Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features

Optimized magnetic resonance imaging (MRI) features and abnormalities of brain network architectures may allow earlier detection and accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). In this study, we proposed a classification framework to distinguish MCI converters (MCIc) from MCI non-converters (MCInc) by using a combination of FreeSurfer-derived MRI features and nodal features derived from the thickness network. At the feature selection step, we first employed sparse linear regression with stability selection, for the selection of discriminative features in the iterative combinations of MRI and network measures. Subsequently the top K features of available combinations were selected as optimal features for classification. To obtain unbiased results, support vector machine (SVM) classifiers with nested cross validation were used for classification. The combination of 10 features including those from MRI and network measures attained accuracies of 66.04, 76.39, 74.66, and 73.91% for mixed conversion time, 6, 12, and 18 months before diagnosis of probable AD, respectively. Analysis of the diagnostic power of different time periods before diagnosis of probable AD showed that short-term prediction (6 and 12 months) achieved more stable and higher AUC scores compared with long-term prediction (18 months), with K-values from 1 to 30. The present results suggest that meaningful predictors composed of MRI and network measures may offer the possibility for early detection of progression from MCI to AD.
Comparison of Algorithms for Document Clustering

Clustering is "the method of organizing objects into groups whose members are related in some way". A cluster is therefore a collection of objects which are coherent internally, but clearly dissimilar to the objects belonging to other clusters. Document clustering is used in many fields such as data mining and information retrieval. Thus, the main goals of this paper are to identify the comparison of the performance of criterion function in the context of partition clustering approach, k means, and agglomerative hierarchical approach. By comparing all this we establish right clustering algorithm to produce qualitative clustering of real world document. And also modify existing algorithm to establish right algorithm which we try to make more efficient than existing algorithms which we are study in this paper.
A pancake droplet translating in a Hele-Shaw cell: lubrication film and flow field

We adopt a boundary integral method to study the dynamics of a translating droplet confined in a Hele-Shaw cell in the Stokes regime. The droplet is driven by the motion of the ambient fluid with the same viscosity. We characterize the three-dimensional (3D) nature of the droplet interface and of the flow field. The interface develops an arc-shaped ridge near the rear-half rim with a protrusion in the rear and a laterally symmetric pair of higher peaks; this pair of protrusions has been identified by recent experiments (Huerre et al., Phys. Rev. Lett., vol. 115 (6), 2015, 064501) and predicted asymptotically (Burgess & Foster, Phys. Fluids A, vol. 2 (7), 1990, pp. 1105–1117). The mean film thickness is well predicted by the extended Bretherton model (Klaseboer et al., Phys. Fluids, vol. 26 (3), 2014, 032107) with fitting parameters. The flow in the streamwise wall-normal middle plane is featured with recirculating zones, which are partitioned by stagnation points closely resembling those of a two-dimensional droplet in a channel. Recirculation is absent in the wall-parallel, unconfined planes, in sharp contrast to the interior flow inside a moving droplet in free space. The preferred orientation of the recirculation results from the anisotropic confinement of the Hele-Shaw cell. On these planes, we identify a dipolar disturbance flow field induced by the travelling droplet and its $1/r^{2}$ spatial decay is confirmed numerically. We pinpoint counter-rotating streamwise vortex structures near the lateral interface of the droplet, further highlighting the complex 3D flow pattern.
The origins of syntax: from navigation to language

This article suggests that the parser underlying human syntax may have originally evolved to assist navigation, a claim supported by computational simulations as well as evidence from neuroscience and psychology. We discuss two independent conjectures about the way in which navigation could have supported the emergence of this aspect of the human language faculty: firstly, by promoting the development of a parser; and secondly, by possibly providing a topic of discussion to which this parser could have been applied with minimum effort. The paper summarizes our previously published experiments and provides original results in support of the evolutionary advantages this type of communication can provide, compared with other foraging strategies. Another aspect studied in the experiments is the combination and range of environmental factors that make communication beneficial, focusing on the availability and volatility of resources. We suggest that the parser evolved for navigation might initially have been limited to handling regular languages, and describe a mechanism that may have created selective pressure for a context-free parser.
A Smart Decision System for Digital Farming

New technologies have the potential to transform agriculture and to reduce environmental impact through a green revolution. Internet of Things (IoT)-based application development platforms have the potential to run farm management tools capable of monitoring real-time events when integrated into interactive innovation models for fertirrigation. Their capabilities must extend to flexible reconfiguration of programmed actions. IoT platforms require complex smart decision-making systems based on data-analysis and data mining of big data sets. In this paper, the advantages are demonstrated of a powerful tool that applies real-time decisions from data such as variable rate irrigation, and selected parameters from field and weather conditions. The field parameters, the index vegetation (estimated using aerial images), and the irrigation events, such as flow level, pressure level, and wind speed, are periodically sampled. Data is processed in a decision-making system based on learning prediction rules in conjunction with the Drools rule engine. The multimedia platform can be remotely controlled, and offers a smart farming open data network with shared restriction levels for information exchange oriented to farmers, the fertilizer provider, and agricultural technicians that should provide the farmer with added value in the form of better decision making or more efficient exploitation operations and management.
Does social identity affect business performance

The business founder’s social identity is crucial to explaining his or her behaviour and attitude in business decision-making. Drawing on three types of entrepreneurial social identity identified by Fauchart and Gruber (2011), this study examines how social identities influence the entrepreneur’s way of managing his/her firm and its consequences for business performance. Based on a survey of newly created firms, the results support the conclusion that effectuation channels the effects of specific identities - Darwinian and missionary- on business performance.
A Pheromone-Like Model for Semantic Context Extraction from Collaborative Networks

The extraction of semantic contexts is a relevant issue in information retrieval to provide high quality query results. This paper introduces the semantic context underlying a set of given input concepts as defined by the relevant multiple explanation paths connecting the input concepts in a collaborative network. A pheromone-like model based on this approach is introduced for the detection and the extraction of multiple paths of explanation between seed concepts. The exploration of the online collaborative network of explanation uses a heuristic driven random walk, based on semantic proximity measures. Random walks distribute pheromone on the traversed arcs used to evaluate the relevance of concepts in the multiple explanatory paths to be extracted. Experimental results obtained on accepted datasets and contexts extracted from the Wikipedia collaborative network show that the proposed algorithm can extract contexts with high relevance degree, which outperforms other methods. The approach has a general applicability and can be extended to other explanation-based online collaborative networks.
Semantic scene detection system for baseball videos based on the MPEG-7 specification

In this paper, we proposed a content-based multimedia analysis/retrieval system mainly based on the MPEG-7 specification which is capable of handling the high-level content analysis such as the semantic scene detection for baseball broadcast videos. Here, eight semantic scene classes were predefined for baseball videos. First, an effective shot boundary detection scheme based on scalable color histogram was proposed to segment a video into many shots. Then, various visual features including field color features, skin color features, and camera motion information were extracted to analyze the semantics for each shot. According to the visual properties of different scenes, we developed a two-stage classification strategy for the semantic scene detection. Finally, the experimental results showed that the proposed framework identifies eight semantic baseball scenes with 81% of precision rates and 84% of recall rates.
A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation

The sparse inverse covariance estimation problem arises in many statistical applications in machine learning and signal processing. In this problem, the inverse of a covariance matrix of a multivariate normal distribution is estimated, assuming that it is sparse. An l1 regularized log-determinant optimization problem is typically solved to approximate such matrices. Because of memory limitations, most existing algorithms are unable to handle large scale instances of this problem. In this paper we present a new block-coordinate descent approach for solving the problem for large-scale data sets. Our method treats the sought matrix block-by-block using quadratic approximations, and we show that this approach has advantages over existing methods in several aspects. Numerical experiments on both synthetic and real gene expression data demonstrate that our approach outperforms the existing state of the art methods, especially for large-scale problems.
A Discriminative Training Procedure for Continuous Translation Models

Continuous-space translation models have recently emerged as extremely powerful ways to boost the performance of existing translation systems. A simple, yet effective way to integrate such models in inference is to use them in an N -best rescoring step. In this paper, we focus on this scenario and show that the performance gains in rescoring can be greatly increased when the neural network is trained jointly with all the other model parameters, using an appropriate objective function. Our approach is validated on two domains, where it outperforms strong baselines.
Familiar Faces: Can Having Similar Classmates from Last Year Link to Better School Attendance This Year?

Identifying driving factors of school attendance in elementary grades has come to the forefront of policy discussion. Little research has been dedicated to understanding the role of classroom context, and no studies have examined whether having classmates from the previous school year who are also present in the current classroom might influence absenteeism (i.e., familiar faces). This study uses district data to explore whether the percentage of familiar faces is associated with absence outcomes for students. The findings suggest that having familiar faces from the previous school year was linked to lower numbers of unexcused absences and lower odds of chronic absenteeism. This suggests that elementary school students might benefit from peer stability, and policy makers and educational stakeholders might help students maintain some degree of familiarity by considering the role of consistent classmates.
Luminous red galaxies in clusters: central occupation, spatial distributions and miscentring

Luminous Red Galaxies (LRG) from the Sloan Digital Sky Survey are considered among the best understood samples of galaxies, and they are employed in a broad range of cosmological studies. Because they form a relatively homogeneous population, with high stellar masses and red colors, they are expected to occupy halos in a relatively simple way. In this paper, we study how LRGs occupy massive halos via direct counts in clusters and we reveal several unexpected trends suggesting that the connection between LRGs and dark matter halos may not be straightforward. Using the redMaPPer cluster catalog, we derive the central occupation of LRGs as a function richness, Ncen({\lambda}). Assuming no correlation between cluster mass and central galaxy luminosity at fixed richness, we show that clusters contain a significantly lower fraction of central LRGs than predicted from the two-point correlation function. At halo masses of 10^14.5 Msun, we find Ncen=0.73, compared to Ncen of 0.89 from correlation studies. Our central occupation function for LRGs converges to 0.95 at large halo masses. A strong anti-correlation between central luminosity and cluster mass at fixed richness is required to reconcile our results with those based on clustering studies. We also derive P_BNC, the probability that the brightest cluster member is not the central galaxy. We find P_BNC ~ 20-30% which is a factor of ~2 lower than the value found by Skibba et al. 2011. Finally, we study the radial offsets of bright non-central LRGs from cluster centers and show that bright non-central LRGs follow a different radial distribution compared to red cluster members, which follow a Navarro-Frank-White profile. This work demonstrates that even the most massive clusters do not always have an LRG at the center, and that the brightest galaxy in a cluster is not always the central galaxy.
On the performance of V-BLAST with zero-forcing successive interference cancellation receiver

The performance analysis of the zero-forcing successive interference cancellation (ZF-SIC) detector in vertical Bell Labs layered space-time (V-BLAST) is generally considered difficult, mainly because of the nonlinear error propagation effect. In this paper, we first present the nearly exact bit error rate (BER) analysis of ZF-SIC V-BLAST with binary phase-shift keying (BPSK) modulation in the richly scattered Rayleigh-fading multiple-antenna channel. We derive the closed-form expressions of the error probability of each substream under a given number of transmit antennas. We further give a general recursive procedure to calculate the BER of each substream with arbitrary number of transmit and receive antennas. The procedure we give is easy to follow and is feasible in evaluating the BER performance of the V-BLAST with ZF-SIC receiver. It can be further utilized to develop an optimal transmission strategy for the open-loop V-BLAST. Our analytical results show excellent matches with the Monte Carlo simulation results.
N-Ethylmaleimide-Sensitive Factor b (nsfb) Is Required for Normal Pigmentation of the Zebrafish Retinal Pigment Epithelium.

PURPOSE Despite the number of albinism-causing mutations identified in human patients and animal models, there remain a significant number of cases for which no mutation has been identified, suggesting that our understanding of melanogenesis is incomplete. Previously, we identified two oculocutaneous albinism mutations in zebrafish, au13 and au18. Here, we sought to identify the mutated loci and determine how the affected proteins contribute to normal pigmentation of the retinal pigment epithelium (RPE). METHODS Complementation analyses revealed that au13 and au18 belonged to a single complementation group, suggesting that they affected the same locus. Whole-genome sequencing and single nucleotide polymorphism (SNP) analysis was performed to identify putative mutations, which were confirmed by cDNA sequencing and mRNA rescue. Transmission electron microscopy (TEM) and image quantification were used to identify the cellular basis of hypopigmentation. RESULTS Whole-genome sequencing and SNP mapping identified a nonsense mutation in the N-ethylmaleimide-sensitive factor b (nsfb) gene in au18 mutants. Complementary DNA sequencing confirmed the presence of the mutation (C893T), which truncates the nsfb protein by roughly two-thirds (Y297X). No coding sequence mutations were identified in au13, but quantitative PCR revealed a significant decrease in nsfb expression, and nsfb mRNA injection rescued the hypopigmentation phenotype, suggesting a regulatory mutation. In situ hybridization revealed that nsfb is broadly expressed during embryonic development, including in the RPE. Transmission electron microscopy analyses indicated that average melanosome density and maturity were significantly decreased in nsfb mutants. CONCLUSIONS au18 and au13 contain mutations in nsfb, which encodes a protein that is required for the maturation of melanosomes in zebrafish RPE.
Aberrant Location of Inhibitory Synaptic Marker Proteins in the Hippocampus of Dystrophin-Deficient Mice: Implications for Cognitive Impairment in Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a neuromuscular disease that arises from mutations in the dystrophin-encoding gene. Apart from muscle pathology, cognitive impairment, primarily of developmental origin, is also a significant component of the disorder. Convergent lines of evidence point to an important role for dystrophin in regulating the molecular machinery of central synapses. The clustering of neurotransmitter receptors at inhibitory synapses, thus impacting on synaptic transmission, is of particular significance. However, less is known about the role of dystrophin in influencing the precise expression patterns of proteins located within the pre- and postsynaptic elements of inhibitory synapses. To this end, we exploited molecular markers of inhibitory synapses, interneurons and dystrophin-deficient mouse models to explore the role of dystrophin in determining the stereotypical patterning of inhibitory connectivity within the cellular networks of the hippocampus CA1 region. In tissue from wild-type (WT) mice, immunoreactivity of neuroligin2 (NL2), an adhesion molecule expressed exclusively in postsynaptic elements of inhibitory synapses, and the vesicular GABA transporter (VGAT), a marker of GABAergic presynaptic elements, were predictably enriched in strata pyramidale and lacunosum moleculare. In acute contrast, NL2 and VGAT immunoreactivity was relatively evenly distributed across all CA1 layers in dystrophin-deficient mice. Similar changes were evident with the cannabinoid receptor 1, vesicular glutamate transporter 3, parvalbumin, somatostatin and the GABAA receptor alpha1 subunit. The data show that in the absence of dystrophin, there is a rearrangement of the molecular machinery, which underlies the precise spatio-temporal pattern of GABAergic synaptic transmission within the CA1 sub-field of the hippocampus.
Emission of monoterpenes from European beech ( Fagus sylvatica L.) as a function of light and temperature

Abstract. Using a dynamic branch enclosure technique European beech (Fagus sylvatica L.) was characterised as a strong emitter of monoterpenes, with sabinene being the predominant compound released. Since monoterpene emission was demonstrated to be a function of light and temperature, application of light and temperature dependent algorithms resulted in reasonable agreement with the measured data. Furthermore, during high temperature periods the depression of net CO2 exchange during midday (midday depression) was accompanied by a depression of monoterpene emission on one occasion. The species dependent standard emission factor and the light and temperature regulated release of monoterpenes is of crucial importance for European VOC emissions. All measurements were performed within the framework of the ECHO project (Emission and CHemical transformation of biogenic volatile Organic compounds) during two intensive field campaigns in the summers of 2002 and 2003.
Exploiting Machine-Transcribed Dialog Corpus to Improve Multiple Dialog States Tracking Methods

This paper proposes the use of unsupervised approaches to improve components of partition-based belief tracking systems. The proposed method adopts a dynamic Bayesian network to learn the user action model directly from a machine-transcribed dialog corpus. It also addresses confidence score calibration to improve the observation model in a unsupervised manner using dialog-level grounding information. To verify the effectiveness of the proposed method, we applied it to the Let's Go domain (Raux et al., 2005). Overall system performance for several comparative models were measured. The results show that the proposed method can learn an effective user action model without human intervention. In addition, the calibrated confidence score was verified by demonstrating the positive influence on the user action model learning process and on overall system performance.
A Novel Merging Method in Watershed Segmentation

The watershed transformation is the primary tool of Mathematical Morphology for image segmentation. However, the resulting image often appears oversegmented into a large number of tiny regions (basins), most of which are not significant in the problem of domain. In this paper, a method for removing non significant basins is presented. The notions of relative significance and intrinsic significance are introduced to restrict the merging of a basin to a number of suitably selected adjacent basins. Such a selection allows one to obtain a segmented image perceptually close to the original image. The good performance of the method is shown for the case of astronomic images.
Age-related obesity and type 2 diabetes dysregulate neuronal associated genes and proteins in humans

Despite numerous developed drugs based on glucose metabolism interventions for treatment of age-related diseases such as diabetes neuropathies (DNs), DNs are still increasing in patients with type 1 or type 2 diabetes (T1D, T2D). We aimed to identify novel candidates in adipose tissue (AT) and pancreas with T2D for targeting to develop new drugs for DNs therapy. AT-T2D displayed 15 (e.g. SYT4 up-regulated and VGF down-regulated) and pancreas-T2D showed 10 (e.g. BAG3 up-regulated, VAV3 and APOA1 down-regulated) highly differentially expressed genes with neuronal functions as compared to control tissues. ELISA was blindly performed to measure proteins of 5 most differentially expressed genes in 41 human subjects. SYT4 protein was upregulated, VAV3 and APOA1 were down-regulated, and BAG3 remained unchanged in 1- Obese and 2- Obese-T2D without insulin, VGF protein was higher in these two groups as well as in group 3- Obese-T2D receiving insulin than 4-lean subjects. Interaction networks analysis of these 5 genes showed several metabolic pathways (e.g. lipid metabolism and insulin signaling). Pancreas is a novel site for APOA1 synthesis. VGF is synthesized in AT and could be considered as good diagnostic, and even prognostic, marker for age-induced diseases obesity and T2D. This study provides new targets for rational drugs development for the therapy of age-related DNs.