Miracle-dz/UniFAR-FTUs huggingface dataset:
Compression and self-entanglement of single DNA molecules under uniform electric field

We experimentally study the effects of a uniform electric field on the conformation of single DNA molecules. We demonstrate that a moderate electric field (∼200 V/cm) strongly compresses isolated DNA polymer coils into isotropic globules. Insight into the nature of these compressed states is gained by following the expansion of the molecules back to equilibrium after halting the electric field. We observe two distinct types of expansion modes: a continuous molecular expansion analogous to a compressed spring expanding, and a much slower expansion characterized by two long-lived metastable states. Fluorescence microscopy and stretching experiments reveal that the metastable states are the result of intramolecular self-entanglements induced by the electric field. These results have broad importance in DNA separations and single molecule genomics, polymer rheology, and DNA-based nanofabrication.
Characterization of indoor radiowave propagation at university buildings

Indoor radiowave propagation is studied by mathematical models of 5 and 7 ray multipath. Frequency response and impulse response are calculated. Measurement of spatial variation of receiver power were performed at 5 sites in university buildings. The power-distance gradients were found. The penetration loss of walls was calculated from measured data. Calculated cumulative distribution function (CDF) showed that spatial distribution of power resembles the log-normal distribution.
Prioritized optimal control: A hierarchical differential dynamic programming approach

This paper deals with the generation of motion for complex dynamical systems (such as humanoid robots) to achieve several concurrent objectives. Hierarchy of tasks and optimal control are two frameworks commonly used to this aim. The first one specifies control objectives as a number of quadratic functions to be minimized under strict priorities. The second one minimizes an arbitrary user-defined function of the future state of the system, thus considering its evolution in time. Our recent work on prioritized optimal control merges the advantages of both these methods. This paper reformulates the original prioritized optimal control algorithm with the precise goal of improving its computational speed. We extend the dynamic programming method to work with a hierarchy of tasks. We compared our approach in simulation with both our previous algorithm and classical optimal control. The measured computational improvement represents another step towards the application of prioritized optimal control for online model predictive control of humanoid robots. We believe that this could be the key to unlock the (so far unexploited) dynamic capabilities of these mechanical systems.
Improving Energy Consumption in Large Scale Wireless Sensor Networks with Multiple Mobile Sinks Deployment

In this paper, we consider the multiple sinks placement problem in energy constrained large-scale Wireless Sensor Networks (WSN). First, some fundamental design parameters in WSNs such as nodes deployment, the network architecture, sink velocity and transmission range, are investigated. Each of these parameters is analysed and discussed according to its influence on the energy consumption in a WSN. Second, a simple and efficient approach for the placement of multiple sinks within large-scale WSNs is proposed. The objective is to determine optimal sinks' positions that maximize the network lifetime by reducing energy consumption related to data transmissions from sensor nodes to different sinks. Balanced graph partitioning techniques are used to split the entire WSN into connected sub-networks. Smaller sub-networks are created, having similar characteristics and where energy consumption can be optimized independently but in the same way. Therefore, different approaches and mechanisms t hat enhance the network lifetime in small-size WSN can be deployed inside each sub-network. Performance results show that the proposed technique significantly enhances the network lifetime
A Bidirectional Dual-Frequency Retrodirective Array for Full-Duplex Communication Applications

A bidirectional dual-frequency retrodirective array is presented, which uses intermediate frequency (IF) phase conjugation as the design concept. The proposed array has the ability of working at two different interrogating frequencies. For each interrogating signal, the frequency is converted to the other. This array contains the attractive features of simultaneously achieving bidirectional dual-frequency retrodirectivity and high isolation between the receiving and retransmitting signals. It is suitable for a full-duplex communication system. The proposed array is fabricated and demonstrated. Monostatic and bistatic measurements have been done to validate the performance of the system.
Scattering from primary feeds with corrugated soft surfaces-effect on the secondary radiation pattern

It is shown that for small and medium-sized circular symmetric reflector antennas it is very important to include the effect of the multiple scattering between the feed and the reflector when determining the secondary pattern. In particular, when corrugated soft surfaces are used to improve the polarization characteristics of primary feeds, the corrugations will cause very high cross-polarization due to multiple scattering. This is very well predicted by the multiple scattering approach. <<ETX>>
Interactive Editing of C1-Continuous 2D Spline Deformations Using ECLES Method

We describe 2DSD, a general algorithm for interactive editing of smooth deformations of the plane defined by polynomial splines of degree 5 on an triangular mesh. 2DSD is based on ECLES, a flexible and general exact method for interactive editing of numeric parameters subject to linear or affine constraints. Each editing action is abstracted as setting a set of anchor points to new positions, and recomputing some derived points so as to satisfy the constraints. When there are multiple solutions, ECLES uses least squares to choose the closest solution to a suggested hint. The advantages of 2DSD include immunity to rounding errors, automatic selection of the derived points for each editing action, and a representation of the deformation whose complexity does not grow with as editing progresses. To validate our algorithm, we re-implemented an editor of space deformations designed to assist the matching of 3D models of flexible microorganisms to microscope images of the same.
Data selection for support vector machine classifiers

The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave minimization problem and solved by a finite number of linear programs. This minimal set of data points, which is the smallest number of support vectors that completely characterize a separating plane classifier, is considerably smaller than that required by a standard 1-norm support vector machine with or without feature selection. The proposed approach also incorporates a feature selection procedure that results in a minimal number of input features used by the classifier. Tenfold cross validation gives as good or better test results using the proposed minimal support vector machine (MSVM) classifier based on the smaller set of data points compared to a standard 1-norm support vector machine classifier. The reduction in data points used by an MSVM classifier over those used by a 1-norm SVM classifier averaged 66% on seven public datasets and was as high as 81%. This makes MSVM a useful incremental classification tool which maintains only a small fraction of a large dataset before merging and processing it with new incoming data.
Visualizing Validation of Protein Surface Classifiers

Many bioinformatics applications construct classifiers that are validated in experiments that compare their results to known ground truth over a corpus. In this paper, we introduce an approach for exploring the results of such classifier validation experiments, focusing on classifiers for regions of molecular surfaces. We provide a tool that allows for examining classification performance patterns over a test corpus. The approach combines a summary view that provides information about an entire corpus of molecules with a detail view that visualizes classifier results directly on protein surfaces. Rather than displaying miniature 3D views of each molecule, the summary provides 2D glyphs of each protein surface arranged in a reorderable, small‐multiples grid. Each summary is specifically designed to support visual aggregation to allow the viewer to both get a sense of aggregate properties as well as the details that form them. The detail view provides a 3D visualization of each protein surface coupled with interaction techniques designed to support key tasks, including spatial aggregation and automated camera touring. A prototype implementation of our approach is demonstrated on protein surface classifier experiments.
Regulation of IL-17A Production Is Distinct from IL-17F in a Primary Human Cell Co-culture Model of T Cell-Mediated B Cell Activation

Improper regulation of B cell responses leads to excessive production of antibodies and contributes to the development of autoimmune disease. T helper 17 (Th17) cells also drive the development of autoimmune disease, but the role of B cells in shaping Th17 cell-mediated immune responses, as well as the reciprocal regulation of B cell responses by IL-17 family cytokines, remains unclear. The aim of this study was to characterize the regulation of IL-17A and IL-17F in a model of T cell-dependent B cell activation. Stimulation of primary human B cell and peripheral blood mononuclear cell (BT) co-cultures with α-IgM and a non-mitogenic concentration of superantigens for three days promoted a Th17 cell response as evidenced by increased expression of Th17-related gene transcripts, including Il17f, Il21, Il22, and Il23r, in CD4 T cells, as well as the secretion of IL-17A and IL-17F protein. We tested the ability of 144 pharmacologic modulators representing 91 different targets or pathways to regulate IL-17A and IL-17F production in these stimulated BT co-cultures. IL-17A production was found to be preferentially sensitive to inhibition of the PI3K/mTOR pathway, while prostaglandin EP receptor agonists, including PGE2, increased IL-17A concentrations. In contrast, the production of IL-17F was inhibited by PGE2, but selectively increased by TLR2 and TLR5 agonists. These results indicate that IL-17A regulation is distinct from IL-17F in stimulated BT co-cultures and that this co-culture approach can be used to identify pathway mechanisms and novel agents that selectively inhibit production of IL-17A or IL-17F.
Gaussian Mixture Model with Local Consistency

Gaussian Mixture Model (GMM) is one of the most popular data clustering methods which can be viewed as a linear combination of different Gaussian components. In GMM, each cluster obeys Gaussian distribution and the task of clustering is to group observations into different components through estimating each cluster's own parameters. The Expectation-Maximization algorithm is always involved in such estimation problem. However, many previous studies have shown naturally occurring data may reside on or close to an underlying submanifold. In this paper, we consider the case where the probability distribution is supported on a submanifold of the ambient space. We take into account the smoothness of the conditional probability distribution along the geodesics of data manifold. That is, if two observations are close in intrinsic geometry, their distributions over different Gaussian components are similar. Simply speaking, we introduce a novel method based on manifold structure for data clustering, called Locally Consistent Gaussian Mixture Model (LCGMM). Specifically, we construct a nearest neighbor graph and adopt Kullback-Leibler Divergence as the distance measurement to regularize the objective function of GMM. Experiments on several data sets demonstrate the effectiveness of such regularization.
Identification of MicroRNAs Differentially Expressed in Lung Carcinoid Subtypes and Progression

Aim: To extensively explore microRNA expression profiles in lung carcinoids in correlation with clinical and pathological features. Methods: A PCR-based array was employed in the screening phase to analyze 752 microRNAs in a discovery set of 12 lung carcinoids, including 6 typical (3 with lymph node metastasis) and 6 atypical (3 with lymph node metastasis). The results were validated by means of real-time PCR in 37 carcinoids, including 22 typical (4 with lymph node metastasis) and 15 atypical (7 with lymph node metastasis), and 19 high-grade neuroendocrine carcinomas. Results: Unsupervised cluster analysis segregated the pilot cases into 3 distinct groups. Twenty-four microRNAs were differentially regulated in atypical versus typical carcinoids, and 29 in metastatic versus nonmetastatic cases. Eleven microRNAs were selected for validation. All but 1 were significantly different among lung neuroendocrine tumor histotypes. Moreover, 5 (miR-129-5p, miR-409-3p, miR-409-5p, miR-185 and miR-497) were significantly upregulated in typical compared to atypical carcinoids. MiR-409-3p, miR-409-5p and miR-431-5p were also significantly downregulated in carcinoids metastatic to the lymph nodes. Predictive in silico analysis of specific target genes showed that these 3 latter microRNAs linked to metastatic potential are implicated in several cellular functions and highlighted several novel genes which may be worth exploring. Conclusions: Our findings demonstrate that lung carcinoids have distinct microRNA expression profiles as compared to high-grade neuroendocrine carcinomas and that specific microRNAs might have potential implications as diagnostic tools or clinical biomarkers.
Active control of the lateral buckling of nitinol-reinforced composite beams

The lateral buckling characteristics of flexible composite beams are actively controlled by activating sets of shape memory alloy (NITINOL) fibers which are embedded along the mid- plane of these beams. With such active control capabilities, the beams can be manufactured from light weight sections without compromising their elastic stability. This feature will be invaluable in building light weight structures that have high resistance to failure due to buckling. Finite element models are developed to analyze the mechanical and thermal characteristics of the composite beams. The models account for the individual contributions of the matrix, the NITINOL fibers and the shape memory effect to the overall performance of the composite beams. A closed-loop computer-controlled system is built to validate the finite element model. The system is used to control the lateral buckling of a composite beam which is 40.6 cm long, 5.1 cm wide and . 25 cm thick which is reinforced with two NITINOL-55 fibers that are 0.55 mm in diameter. The results obtained confirm the validity of the developed theoretical model and indicate that the buckled beams can be brought back completely to their unbuckled configuration by proper activation of the shape memory effect. Also, the results obtained suggest that reinforcing the beams with NITINOL fibers is effective in enhancing their lateral buckling characteristics.
Fault Localization in Multi-threaded C Programs Using Bounded Model Checking

Software debugging is a very time-consuming process, which is even worse for multi-threaded programs, due to the non-deterministic behavior of thread-scheduling algorithms. However, the debugging time may be greatly reduced, if automatic methods are used for localizing faults. In this study, a new method for fault localization, in multi-threaded C programs, is proposed. It transforms a multi-threaded program into a corresponding sequential one and then uses a fault-diagnosis method suitable for this type of program, in order to localize faults. The code transformation is implemented with rules and context switch information from counterexamples, which are typically generated by bounded model checkers. Experimental results show that the proposed method is effective, in such a way that sequential fault localization methods can be extended to multi-threaded programs.
Load-Deflection and Friction Properties of PEEK Wires as Alternative Orthodontic Wires

Polyetheretherketone (PEEK) is now attracting attention as an alternative to metal alloys in the dental field. In the present study, we evaluated the load-deflection characteristics of PEEK wires in addition to their frictional properties. Three types of PEEK wires are used: two sizes of rectangular shape, 0.016 × 0.022 in2 and 0.019 × 0.025 in2 (19-25PEEK), and rounded shape, diameter 0.016 in (16PEEK). As a control, Ni-Ti orthodontic wire, diameter 0.016 in, was used. The three-point bending properties were evaluated in a modified three-point bending system for orthodontics. The static friction between the orthodontic wire and the bracket was also measured. The load-deflection curves were similar among Ni-Ti and PEEK wires, except for 16PEEK with slot-lid ligation. The bending force of 19-25PEEK wire was comparable with that of Ni-Ti wire. 19-25PEEK showed the highest load at the deflection of 1500 μm (p < 0.05) in the case of slot-lid ligation. No significant differences were seen in the permanent deformation between Ni-Ti and all three PEEK wires (p > 0.05). No significant difference was seen in static friction between all three PEEK wires and Ni-Ti wire (p > 0.05). It is suggested that 19-25PEEK will be applicable for orthodontic treatment with the use of slot-lid ligation.
Preeclampsia and Gestational Hypertension Are Associated With Childhood Blood Pressure Independently of Family Adiposity Measures: The Avon Longitudinal Study of Parents and Children

Background— Offspring of women with hypertensive disorders of pregnancy are at increased risk of cardiovascular complications later in life, but the mechanisms underlying these associations are unclear. Our aim was to examine whether adjusting for birth weight and familial adiposity changed the association of hypertensive disorders of pregnancy with offspring blood pressure. Methods and Results— Using data from 6343 nine-year-old participants in the Avon Longitudinal Study of Parents and Children, we examined the association between hypertensive disorders of pregnancy (preeclampsia and gestational hypertension) and offspring blood pressure. Both preeclampsia and gestational hypertension were associated with systolic and diastolic blood pressures in the 9-year-old offspring; after adjustment for parental and own adiposity and for other potential confounders, the mean difference in systolic blood pressure was 2.05 mm Hg (95% confidence interval, 0.72 to 3.38) and 2.04 mm Hg (95% confidence interval, 1.42 to 2.67) for preeclampsia and gestational hypertension, respectively, compared with those with no hypertensive disorders of pregnancy. Equivalent results for diastolic blood pressure were 1.00 mm Hg (95% confidence interval, −0.01 to 2.10) and 1.07 mm Hg (95% confidence interval, 0.60 to 1.54). The association of preeclampsia with offspring systolic and diastolic blood pressures attenuated toward the null with further adjustment for birth weight and gestational age, whereas these adjustments did not attenuate the association of gestational hypertension with offspring blood pressure. Conclusions— The associations of hypertensive disorders of pregnancy with higher offspring blood pressure are not explained by familial adiposity. The mechanisms linking preeclampsia and gestational hypertension with offspring blood pressure may differ, with the former mediated at least in part by the effect of preeclampsia on intrauterine growth restriction.
Armless mitochondrial tRNAs in Enoplea (Nematoda)

The mitochondrial genome of metazoan animal typically encodes 22 tRNAs. Nematode mt-tRNAs normally lack the T-stem and instead feature a replacement loop. In the class Enoplea, putative mt-tRNAs that are even further reduced have been predicted to lack both the T- and the D-arm. Here we investigate these tRNA candidates in detail. Three lines of computational evidence support that they are indeed minimal functional mt-tRNAs: (1) the high level of conservation of both sequence and secondary structure, (2) the perfect preservation of the anticodons, and (3) the persistence of these sequence elements throughout several genome rearrangements that place them between different flanking genes.
Modeling narrative structure and dynamics with networks, sentiment analysis, and topic modeling

Human communication is invariably executed in the form of a narrative, an account of connected events comprising characters, actions, and settings. A coherent and well-structured narrative is therefore essential for effective communication, confusion caused by a haphazard attempt at storytelling being a common experience. This also suggests that a scientific understanding of how a narrative is formed and delivered is key to understanding human communication and dialog. Here we show that the definition of a narrative lends itself naturally to network-based modeling and analysis, and they can be further enriched by incorporating various text analysis methods from computational linguistics. We model the temporally unfolding nature of narrative as a dynamical growing network of nodes and edges representing characters and interactions, which allows us to characterize the story progression using the network growth pattern. We also introduce the concept of an interaction map between characters based on associated sentiments and topics identified from the text that characterize their relationships explicitly. We demonstrate the methods via application to Victor Hugo’s Les Misérables. Going beyond simple, aggregate occurrence-based methods for narrative representation and analysis, our proposed methods show promise in uncovering its essential nature of a highly complex, dynamic system that reflects the rich structure of human interaction and communication.
Simulation Model for the Studying of the Self-organizing Team

This paper proposes a multi-agent model for the studying of the self-organization team’s behavior. The model is dependent on the interaction rules and the decision factors of team members. Three kinds of team members (Reciprocators, Cooperators and Selfish) are proposed in the paper. The team members work with one of three work attitudes (diligence, average, shirking). A small-world network is suggested as the basic relationships of team members. It is supposed that team member’s decision of choosing work attitude depends on four decision factors, humanity, herd instinct, rationality, and follower tendency. A self-adaptive algorithm is suggested to achieve the four decision factors’ weights. The results of self-adaptive algorithm have different influence on the team’s behaviors under different fractions of Reciprocators. A number of experiments demonstrate that the multi-agent model is beneficial to manage the self-organizing team. The model should be available as a toolkit for the studying of the self-organizing team.