Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicted 3D Model of the Rabies Virus Glycoprotein Trimer

The RABVG ectodomain is a homotrimer, and trimers are often called spikes. They are responsible for the attachment of the virus through the interaction with nicotinic acetylcholine receptors, neural cell adhesion molecule (NCAM), and the p75 neurotrophin receptor (p75NTR). This makes them relevant in viral pathogenesis. The antigenic structure differs significantly between the trimers and monomers. Surfaces rich in hydrophobic amino acids are important for trimer stabilization in which the C-terminal of the ectodomain plays an important role; to understand these interactions between the G proteins, a mechanistic study of their functions was performed with a molecular model of G protein in its trimeric form. This verified its 3D conformation. The molecular modeling of G protein was performed by a I-TASSER server and was evaluated via a Rachamandran plot and ERRAT program obtained 84.64% and 89.9% of the residues in the favorable regions and overall quality factor, respectively. The molecular dynamics simulations were carried out on RABVG trimer at 310 K. From these theoretical studies, we retrieved the RMSD values from Cα atoms to assess stability. Preliminary model of G protein of rabies virus stable at 12 ns with molecular dynamics was obtained.
Non-operative management (NOM) of blunt hepatic trauma: 80 cases.

BACKGROUND Liver is the most frequently injured organ upon abdominal trauma. We present a group of patients with blunt hepatic trauma who were managed without any invasive diagnostic tools and/or surgical intervention. METHODS A total of 80 patients with blunt liver injury who were hospitalized to the general surgery clinic or other clinics due to the concomitant injuries were followed non-operatively. The normally distributed numeric variables were evaluated by Student's t-test or one way analysis of variance, while non-normally distributed variables were analyzed by Mann-Whitney U-test or Kruskal-Wallis variance analysis. Chi-square test was also employed for the comparison of categorical variables. Statistical significance was assumed for p<0.05. RESULTS There was no significant relationship between patients' Hgb level and liver injury grade, outcome, and mechanism of injury. Also, there was no statistical relationship between liver injury grade, outcome, and mechanism of injury and ALT levels as well as AST level. There was no mortality in any of the patients. CONCLUSION During the last quarter of century, changes in the diagnosis and treatment of liver injury were associated with increased survival. NOM of liver injury in patients with stable hemodynamics and hepatic trauma seems to be the gold standard.
Human box C/D snoRNAs with miRNA like functions: expanding the range of regulatory RNAs

Small nucleolar RNAs (snoRNAs) and microRNAs are two classes of non-protein-coding RNAs with distinct functions in RNA modification or post-transcriptional gene silencing. In this study, we introduce novel insights to RNA-induced gene activity adjustments in human cells by identifying numerous snoRNA-derived molecules with miRNA-like function, including H/ACA box snoRNAs and C/D box snoRNAs. In particular, we demonstrate that several C/D box snoRNAs give rise to gene regulatory RNAs, named sno-miRNAs here. Our data are complementing the increasing number of studies in the field of small RNAs with regulatory functions. In massively deep sequencing of small RNA fractions we identified high copy numbers of sub-sequences from >30 snoRNAs with lengths of ≥18 nt. RNA secondary structure prediction indicated for a majority of candidates a location in predicted stem regions. Experimental analysis revealed efficient gene silencing for 11 box C/D sno-miRNAs, indicating cytoplasmic processing and recruitment to the RNA silencing machinery. Assays in four different human cell lines indicated variations in both the snoRNA levels and their processing to active sno-miRNAs. In addition we show that box D elements are predominantly flanking at least one of the sno-miRNA strands, while the box C element locates within the sequence of the sno-miRNA guide strand.
Virtual Genetic Algorithms: First Results

An important goal of the theory of genetic algorithms is to build models that predict how well genetic algorithms are expected to perform on a given fitness landscape (i.e., a given combination of representation, fitness function, and set of genetic operators). This paper describes the design of a software tool called a virtual genetic algorithm (VGA) that predicts the behavior of a genetic algorithm. The VGA operates like a genetic algorithm except that evaluations of individuals are based on empirically derived statistical fitness models. Because it by-passes the evaluation process, the VGA can be executed in a fraction of the time of the GA that it models, allowing multiple exploratory runs that produce average-case, best-case and worst-case predictions. We discuss ways to build the required models based on a preliminary exploration of the fitness landscape. Our initial results show that the VGA can provide a cost effective way to explore the likely performance of alternative genetic representations and operators.
How do approaches to solving open-ended problems vary within the science disciplines?

ABSTRACT This paper describes the results of a comparative study into the approaches used by science undergraduates when solving open-ended problems. This study adopted a pseudo-grounded theory framework to analyse six case studies, one from each of the science disciplines studied. The study involved 70 participants from 5 institutions solving open-ended problems using a think-aloud protocol. Analysis of the data identified a number of different approaches used by each subject group. Participants in psychology, pharmacy and sports rehabilitation used a greater number of novice-like approaches than participants in chemistry, physics and interdisciplinary science. The approaches used by physical science participants were very similar whereas those from pharmacy, sports rehabilitation and psychology were more varied and relied on students’ ability to relate to a familiar context. Evaluation of the relative quality of solutions showed variation with discipline.
An automobile detection algorithm development for automated emergency braking system

Automated emergency braking (AEB) systems become more and more important than ever in modern vehicles for assisting drivers in emergency driving situations. They mostly require fusion techniques for vehicle detection (camera and radar or stereo-vision system) that require complicated algorithms and additional costs. These have caused AEB systems less attractive to the market. This paper presents an automobile detection algorithm using single camera for the AEB system. The algorithm contains three main steps: background subtraction, thresholding, and inverted U-shape back wheel detection. The simulation under MATLAB environment provides 87.25% and 78% of detection rate and accuracy, respectively for a 1080×1920 pixel input image; 88.25% and 73.5% of detection rate and accuracy for a 480×640 pixel input image. Processing time achieved are 0.156s and 0.0297s accordingly.
A methodology for the development of distributed real-time control applications with focus on task allocation in heterogeneous systems

A distributed application development methodology is necessary to define specific steps, through which the application specification can be successfully mapped to the system devices. Interoperability and real-timeliness are two major issues in distributed control application development. The main focus of this paper is a generic device model, developed to provide interoperability, and the function block allocation (FBALL) algorithm, defined to guarantee that real-time requirements are met. FBALL is a hybrid approach targeted for the nature of distributed control applications, resulting in an assignment of the application tasks to the system resources, as well as a feasible schedule that meets the real-time constraints. Based on these solutions to interoperability and real-time criticality, a methodology is presented, supporting the distributed control application specification, modeling, and implementation to heterogeneous systems.
Tracking Value at Risk

The focus of this work is on the problem of tracking parameters describing both the Risk Neutral and the Objective measures over time with the aim of monitoring Value at Risk. The methodology presented incorporates information both from contingent claim prices over time as well as from the underlying instrument’s price over time in order to perform parameter estimation. The models adopted for the Objective measure are based on Variance Gamma and Normal Inverse Gaussian processes, and new Monte Carlo based methods for pricing options under Risk Neutral versions of these measures are presented. A parametric model of the Stochastic Discount Factor is introduced based on empirical results in the literature. This, combined with the Objective measure discussed results in option prices which are found through a novel Monte Carlo based method. A Sequential Monte Carlo approach to tracking the parameters of these models, while considering prices as imperfect observations is developed, incorporating optimal updates in the sense of minimum variance where possible. This results in the successful tracking of these parameters and comparative results for the two models are presented. These show that both models appear to track realistically, however the Variance Gamma model showed far more variability in its estimation of expected daily price moves than the Normal Inverse Gaussian model. Conversely the Normal Inverse Gaussian model shows more variability in its estimation of the probability of large daily moves.
The enhanced cognitive interview: could individual differences in visuo-spatial working memory explain differences in recalling an event?

ABSTRACT The Enhanced Cognitive Interview (ECI) is one of the most useful witness interviews, consisting of 5 techniques (context reinstatement: CR; report everything: RE, mental imagery: MI, change order: CO and change of perspectives: CP) aimed at increasing both the quantity and the quality of elements recalled. All the techniques require mental imagery skills. The present study investigates the relationship between forward and backward visuo-spatial working memory (VSWM) and correct elements recalled through the ECI with respect to its techniques. 99 college students (48 males) watched a color videotape of a fictional crime and performed a visuo-spatial working memory task. The following day they were interviewed about what they had seen. Results showed that high forward VSWM capacity has a role in favoring the total amount of correct information recalled, specifically in the MI technique. This suggests that maintaining and processing mood-related contents in VSWM is easier for individuals with high working memory skills.
Analysis of the transcriptional networks underpinning the activation of murine macrophages by inflammatory mediators

Macrophages respond to the TLR4 agonist LPS with a sequential transcriptional cascade controlled by a complex regulatory network of signaling pathways and transcription factors. At least two distinct pathways are currently known to be engaged by TLR4 and are distinguished by their dependence on the adaptor molecule MyD88. We have used gene expression microarrays to define the effects of each of three variables—LPS dose, LPS versus IFN‐β and ‐γ, and genetic background—on the transcriptional response of mouse BMDMs. Analysis of correlation networks generated from the data has identified subnetworks or modules within the macrophage transcriptional network that are activated selectively by these variables. We have identified mouse strain‐specific signatures, including a module enriched for SLE susceptibility candidates. In the modules of genes unique to different treatments, we found a module of genes induced by type‐I IFN but not by LPS treatment, suggesting another layer of complexity in the LPS‐TLR4 signaling feedback control. We also observe that the activation of the complement system, in common with the known activation of MHC class 2 genes, is reliant on IFN‐γ signaling. Taken together, these data further highlight the exquisite nature of the regulatory systems that control macrophage activation, their likely relevance to disease resistance/susceptibility, and the appropriate response of these cells to proinflammatory stimuli.
GmNAC5, a NAC Transcription Factor, Is a Transient Response Regulator Induced by Abiotic Stress in Soybean

GmNAC5 is a member of NAM subfamily belonging to NAC transcription factors in soybean (Glycine max (L.) Merr. ). Studies on NAC transcription factors have shown that this family functioned in the regulation of shoot apical meristem (SAM), hormone signalling, and stress responses. In this study, we examined the expression levels of GmNAC5. GmNAC5 was highly expressed in the roots and immature seeds, especially strongly in immature seeds of 40 days after flowering. In addition, we found that GmNAC5 was induced by mechanical wounding, high salinity, and cold treatments but was not induced by abscisic acid (ABA). The subcellular localization assay suggested that GmNAC5 was targeted at nucleus. Together, it was suggested that GmNAC5 might be involved in seed development and abiotic stress responses in soybean.
MIMO Channel identification using the DOA and DOD estimation

Channel identification finds direct application in the growing field of mobile wireless communications. In this paper, we address this problem in the context of multi-users: multiple-input multiple-output (MIMO) channels. To avoid scheduling at the reception we will proceed to a source separation (DOA and DOD estimation) then we will deduct the channel parameters. The estimation of the directions of departure (DOD) and directions of arrival (DOA), is based on the strong algebraic structure of tensor algebra: PARAFAC method. The Second Order Statistics (SOS) are used for the estimation of the impulse response's parameters of the MIMO channel. The performance of the proposed method will be proved by the simulation study.
Money Growth and Inflation: A Regime Switching Approach

We develop a time-varying transition probabilities Markov Switching model in which inflation is characterised by two regimes (high and low inflation). Using Bayesian techniques, we apply the model to the euro area, Germany, the US, the UK and Canada for data from the 1960s up to the present. Our estimates suggest that a smoothed measure of broad money growth, corrected for real-time estimates of trend velocity and potential output growth, has important leading indicator properties for switches between inflation regimes. Thus money growth provides an important early warning indicator for risks to price stability.
magma: a three-dimensional, Lagrangian magnetohydrodynamics code for merger applications

We present a new, completely Lagrangian magnetohydrodynamics (MHD) code that is based on the smoothed particle hydrodynamics (SPH) method. The equations of self-gravitating hydrodynamics are derived self-consistently from a Lagrangian and account for variable smoothing length (‘grad-h’) terms in both the hydrodynamic and the gravitational acceleration equations. The evolution of the magnetic field is formulated in terms of so-called Euler potentials which are advected with the fluid and thus guarantee the MHD flux-freezing condition. This formulation is equivalent to a vector potential approach and therefore fulfils the ∇· B = 0 constraint by construction. Extensive tests in one, two and three dimensions are presented. The tests demonstrate the excellent conservation properties of the code and show the clear superiority of the Euler potentials over earlier magnetic SPH formulations.
Pattern Classiication

Pattern classiication consists in assigning entities, described by feature vectors, to predeened groups of patterns. When the statistical characteristics of the problem under consideration are perfectly known, minimal error probability can be achieved by means of the Bayes decision rule. In practice, however, a sub-optimal classiier has to be constructed from training data. Several neural network approaches to this problem have been proposed. Nearest-neighbor models are based on assessing the similarity between the input pattern and a set of reference patterns with known classiication. The regression approach consists in predicting category from pattern by minimizing a certain error criterion. In the nite sample case, the deenition of the structural complexity of these models is shown to have considerable innuence on classiication error. Finally, a taxonomy of the main neural network and alternative techniques to pattern classiication is presented.
and Mice F-FDG PET Studies in Rats 18 Image-Derived Input Function from the Vena Cava for

Published online: June 9, 2014. 2014;55:1380-1388. J Nucl Med. Bernard Lanz, Carole Poitry-Yamate and Rolf Gruetter and Mice F-FDG PET Studies in Rats 18 Image-Derived Input Function from the Vena Cava for http://jnm.snmjournals. org/content/55/8/1380 This article and updated information are available at: http://jnm.snmjournals. org/site/subscriptions/online.xhtml Information about subscriptions to JNM can be found at: http://jnm.snmjournals. org/site/misc/permission.xhtml Information about reproducing figures, tables, or other portions of this article can be found online at:
The plug-based nanovolume Microcapillary Protein Crystallization System (MPCS)

The Microcapillary Protein Crystallization System (MPCS) is a new protein-crystallization technology used to generate nanolitre-sized crystallization experiments for crystal screening and optimization. Using the MPCS, diffraction-ready crystals were grown in the plastic MPCS CrystalCard and were used to solve the structure of methionine-R-sulfoxide reductase.
Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics

Understanding the mechanisms of glioblastoma at the molecular and structural level is not only interesting for basic science but also valuable for biotechnological application, such as the clinical treatment. In the present study, bioinformatics analysis was performed to reveal and identify the key genes of glioblastoma multiforme (GBM). The results obtained in the present study signified the importance of some genes, such as COL3A1, FN1, and MMP9, for glioblastoma. Based on the selected genes, a prediction model was built, which achieved 94.4% prediction accuracy. These findings might provide more insights into the genetic basis of glioblastoma.
Measurements, analysis, and modeling of BitTorrent-like systems

Existing studies on BitTorrent systems are single-torrent based, while more than 85% of all peers participate in multiple torrents according to our trace analysis. In addition, these studies are not sufficiently insightful and accurate even for single-torrent models, due to some unrealistic assumptions. Our analysis of representative Bit-Torrent traffic provides several new findings regarding the limitations of BitTorrent systems: (1) Due to the exponentially decreasing peer arrival rate in reality, service availability in such systems becomes poor quickly, after which it is difficult for the file to be located and downloaded. (2) Client performance in the BitTorrent-like systems is unstable, and fluctuates widely with the peer population. (3) Existing systems could provide unfair services to peers, where peers with high downloading speed tend to download more and upload less. In this paper, we study these limitations on torrent evolution in realistic environments. Motivated by the analysis and modeling results, we further build a graph based multi-torrent model to study inter-torrent collaboration. Our model quantitatively provides strong motivation for inter-torrent collaboration instead of directly stimulating seeds to stay longer. We also discuss a system design to show the feasibility of multi-torrent collaboration.
A bottom-up oblique decision tree induction algorithm

Decision tree induction algorithms are widely used in knowledge discovery and data mining, specially in scenarios where model comprehensibility is desired. A variation of the traditional univariate approach is the so-called oblique decision tree, which allows multivariate tests in its non-terminal nodes. Oblique decision trees can model decision boundaries that are oblique to the attribute axes, whereas univariate trees can only perform axis-parallel splits. The majority of the oblique and univariate decision tree induction algorithms perform a top-down strategy for growing the tree, relying on an impurity-based measure for splitting nodes. In this paper, we propose a novel bottom-up algorithm for inducing oblique trees named BUTIA. It does not require an impurity-measure for dividing nodes, since we know a priori the data resulting from each split. For generating the splitting hyperplanes, our algorithm implements a support vector machine solution, and a clustering algorithm is used for generating the initial leaves. We compare BUTIA to traditional univariate and oblique decision tree algorithms, C4.5, CART, OC1 and FT, as well as to a standard SVM implementation, using real gene expression benchmark data. Experimental results show the effectiveness of the proposed approach in several cases.
The Meaning of “Near” and “Far”: The Impact of Structuring Design Databases and the Effect of Distance of Analogy on Design Output

This work lends insight into the meaning and impact of “near” and “far” analogies. A cognitive engineering design study is presented that examines the effect of the distance of analogical design stimuli on design solution generation, and places those findings in context of results from the literature. The work ultimately sheds new light on the impact of analogies in the design process and the significance of their distance from a design problem. In this work, the design repository from which analogical stimuli are chosen is the U.S. patent database, a natural choice, as it is one of the largest and easily accessed catalogued databases of inventions. The “near” and “far” analogical stimuli for this study were chosen based on a structure of patents, created using a combination of Latent Semantic Analysis and a Bayesian based algorithm for discovering structural form, resulting in clusters of patents connected by their relative similarity. The findings of this engineering design study are contextualized with the findings of recent work in design by analogy, by mapping the analogical stimuli used in the earlier work into similar structures along with the patents used in the current study. Doing so allows the discovery of a relationship between all of the stimuli and their relative distance from the design problem. The results confirm that “near” and “far” are relative terms, and depend on the characteristics of the potential stimuli. Further, although the literature has shown that “far” analogical stimuli are more likely to lead to the generation innovative solutions with novel characteristics, there is such a thing as too far. That is, if the stimuli are too distant, they then can become harmful to the design process. Importantly, as well, the data mapping approach to identify analogies works, and is able to impact the effectiveness of the design process. This work has implications not only in the area of finding inspirational designs to use for design by analogy processes in practice, but also for synthesis, or perhaps even unification, of future studies in the field of design by analogy.