Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Evolution of Puumala Hantavirus

ABSTRACT Puumala virus (PUUV) is a negative-stranded RNA virus in the genusHantavirus, family Bunyaviridae. In this study, detailed phylogenetic analysis was performed on 42 complete S segment sequences of PUUV originated from several European countries, Russia, and Japan, the largest set available thus far for hantaviruses. The results show that PUUV sequences form seven distinct and well-supported genetic lineages; within these lineages, geographical clustering of genetic variants is observed. The overall phylogeny of PUUV is star-like, suggesting an early split of genetic lineages. The individual PUUV lineages appear to be independent, with the only exception to this being the Finnish and the Russian lineages that are closely connected to each other. Two strains of PUUV-like virus from Japan form the most ancestral lineage diverging from PUUV. Recombination points within the S segment were searched for and evidence for intralineage recombination events was seen in the Finnish, Russian, Danish, and Belgian lineages of PUUV. Molecular clock analysis showed that PUUV is a stable virus, evolving slowly at a rate of 0.7 × 10−7 to 2.2 × 10−6 nt substitutions per site per year.
Analyses of phase noise reduction techniques in CMOS Hartley oscillator topology at the mm-waves: Inductive degeneration and optimum current density

This paper reports the analyses of two techniques for phase noise reduction in the CMOS Hartley oscillator circuit topology. Namely, the two techniques inductive degeneration and optimum current density are investigated with the objective of exploring the potential benefits in the mm-waves frequency range. The circuit sizing is carried out in 28 nm bulk CMOS technology. Overall, the analyses show that the adoption of these techniques may lead to a potential phase noise reduction up to 6 dB at a 1 MHz frequency offset for an oscillation frequency of 100 GHz.
Design and Implementation of Logical Topology in Sensor Network for an Industrial Stack Monitoring

Wireless Sensor Network (WSN) has gained worldwide attention in recent years. The design of a WSN depends significantly on the application. In sensor network design, physical and logical topology plays a major role. Transfer information in real time means, to provide a required result in a bounded time. Large-scale sensor networks will not be able to simply send all the raw sensed sample data to a sink. This paper presents an approach of simulation of various sampling techniques (logical topology) of industrial stacks emission and also defines a network with rough node deployment. The simulation of the sampling techniques is carried out using Castalia simulator based on OMNET++ language.
A Longitudinal Cardiovascular Autonomic Function Study in Mild Guillain-Barré Syndrome

To identify subclinical autonomic dysfunction in mild Guillain-Barré syndrome (GBS), a set of autonomic function tests was serially performed for up to 6 months in 5 GBS patients with mild disability at the nadir. Parasympathetic autonomic function tests consisted of Valsalva ratio and R-R interval variation during rest and deep breathing. Sympathetic autonomic function was evaluated by blood pressure responses to sustained handgrip, hand immersion in ice water, and active standing. The results showed that abnormal parasympathetic and sympathetic function was frequently encountered in all 5 patients during the acute stage of the illness. Autonomic dysfunction occurred both in axonal and demyelinating types of GBS. There was a trend of improvement in most autonomic function tests after 3 months, comparable to the recovery of motor function. In conclusion, subclinical autonomic dysfunction was present in mild GBS. It was temporary and would resolve spontaneously.
Roughness Perception of Haptically Displayed Fractal Surfaces

Surface profiles were generated by a fractal algorithm and haptically rendered on a force feedback joystick. Subjects were asked to use the joystick to explore pairs of surfaces and report to the experimenter which of the surfaces they felt was rougher. Surfaces were characterized by their root mean square (RMS) amplitude and their fractal dimension. The most important factor affecting the perceived roughness of the fractal surfaces was the RMS amplitude of the surface. When comparing surfaces of fractal dimension 1.2–1.35 it was found that the fractal dimension was negatively correlated with perceived roughness.
Dimension theory of iterated function systems

Let {Si} i = 1𝓁 be an iterated function system (IFS) on ℝd with attractor K. Let (Σ, σ) denote the one‐sided full shift over the alphabet {1, …, 𝓁}. We define the projection entropy function hπ on the space of invariant measures on Σ associated with the coding map π : Σ → K and develop some basic ergodic properties about it. This concept turns out to be crucial in the study of dimensional properties of invariant measures on K. We show that for any conformal IFS (respectively, the direct product of finitely many conformal IFSs), without any separation condition, the projection of an ergodic measure under π is always exactly dimensional and its Hausdorff dimension can be represented as the ratio of its projection entropy to its Lyapunov exponent (respectively, the linear combination of projection entropies associated with several coding maps). Furthermore, for any conformal IFS and certain affine IFSs, we prove a variational principle between the Hausdorff dimension of the attractors and that of projections of ergodic measures. © 2008 Wiley Periodicals, Inc.
Supervised learning for optimal power flow as a real-time proxy

In this work we design and compare different supervised learning algorithms to compute the cost of Alternating Current Optimal Power Flow (ACOPF). The motivation for quick calculation of OPF cost outcomes stems from the growing need of algorithmic-based long-term and medium-term planning methodologies in power networks. Integrated in a multiple time-horizon coordination framework, we refer to this approximation module as a proxy for predicting short-term decision outcomes without the need of actual simulation and optimization of them. Our method enables fast approximate calculation of OPF cost with less than 1% error on average, achieved in run-times that are several orders of magnitude lower than of exact computation. Several test-cases such as IEEE-RTS96 are used to demonstrate the efficiency of our approach.
A Semantic Hybrid Approach for Sound Recommendation

In this work we describe a hybrid recommendation approach for recommending sounds to users by exploiting and semantically enriching textual information such as tags and sounds descriptions. As a case study we used Freesound, a popular site for sharing sound samples which counts more than 4 million registered users. Tags and textual sound descriptions are exploited to extract and link entities to external ontologies such as WordNet and DBpedia. The enriched data are eventually merged with a domain specific tagging ontology to form a knowledge graph. Based on this latter, recommendations are then computed using a semantic version of the feature combination hybrid approach. An evaluation on historical data shows improvements with respect to state of the art collaborative algorithms.
Community structure of soil phototrophs along environmental gradients in arid Himalaya.

The well-developed biological soil crusts cover up to 40% of the soil surface in the alpine and subnival zones of the Tibetan Plateau, accounting for a vast area of Asia. We investigated the diversity and biomass of the phototrophic part (Cyanobacteria) of the microbial community inhabiting biological soil crusts and uncrusted soils in their surroundings on the elevation gradient of 5200-5900 m a.s. l. The influence of soil physico-chemical properties on phototrophs was studied. The ability of high-altitude phototrophs to fix molecular nitrogen was also determined under laboratory conditions. The biological soil crust phototroph community did not differ from that living in uncrusted soil in terms of the species composition, but the biomass is three-to-five times higher. An increasing trend in the cyanobacterial biomass from the biological soil crusts with elevation was observed, with the genera Nostoc spp. , Microcoleus vaginatus and Phormidium spp. contributing to this increase. Based on the laboratory experiments, the highest nitrogenase activity was recorded in the middle elevations, and the rate of nitrogen fixation was not correlated with the cyanobacterial biomass.
Mathematical Modeling of the Parabolic Trough Collector Field of the TCP-100 Research Plant

There are two main drawbacks when operating solar energy systems: a) the resulting energy costs are not yet competitive and b) solar energy is not always available when needed. In order to improve the overall solar plants efficiency, advances control techniques play an important role. In order to develop efficient and robust control techniques, the use of accurate mathematical models is crucial. In this paper, the mathematical modeling of the new TCP100 parabolic trough collector (PTC) research facility at the Plataforma Solar de Almería is presented. Some simulations are shown to demonstrate the adequate behavior of the model compared to the facility design conditions.
A coarse-graining approach to rate equations of the composite AC-NBTI model

For ac negative bias temperature instability (NBTI), the composite model that combines the reaction-diffusion (R-D) model for permanent component and the trap-detrap (T-D) model for recoverable component has almost been accepted. However, simple analytical formulas, which are useful for product qualification, have not yet been established. In this paper, we present a coarse-graining approach to analyze the rate equations in the T-D and R-D schemes. Here, the time-averaged rate equations are derived from the equations for stress and recovery phases. The analytical solutions obtained by solving this set of equations can explain the essential features of ac-NBTI, such as the duty-cycle dependence and the memory effect of hole-trapping determined by the standby condition. For the T-D scheme, the exact analytical solutions of the original equations verify this approach, whereas for the R-D scheme, the double-interface R-D model rather than the conventional R-D model captures the duty-cycle dependence observed in the experimental universal curve. Our experimental data on the ac-NBTI are also consistent with the developed analytical model providing a general validity of the approach presented here.
Analytic grasp success prediction with tactile feedback

Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information can alleviate contact placement uncertainties and facilitates contact modeling. We introduce a wrench-based classifier and evaluate it on a large set of real grasps. The key finding of this work is that exploiting tactile information allows wrench-based reasoning to perform on a level with existing methods based on learning or simulation. Different from these methods, the suggested approach has no need for training data, requires little modeling effort and is computationally efficient. Furthermore, our method affords task generalization by considering the capabilities of the grasping device and expected disturbance forces/moments in a physically meaningful way.
Silencing of Parkinson's disease-associated genes with artificial mirtron mimics of miR-1224

Mirtrons are a recently described category of microRNA (miRNA) relying on splicing rather than processing by the microprocessor complex to generate pre-miRNA precursors of the RNA interference (RNAi) pathway. Their discovery and subsequent verification provides important information about a distinct class of miRNA and inherent advantages that could be exploited to silence genes of interest. These include micro-processor-independent biogenesis, pol-II-dependent transcription, accurate species generation and the delivery of multiple artificial mirtrons as introns within a single host transcript. Here we determined the sequence motifs required for correct processing of the mmu-miR-1224 mirtron and incorporated these into artificial mirtrons targeting Parkinson’s disease-associated LRRK2 and α-synuclein genes. By incorporating these rules associated with processing and splicing, artificial mirtrons could be designed and made to silence complementary targets either at the mRNA or protein level. We further demonstrate with a LRRK2 targeting artificial mirtron that neuronal-specific silencing can be directed under the control of the human synapsin promoter. Finally, multiple mirtrons were co-delivered within a single host transcript, an eGFP reporter, to allow simultaneous targeting of two or more targets in a combinatorial approach. Thus, the unique characteristics of artificial mirtrons make this an attractive approach for future RNAi applications.
Case Prediction for Morphologically Poor Languages

In the context of machine translation, languages can take on implicit morphology. For instance, nouns in a morphologically poor language can be assigned implicit cases when being translated into a morphologically rich language. We investigated a technique of predicting cases for English nouns, in the context of English-Czech translation. We used a parser trained on Czech treebank to annotate the target side of the word-aligned parallel corpus. The cases were then projected from Czech into English via the alignments. Finally, a maximum entropy classifier was trained to predict cases of English nouns in unseen sentences. We evaluated our technique on a held-out set of word-aligned data, achieving 62.5 precision, and 20.9 recall (F-score of 31.4). Extensive error analysis showed that contextual and POS features acted as most useful for the classification task. Overall, we showed that one can produce a generalized case annotator for a morphologically poor language, given a morphologically rich one. The achieved precision suggests this technique might be useful for the task of machine translation.
Gaze-Assisted Multi-Stream Deep Neural Network for Action Recognition

There are two important aspects in human action recognition. The first one is how to locate the area that better indicates what the subjects in the videos are doing. The second one is how we can utilize the appearance and motion information from the video data. In this paper, we propose a gaze-assisted deep neural network, which performs the action recognition task with the help of human visual attention. Based on the above-mentioned consideration, we first collect a large number of human gaze data by recording the eye movements of human subjects when they watch the video. Then, we employ a fully convolutional network to learn to predict the human gaze. To efficiently utilize the human gaze, inspired by the rank pooling concept, which can encode the video into one image, we design a novel video representation named by dynamic gaze. The proposed dynamic gaze captures both the appearance and motion information from the video, and our human gaze data can better locate the area of interest. Based on the dynamic gaze, we build our dynamic gaze stream. We combine the proposed dynamic gaze stream together with the two-stream architecture as our final multi-stream architecture. We have collected over 300-k human gaze maps for the J-HMDB data set in this paper, and experiments show that the proposed multi-stream architecture can achieve comparable results with the state of the art in the task of action recognition with both collected human gaze data and predicted human gaze data.
Generation of conditional auxin-inducible degron (AID) cells and tight control of degron-fused proteins using the degradation inhibitor auxinole

Controlling protein expression using a degron is drawing more attention because the protein of interest can be rapidly depleted in a reversible manner. We pioneered the development of the auxin-inducible degron (AID) technology by transplanting a plant-specific degradation pathway to non-plant cells. In human cells expressing an E3 ligase component, OsTIR1, it is possible to degrade a degron-fused protein with a half-life of 15–45 min in the presence of the phytohormone auxin. We reported previously the generation of human HCT116 mutants in which the C terminus of endogenous proteins was fused with the degron by CRISPR–Cas9-based knock-in. Here, we show new plasmids for N-terminal tagging and describe a detailed protocol for the generation of AID mutants of human HCT116 and DLD1 cells. Moreover, we report the use of an OsTIR1 inhibitor, auxinole, to suppress leaky degradation of degron-fused proteins. The addition of auxinole is also useful for rapid re-expression after depletion of degron-fused proteins. These improvements enhance the utility of AID technology for studying protein function in living human cells.
Muffled Semi-Supervised Learning

We explore a novel approach to semi-supervised learning. This approach is contrary to the common approach in that the unlabeled examples serve to "muffle," rather than enhance, the guidance provided by the labeled examples. We provide several variants of the basic algorithm and show experimentally that they can achieve significantly higher AUC than boosted trees, random forests and logistic regression when unlabeled examples are available.
A type discipline for authorization policies

Distributed systems and applications are often expected to enforce high-level authorization policies. To this end, the code for these systems relies on lower-level security mechanisms such as digital signatures, local ACLs, and encrypted communications. In principle, authorization specifications can be separated from code and carefully audited. Logic programs in particular can express policies in a simple, abstract manner. We consider the problem of checking whether a distributed implementation based on communication channels and cryptography complies with a logical authorization policy. We formalize authorization policies and their connection to code by embedding logical predicates and claims within a process calculus. We formulate policy compliance operationally by composing a process model of the distributed system with an arbitrary opponent process. Moreover, we propose a dependent type system for verifying policy compliance of implementation code. Using Datalog as an authorization logic, we show how to type several examples using policies and present a general schema for compiling policies.
Using CVIS to process the concurrent signal priority requirements: A cooperative optimization model and its hardware-in-the-loop field tests

Various signal priority models have been proposed for different types of special vehicles (e.g. emergency vehicles or buses). However, they are generally difficult to be compatible for each other and fail to be used in one road network. In this paper, a cooperative optimization model is proposed to process concurrent priority requirements in traffic signal control. First, the standard of the priority requirements is unified so as to enable the concurrent processing. And a signal optimization model is proposed to coordinate the conflicting requirements. The strategy relies on real-time location and speed adaption of vehicles provided by CVIS (cooperative vehicle infrastructure system). A HIL (hardware-in-the-loop) field test was conducted to evaluate the developed strategy and the results are encouraging.
Analysis on Ampacity of Overhead Transmission Lines Being Operated

Dynamic thermal rating (DTR) system is an effective method to improve the capacity of existing overhead line. According to the methodology based on CIGRE (International Council on Large Electric systems) standard, ampacity values under steady-state heating balance can be calculated from ambient environmental conditions. In this study, simulation analysis of relations between parameters and ampacity is described as functional dependence, which can provide an effective basis for the design and research of overhead transmission lines. The simulation of ampacity variation in different rating scales is described in this paper, which are determined from real-time meteorological data and conductor state parameters. To test the performance of DTR in different rating scales, capacity improvement and risk level are presented. And the experimental results show that the capacity of transmission line by using DTR has significant improvement, with low probability of risk. The information of this study has an important reference value to the operation management of power grid.
High Yield of Bioactive Abietane Diterpenes in Salvia sclarea Hairy Roots by Overexpressing Cyanobacterial DXS or DXR Genes

Abstract Abietane diterpenoids, containing a quinone moiety, are synthesized in the roots of several Salvia species. Promising cytotoxicity and antiproliferative activities have been reported for these compounds in various cell and animal models. We have recently shown that aethiopinone, an o-naphto-quinone diterpene, produced in the roots of different Salvia species, is selectively cytotoxic against the A375 melanoma cell line. To enhance the synthesis of this abietane diterpenoid, we have engineered the plastidial 2-C-methyl-D-erythritol 4-phosphate-derived isoprenoid pathway in Salvia sclarea hairy roots by ectopic expression and plastid targeting of cyanobacterial genes encoding the 1-deoxy-D-xylulose 5-phosphate synthase or 1-deoxy-D-xylulose-5-phosphate reductoisomerase gene, the first two enzymatic steps of the plastidial MEP pathway, from which plant diterpenes primarily derive. Plastid-targeted expression of 1-deoxy-D-xylulose 5-phosphate synthase and 1-deoxy-D-xylulose-5-phosphate reductoisomerase proteins significantly enhanced the yield of aethiopinone by a 3-fold and about 6-fold increase, respectively. The accumulation of other abietane-type diterpenes (ferruginol, salvipisone, and carnosic acid), with interesting antiproliferative activity, was also increased. Compared to our previous data obtained by overexpressing the plant orthologous 1-deoxy-D-xylulose 5-phosphate synthase and 1-deoxy-D-xylulose-5-phosphate reductoisomerase genes in S. sclarea hairy roots, the results presented here confirm that the bacterial 1-deoxy-D-xylulose-5-phosphate reductoisomerase enzyme plays a major role than the DXS enzyme in the biosynthetic pathway of this class of compounds and that its ectopic expression does not conflict with active hairy root growth, resulting in a balanced trade-off between the transgenic hairy root final biomass and the increased content of o-naphto-quinone diterpenes, with interesting biological activities.
Making sense of virtual environments: action representation, grounding and common sense

The development of complex interactive 3D systems raises the need for representations supporting more abstract descriptions of world objects, their behaviour and the world dynamics. The inclusion of Artificial Intelligence representations and their use within 3D graphic worlds face both fundamental and technical issues due to the difference in representational logic between computer graphics and knowledge-based systems. We present a framework for such an integration illustrated by a first prototype.