Miracle-dz/UniFAR-FTUs huggingface dataset:
Packaging-Competent Capsids of a Herpes Simplex Virus Temperature-Sensitive Mutant Have Properties Similar to Those of In Vitro-Assembled Procapsids

ABSTRACT Newcomb and coworkers (W. W. Newcomb, F. L. Homa, D. R. Thomsen, F. P. Booy, B. L. Trus, A. C. Steven, J. V. Spencer, and J. C. Brown, J. Mol. Biol. 263:432–446, 1996; W. W. Newcomb, F. L. Homa, D. R. Thomsen, Z. Ye, and J. C. Brown, J. Virol. 68:6059–6063, 1994) have recently described an in vitro herpes simplex virus (HSV) capsid assembly product which, because of certain parallels between its properties and those of bacteriophage proheads, they have designated the procapsid. As in their bacteriophage counterparts, there are marked differences between the structures of the two types of particle, and conversion from the procapsid to the capsid form requires extensive reconfiguration of the subunits. This reconfiguration occurs spontaneously upon extended in vitro incubation. One of the distinctive features of the HSV procapsids is that, unlike mature capsids, they are unstable and disassemble upon storage at 2°C. Using a mutant of HSV type 1 (ts1201), which has a lesion in the protease responsible for maturational cleavage of the scaffolding protein, we have demonstrated that capsids present within cells infected at nonpermissive temperatures are also cryosensitive and disappear if the cells are incubated at 0°C. This suggests that ts1201 capsids may resemble procapsids in structure. However,ts1201 capsids remain cryosensitive following extended incubation at an elevated temperature and, therefore, do not appear to undergo the spontaneous reconfiguration seen with in vitro-assembled procapsids. The lesion in ts1201 is reversible, and capsids formed at the nonpermissive temperature can undergo maturational cleavage and go on to form infectious virions following downshift to permissive temperatures. The sensitivity of ts1201 capsids to low temperatures is closely correlated with the cleavage status of the scaffolding protein, suggesting that proteolysis may act to trigger their conversion to the stable form. The experiments described here provide the firmest evidence yet that the procapsid has a biologically relevant role in the virus life cycle.
The effects of unnatural base pairs and mispairs on DNA duplex stability and solvation

In an effort to develop unnatural DNA base pairs we examined six pyridine-based nucleotides, d3MPy, d4MPy, d5MPy, d34DMPy, d35DMPy and d45DMPy. Each bears a pyridyl nucleobase scaffold but they are differentiated by methyl substitution, and were designed to vary both inter- and intra-strand packing within duplex DNA. The effects of the unnatural base pairs on duplex stability demonstrate that the pyridine scaffold may be optimized for stable and selective pairing, and identify one self pair, the pair formed between two d34DMPy nucleotides, which is virtually as stable as a dA:dT base pair in the same sequence context. In addition, we found that the incorporation of either the d34DMPy self pair or a single d34DMPy paired opposite a natural dA significantly increases oligonucleotide hybridization fidelity at other positions within the duplex. Hypersensitization of the duplex to mispairing appears to result from global and interdependent solvation effects mediated by the unnatural nucleotide(s) and the mispair. The results have important implications for our efforts to develop unnatural base pairs and suggest that the unnatural nucleotides might be developed as novel biotechnological tools, diagnostics, or therapeutics for applications where hybridization stringency is important.
Design of Non-Critical Path Resource Distributor for SMT Processors

The performance in simultaneous multithreading (SMT) processors is mainly determined by the distribution of the common resources among the threads. However, resource distribution methods often take cycles to calculate a resource allocation solution. Implementing a many-cycles resource distributor in the critical pipeline path will result in an innegligible impact on SMT processor performance. This work proposes a design of non-critical path resource distributor (NCPRD) for SMT processors, which separates the resource distribution from the critical pipeline path to avoid the clock wastage caused by the computation on the allocation solution. Our limit case study shows that, NCPRD benefits from its asynchronous work mode under both throughput and fairness metric in all type workloads but the memory-intensive workloads, and obtains more gains over the critical-path-involved resource distributor when the computation on the allocation solution spends more cycles.
Novel inclusion bodies in Betz cells of cortical area 4 of aged rhesus monkeys

Seven adult rhesus monkeys ranging in age from 5 to 35 years were studied in an ultrastructural investigation of cortical area 4. Some Betz cells of layer 5 contained a perikaryal inclusion body (IB) of a unique and characteristic ultrastructural configuration that has not previously been described. The IBs consist of a regular array of three sets of equidistant parallel sheets and are not membrane bound. The distance between the sheets measures ∼ 80 nm. The three sets of sheets intersect at an invariable angle of 60°, forming a regular geometric structure. Each sheet comprises a single layer of parallel and densely packed straight filaments, which are ∼10 nm thick. Thus the IBs present highly ordered crystalline arrays of filaments. The occurrence and number of IBs may be age related, since they appeared only in monkeys in mid to late adulthood and were most numerous in the oldest monkey. © 1992 Wiley‐Liss, Inc.
Content-based music similarity computation with relevant component analysis

Content-based music similarity is becoming important because of the millions of songs with online distribution. However, current methods have to treat the same attention on unrelated information or different informative information. In this paper, a new method is proposed to compute the music similarity with relevant component analysis. Considering the different weights for different parts, this method pays more attention on those parts with informative features by ranking the Mel-frequency cepstral coefficients frames on energy, and masters the song features more precisely compared with other methods. Experimental results on public dataset MusiClef show that this method works faster on music similarity computation task without sacrificing the accuracy of the similarity measurement.
Information Theoretic Approach to Address Delay and Reliability in Long On-Chip Interconnects

With shrinking feature size and growing integration density in the deep sub-micron technologies, the global buses are fast becoming the "weakest-links" in VLSI design. They have large delays and are error-prone. Especially, in system-on-chip (SoC) designs, where parallel interconnects run over large distances, the effects of crosstalk are detrimental to the overall system performance due to the large delays and un-reliability involved. This paper presents an information theoretic approach to address delay and reliability in long interconnects. A framework to calculate the capacity of a physical wire is laid out herein. The results for 8-bit wide buses of varying lengths in 0.1/mum technology are also presented. The wires are modeled based on their calculated parasitic (R,L,C) values and the coupling (C,L) parameters. Using this model, results are obtained for the data transfer capacity of long interconnects. It is seen that for wide buses, the signal delay distribution has a long tail, meaning that most signals arrive at the output much faster than the worst case delay. Using communication-theory, these "good" signals arriving early can be used to predict/correct the "few" signals arriving late. Further, results show that for every bus configuration, there exists an optimal frequency of transmission that will result in the maximum data transfer rate. Also, this optimal frequency is higher than the pessimistic worst case delay based clock design
The evolution of pattern camouflage strategies in waterfowl and game birds

Visual patterns are common in animals. A broad survey of the literature has revealed that different patterns have distinct functions. Irregular patterns (e.g., stipples) typically function in static camouflage, whereas regular patterns (e.g., stripes) have a dual function in both motion camouflage and communication. Moreover, irregular and regular patterns located on different body regions (“bimodal” patterning) can provide an effective compromise between camouflage and communication and/or enhanced concealment via both static and motion camouflage. Here, we compared the frequency of these three pattern types and traced their evolutionary history using Bayesian comparative modeling in aquatic waterfowl (Anseriformes: 118 spp.), which typically escape predators by flight, and terrestrial game birds (Galliformes: 170 spp.), which mainly use a “sit and hide” strategy to avoid predation. Given these life histories, we predicted that selection would favor regular patterning in Anseriformes and irregular or bimodal patterning in Galliformes and that pattern function complexity should increase over the course of evolution. Regular patterns were predominant in Anseriformes whereas regular and bimodal patterns were most frequent in Galliformes, suggesting that patterns with multiple functions are broadly favored by selection over patterns with a single function in static camouflage. We found that the first patterns to evolve were either regular or bimodal in Anseriformes and either irregular or regular in Galliformes. In both orders, irregular patterns could evolve into regular patterns but not the reverse. Our hypothesis of increasing complexity in pattern camouflage function was supported in Galliformes but not in Anseriformes. These results reveal a trajectory of pattern evolution linked to increasing function complexity in Galliformes although not in Anseriformes, suggesting that both ecology and function complexity can have a profound influence on pattern evolution.
Dietary intakes of elite female athletes in Greece.

BACKGROUND Although there is a great interest in sports in Greece, there are very few data regarding dietary intakes and habits of Greek elite female athletes. The present study assesses the dietary intakes and the energy balance of elite female athletes of four different sports (volleyball, middle distance running, ballet dancing, and swimming) and a non-athletic control group. METHODS Data were collected over two seasons, the training and the competitive, using 7-day weighed dietary records. Energy expenditure was calculated from 7-day activity records. Anthropometric measurements were also taken for all athletes. RESULTS Athletes and controls had similar BMI values. Per cent body fat was lower for athletes compared with controls. Between sports, middle distance runners had the lowest per cent body fat. No significant differences were found between mean energy intake of athletes and controls. Mean energy intake was found lower than calculated energy expenditure, for all four teams. Macronutrient and micronutrient intakes of the athletes were not statistically different from those of the non-athletic control group. Mean micronutrient intakes were found above the recommended values with the exception of iron. Both athletes and controls had a high intake of vitamin C that is a characteristic of the population of the Mediterranean countries. CONCLUSIONS Energy intakes varied between sports and between athletes of the same sport. Calculated energy expenditure was higher from the reported energy intake for most athletes. Athletes with the lowest energy intakes reported menstrual abnormalities.
Robust DOA estimation of heavily noisy gunshot signals

Direction of Arrival (DOA) estimation of gunshots is an important asset to law-enforcement agencies and defense forces, for shooter localization is key to improving public and troop safety. Solutions vary according to scenario, application, and available resources. As the distance between the firing position and the sensor array increases, as in a typical sniper scenario, signal to noise ratio decreases and the estimation degrades. This paper proposes a gunshot DoA estimation algorithm to be used with highly noisy signals. We combine exhaustive search for selecting pairs of microphones from the array to attain the best DOA estimation results and fast response time for different shooting scenarios. We are particularly interested in highly corrupted signals for which state-of-the-art algorithms fail. Experimental results from simulated and recorded gunshot signals were used to evaluate the performance of the proposed scheme.
Robustness Issues in a Data-Driven Spoken Language Understanding System

Robustness is a key requirement in spoken language understanding (SLU) systems. Human speech is often ungrammatical and ill-formed, and there will frequently be a mismatch between training and test data. This paper discusses robustness and adaptation issues in a statistically-based SLU system which is entirely data-driven. To test robustness, the system has been tested on data from the Air Travel Information Service (ATIS) domain which has been artificially corrupted with varying levels of additive noise. Although the speech recognition performance degraded steadily, the system did not fail catastrophically. Indeed, the rate at which the end-to-end performance of the complete system degraded was significantly slower than that of the actual recognition component. In a second set of experiments, the ability to rapidly adapt the core understanding component of the system to a different application within the same broad domain has been tested. Using only a small amount of training data, experiments have shown that a semantic parser based on the Hidden Vector State (HVS) model originally trained on the ATIS corpus can be straightforwardly adapted to the somewhat different DARPA Communicator task using standard adaptation algorithms. The paper concludes by suggesting that the results presented provide initial support to the claim that an SLU system which is statistically-based and trained entirely from data is intrinsically robust and can be readily adapted to new applications.
Inhibition of the Progesterone Nuclear Receptor during the Bone Linear Growth Phase Increases Peak Bone Mass in Female Mice

Augmentation of the peak bone mass (PBM) may be one of the most effective interventions to reduce the risk of developing osteoporosis later in life; however treatments to augment PBM are currently limited. Our study evaluated whether a greater PBM could be achieved either in the progesterone nuclear receptor knockout mice (PRKO) or by using a nuclear progesterone receptor (nPR) antagonist, RU486 in mice. Compared to their wild type (WT) littermates the female PRKO mice developed significantly higher cancellous and cortical mass in the distal femurs, and this was associated with increased bone formation. The high bone mass phenotype was partially reproduced by administering RU486 in female WT mice from 1–3 months of age. Our results suggest that the inhibition of the nPR during the rapid bone growth period (1–3 months) increases osteogenesis, which results in acquisition of higher bone mass. Our findings suggest a crucial role for progesterone signaling in bone acquisition and inhibition of the nPR as a novel approach to augment bone mass, which may have the potential to reduce the burden of osteoporosis.
Privacy-Preserving Access Control in Cloud Federations

A Cloud federation is a collaboration of organizations sharing data hosted on their private cloud infrastructures in order to exploit a common business opportunity. However, the adoption of cloud federations is hindered by member organizations' concerns on sharing their data with potentially competing organizations. For cloud federations to be viable, federated organizations' privacy concerns should be alleviated by providing mechanisms that allow organizations to control which users from other federated organizations can access which data. We propose the architecture of a novel identity and access management system part of FaaS, a cloud federation service developed by the H2020 SUNFISH project. Our system allows federated organizations to enforce attribute-based access control policies on their data in a privacy-preserving fashion. Users are granted access to federated data when their identity attributes match the policies, but without revealing their attributes in clear. The architecture relies on two novel technologies, blockchain and Intel SGX hardware platform to guarantee integrity of the policy evaluation process.
Classifier ensemble optimization for human activity recognition in smart homes

Recognizing human activities is an active research area due to its applicability in many applications, such as assistive living and healthcare. Currently, the major challenges in activity recognition include the reliability of prediction of each classifier as they differ according to smart homes characteristics. It is not possible that one classifier always performs better than all the other classifiers for every possible situation. Therefore, in this paper, a method for activity recognition is proposed by optimizing the output of multiple classifiers with evolutionary algorithm. We combine the measurement level output of different classifiers in terms of weights for each activity class to make up the ensemble. Classifier ensemble learner generates activity rules by optimizing the prediction accuracy of weighted feature vectors to obtain significant improvement over raw classification. For the evaluation of the proposed method, experiments are performed on two real datasets from CASAS smart home. The results show that our method systematically outperforms single classifier and traditional multiclass models.
Optimal subspaces and constrained principal component analysis

The main result of this article allows formulas of analytic geometry to be elegantly unified,by readily providing parametric as well as cartesian systems of equations. These systems characterize affine subspaces in Rp passing through specified affine subspaces of lower dimension. The problem solved is closely related to constrained principal component analysis. A few interest- ing applications are pointed out,notably a measure of the relative loss of optimality due to the constraints. The results pave the way for further research.
Dual-channel fiber surface plasmon resonance biological sensor based on a hybrid interrogation of intensity and wavelength modulation

Abstract. We investigate an intensity and wavelength modulation combined plasmon resonance-based fiber-optic sensor technology. Composed of gold nanoparticles (GNPs) and sandwich configuration of Au/indium tin oxide (ITO)/Au film, two sensing regions are fabricated separately along with unclad portions of the fiber-optic probe. It can simultaneously monitor both the light intensity from the Au NP channel and the wavelength from the Au/ITO/Au film channel with a single detector. As the refractive index (RI) of the external environment changes, the transmission intensity and resonance wavelength in the two channels are modified, which provides an interrogation of intensity and wavelength modulation. The sandwich film structure is formed using magnetron sputtering technology, and the GNPs functioning as localized surface plasmon resonators are coated on a multimode optical fiber via the layer-by-layer method. The experimental results reveal that the RI sensitivities of the two sensing channels are 334.1% RIU−1 and 1963.2 nm/RIU, respectively. Based on the above sensing design, we conduct real-time and label-free monitoring of IgG/anti-IgG and Con A/RNase B biomolecular interaction. The resonant dips excited by different sensing modes make it more attractive as a multichannel surface plasmon resonance analysis technology, which is valuable in biological and life sciences research and rapid diagnostics.
Bulge Hair Follicle Stem Cells Accelerate Cutaneous Wound Healing in Rats.

OBJECTIVE Skin wound healing is a serious clinical problem especially after surgery and severe injury of the skin. Cell therapy is an innovative technique that can be applied to wound healing. One appropriate source of stem cells for therapeutic use is stem cells from the adult bulge of hair follicles. This study examined the effects of adult bulge hair follicle stem cells (HFSC) in wound healing. MATERIALS AND METHODS Hair follicle stem cells were obtained from rat vibrissa and labeled with DiI (Invitrogen, Carlsbad, CA), then special markers were detected using flow cytometry. A full-thickness excisional wound model was created and DiI-labeled HFSC were injected around the wound bed. Wound healing was recorded with digital photographs. Animals were sacrificed at 3, 7, or 14 days after surgery, and were used for the following histological analyses. RESULTS Flow cytometry analysis showed that HFSC were CD34 positive and nestin positive, but K15 negative. Morphological analysis of HFSC-treated wounds exhibited accelerated wound closure. Histological analysis of hematoxylin and eosin stained and Masson's trichrome-stained photomicrographs showed significantly more re-epithelialization and dermal structural regeneration in HFSC-treated wounds than in the control group. Immunohistochemical analysis of CD31 protein-positive cells showed angiogenesis was also more significant in HFSC-treated wounds than in the control group. CONCLUSION Hair follicle stem cells accelerate skin wound healing. Isolating HFSC from a small skin biopsy could repair less-extensive full-thickness skin wounds by autologous stem cells and overcome major challenges regarding the use of stem cells in clinical application, while avoiding immune rejection and ethical concerns.
Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation

The paper addresses the problem of optimizing a class of composite functions on Riemannian manifolds and a new first order optimization algorithm (FOA) with a fast convergence rate is proposed. Through the theoretical analysis for FOA, it has been proved that the algorithm has quadratic convergence. The experiments in the matrix completion task show that FOA has better performance than other first order optimization methods on Riemannian manifolds. A fast subspace pursuit method based on FOA is proposed to solve the low-rank representation model based on augmented Lagrange method on the low rank matrix variety. Experimental results on synthetic and real data sets are presented to demonstrate that both FOA and SP-RPRG(ALM) can achieve superior performance in terms of faster convergence and higher accuracy.
Parameter estimation and order selection for linear regression problems

Parameter estimation and model order selection for linear regression models are two classical problems. In this article we derive the minimum mean-square error (MMSE) parameter estimate for a linear regression model with unknown order. We call the so-obtained estimator the Bayesian Parameter estimation Method (BPM). We also derive the model order selection rule which maximizes the probability of selecting the correct model. The rule is denoted BOSS-Bayesian Order Selection Strategy. The estimators have several advantages: They satisfy certain optimality criteria, they are non-asymptotic and they have low computational complexity. We also derive “empirical Bayesian” versions of BPM and BOSS, which do not require any prior knowledge nor do they need the choice of any “user parameters”. We show that our estimators outperform several classical methods, including the AIC and BIC for order selection.