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Respondent Driven Sampling (RDS) is a popular network-based method for sampling from hidden population. This method is a type of chain referral (or snowball) sampling in which an estimator is used to infer the proportion of the population with that property. Existing RDS estimators are asymptotically unbiased based on various underlying assumptions. However, these assumptions are often violated in practice, and little attention has been given to violation of one of these assumptions on accurately reporting the degree by all nodes. In this paper, we address the violation of this assumption and propose a new estimator based on fuzzy computing. In particular, the number of an individual’s contacts can be a fuzzy concept. Using fuzzy functions, we transform the reported degrees to fuzzy numbers and estimate the infection prevalence in the hidden population by the proposed estimator. We simulate RDS method under the condition that all assumptions are satisfied except the one for the degree, and then evaluate the proposed estimator in synthetic and real datasets. Our results show that the fuzzy-based estimator can reduce the sampling bias in average 54% as compared to the existing methods. [ABSTRACT FROM AUTHOR]

Background: Recent evidence indicates that the PVT1, CCDC26, and CCAT1 long noncoding RNAs (lncRNAs) are involved in the leukemogenic process. This study quantified the expression levels of the PVT1, CCDC26, and CCAT1 lncRNAs in patients with acute myeloid leukemia (AML) and also correlated their expression levels with the clinicopathological features of the patients. Materials and Methods: The expression levels of the PVT1, CCDC26, and CCAT1 lncRNAs were analyzed using quantitative reverse transcription–polymerase chain reaction of bone marrow specimens obtained from 86 AML patients, 48 AML-M3 patients, and 40 normal controls. Results: No differences were found between the combined AML patient populations and the healthy controls with respect to the expression levels of PVT1, CCDC26, and CCAT1 (p = 0.35, p = 0.09, and p = 0.77, respectively). However, compared with the controls, the AML-M3 patients had higher PVT1 expression (p = 0.017). Furthermore, high-risk AML-M3 patients manifested higher expression levels of PVT1 than low- and intermediate-risk groups. In addition, distinctive CCDC26 and CCAT1 expression levels were observed among patients with different French–American–British subtypes (p = 0.001 for CCDC26 and p = 0.013 for CCAT1). Compared with the healthy controls, AML-M4 and M5 had higher CCAT1 expression (p = 0.04) and AML-M2 and AML-M4/M5 patients had higher CCDC26 expression (p < 0.001 and p = 0.02, respectively). In addition, different patterns of CCDC26 expression were found among the different cytogenetic risk subtypes (p = 0.005). Finally, patients with intermediate cytogenetic risk showed higher CCDC26 expression levels. Conclusion: The differential expression of the PVT1, CCDC26, and CCAT1 lncRNAs in different AML subtypes suggests that the deregulation of these transcripts may function in the multistep leukemogenic process and that they may serve as new therapeutic targets for this malignancy. [ABSTRACT FROM AUTHOR]

Reducing emissions and energy consumption of autonomous vehicles is critical in the modern era. This paper presents an intelligent energy management system based on Reinforcement Learning (RL) for conventional autonomous vehicles. Furthermore, in order to improve the efficiency, a new exploration strategy is proposed to replace the traditional decayed ε-greedy strategy in the Q-learning algorithm associated with RL. Unlike traditional Q-learning algorithms, the proposed self-adaptive Q-learning (SAQ-learning) can be applied in real-time. The learning capability of the controllers can help the vehicle deal with unknown situations in real-time. Numerical simulations show that compared to other controllers, Q-learning and SAQ-learning controllers can generate the desired engine torque based on the vehicle road power demand and control the air/fuel ratio by changing the throttle angle efficiently in real-time. Also, the proposed real-time SAQ-learning is shown to improve the operational time by 23% compared to standard Q-learning. Our simulations reveal the effectiveness of the proposed control system compared to other methods, namely dynamic programming and fuzzy logic methods. [ABSTRACT FROM AUTHOR]

Recently network embedding has gained increasing attention due to its advantages in facilitating network computation tasks such as link prediction, node classification and node clustering. The objective of network embedding is to represent network nodes in a low-dimensional vector space while retaining as much information as possible from the original network including structural, relational, and semantic information. However, asymmetric nature of directed networks poses many challenges as how to best preserve edge directions during the embedding process. Here, we propose a novel deep asymmetric attributed network embedding model based on the convolutional graph neural network, called AAGCN. The main idea is to maximally preserve the asymmetric proximity and asymmetric similarity of directed attributed networks. AAGCN introduces two neighbourhood feature aggregation schemes to separately aggregate the features of a node with the features of its in- and out- neighbours. Then, it learns two embedding vectors for each node, one source embedding vector and one target embedding vector. The final representations are the results of concatenating source and target embedding vectors. We test the performance of AAGCN on four real-world networks for network reconstruction, link prediction, node classification and visualization downstream tasks and investigate the impact of hyperparameters of the proposed method on the performance of the tasks. The experimental results show the superiority of AAGCN against state-of-the-art embedding methods. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to evaluate DNMT3A exon 23 mutations and their prognostic impacts in the presence of NPM1 and FLT3 mutations in acute myeloid leukemia (AML) patients who underwent allogeneic hematopoietic stem cell transplantation (HSCT). Materials and Methods: This study comprised 128 adult AML patients referred to the Hematology-Oncology and Stem Cell Research Center of Shariati Hospital. NPM1 and FLT3-ITD mutations were detected by fragment analysis. For DNMT3A exon 23 mutation analysis, we used Sanger sequencing. Overall survival (OS) and relapse-free survival (RFS) curves were estimated by the Kaplan-Meier method and the logrank test was used to calculate differences between groups. Results: The prevalence of DNMT3A exon 23 mutations was 15.6% and hotspot region R882 mutations were prominent. RFS and OS were compared in patients with and without DNMT3A exon 23 mutations using univariate analysis and there was no significant difference between these groups of patients. On the contrary, the FLT3-ITD mutation significantly reduced the OS (p=0.009) and RFS (p=0.006) in AML patients after allogeneic HSCT. In the next step, patients with AML were divided into four groups regarding FLT3-ITD and DNMT3A mutations. Patients with DNMT3A R882mut/FLT3-ITDpos had the worst OS and RFS. These results indicate that DNMT3A mutations alone do not affect the clinical outcomes of AML patients undergoing allogeneic HSCT, but when accompanied by FLT3-ITD mutations, the OS was significantly reduced (5-year OS 0% for DNMT3A R882mut/ FLT3-ITDpos patients vs. 62% DNMT3A R882wt/FLT3-ITDneg, p=0.025) and the relapse rate increased. Conclusion: It can be deduced that DNMT3A R882mut/FLT3-ITDpos is an unfavorable prognostic factor in AML patients even after allogeneic HSCT. [ABSTRACT FROM AUTHOR]

With increasing number of documents in digital format, automatic text categorization has become a crucial task in pattern recognition problems. To ease the classification task, feature selection methods have been introduced to reduce the dimensionality of the feature space, and thus improve the classification performance. In this paper a novel filter method for feature selection, called Multivariate Relative Discrimination Criterion (MRDC), is proposed for text classification. The proposed method focuses on the reduction of redundant features using minimal-redundancy and maximal-relevancy concepts. To this end, the proposed method takes into account document frequencies for each term, while estimating their usefulness. The proposed method not only selects the features with maximum relevancy, but also the redundancy between them is takes into account using a correlation metric. MRDC does not employ any learning algorithm to evaluate the usefulness of the selected features, and thus it can be categorized as a filter method. In order to assess the effectiveness of the proposed method, several experiments are performed on three real-world datasets. The obtained results are compared to the state-of-the-art filter methods. The reported results show that in most cases MRDC results in better classification performance than others. [ABSTRACT FROM AUTHOR]

The aims of this study are to determine the replacement rate of damaged hepatocytes by donor-derived cells in sex-mismatched recipient patients with thalassemia major and to determine whether co-transplantation of mesenchymal stem cells and hematopoietic stem cells (HSCs) can alleviate liver fibrosis. Ten sex-mismatched donor-recipient pairs who received co-transplantation of HSCs with mesenchymal stem cells were included in our study. Liver biopsy was performed before transplantation. Two other liver biopsies were performed between 2 and 5 years after transplantation. The specimens were studied for the presence of donor-derived epithelial cells or hepatocytes using fluorescence in situ hybridization by X- and Y-centromeric probes and immunohistochemical staining for pancytokeratin, CD45, and a hepatocyte-specific antigen. All sex-mismatched tissue samples demonstrated donor-derived hepatocyte independent of donor gender. XY-positive epithelial cells or hepatocytes accounted for 11 to 25% of the cells in histologic sections of female recipients in the first follow-up. It rose to 47-95% in the second follow-up. Although not statistically significant, four out of ten patients showed signs of improvement in liver fibrosis. Our results showed that co-transplantation of HSC with mesenchymal stem cells increases the rate of replacement of recipient hepatocytes by donor-derived cells and may improve liver fibrosis. [ABSTRACT FROM AUTHOR]

Recommender systems are widely used to provide e-commerce users appropriate items. Collaborative filtering is one of the most successful recommender approaches which recommends items to a given user based on the opinions of his/her like-minded neighbors. However, the user-item ratings matrix, which is used as an input to the recommendation algorithm, is often highly sparse, leading to unreliable predictions. Recent studies demonstrated that information from social networks such as trust statements can be employed to improve accuracy of recommendations. However, there are not explicit trust relationships between most of users in many e-commerce applications. In this manuscript, we propose a method to identify implicit trust statements by applying a specific reliability measure. The Pareto dominance and confidence concepts are used to identify the most prominent users of which opinions are employed in the recommendation process. The proposed recommendation algorithm shows significant improvements in terms of accuracy and coverage measures as compared to the state-of-the-art recommenders. [ABSTRACT FROM AUTHOR]

Matrix-Factorization (MF) is an accurate and scalable approach for collaborative filtering (CF)-based recommender systems. The performance of matrix MF methods depends on how the system is modeled to mitigate the data sparsity and over-fitting problems. In this paper we aim at improving the performance of MF-based methods through employing imputed ratings of unknown entries. A novel algorithm is proposed based on the classic Multiplicative update rules (MULT), which utilizes imputed ratings to overcome the sparsity problem. Experimental results on three real-world datasets including MovieLens , Jester , and EachMovie reveal the effectiveness of the proposed strategy over state of the art methods. The proposed method is more tolerant against the sparsity of the datasets as compared to other methods including Alternating Least Squares (ALS), Stochastic Gradient Descent (SGD), Regularized Stochastic Gradient Descent (RSGD), Singular Value Decomposition Plus Plus (SVD++) and MULT methods. [ABSTRACT FROM AUTHOR]

Recommender systems have been accompanied by many applications in both academia and industry. Among different algorithms used to construct a recommender system, collaborative filtering methods have attracted much attention and been used in many commercial applications. Incorporating the time into the recommendation algorithm can greatly enhance its performance. In this paper, we propose a novel time-aware model-based recommendation system. We show that future ratings of a user can be inferred from his/her rating history. We assume that there is cascade of information between the items such that rating an item can lead to other items being rated. There is indeed a hidden network structure among the items and each user tracks a sequence of items in this network. The dependencies between the items are modeled based on statistical diffusion models and the parameters are obtained through maximum-likelihood estimation. We show that under some mild assumptions, the estimation task becomes a convex optimization problem. A major advantage of the proposed method over classical recommender systems is the ability to include novel items in its recommendation lists besides providing accurate recommendations. The proposed model also results in personalized and diverse recommendations. Experimental evaluations show that the model can be trained based on the ratings of a limited number of users. Furthermore, the proposed model outperforms classical recommendation algorithms in terms of both accuracy and novelty. [ABSTRACT FROM AUTHOR]

Discovering community structure is one of the most intensively studied problems in network science. Many real networks are composed of nodes belonging to multiple communities. In this manuscript, a new overlapping community detection algorithm is proposed based on neutrosophic set (NS) theory. The proposed community detection method manages uncertainty arisen from imprecise definition of communities, by handling boundary and outlier nodes. In the first step, the proposed algorithm calculates the dissimilarity index between each pair of nodes in the network. Then, in order to keep the original distance between nodes as much as possible, the network structure is mapped into a low-dimensional space by multidimensional scaling. Finally, the neutrosophic c-means algorithm is employed to find communities in the network. The experimental results show that the proposed algorithm can detect communities on real and artificial datasets effectively and accurately. [ABSTRACT FROM AUTHOR]

A class of networks are those with both positive and negative links. In this manuscript, we studied the interplay between positive and negative ties on mesoscopic level of these networks, i.e., their community structure. A community is considered as a tightly interconnected group of actors; therefore, it does not borrow any assumption from balance theory and merely uses the well-known assumption in the community detection literature. We found that if one detects the communities based on only positive relations (by ignoring the negative ones), the majority of negative relations are already placed between the communities. In other words, negative ties do not have a major role in community formation of signed networks. Moreover, regarding the internal negative ties, we proved that most unbalanced communities are maximally balanced, and hence they cannot be partitioned into k nonempty sub-clusters with higher balancedness (k≥2). Furthermore, we showed that although the mediator triad ++- (hostile-mediator-hostile) is underrepresented, it constitutes a considerable portion of triadic relations among communities. Hence, mediator triads should not be ignored by community detection and clustering algorithms. As a result, if one uses a clustering algorithm that operates merely based on social balance, mesoscopic structure of signed networks significantly remains hidden. [ABSTRACT FROM AUTHOR]

Liver biopsy has been performed for many decades for classifying the patients with TM. Meanwhile, using non-invasive methods such as T2* MRI technique has been recently much more considered to determine the hepatic iron overload. Ninety-three pediatric HSCT candidates with TM who underwent liver biopsy were included in this study. Hepatic T2* MRI values and serum ferritin concentrations were assessed to investigate and determine the useful method in detection of patients with TM class III whom received different conditioning regimens, in comparison with class I and II. Twenty (21.5%) patients were categorized as class III. Hepatic T2* MRI could detect TM class III patients with 60% sensitivity and 87.67% specificity ( LR+: 4.867, accuracy: 81.72%), while predictive feature of ferritin values for distinguishing patients with TM class III was not statistically significant (p-value >0.01). Combination of T2* MRI with age (T2*-age) could detect TM class III with 85% sensitivity and 72.6% specificity ( LR+: 3.1, accuracy: 75.27%).T2*-age may be considered as an alternative and non-invasive method to liver biopsy for differentiation and classification of patients with TM before transplantation. [ABSTRACT FROM AUTHOR]

Hamidieh AA, Hadjibabaie M, Taghizadeh Ghehi M, Jalili M, Hosseini A, Pasha F, Behfar M, Ghavamzadeh A. Long-term follow-up of children treated with daclizumab for steroid-refractory GI GvHD in a prospective study. Abstract: Daclizumab, a humanized MoAB to IL-2Ra, has been found to be safe and effective in adults with refractory GvHD; however, data in children are limited. The aim of this prospective study was to evaluate the long-term safety and efficacy of daclizumab in children with steroid-refractory GI aGvHD. This study included 13 children who developed steroid-refractory GI GvHD between 2007 and 2009. When first-line treatment failed, daclizumab was given in a regimen of 1 mg/kg intravenously and then repeated on a 10- to 14-day interval for maximum five doses if necessary. Daclizumab was well tolerated, but infections were common. Ten patients responded to daclizumab completely, one patient responded partially, and two patients failed to respond. With a median follow-up of 630 days, 10 patients were alive and free of severe infections, but among them, four patients were suffering from cGvHD. Of the three remaining patients, one died because of bacterial meningitis, and the other two patients died because of severe refractory GI GvHD. This long-term evaluation showed that daclizumab could be an effective and relatively safe treatment in most of the pediatric patients with severe steroid-refractory GI GvHD. [ABSTRACT FROM AUTHOR]

Some networked systems can be better modeled by multilayer structure where the individual nodes develop relationships in multiple layers. Multilayer networks with similar nodes across layers are also known as multiplex networks. This manuscript proposes a novel framework for predicting forthcoming or missing links in multiplex networks. The link prediction problem in multiplex networks is how to predict links in one of the layers, taking into account the structural information of other layers. The proposed link prediction framework is based on interlayer similarity and proximity-based features extracted from the layer for which the link prediction is considered. To this end, commonly used proximity-based features such as Adamic–Adar and Jaccard Coefficient are considered. These features that have been originally proposed to predict missing links in monolayer networks, do not require learning, and thus are simple to compute. The proposed method introduces a systematic approach to take into account interlayer similarity for the link prediction purpose. Experimental results on both synthetic and real multiplex networks reveal the effectiveness of the proposed method and show its superior performance than state-of-the-art algorithms proposed for the link prediction problem in multiplex networks. • We propose an interlayer similarities measure for multiplex networks. • We introduce a link prediction framework based on interlayer similarities for multiplex networks. • We examine the Impact of interlayer similarities on link prediction in synthetic and real multiplex networks. [ABSTRACT FROM AUTHOR]