Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrodeposition of Mn-Co/Polypyrrole Nanocomposites: An Electrochemical and In Situ Soft-X-ray Microspectroscopic Investigation

Understanding the lateral variations in the elemental and chemical state of constituents induced by electrochemical reactions at nanoscales is crucial for the advancement of electrochemical materials science. This requires in situ studies to provide observables that contribute to both modeling beyond the phenomenological level and exactly transducing the functionally relevant quantities. A range of X-ray coherent diffraction imaging (CDI) approaches have recently been proposed for imaging beyond the diffraction limit with potentially dramatic improvements in time resolution with chemical sensitivity. In this paper, we report a selection of ptychography results obtained in situ during the electrodeposition of a metal–polymer nanocomposite. Our selection includes dynamic imaging during electrochemically driven growth complemented with absorption and phase spectroscopy with high lateral resolution. We demonstrate the onset of morphological instability feature formation and correlate the chemical state of Mn with the local growth rate controlled by the current density distribution resulting from morphological evolution.
A comparison of the PSCz and Stromlo–APM redshift surveys

We present a direct comparison of the clustering properties of two redshift surveys covering a common volume of space: the recently completed IRAS Point Source Catalogue redshift survey (PSCz) containing 14500 galaxies with a limiting flux of 0.6 Jy at 60 � m, and the optical Stromlo-APM survey containing 1787 galaxies in a region of 4300 deg 2 in the south Galactic cap. We use three methods to compare the clustering properties: the counts-in-cells comparison of Efstathiou (1995, hereafter E95), the two-point cross correlation function, and the Tegmark (1998) ‘null-buster’ test. We find that the Stromlo variances are systematically higher than those of PSCz, as expected due to the deficit of early-type galaxies in IRAS samples. However we find that the differences between the cell counts are consistent with a linear bias between the two surveys, with a relative bias parameter brel ≡ bStromlo/bPSCz ≈ 1.3 which appears approximately scale-independent. The correlation coefficientR between optical and IRAS densities on scales ∼ 20 h −1 Mpc is R ≥ 0.72 at 95% c.l. , placing limits on types of ‘stochastic bias’ which affect optical and IRAS galaxies differently.
Predicting the short-term success of human influenza A variants with machine learning

Seasonal influenza viruses are constantly changing, and produce a different set of circulating strains each season. Small genetic changes can accumulate over time and result in antigenically different viruses; this may prevent the body’s immune system from recognizing those viruses. Due to rapid mutations, in particular in the hemagglutinin gene, seasonal influenza vaccines must be updated frequently. This requires choosing strains to include in the updates to maximize the vaccines’ benefits, according to estimates of which strains will be circulating in upcoming seasons. This is a challenging prediction task. In this paper we use longitudinally sampled phylogenetic trees based on hemagglutinin sequences from human influenza viruses, together with counts of epitope site polymorphisms in hemagglutinin, to predict which influenza virus strains are likely to be successful. We extract small groups of taxa (subtrees) and use a suite of features of these subtrees as key inputs to the machine learning tools. Using a range of training and testing strategies, including training on H3N2 and testing on H1N1, we find that successful prediction of future expansion of small subtrees is possible from these data, with accuracies of 0.71-0.85 and a classifier ‘area under the curve’ (AUC) 0.75-0.9.
Purification and partial characterisation of a lectin from the red marine alga Vidalia obtusiloba C. Agardh

A lectina da alga marinha vermelha Vidalia obtusiloba foi purificada atraves da combinacao de precipitacao com sulfato de amonio, cromatografia de troca ionica em DEAE-celulose e cromatografia de afinidade em goma de guar reticulada. A lectina preferencialmente aglutinou eritrocitos do grupo humano O, nativos e tratados com bromelaina. A atividade hemaglutinante revelou que a lectina era dependente de cations divalentes (Ca++ ou Mn++) e inibida por N-acetil-galactosamina, D-galactosamina, a-lactose and D-galactose e pela glicoproteina mucina de estomago de porco. A massa molecular da lectina, estimada por filtracao em gel, foi de 78,9 kDa, enquanto por SDS-PAGE, em presenca de b-mercaptoetanol, a lectina exibiu duas subunidades proteicas diferentes com Mr de 59,6 e 15,2 kDa, sugerindo que a lectina e uma proteina dimerica. Focalizacao isoeletrica revelou a presenca de uma proteina acida simples, com um ponto isoeletrico entre 4 e 5. A lectina purificada exibiu um conteudo de carboidrato de 43,2% e uma predominância dos aminoacidos Asp/Asn, Glu/Gln e Leu. A energia de ativacao (DG') para a desnaturacao da lectina foi calculada como sendo 25,4 kcalm.mol-1 a 90 oC. Ensaios imunoquimicos usando um anti-soro de coelho produzido contra a lectina purificada de V. obtusiloba mostraram que foi possivel detectar a presenca da lectina em diferentes etapas do processo de purificacao. Western blotting de geis de SDS-PAGE mostraram imunocoramento apenas da maior das subunidades da lectina.
Characterization of physiological noise in resting-state fMRI data at 7 T

An increasing interest exists in the scientific community in studying functional brain networks measured by resting-state fMRI (rs-fMRI). The development of new fMRI acquisition techniques at ultra-high-field (7 Tesla), that allow full brain coverage with high sensitivity and spatial resolution, promises to provide significant insights into functional brain networks. Because measuring functional brain connectivity is based on the detection of synchronous neuronal activity between different regions, non-neuronal mechanisms producing correlated signal fluctuations will be reflected on the connectivity measurements. The characterization and removal of these spurious sources, most often referred to as physiological noise, has therefore become a major subject of concern, especially when going to ultra-high-field strengths where their contribution is significantly increased. This work aims to quantitatively characterize the correlated, physiological noise fluctuations in whole-brain, 1 mm-resolution rs-fMRI data collected from a group of healthy participants at 7 Tesla, both at a whole-brain level and at a more specific, region-based, tier. To that end, a nested-model general linear modeling (GLM) optimization approach was employed, which combines and compares a number of different well-established and promising techniques to model the main sources of physiological noise in the rs-fMRI signal. The optimal model that was found explained a total of 33.8%±5.9 SE of variance in gray matter, with the main source of signal fluctuations being explained by low-frequency drifts (19.6±3.3%), followed by cardiac and respiratory mechanisms (6.6±1.2 %), voxel displacements caused by motion (5.9±1.2%), and global fluctuations in the cerebrospinal fluid and white matter (1.3±0.2%). Correction techniques based on external physiological acquisitions proved to be invaluable as only 17.6±1.9% of spurious signal variance introduced by cardiac and respiratory mechanisms is expected to be alternatively removed by using data-based techniques alone. Subtle voxel displacements (5.0±0.8%), rather than large, abrupt movement (0.9±0.4%) were found to introduce the majority of motion-induced artefacts in the signal. Improvements in intrinsically problematic regions such as the brainstem were more modest, however the model still identified 25.8% putatively attributed to nuisance sources. In conclusion, we compared different methods for modelling correlated, physiological noise fluctuations in rs-fMRI data collected at 7 Tesla, and propose an optimal methodology that explains maximum signal variance. Future work will investigate the effects of the improved physiological noise correction on the identification and analysis of resting-state networks.
PSISA: an algorithm for indexing and searching protein structure using suffix arrays

Protein Structure Indexing using Suffix Array (PSISA) is a new technique provides the ability to retrieve similarities of proteins based on the proteins structures. Indexing the protein structure is one approach of searching for protein similarities. In this paper we developed our proposed technique based on novel use of suffix array. We start by converting protein structure into a sequence by extracting local feature vectors; normalization is applied to these vectors components and converts these normalized vectors into a sequence. Sequence is indexed using the suffix array structure, which is used effectively in the searching process to retrieve proteins with similar structure. Proteins with high structural similarities are ranked according to their alignment score against the query protein. The experimental results, which based on the structural classification of proteins (SCOP) dataset, show that our method outperforms existing similar methods in memory utilization. Our results show an enhancement in the memory usage with factor exceeds 35%.
Distinct Effects of Carrageenan and High-Fat Consumption on the Mechanisms of Insulin Resistance in Nonobese and Obese Models of Type 2 Diabetes

Exposure to low concentration of the common food additive carrageenan (10 mg/L) for only six days led to glucose intolerance and insulin resistance in the C57BL/6J mouse. Longer exposure produced fasting hyperglycemia but with no increase in weight, in contrast to the HFD. Glucose intolerance was attributable to carrageenan-induced inflammation and to increased expression of GRB10. Both HFD and carrageenan increased p(Ser32)-IκBα and p(Ser307)-IRS1, and the increases were greater following the combined exposure. The effects of carrageenan were inhibited by the combination of the free radical inhibitor Tempol and BCL10 siRNA, which had no impact on the HFD-mediated increase. In contrast, the PKC inhibitor sotrastaurin blocked the HFD-induced increases, without an effect on the carrageenan-mediated effects. HFD had no impact on the expression of GRB10. Both carrageenan and high fat increased hepatic infiltration by F4/80-positive macrophages. Serum galectin-3 and galectin-3 binding to the insulin receptor increased by carrageenan and by HFD. Tyrosine phosphorylation of the insulin receptor declined following either exposure and was further reduced by their combination. Carrageenan reduced the activity of the enzyme N-acetylgalactosamine-4-sulfatase (ARSB; arylsulfatase B), which was unchanged following HFD. Dietary exposure to both high fat and carrageenan can impair insulin signaling through both similar and distinct mechanisms.
An Automated Algorithm to Extract Time Plane Features from the PPG Signal and its Derivatives for Personal Health Monitoring Application

Recently, photoplethysmogram (PPG) signal is widely adopted in health monitoring devices for automated assessment of different cardiovascular parameters. However, research in the area of computerized health analysis using PPG signal features is still lagging behind. In this paper, a robust, automated yet simple algorithm is proposed for accurate detection of characteristic points from the PPG signal and its derivatives. The methodology follows amplitude thresholding, slope-reversal, and an empirical formula based approach. Finally, Baseline modulation is removed from the PPG dataset and features are extracted from the amplitude normalized PPG signal and its derivatives. Performance of the proposed algorithm is evaluated over MIMIC database as well as over real PPG data acquired from both healthy volunteers and cardiac patients. The algorithm exhibits high efficiency for all detected fiducial points with an average sensitivity, positive predictivity and detection accuracy of 99.80%, 99.84%, and 99.65%, respectively. Compared to the existing methods, the proposed algorithm offers complete characterization of the PPG signal and its derivatives.
Supervised adaptation of MCE-trained CDHMMS using minimum classification error linear regression

In this paper, we present a formulation of minimum classification error linear regression (MCELR) for adaptation of Gaussian mixture continuous density HMM (CDHMM) parameters. We demonstrate that the MCELR can be used to adapt the MCE-trained HMM parameters under a consistent criterion. In a supervised speaker adaptation application, we observe that such adapted models perform better than the ones adapted using MLLR from the ML-trained seed models. We also observe that the MCELR performs consistently better than the MLLR for either sets of seed models.
Evaluating Adoption of Innovations of Mobile Devices and Desktops within Collaborative Environments in a Higher Education Context

This study aims to evaluate if students that use laptops or desktops learn when using Google Groups, involving one hundred and twelve students in a higher education context. The research main goal is to validate which of the students, involved in this study, are in the initial and the majority market of adopters and also classify which of them are more in the flow experience. This study is based on the categories proposed by Roger (1995) for the adoption of innovations, and in the 0low experience concept, introduced by Csikszentmihalyi (1975). The main purpose of this study is to establish whether the laptops or desktops users in the initial and the majority market of adopters are in the flow experience when using a set of the available Google applications. At the end of the study, it was possible to conclude that students have experienced the flow state and it had a positive effect on their learning experiences both by students using laptops and those, using desktops. For both desktop and laptop users who belong to the initial market, these have a value slightly lower for the flow experience than the users who belong to the majority market. With this we can say that the users that belong to the majority market learn more when using Google Groups than the users that belong to the initial market.
Application of a Multiseed-Based Clustering Technique for Automatic Satellite Image Segmentation

The problem of classifying an image into different homogeneous regions is viewed as a task of clustering the pixels in the intensity space. In this letter, a newly developed genetic clustering technique is used for automatically segmenting remote sensing satellite images. Each cluster is divided into several small hyperspherical subclusters, and the centers of all these small subclusters are encoded in a chromosome to represent the whole clustering. For assigning points to different clusters, these local subclusters are considered individually. For the purpose of objective function evaluation, these subclusters are merged appropriately to form a variable number of global clusters. A newly proposed point-symmetry-distance-based cluster validity index, Sym index, is used as a measure of the validity of the corresponding segment. The effectiveness of the proposed technique compared to a fuzzy C-means clustering technique, a recently proposed GAPS clustering with Sym-index-based method, and a subtractive clustering technique is demonstrated in identifying different land cover regions from two numeric image data sets and a remote sensing image of a part of the city of Kolkata.
An experimental study of the inheritance of intelligence.

The Primary Mental Abilities Tests and the Furneaux Test of Intellectual Level and Intellectual Speed were administered to a group of 52 pairs of like-sexed twins between the ages of 12 and 15 years. The sample was evenly divided in terms of sex and zygosity. Test results were intercorrelated and the factor analysis was carried out using the Thurstone method. The first factor, accounting for 20 per cent. of the variance, was identified as an intelligence factor. Factor scores were determined on the basis of the regression equation. h2 values were calculated for these factor scores. The first of the hypotheses put forward in regard to intelligence was supported by the data. The total score for the Thurstone Primary Mental Abilities Tests weighted in accordance with the factor loadings of the tests on Thurstone's second order factor and the total score weighted on the basis of the present analysis give h2 values of ·594 and ·549 respectively. In each case the intra-identical correlation exceeds the intra-fraternal correlation, the difference being significant beyond the ·01 level of confidence. The second hypothesis stated that the total score would be determined in larger part by inheritance than would any of the individual Primary Mental Abilities scores. The data did not support this hypothesis since the h2 values of three of the Primary Mental Abilities tests—Verbal Comprehension, Space and Reasoning—exceeded those of the total scores.
ICE: Information Credibility Evaluation on Social Media via Representation Learning

With the rapid growth of social media, rumors are also spreading widely on social media and bring harm to people's daily life. Nowadays, information credibility evaluation has drawn attention from academic and industrial communities. Current methods mainly focus on feature engineering and achieve some success. However, feature engineering based methods require a lot of labor and cannot fully reveal the underlying relations among data. In our viewpoint, the key elements of user behaviors for evaluating credibility are concluded as "who", "what", "when", and "how". These existing methods cannot model the correlation among different key elements during the spreading of microblogs. In this paper, we propose a novel representation learning method, Information Credibility Evaluation (ICE), to learn representations of information credibility on social media. In ICE, latent representations are learnt for modeling user credibility, behavior types, temporal properties, and comment attitudes. The aggregation of these factors in the microblog spreading process yields the representation of a user's behavior, and the aggregation of these dynamic representations generates the credibility representation of an event spreading on social media. Moreover, a pairwise learning method is applied to maximize the credibility difference between rumors and non-rumors. To evaluate the performance of ICE, we conduct experiments on a Sina Weibo data set, and the experimental results show that our ICE model outperforms the state-of-the-art methods.
Xenotransplantation of human dental pulp stem cells in platelet-rich plasma for the treatment of full-thickness articular cartilage defects in a rabbit model

Stem cells in platelet-rich plasma (PRP) scaffolds may be a promising treatment for cartilage repair. Human dental pulp stem cell (hDPSC) subpopulations have been identified to have substantial angiogenic, neurogenic and regenerative potential when compared with other stem cell sources. The present study evaluated the potential of hDPSCs in a PRP scaffold to regenerate full-thickness cartilage defects in rabbits. Full-thickness articular cartilage defects were created in the patellar groove of the femur of 30 rabbits allocated into three experimental groups: Those with an untreated critical defect (CTL), those treated with PRP (PRP) and those treated with stem cells in a PRP scaffold (PRP+SC). The patellar grooves of the femurs from the experimental groups were evaluated macroscopically and histologically at 6 and 12 weeks post-surgery. The synovial membranes were also collected and evaluated for histopathological analysis. The synovial lining cell layer was enlarged in the CTL group compared with the PRP group at 6 weeks (P=0.037) but not with the PRP+SC group. All groups exhibited low-grade synovitis at 6 weeks and no synovitis at 12 weeks. Notably, macroscopic grades for the area of articular cartilage repair for the PRP+SC group were significantly improved compared with those in the CTL (P=0.001) and PRP (P=0.049) groups at 12 weeks. Furthermore, histological scores (modified O'Driscoll scoring system) of the patellar groove articular cartilage in the PRP+SC and PRP groups, in which the articular cartilage was primarily hyaline-like, were significantly higher compared with those in the CTL group at 12 weeks (P=0.002 and P=0.007, respectively). The present results support the therapeutic use of hDPSCs for the treatment of full-thickness articular cartilage defects.
Bio-Medical Image Retrieval Using SVM

— This paper presents an image retrieval system in a computer system for browsing, searching and retrieving images from a large database of digital images. The support vector machine (SVM) is a concept based on learning methods that evaluate data and identify patterns, used for classification and regression analysis. In this work, the prediction of database and query image depends on the outputs of a multiclass support vectormachine (SVM) classifier. The irrelevant images are first to filter out, for similarity matching. The different low-level featuresmodalities are used in this framework. When compare to the query image, the main difficulty is to find unique feature. So an adaptive linear combination of similarity matching and user’s feedback information is used here. The predicted output of classifier may be inexact in some cases. Hence, the feature weights are calculated by considering both precision and rank order information based on users feedback. As a result, using SVM classifier with the retrieval performance clearly shows the advantageof searching images based on similarity fusion and filtering in terms of competent and success.
N-(4-Methoxyphenyl)-2,6-dimethyl-1,3-dioxan-4-amine

In the title compound, C13H19NO3, the dioxane ring adopts a chair conformation. Its mean plane is inclined to the 4-methoxyphenyl ring by 70.34 (9)°. In the crystal, molecules are linked by pairs of C—H⋯O hydrogen bonds, forming inversion dimers with an R 2 2(16) ring motif. The dimers are linked via C—H⋯π interactions, forming two-dimensional networks lying parallel to the ac plane.
Knowledge-Intensive Collaborative Decision Support for Design Process , Part I : A Hybrid Decision Model and Multi-agent Framework

Engineering design is essentially a collaborative decision-making process that requires rigorous evaluation, comparison and selection of design alternatives and optimization from a global perspective. Increasing design knowledge and supporting designers to make intelligent and correct decisions can result in higher quality designs. In this paper we present a hybrid decision support model and framework, focused on facilitating the integration of objective and subjective aspects of design, which can be extensively applied for engineering systems. The proposed system will facilitate the seamless/smooth integration of the stakeholder involved in collaborative product development and improve the likelihood of optimal product performance. The work focuses on the provision of methodologies/algorithms and a framework for knowledge-based intelligent design decision-making for improved product development and realization of business strategies. The reported hybrid decision model, which integrates the compromise Decision Support Problem (cDSP) and the fuzzy synthetic model (FSD), can quantitatively incorporate qualitative design knowledge and preferences for multiple, conflicting attributes stored in a knowledge repository so that a better understanding of the consequences of design decisions can be achieved from an overall perspective. The developed model and framework are generic and flexible enough to be used in a variety of design decision problems. The framework is illustrated with an application in concept evaluation and selection in product family design for mass customization.