Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequence-based Gaussian network model for protein dynamics

Motivation: Gaussian network model (GNM) is widely adopted to analyze and understand protein dynamics, function and conformational changes. The existing GNM-based approaches require atomic coordinates of the corresponding protein and cannot be used when only the sequence is known. Results: We report, first of its kind, GNM model that allows modeling using the sequence. Our linear regression-based, parameter-free, sequence-derived GNM (L-pfSeqGNM) uses contact maps predicted from the sequence and models local, in the sequence, contact neighborhoods with the linear regression. Empirical benchmarking shows relatively high correlations between the native and the predicted with L-pfSeqGNM B-factors and between the cross-correlations of residue fluctuations derived from the structure- and the sequence-based GNM models. Our results demonstrate that L-pfSeqGNM is an attractive platform to explore protein dynamics. In contrast to the highly used GNMs that require protein structures that number in thousands, our model can be used to study motions for the millions of the readily available sequences, which finds applications in modeling conformational changes, protein–protein interactions and protein functions. Contact: zerozhua@126.com Supplementary information: Supplementary data are available at Bioinformatics online.
Minimum Message Length and Statistically Consistent Invariant (Objective?) Bayesian Probabilistic Inference—From (Medical) “Evidence”

“Evidence” in the form of data collected and analysis thereof is fundamental to medicine, health and science. In this paper, we discuss the “evidence‐based” aspect of evidence‐based medicine in terms of statistical inference, acknowledging that this latter field of statistical inference often also goes by various near‐synonymous names—such as inductive inference (amongst philosophers), econometrics (amongst economists), machine learning (amongst computer scientists) and, in more recent times, data mining (in some circles). Three central issues to this discussion of “evidence‐based” are (i) whether or not the statistical analysis can and/or should be objective and/or whether or not (subjective) prior knowledge can and/or should be incorporated, (ii) whether or not the analysis should be invariant to the framing of the problem (e.g. does it matter whether we analyse the ratio of proportions of morbidity to non‐morbidity rather than simply the proportion of morbidity? ), and (iii) whether or not, as we get more and more data, our analysis should be able to converge arbitrarily closely to the process which is generating our observed data. For many problems of data analysis, it would appear that desiderata (ii) and (iii) above require us to invoke at least some form of subjective (Bayesian) prior knowledge. This sits uncomfortably with the understandable but perhaps impossible desire of many medical publications that at least all the statistical hypothesis testing has to be classical non‐Bayesian—i.e. it is not permitted to use any (subjective) prior knowledge.
A study of existing Ontologies in the IoT-domain

Several domains have adopted the increasing use of IoT-based devices to collect sensor data for generating abstractions and perceptions of the real world. This sensor data is multi-modal and heterogeneous in nature. This heterogeneity induces interoperability issues while developing cross-domain applications, thereby restricting the possibility of reusing sensor data to develop new applications. As a solution to this, semantic approaches have been proposed in the literature to tackle problems related to interoperability of sensor data. Several ontologies have been proposed to handle different aspects of IoT-based sensor data collection, ranging from discovering the IoT sensors for data collection to applying reasoning on the collected sensor data for drawing inferences. In this paper, we survey these existing semantic ontologies to provide an overview of the recent developments in this field. We highlight the fundamental ontological concepts (e.g., sensor-capabilities and context-awareness) required for an IoT-based application, and survey the existing ontologies which include these concepts. Based on our study, we also identify the shortcomings of currently available ontologies, which serves as a stepping stone to state the need for a common unified ontology for the IoT domain.
Sealing ability and adaptation of root‐end filling materials in cavities prepared with different techniques

The aim of this study was to evaluate the sealing ability and marginal adaptation of calcium silicate‐based cements (CSCs) in root‐end cavities prepared by ultrasonic and laser tips. A total of 72 extracted human maxillary incisor teeth were randomly divided as 60 teeth in experimental groups and 6 teeth each for positive and negative control groups. Specimens in experimental groups were obturated, their root‐end resections were performed and randomly divided into six groups (n = 10) as follows: G1: Ultrasonic retrotip + MTA, G2: Ultrasonic retrotip + Calcium Enriched Mixture (CEM), G3: Ultrasonic retrotip + Biodentine, G4: Er:YAG laser tip + MTA, G5: Er:YAG laser tip + CEM, G6: Er:YAG laser tip + Biodentine. The sealing ability was measured by fluid transport method. Six specimens from each experimental group were randomly selected to analyze marginal adaptation and prepared for scanning electron microscopy (SEM) analysis. Micrographs were scored and also analyzed using Image J software. Data were analyzed with; two‐way ANOVA, Bonferroni, Kruskall–Wallis, Mann–Whitney‐U, Siegel & Castellan, and Spearman correlation coefficient tests. No significant difference was found between materials regarding the sealing ability and marginal adaptation (p > 0.05). Significantly greater fluid movement and poor marginal adaptation were seen for materials placed in cavities prepared by laser tips (p < 0.05). Positive correlation was found between the results of scoring and Image J analysis of SEM images (r = 0.596, p < 0.001). Fluid transport method and SEM analysis gave similar results suggesting the use of ultrasonic‐retrotips for preparing root‐end cavities which are going to be filled with one of these CSCs.
A cis-Acting Replication Element in the Sequence Encoding the NS5B RNA-Dependent RNA Polymerase Is Required for Hepatitis C Virus RNA Replication

ABSTRACT RNA structures play key roles in the replication of RNA viruses. Sequence alignment software, thermodynamic RNA folding programs, and classical comparative phylogenetic analysis were used to build models of six RNA elements in the coding region of the hepatitis C virus (HCV) RNA-dependent RNA polymerase, NS5B. The importance of five of these elements was evaluated by site-directed mutagenesis of a subgenomic HCV replicon. Mutations disrupting one of the predicted stem-loop structures, designated 5BSL3.2, blocked RNA replication, implicating it as an essential cis-acting replication element (CRE). 5BSL3.2 is about 50 bases in length and is part of a larger predicted cruciform structure (5BSL3). As confirmed by RNA structure probing, 5BSL3.2 consists of an 8-bp lower helix, a 6-bp upper helix, a 12-base terminal loop, and an 8-base internal loop. Mutational analysis and structure probing were used to explore the importance of these features. Primary sequences in the loops were shown to be important for HCV RNA replication, and the upper helix appears to serve as an essential scaffold that helps maintain the overall RNA structure. Unlike certain picornavirus CREs, whose function is position independent, 5BSL3.2 function appears to be context dependent. Understanding the role of 5BSL3.2 and determining how this new CRE functions in the context of previously identified elements at the 5′ and 3′ ends of the RNA genome should provide new insights into HCV RNA replication.
Numerical time integration for air pollution models

textabstractDue to the large number of chemical species and the three space dimensions, off-the-shelf stiff ODE integrators are not feasible for the numerical time integration of stiff systems of advection-diffusion-reaction equations [ fracpar{c{t + nabla cdot left( vu{u c right) = nabla cdot left( K ,nabla, c right) + R left( c right), c=c(vu{x,t), c in IR^m, vu{x in Omega subset IR^3 ] from the field of air pollution modelling. This has led to the use of special time integration techniques. This paper is devoted to a survey of such techniques, encompassing stiff chemistry solvers, positive advection schemes, time or operator splitting, implicit-explicit methods and approximate matrix factorization solutions. Of great importance in practice is high performance computing due to the huge problem scales, in particular for global models. We will therefore also report on experiences with vector/parallel shared memory and massively parallel distributed memory architectures and clusters of workstations. The survey is not entirely unique to air pollution models and biased towards work done at CWI over approximately the last 5 years.
Perceived quality of DIBR-based synthesized views

This paper considers the reliability of usual assessment methods when evaluating virtual synthesized views in the multiview video context. Virtual views are generated from Depth Image Based Rendering (DIBR) algorithms. Because DIBR algorithms involve geometric transformations, new types of artifacts come up. The question regards the ability of commonly used methods to deal with such artifacts. This paper investigates how correlated usual metrics are to human judgment. The experiments consist in assessing seven different view synthesis algorithms by subjective and objective methods. Three different 3D video sequences are used in the tests. Resulting virtual synthesized sequences are assessed through objective metrics and subjective protocols. Results show that usual objective metrics can fail assessing synthesized views, in the sense of human judgment.
Two-component jet simulations I. Topological stability of analytical MHD outflow solutions

Context. Observations of collimated outflows in young stellar objects indicate that several features of the jets can be understood by adopting the picture of a two-component outflow, wherein a central stellar component around the jet axis is surrounded by an extended disk wind. The precise contribution of each component may depend on the intrinsic physical properties of the YSO-disk system as well as its evolutionary stage. Aims. This article reports a systematic separate investigation of these jet components via time-dependent simulations of two prototypical and complementary analytical solutions, each closely related to the properties of stellar outflows and disk winds. These models describe a meridionally and a radially self-similar exact solution of the steady-state, ideal hydromagnetic equations, respectively. Methods. Using the PLUTO code to carry out the simulations, the study focuses on the topological stability of each of the two analytical solutions, which are successfully extended to all space by removing their singularities. In addition, their behavior and robustness over several physical and numerical modifications is extensively examined. Therefore, this work serves as the starting point for the analysis of the two-component jet simulations. Results. It is found that radially self-similar solutions (disk winds) always reach a final steady-state while maintaining all their welldefined properties. The different ways to replace the singular part of the solution around the symmetry axis, being a first approximation towards a two-component outflow, lead to the appearance of a shock at the super-fast domain corresponding to the fast magnetosonic separatrix surface. These conclusions hold true independently of the numerical modifications and/or evolutionary constraints that the models have undergone, such as starting with a sub-modified-fast initial solution or different types of heating/cooling assumptions. Furthermore, the final outcome of the simulations remains close enough to the initial analytical configurations, thus showing their topological stability. Conversely, the asymptotic configuration and the stability of meridionally self-similar models (stellar winds) is related to the heating processes at the base of the wind. If the heating is modified by assuming a polytropic relation between density and pressure, a turbulent evolution is found. On the other hand, adiabatic conditions lead to the replacement of the outflow by an almost static atmosphere.
Discriminant validity of a reduced set of Mini‐Mental State Examination items for dementia and Alzheimer's disease

Linear discriminant analysis was used to construct a series of discriminant functions including subsets of demographic variables and Mini‐Mental State Examination item responses for a case series and a population sample. A 9‐item discriminant function including the variables for time orientation, recall, calculation, copying a figure, age, writing, 3‐step command, naming, and race distinguished demented subjects from community controls with 91% sensitivity and 88% specificity. The same discriminant function classified Alzheimer's disease patients and controls with 96% sensitivity and 98% specificity. This discriminant function has been cross‐validated as a potential screening instrument for Alzheimer's disease in a community‐based sample.
Canonical form functions as a simple means for genetic programming to evolve human-interpretable functions

In this paper, we investigate the use of canonical form functions to evolve human-interpretable expressions for symbolic regression problems. The approach is simple to apply, being mostly a grammar that fits into any grammar-based Genetic Programming (GP) system. We demonstrate the approach, dubbed CAFFEINE, in producing highly predictive, interpretable expressions for six circuit modeling problems. We investigate variations of CAFFEINE, including Grammatical Evolution vs. Whigham-style, grammar-defined introns, and smooth uniform crossover with smooth point mutation (SUX/SM). The fastest CAFFEINE variant, SUX/SM, is only moderately slower than non-grammatical GP - a reasonable price to pay when the user wants immediately interpretable results.
The Colletotrichum boninense species complex

Although only recently described, Colletotrichum boninense is well established in literature as an anthracnose pathogen or endophyte of a diverse range of host plants worldwide. It is especially prominent on members of Amaryllidaceae, Orchidaceae, Proteaceae and Solanaceae. Reports from literature and preliminary studies using ITS sequence data indicated that C. boninense represents a species complex. A multilocus molecular phylogenetic analysis (ITS, ACT, TUB2, CHS-1, GAPDH, HIS3, CAL) of 86 strains previously identified as C. boninense and other related strains revealed 18 clades. These clades are recognised here as separate species, including C. boninense s. str. , C. hippeastri, C. karstii and 12 previously undescribed species, C. annellatum, C. beeveri, C. brassicicola, C. brasiliense, C. colombiense, C. constrictum, C. cymbidiicola, C. dacrycarpi, C. novae-zelandiae, C. oncidii, C. parsonsiae and C. torulosum. Seven of the new species are only known from New Zealand, perhaps reflecting a sampling bias. The new combination C. phyllanthi was made, and C. dracaenae Petch was epitypified and the name replaced with C. petchii. Typical for species of the C. boninense species complex are the conidiogenous cells with rather prominent periclinal thickening that also sometimes extend to form a new conidiogenous locus or annellations as well as conidia that have a prominent basal scar. Many species in the C. boninense complex form teleomorphs in culture. Taxonomic novelties: New combination - Colletotrichum phyllanthi (H. Surendranath Pai) Damm, P.F. Cannon & Crous. Name replacement - C. petchii Damm, P.F. Cannon & Crous. New species - C. annellatum Damm, P.F. Cannon & Crous, C. beeveri Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. brassicicola Damm, P.F. Cannon & Crous, C. brasiliense Damm, P.F. Cannon, Crous & Massola, C. colombiense Damm, P.F. Cannon, Crous, C. constrictum Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. cymbidiicola Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. dacrycarpi Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. novae-zelandiae Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. oncidii Damm, P.F. Cannon & Crous, C. parsonsiae Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. torulosum Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir. Typifications: Epitypifications - C. dracaenae Petch.
Laminin 5 Regulates Polycystic Kidney Cell Proliferation and Cyst Formation*

Renal cyst formation is the hallmark of autosomal dominant polycystic kidney disease (ADPKD). ADPKD cyst-lining cells have an increased proliferation rate and are surrounded by an abnormal extracellular matrix (ECM). We have previously shown that Laminin 5 (Ln-5, a α3β3γ2 trimer) is aberrantly expressed in the pericystic ECM of ADPKD kidneys. We report that ADPKD cells in primary cultures produce and secrete Ln-5 that is incorporated to the pericystic ECM in an in vitro model of cystogenesis. In monolayers, purified Ln-5 induces ERK activation and proliferation of ADPKD cells, whereas upon epidermal growth factor stimulation blocking endogenously produced Ln-5 with anti-γ2 chain antibody reduces the sustained ERK activation and inhibits proliferation. In three-dimensional gel culture, addition of purified Ln-5 stimulates cell proliferation and cyst formation, whereas blocking endogenous Ln-5 strongly inhibits cyst formation. Ligation of α6β4 integrin, a major Ln-5 receptor aberrantly expressed by ADPKD cells, induces β4 integrin phosphorylation, ERK activation, cell proliferation, and cyst formation. These findings indicate that Ln-5 is an important regulator of ADPKD cell proliferation and cystogenesis and suggest that Ln-5 γ2 chain and Ln-5-α6β4 integrin interaction both contribute to these phenotypic changes.
Investigation of Question Classifier in Question Answering

In this paper, we investigate how an accurate question classifier contributes to a question answering system. We first present a Maximum Entropy (ME) based question classifier which makes use of head word features and their WordNet hypernyms. We show that our question classifier can achieve the state of the art performance in the standard UIUC question dataset. We then investigate quantitatively the contribution of this question classifier to a feature driven question answering system. With our accurate question classifier and some standard question answer features, our question answering system performs close to the state of the art using TREC corpus.
A Structural Equation Modeling Approach to Examining Factors Influencing Outcomes with Cochlear Implant in Mandarin-Speaking Children

Objective To examine the direct and indirect effects of demographical factors on speech perception and vocabulary outcomes of Mandarin-speaking children with cochlear implants (CIs). Methods 115 participants implanted before the age of 5 and who had used CI before 1 to 3 years were evaluated using a battery of speech perception and vocabulary tests. Structural equation modeling was used to test the hypotheses proposed. Results Early implantation significantly contributed to speech perception outcomes while having undergone a hearing aid trial (HAT) before implantation, maternal educational level (MEL), and having undergone universal newborn hearing screening (UNHS) before implantation had indirect effects on speech perception outcomes via their effects on age at implantation. In addition, both age at implantation and MEL had direct and indirect effects on vocabulary skills, while UNHS and HAT had indirect effects on vocabulary outcomes via their effects on age at implantation. Conclusion A number of factors had indirect and direct effects on speech perception and vocabulary outcomes in Mandarin-speaking children with CIs and these factors were not necessarily identical to those reported among their English-speaking counterparts.
Theoretical Study of Realistic Graphene Nanoribbons

In this thesis, the electronic and magnetic properties of experimentally accessible graphene nanoribbons (GNRs) by using density functional theory (DFT) are presented. GNRs, where quantum confinement opens an energy gap in the electronic spectrum of graphene, hold promise for future electronic devices. Structurally well-defined graphene nanoribbons with tunable band gaps are emerging materials to unravel the great potential of semimetallic graphene. To utilize the full potential of these materials, atomic-scale control over their edges is required. The experimental realization of atomically precise zigzag GNRs (6-ZGNRs) through a bottom-up approach is a major step towards spintronics devices. We investigate the electronic and magnetic properties of such realistic ZGNRs by using DFT. The ZGNRs show edge-localized states with large energy splittings in scanning tunnelling spectroscopy experiments that are confirmed in DFT calculations. An elegant approach to modify the electronic properties of graphene nanostructures is based on the well established technique of chemical functionalization. Phenyl functionalization of pristine ZGNR (ZGNR+) preserves the electronic properties by reducing the energy gap which renders edge magnetism unstable to environmental influences. The magnetic instability is evident when modelling the Au(111) adsorbed ZGNR+ using different van der Waals parameterizations. Different parameterizations lead to an inversion of the frontier states at the Fermi energy by decreasing the equilibrium distance between the ribbon and the underlying metal substrate, and to a change in the net magnetization. Deviations from the pristine edges with periodic distribution of zigzag and armchair sites, chiral GNRs, show localized states at the zigzag edges. On the other hand, ZGNRs containing edge perturbations, e.g. cove defects, show removal of edge states at the Fermi energy which leads to a reduction in magnetism at the edges. My findings show moreover that the selection of an appropriate precursor molecule provides a route to engineer the electronic structure of GNRs.
Minimum BER block precoders for zero-forcing equalization

In this paper we derive an analytic expression for the linear precoder which minimizes the bit error rate (BER) for block transmission systems with zero-forcing equalization and threshold detection. The design is developed for the two standard schemes for eliminating inter-block interference; viz, zero padding (ZP) and cyclic prefix (CP). The CP minimum BER precoder has a structure similar to that of the conventional water-filling discrete multitone (DMT) modulation scheme, but the diagonal water-filling power loading matrix is replaced by a full matrix consisting of a diagonal minimum mean square error (MMSE) power loading matrix post-multiplied by a Discrete Fourier Transform (DFT) matrix. The ZP minimum BER precoder has a corresponding structure. Performance evaluations indicate that the signal-to-noise ratio (SNR) gain of the ZP and CP minimum BER precoders over conventional water-filling DMT, MMSE, and orthogonal frequency division multiplexing (OFDM) schemes can be as much as several decibels.
Carrier frequency offset compensation for uplink of OFDM-FDMA systems

A frequency offset compensation method for OFDM-FDMA, that can correct offsets after the DFT via circular convolution, is proposed as an alternative to the direct method that corrects frequency offsets by multiplying the complex exponents of the offset estimates before the DFT. In contrast to the direct method whose complexity increases proportional to the number of users, the computational load of the proposed scheme decreases as the number of users increases. It is shown that the proposed method is simpler to implement than the direct method when the number of users is greater than two. Furthermore, the former can outperform the latter, because a frequency offset compensation for one user after the DFT does not affect the data of other users. Computer simulation results demonstrate the advantage of the proposed method.