. By associating the human continuous movements with a series of human motion primitives, the complex human motion could be analysed as the same process as recognizing a word by alphabet. However, because the human movements can be multipaths and inherently stochastic, it is indisputable that a more sophisticated framework must be applied to reveal the statistic relationships among the different human motion primitives. Hence, based on the human motion recognition results derived from the fuzzy clustering function, HMM is modified by changing the formulation of the emission and transition matrices to analyse the human wrist motion. According to the experimental results, the complex human wrist motion sequence can be identified by the novel HMM holistically and efficiently.
Determining students’ academic failure profile founded on data mining methods

Exams failure among university students has long fed a large number of debates, many education experts seeking to comprehend and explicate it, and many statisticians have tried to predict it. Understanding, predicting and preventing the academic failure are complex and continuous processes anchored in past and present information collected from scholastic situations and studentspsila surveys, but also on scientific research based on data mining technologies. In the current article the authors illustrate their experiments in the educational area, based on classification learning and data clustering techniques, made in order to draw up the studentspsila profile for exam failure/success.
Achievements and Challenges of Metamorphic Testing

A fundamental problem software testing faces is the so called "test oracle problem", which restricts the development of software testing techniques significantly. Metamorphic Testing (MT) can provide a way to determine the correctness of testing outputs with metamorphic relations between a series of outputs that correspond to a series of inputs, which is regarded as one of the most effective techniques to alleviate oracle problem. This paper reviews previous works on MT and proposes the major challenges of MT, with the goal of supporting future practices and researches in this area.
Aptamer-Dendrimer Bioconjugates for Targeted Delivery of miR-34a Expressing Plasmid and Antitumor Effects in Non-Small Cell Lung Cancer Cells

Metastasis and drug resistance are major barriers for the treatment of non-small cell lung cancer (NSCLC). To explore new therapeutic options, we successfully encapsulated MicroRNA-34a (miR-34a), a potent endogenous tumor suppressor in NSCLC into S6 aptamer-conjugated dendrimer to form lung cancer-targeted gene delivery nanoparticles (PAM-Ap/pMiR-34a NPs). PAM-Ap/pMiR-34a NPs had a diameter of 100–200 nm and Zeta potential of ~30 mV at applied N/P ratio. The aptamer conjugation significantly improved cellular uptake as well as gene transfection efficiency of PAM-Ap/pMiR-34a NPs in cultured NSCLC cells. We showed that PAM-Ap/pMiR-34a NPs enhanced the regulation of targeted genes, BCL-2 and p53 in vitro. In addition, we revealed PAM-Ap/pMiR-34a NPs significantly inhibited cell growth, migration, invasion and induced apoptosis of lung cancer cells compared with non-targeted NPs. The method provided a novel therapeutic strategy for the experimental treatment of NSCLC.
Superficial Harmony and Conflict Avoidance Resulting from Negative Anticipation in the Workplace

ABSTRACT This research examines how people perceive and respond to potential conflict in work settings. When individuals highly value their interpersonal relationships with others, they may take the potential costs to relationships into consideration in deciding how to handle conflict. We propose that individuals take an avoidance approach to conflict to prevent disruption in relationships from confrontation. Specifically, the value that individuals place on superficial harmony is positively related to their negative anticipation of relationship costs, which in turn leads to conflict avoidance. Furthermore, the direct relationship between superficial harmony and negative anticipation and the indirect relationship between superficial harmony and conflict avoidance are negatively moderated by the closeness of relations between the parties involved. The results of two studies conducted in workplace settings supported our hypothesized moderated mediation model. Highlighting the role of superficial harmony in conflict avoidance, this research contributes to the existing literature on conflict management and has practical implications for effectively managing conflict in the workplace.
DiNAMIC: a method to identify recurrent DNA copy number aberrations in tumors

Motivation: DNA copy number gains and losses are commonly found in tumor tissue, and some of these aberrations play a role in tumor genesis and development. Although high resolution DNA copy number data can be obtained using array-based techniques, no single method is widely used to distinguish between recurrent and sporadic copy number aberrations. Results: Here we introduce Discovering Copy Number Aberrations Manifested In Cancer (DiNAMIC), a novel method for assessing the statistical significance of recurrent copy number aberrations. In contrast to competing procedures, the testing procedure underlying DiNAMIC is carefully motivated, and employs a novel cyclic permutation scheme. Extensive simulation studies show that DiNAMIC controls false positive discoveries in a variety of realistic scenarios. We use DiNAMIC to analyze two publicly available tumor datasets, and our results show that DiNAMIC detects multiple loci that have biological relevance. Availability: Source code implemented in R, as well as text files containing examples and sample datasets are available at http://www.bios.unc.edu/research/genomic_software/DiNAMIC. Contact: vwalter@email.unc.edu; fwright@bios.unc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Functional consequences of prolactin signalling in endothelial cells: a potential link with angiogenesis in pathophysiology?

Prolactin is best known as the polypeptide anterior pituitary hormone, which regulates the development of the mammary gland. However, it became clear over the last decade that prolactin contributes to a broad range of pathologies, including breast cancer. Prolactin is also involved in angiogenesis via the release of pro‐angiogenic factors by leukocytes and epithelial cells. However, whether prolactin also influences endothelial cells, and whether there are functional consequences of prolactin‐induced signalling in the perspective of angiogenesis, remains so far elusive. In the present study, we show that prolactin induces phosphorylation of ERK1/2 and STAT5 and induces tube formation of endothelial cells on Matrigel. These effects are blocked by a specific prolactin receptor antagonist, del1‐9‐G129R‐hPRL. Moreover, in an in vivo model of the chorioallantoic membrane of the chicken embryo, prolactin enhances vessel density and the tortuosity of the vasculature and pillar formation, which are hallmarks of intussusceptive angiogenesis. Interestingly, while prolactin has only little effect on endothelial cell proliferation, it markedly stimulates endothelial cell migration. Again, migration was reverted by del1‐9‐G129R‐hPRL, indicating a direct effect of prolactin on its receptor. Immunohistochemistry and spectral imaging revealed that the prolactin receptor is present in the microvasculature of human breast carcinoma tissue. Altogether, these results suggest that prolactin may directly stimulate angiogenesis, which could be one of the mechanisms by which prolactin contributes to breast cancer progression, thereby providing a potential tool for intervention.
Radio Sleep Mode Optimization in Wireless Sensor Networks

Energy efficiency is a central challenge in sensor networks, and the radio is a major contributor to overall energy node consumption. Current energy-efficient MAC protocols for sensor networks use a fixed low-power radio mode for putting the radio to sleep. Fixed low-power modes involve an inherent trade-off: deep sleep modes have low current draw and high energy cost and latency for switching the radio to active mode, while light sleep modes have quick and inexpensive switching to active mode with a higher current draw. This paper proposes adaptive radio low-power sleep modes based on current traffic conditions in the network. It first introduces a comprehensive node energy model, which includes energy components for radio switching, transmission, reception, listening, and sleeping, as well as the often disregarded microcontroller energy component for determining the optimal sleep mode and MAC protocol to use for given traffic scenarios. The model is then used for evaluating the energy-related performance of our recently proposed RFID impulse protocol enhanced with adaptive low-power modes, and comparing it against BMAC and IEEE 802.15. 4, for both MicaZ and TelosB platforms under varying data rates. The comparative analysis confirms that RFID impulse with adaptive low-power modes provides up to 20 times lower energy consumption than IEEE 802.15. 4 in low traffic scenario. The evaluation also yields the optimal settings of low-power modes on the basis of data rates for each node platform, and provides guidelines and a simple algorithm for the selection of appropriate MAC protocol, low-power mode, and node platform for a given set of traffic requirements of a sensor network application.
Reversible binding of actin to gelsolin and profilin in human platelet extracts

This paper documents the reversible appearance of high-affinity complexes of profilin and gelsolin with actin in extracts of platelets undergoing activation and actin assembly. Sepharose beads coupled to either monoclonal anti-gelsolin antibodies or to polyproline were used to extract gelsolin and profilin, respectively, from EGTA-containing platelet extracts and determine the proportion of these molecules bound to actin with sufficient affinity to withstand dilution (high-affinity complexes). Resting platelets (incubated for 30 min at 37 degrees C after gel filtration) contained nearly no high-affinity actin/gelsolin or actin/profilin complexes. Thrombin, within seconds, caused quantitative conversion of platelet profilin and gelsolin to high- affinity complexes with actin, but these complexes were not present 5 min after stimulation. The calcium-dependent actin filament-severing activity of platelet extracts, a function of free gelsolin, fell in concert with the formation of EGTA-stable actin/gelsolin complexes, and rose when the adsorption experiments indicated that free gelsolin was restored. The dissociation of high-affinity complexes was temporally correlated with the accumulation of actin in the Triton-insoluble cytoskeleton.
Comb-drive actuators for large displacements

The design, fabrication and experimental results of lateral-comb-drive actuators for large displacements at low driving voltages is presented. A comparison of several suspension designs is given, and the lateral large deflection behaviour of clamped - clamped beams and a folded flexure design is modelled. An expression for the axial spring constant of folded flexure designs including bending effects from lateral displacements, which reduce the axial stiffness, is also derived. The maximum deflection that can be obtained by comb-drive actuators is bounded by electromechanical side instability. Expressions for the side-instability voltage and the resulting displacement at side instability are given. The electromechanical behaviour around the resonance frequency is described by an equivalent electric circuit. Devices are fabricated by polysilicon surface micromachining techniques using a one-mask fabrication process. Static and dynamic properties are determined experimentally and are compared with theory. Static properties are determined by displacement-to-voltage, capacitance-to-voltage and pull-in voltage measurements. Using a one-port approach, dynamic properties are extracted from measured admittance plots. Typical actuator characteristics are deflections of about at driving voltages around 20 V, a resonance frequency around 1.6 kHz and a quality factor of approximately 3.
Four-dimensional realistic modeling of pancreatic organogenesis

Organogenesis, the process by which organs develop from individual precursor stem cells, requires that the precursor cells proliferate, differentiate, and aggregate to form a functioning structure. This process progresses through changes in 4 dimensions: time and 3 dimensions of space—4D. Experimental analysis of organogenesis, by its nature, cuts the 4D developmental process into static, 2D histological images or into molecular or cellular markers and interactions with little or no spatial dimensionality and minimal dynamics. Understanding organogenesis requires integration of the piecemeal experimental data into a running, realistic and interactive 4D simulation that allows experimentation and hypothesis testing in silico. Here, we describe a fully executable, interactive, visual model for 4D simulation of organogenic development using the mouse pancreas as a representative case. Execution of the model provided a dynamic description of pancreas development, culminating in a structure that remarkably recapitulated morphologic features seen in the embryonic pancreas. In silico mutations in key signaling molecules resulted in altered patterning of the developing pancreas that were in general agreement with in vivo data. The modeling approach described here thus typifies a useful platform for studying organogenesis as a phenomenon in 4 dimensions.
An Efficient Approach to Processing Massive RFID Data in Supply Chain

Radio Frequency Identification (RFID) is widely used to track and trace objects in supply chain management. However, massive uncertain data produced by RFID readers are not suitable for directly use in RFID applications. This is due to repetitive readings which are unnecessary because it contains only the same information. Thus, an approach to remove repetitive readings in supply chain is paramount to minimize massive data storage that could affect query performances. We propose a simplified approach, which is suitable for a wide range of application scenarios. Experimental evaluations show that our approach is effective and efficient in terms of the removing duplicate readings and compressing the massive data significantly.
ADAPTED DOWNHILL SIMPLEX METHOD 131 Convertible bonds are characterized by the following options : The Issuers Options 1

The paper is devoted to modeling optimal exercise strategies of the behavior of investors and issuers working with convertible bonds. This implies solution of the problems of stock price modeling, payoff computation and minimax optimization. Stock prices (underlying asset) were modeled under the assumption of the geometric Brownian motion of their values. The Monte Carlo method was used for calculating the real payoff which is the objective function. The minimax optimization problem was solved using the derivative-free Downhill Simplex method. The performed numerical experiments allowed to formulate recommendations for the choice of appropriate size of the initial simplex in the Downhill Simplex Method, the number of generated trajectories of underlying asset, the size of the problem and initial trajectories of the behavior of investors and issuers.
Metallothionein 2 (SaMT2) from Sedum alfredii Hance Confers Increased Cd Tolerance and Accumulation in Yeast and Tobacco

Metallothioneins are cysteine-rich metal-binding proteins. In the present study, SaMT2, a type 2 metallothionein gene, was isolated from Cd/Zn co-hyperaccumulator Sedum alfredii Hance. SaMT2 encodes a putative peptide of 79 amino acid residues including two cysteine-rich domains. The transcript level of SaMT2 was higher in shoots than in roots of S. alfredii, and was significantly induced by Cd and Zn treatments. Yeast expression assay showed SaMT2 significantly enhanced Cd tolerance and accumulation in yeast. Ectopic expression of SaMT2 in tobacco enhanced Cd and Zn tolerance and accumulation in both shoots and roots of the transgenic plants. The transgenic plants had higher antioxidant enzyme activities and accumulated less H2O2 than wild-type plants under Cd and Zn treatment. Thus, SaMT2 could significantly enhance Cd and Zn tolerance and accumulation in transgenic tobacco plants by chelating metals and improving antioxidant system.
Through a different gate: An automated content analysis of how online news and print news differ

We investigate how news values differ between online and print news articles. We hypothesize that print and online articles differ in terms of news values because of differences in the routines used to produce them. Based on a quantitative automated content analysis of N = 762,095 Dutch news items, we show that online news items are more likely to be follow-up items than print items, and that there are further differences regarding news values like references to persons, the power elite, negativity, and positivity. In order to conduct this large-scale analysis, we developed innovative methods to automatically code a wide range of news values. In particular, this article demonstrates how techniques such as sentiment analysis, named entity recognition, supervised machine learning, and automated queries of external databases can be combined and used to study journalistic content. Possible explanations for the difference found between online and offline news are discussed.
Usage-based Traffic Control for P2P Content Delivery

Recently, content delivery services have grown rapidly over the Internet. For ASPs (Application Service Provider) providing content delivery services, P2P architecture is beneficial to reduce outgoing traffic from content servers. On the other hand, ISPs are suffering from the increase in P2P traffic. The P2P traffic is unnecessarily redundant because the same content or the same fractions of content are transferred through an inter-ISP link several times. Subscriber ISPs have to pay a transit fee to upstream ISPs based on the volume of inter-ISP traffic. In order to solve such problems, several works have been done for the purpose of P2P traffic reduction. However, these existing works cannot control the traffic volume of a certain link. In order to solve such an ISP’s operational requirement, we propose a method to control traffic volume for a link within a preconfigured upper bound value. We evaluated that the proposed method works well by conducting a simulation on a 1,000-user scale. We confirm that the traffic volume could be controlled at a lower level than the upper bound for all evaluated conditions. Moreover, our method could control the traffic volume at 98.95% link usage against the target value. Keywords—P2P, traffic control, traffic localization, ALTO.