Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Human Auricular Cartilage to Guide Tissue-Engineered Nanofiber-Based Chondrogenesis

Objective. Nanofiber-supported, in vitro–generated cartilage may represent an optimal starting material for the development of a cartilage implant for use in microtia reconstruction. To do so, the authors aim to first characterize the molecular composition of endogenous auricular cartilage and determine if human umbilical cord mesenchymal stem cells (hUCMSCs) can be differentiated into cartilage in vitro. Study Design. Prospective, controlled. Setting. Academic research laboratory. Subjects and Methods. Human ear cartilage from normal adults, pediatric patients with microtia, and pediatric patients with preauricular appendages (n = 2) was analyzed for collagens I, II, and X and elastin expression. In parallel, hUCMSCs were cultured on either polycaprolactone (PCL) or D, L-lactide-co-glycolic acid (PLGA) nanofiber scaffolds for 21 days under chondrogenic conditions. Cells were harvested for histologic, biochemical, and quantitative polymerase chain reaction analysis. Control cells were grown under both chondrogenic and nonchondrogenic conditions in the absence of nanofiber scaffolds. Results. Histological analysis of human ear cartilage revealed similar levels and distribution of collagens I and X and elastin. Collagen II was not highly expressed in the microtia samples. hUCMSC cultures stained positively for glycosaminosglycans (GAG) and sulfated proteoglycans. Compared to control cells, hUCMSCs grown on PLGA nanofiber scaffolds had a higher differentiation index (P ≤ .012) and higher levels of collagen X mRNA expression (P ≤ .006). Conclusion. These data provide information regarding the composition of endogenous ear cartilage and suggest that hUCMSCs grown on PLGA nanofiber scaffolds may represent an optimal starting material for the development of a cartilage implant for use in microtia reconstruction.
A genetic-fuzzy algorithm for the articulatory imitation of facial movements during vocalization of a humanoid robot

In human heads there is a strong structural linkage between vocal tract and facial behavior during speech. For a robotic talking head to have a human-like behavior, this linkage should be emulated. One way to do that is to compute an estimate of the articulatory features which produce a given utterance and then to transform them into facial animation. We present a computational model of human vocalization which is aimed at describing the articulatory mechanisms which produce spoken phonemes. It uses a set of fuzzy rules and genetic optimization. The former represents the relationships between places of articulations and speech acoustic parameters, while the latter estimates the degrees of membership of the places of articulation. That is, the places of articulation are considered as fuzzy sets whose degrees of membership are the articulatory features. The trajectories of articulatory parameters can be used to control a graphical or mechanical talking head. We verify the model presented here by generating and listening to artificial sentences. Subjective listening tests of artificially generated sentences from the articulatory description resulted in an average phonetic accuracy of about 79 %. Through the analysis of a large amount of natural speech, the algorithm can be used to learn the places of articulation of all phonemes of a given speaker
Rate Theory for Dislocation Loops Evolution in AL-6XN Austenitic Stainless Steel under Proton Irradiation

The average size and density evolution of dislocation loops in AL-6XN austenitic stainless steel, a candidate fuel cladding material for supercritical water-cooled reactor, under proton irradiation were simulated through a rate theory model. The simulation results exhibit relatively good agreement with the experimental results at 563 K. The size and density of defect clusters are calculated under irradiation temperature between 550 K and 900 K and irradiation doses up to 15 dpa which satisfies the working condition in supercritical water-cooled reactor. The fast nucleation between self-interstitials happens at the initial stage of irradiation. The average size of dislocation loops increases while the average density of these loops reduces with the increasing temperature, and the average density approaches to a constant when irradiated at higher irradiation doses. The mechanism is discussed based on the variation of rate constants of defect reactions and the variation of the diffusion coefficients of interstitials and dislocation loops with dose and temperature.
A Hardware-Based Software Protection Systems - Analysis of Security Dongles with Time Meters

Software piracy is the illegal duplication or the installation of copyrighted software on more computers than authorized under terms of the software license agreement. Globally, businesses and consumers will spend $300 billion on PC software over the next four years, according to International Data Corporation (IDC) estimates. Given the current market growth by country and current country-level piracy rates, during the same four-year period, we can expect almost $200 billion worth of PC software to be pirated [1]. Protecting application against not authorized copying using program solutions is impossible. If there is a possibility to start program, there is also a possibility to crackit. Cracking means that application can be used without being registered or to the application there is a mechanism added which enables to obviate implemented there protections. The device protection of software (dongles) has existed on the market for many years and it has functioned as devices protecting the software against not authorized copying and usage. The dongle significantly increases the level of security in a non-linear way, because a hardware key is an external device that is controlled and protected by the security solution provider and not the end-user. This gives the software security vendor freedom to develop specific hardware only for the purposes of enhancing security. This is not available in a software-based protection solution as it is dependent on the PC controlled by the end-user. The dissertation presents tools and techniques used to break devices' protection of software with time meters. The dongles with time meters contains an internal real-time clock, indicating the exact time and date. The dissertation provides information about the applied methods and techniques of breaking the hypothetical construction of device's protection with time meter. The research is the introduction to designing the hardware protection of programmes resistant to well-known methods of breaking through safety of electronic devices' keys.
Stereochemical Control in the Still-Wittig Rearrangement Synthesis of Cyclohexyl (Z)-Alkene Inhibitors of Pin1

Three stereoisomeric inhibitors of Pin1: (2R,5S)-, (2S,5R)- and (2S,5S)-Ac–pSer–Ψ[(Z)CH = C]–pipecolyl(Pip)–2-(2-naphthyl)ethylamine 1, that mimic L-pSer–D-Pro, D-pSer–L-Pro, and D-pSer–D-Pro amides respectively, were synthesized by a 13-step route. The newly formed stereogenic centers in the pipecolyl ring were introduced by Luche reduction, followed by stereospecific [2,3]-Still-Wittig rearrangement. The (Z)- to (E)-alkene ratio in the rearrangements were consistently 5.5 to 1. The stereochemistry at the original Ser α-carbon controlled the stereochemistry of the Luche reduction, but it did not affect the stereochemical outcome of the rearrangement, which consistently gave the (Z)-alkene. The epimerized by-product, (2S,5S)-10, resulting from the work-up after Na/NH3 debenzylation of (2S,5R)-9, was carried on to the (2S,5S)-1 isomer. Compound (2S,5S)-10 was resynthesized from the Luche reduction by-product, (2R,3R)-3, and the stereochemistry was confirmed by comparison of the optical rotations. The IC50 values for (2R,5S)-1, (2S,5R)-1 and (2S,5S)-1 Pin1 inhibition were: 52, 85, and 140 μM, respectively.
Classification with Ultrahigh-Dimensional Features

Although much progress has been made in classification with high-dimensional features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014}, classification with ultrahigh-dimensional features, wherein the features much outnumber the sample size, defies most existing work. This paper introduces a novel and computationally feasible multivariate screening and classification method for ultrahigh-dimensional data. Leveraging inter-feature correlations, the proposed method enables detection of marginally weak and sparse signals and recovery of the true informative feature set, and achieves asymptotic optimal misclassification rates. We also show that the proposed procedure provides more powerful discovery boundaries compared to those in \citet{CaiSun:2014} and \citet{JJin:2009}. The performance of the proposed procedure is evaluated using simulation studies and demonstrated via classification of patients with different post-transplantation renal functional types.
Numerical Study On Multi Layered Target Material Subjected To Impact Loading

In this study, the glass/epoxy composite laminate is layered with polyurethane foam/ polyurethane sheet and silicon carbide to analyse their response during high mass and low velocity impact. The silicon carbide is layered in two forms, one is as plate and the other is as inserts. The target materials are prepared in various combinations and the bonding of layers is done by using epoxy. Effectiveness of silicon carbide inserts and plates are compared in terms of their energy absorbing capacities. The numerical simulation is also carried for the target material with the same experimental conditions. The experimental results are compared with the numerical results for validation and a reasonably good agreement is found. Further, the validated numerical model is extended to understand the ballistic performance of the target material. It is observed that the introduction of silicon carbide as front layer improves both the structural and ballistic performance. Also, the damage in case of samples with silicon carbide inserts is localized as opposed to that of silicon carbide plate.
Robust receiver algorithms to mitigate partial-band and partial-time interference in ldpc-coded OFDM systems

Orthogonal frequency division multiplexing (OFDM) systems are vulnerable to narrow-band jamming signals. We jointly tackle two problems: channel estimation in the presence of unknown interference, and decoding with imperfect channel knowledge. In this paper, we propose robust, yet simple, receiver algorithms consisting of both channel estimation and information decoding. The receiver conducts threshold tests to detect interference followed by pilot erasure and channel estimation. Then, channel estimation error and unknown interference statistics are dealt with by the robust log-likelihood ratio (LLR) calculations for soft iterative decoding. The proposed receiver algorithm does not require any statistical knowledge of interference and its complexity is linear against the length of codewords. Simulation results show that the bit-error-rate (BER) performance of the proposed system is only 2~3 dB away from a genie system where channel information and interference parameters are perfectly known. We also demonstrate that soft decision feedback from a decoder to enhance channel estimation achieves additional 0.5 ~ 1dB improvement.
Morphological reconstruction of fluorescence molecular tomography based on nonlocal total variation regularization for tracer distribution in glioma

The high sensitivity and low cost of fluorescence imaging enables fluorescence molecular tomography (FMT) as a powerful noninvasive technique in applications of tracer distribution visualization. With the development of targeted fluorescence tracer, FMT has been widely used to localize the tumor. However, the visualization of probe distribution in tumor and surrounding region is still a challenge for FMT reconstruction. In this study, we proposed a novel nonlocal total variation (NLTV) regularization method, which is based on structure prior information. To build the NLTV regularization term, we consider the first order difference between the voxel and its four nearest neighbors. Furthermore, we assume that the variance of fluorescence intensity between any two voxels has a non-linear inverse correlation with their Gaussian distance. We adopted the Gaussian distance between two voxels as the weight of the first order difference. Meanwhile, the split Bregman method was applied to minimize the optimization problem. To evaluate the robustness and feasibility of our proposed method, we designed numerical simulation experiments and in vivo experiments of xenograft orthotopic glioma models. The ex vivo fluorescent images of cryoslicing specimens were regarded as gold standard of probe distribution in biological tissue. The results demonstrated that the proposed method could recover the morphology of the tracer distribution more accurately compared with fast iterated shrinkage (FIS) method, Split Bregman-resolved TV (SBRTV) regularization method and Gaussian weighted Laplace prior (GWLP) regularization method. These results demonstrate the potential of our method for in vivo visualization of tracer distribution in xenograft orthotopic glioma models.
C-MAC: Model-Driven Concurrent Medium Access Control for Wireless Sensor Networks

This paper presents C-MAC, a new MAC protocol designed to achieve high-throughput bulk communication for data-intensive sensing applications. C-MAC exploits concurrent wireless channel access based on empirical power control and physical interference models. Nodes running C-MAC estimate the level of interference based on the physical signal-to-interference-plus-noise-ratio (SINR) model and adjust the transmission power accordingly for concurrent channel access. C-MAC employs a block-based communication mode that not only amortizes the overhead of channel assessment, but also improves the probability that multiple nodes within the interference range of each other can transmit concurrently. C-MAC has been implemented in TinyOS-1.x and extensively evaluated on Tmote nodes. Our experiments show that C-MAC significantly outperforms the state-of-art CSMA protocol in TinyOS with respect to system throughput, delay and energy consumption.
Performance evaluation of evolutionary algorithms in classification of biomedical datasets

Biomedical datasets pose a unique challenge for machine learning and data mining techniques to extract accurate, comprehensible and hidden knowledge from them. In this paper, we comprehensively investigate the role of a biomedical dataset on the classification accuracy of an algorithm. To this end, we quantify the complexity of a biomedical dataset in terms of its missing values, imbalance ratio, noise and information gain. We have performed our experiments using six well-known evolutionary rule learning algorithms: XCS, UCS, GAssist, cAnt-Miner, SLAVE and Ishibuchi, on 31 publicly available biomedical datasets. The results of our experiments show that GAssist gives better classification accuracy among the compared schemes. However, the nature of a biomedical dataset -- not the selection of evolutionary algorithm -- plays a major role in determining the classification accuracy of a dataset. We further show that noise is a dominating factor in determining the complexity of a dataset and it is inversely proportional to the classification accuracy of all the algorithms. The complexity of biomedical dataset will prove useful to researchers in evaluating the classification potential of their dataset for automatic knowledge extraction.
Temporal Support for Persistent Stored Modules

We show how to extend temporal support of SQL to the Turing-complete portion of SQL, that of persistent stored modules (PSM). Our approach requires minor new syntax beyond that already in SQL/Temporal to define and to invoke PSM routines, thereby extending the current, sequenced, and non-sequenced semantics of queries to PSM routines. Temporal upward compatibility (existing applications work as before when one or more tables are rendered temporal) is ensured. We provide a transformation that converts Temporal SQL/PSM to conventional SQL/PSM. To support sequenced evaluation of PSM routines, we define two different slicing approaches, maximal slicing and per-statement slicing. We compare these approaches empirically using a comprehensive benchmark and provide a heuristic for choosing between them.
Rapid Response of the Yeast Plasma Membrane Proteome to Salt Stress*

The plasma membrane separates the cell from the external environment and plays an important role in the stress response of the cell. In this study, we compared plasma membrane proteome modifications of yeast cells exposed to mild (0.4 m NaCl) or high (1 m NaCl) salt stress for 10, 30, or 90 min. Plasma membrane-enriched fractions were isolated, purified, and subjected to iTRAQ labeling for quantitative analysis. In total, 88–109 plasma membrane proteins were identified and quantified. The quantitative analysis revealed significant changes in the abundance of several plasma membrane proteins. Mild salt stress caused an increase in abundance of 12 plasma membrane proteins, including known salt-responsive proteins, as well as new targets. Interestingly, 20 plasma membrane proteins, including the P-type H+-ATPase Pma1, ABC transporters, glucose and amino acid transporters, t-SNAREs, and proteins involved in cell wall biogenesis showed a significant and rapid decrease in abundance in response to both 0.4 m and 1 m NaCl. We propose that rapid protein internalization occurs as a response to hyper-osmotic and/or ionic shock, which might affect plasma membrane morphology and ionic homeostasis. This rapid response might help the cell to survive until the transcriptional response takes place.
Kernel Learning for Extrinsic Classification of Manifold Features

In computer vision applications, features often lie on Riemannian manifolds with known geometry. Popular learning algorithms such as discriminant analysis, partial least squares, support vector machines, etc., are not directly applicable to such features due to the non-Euclidean nature of the underlying spaces. Hence, classification is often performed in an extrinsic manner by mapping the manifolds to Euclidean spaces using kernels. However, for kernel based approaches, poor choice of kernel often results in reduced performance. In this paper, we address the issue of kernel selection for the classification of features that lie on Riemannian manifolds using the kernel learning approach. We propose two criteria for jointly learning the kernel and the classifier using a single optimization problem. Specifically, for the SVM classifier, we formulate the problem of learning a good kernel-classifier combination as a convex optimization problem and solve it efficiently following the multiple kernel learning approach. Experimental results on image set-based classification and activity recognition clearly demonstrate the superiority of the proposed approach over existing methods for classification of manifold features.
Coordinated Incorporation of Skeletal Muscle Dihydropyridine Receptors and Ryanodine Receptors in Peripheral Couplings of BC3H1 Cells

Rapid release of calcium from the sarcoplasmic reticulum (SR) of skeletal muscle fibers during excitation–contraction (e–c) coupling is initiated by the interaction of surface membrane calcium channels (dihydropyridine receptors; DHPRs) with the calcium release channels of the SR (ryanodine receptors; RyRs, or feet). We studied the early differentiation of calcium release units, which mediate this interaction, in BC3H1 cells. Immunofluorescence labelings of differentiating myocytes with antibodies against α1 and α2 subunits of DHPRs, RyRs, and triadin show that the skeletal isoforms of all four proteins are abundantly expressed upon differentiation, they appear concomitantly, and they are colocalized. The transverse tubular system is poorly organized, and thus clusters of e–c coupling proteins are predominantly located at the cell periphery. Freeze fracture analysis of the surface membrane reveals tetrads of large intramembrane particles, arranged in orderly arrays. These appear concomitantly with arrays of feet (RyRs) and with the appearance of DHPR/RyS clusters, confirming that the four components of the tetrads correspond to skeletal muscle DHPRs. The arrangement of tetrads and feet in developing junctions indicates that incorporation of DHPRs in junctional domains of the surface membrane proceeds gradually and is highly coordinated with the formation of RyR arrays. Within the arrays, tetrads are positioned at a spacing of twice the distance between the feet. The incorporation of individual DHPRs into tetrads occurs exclusively at positions corresponding to alternate feet, suggesting that the assembly of RyR arrays not only guides the assembly of tetrads but also determines their characteristic spacing in the junction.
Dimerization and Protein Binding Specificity of the U2AF Homology Motif of the Splicing Factor Puf60*

PUF60 is an essential splicing factor functionally related and homologous to U2AF65. Its C-terminal domain belongs to the family of U2AF (U2 auxiliary factor) homology motifs (UHM), a subgroup of RNA recognition motifs that bind to tryptophan-containing linear peptide motifs (UHM ligand motifs, ULMs) in several nuclear proteins. Here, we show that the Puf60 UHM is mainly monomeric in physiological buffer, whereas its dimerization is induced upon the addition of SDS. The crystal structure of PUF60-UHM at 2.2 Å resolution, NMR data, and mutational analysis reveal that the dimer interface is mediated by electrostatic interactions involving a flexible loop. Using glutathione S-transferase pulldown experiments, isothermal titration calorimetry, and NMR titrations, we find that Puf60-UHM binds to ULM sequences in the splicing factors SF1, U2AF65, and SF3b155. Compared with U2AF65-UHM, Puf60-UHM has distinct binding preferences to ULMs in the N terminus of SF3b155. Our data suggest that the functional cooperativity between U2AF65 and Puf60 may involve simultaneous interactions of the two proteins with SF3b155.
Optimal scaling of discrete approximations to Langevin diffusions

We consider the optimal scaling problem for proposal distributions in Hastings–Metropolis algorithms derived from Langevin diffusions. We prove an asymptotic diffusion limit theorem and show that the relative efficiency of the algorithm can be characterized by its overall acceptance rate, independently of the target distribution. The asymptotically optimal acceptance rate is 0.574. We show that, as a function of dimension n, the complexity of the algorithm is O(n1/3), which compares favourably with the O(n) complexity of random walk Metropolis algorithms. We illustrate this comparison with some example simulations.