Echinocandin persistence directly impacts the evolution of resistance and survival of the pathogenic fungus Candida glabrata.

Recent epidemiological studies documented an alarming increase in the prevalence of echinocandin-resistant (ECR) Candida glabrata blood isolates. ECR isolates are known to arise from a minor subpopulation of a clonal population, termed echinocandin persisters. Although it is believed that isolates with a higher echinocandin persistence (ECP) are more likely to develop ECR, the implication of ECP needs to be better understood. Moreover, replacing laborious and time-consuming traditional approaches to determine ECP levels with rapid, convenient, and reliable tools is imperative to advance our understanding of this emerging concept in clinical practice. Herein, using extensive ex vivo and in vivo systemic infection models, we showed that high ECP isolates are less effectively cleared by micafungin treatment and exclusively give rise to ECR colonies. Additionally, we developed a flow cytometry-based tool that takes advantage of a SYTOX-based assay for the stratification of ECP levels. Once challenged with various collections of echinocandin-susceptible blood isolates, our assay reliably differentiated ECP levels in vitro and predicted ECP levels in real time under ex vivo and in vivo conditions when compared to traditional methods relying on colony-forming unit counting. Given the high and low ECP predictive values of 92.3% and 82.3%, respectively, our assay showed a high agreement with traditional approach. Collectively, our study supports the concept of ECP level determination in clinical settings and provides a robust tool scalable for high-throughput settings. Application of this tool facilitates the interrogation of mutant and drug libraries to further our understanding of persister biology and designing anti-persister therapeutics. IMPORTANCE: Candida glabrata is a prevalent fungal pathogen able to replicate inside macrophages and rapidly develop resistance against frontline antifungal echinocandins. Multiple studies have shown that echinocandin resistance is fueled by the survival of a small subpopulation of susceptible cells surviving lethal concentrations of echinocandins. Importantly, bacterial pathogens that exhibit high antibiotic persistence also impose a high burden and generate more antibiotic-resistant colonies. Nonetheless, the implications of echinocandin persistence (ECP) among the clinical isolates of C. glabrata have not been defined. Additionally, ECP level determination relies on a laborious and time-consuming method, which is prone to high variation. By exploiting in vivo systemic infection and ex vivo models, we showed that C. glabrata isolates with a higher ECP are associated with a higher burden and more likely develop echinocandin resistance upon micafungin treatment. Additionally, we developed an assay that reliably determines ECP levels in real time. Therefore, our study identified C. glabrata isolates displaying high ECP levels as important entities and provided a reliable and convenient tool for measuring echinocandin persistence, which is extendable to other fungal and bacterial pathogens.

Early development of the functional brain network in newborns.

During the prenatal period and the first postnatal years, the human brain undergoes rapid growth, which establishes a preliminary infrastructure for the subsequent development of cognition and behavior. To understand the underlying processes of brain functioning and identify potential sources of developmental disorders, it is essential to uncover the developmental rules that govern this critical period. In this study, graph theory modeling and network science analysis were employed to investigate the impact of age, gender, weight, and typical and atypical development on brain development. Local and global topologies of functional connectomes obtained from rs-fMRI data were collected from 421 neonates aged between 31 and 45 postmenstrual weeks who were in natural sleep without any sedation. The results showed that global efficiency, local efficiency, clustering coefficient, and small-worldness increased with age, while modularity and characteristic path length decreased with age. The normalized rich-club coefficient displayed a U-shaped pattern during development. The study also examined the global and local impacts of gender, weight, and group differences between typical and atypical cases. The findings presented some new insights into the maturation of functional brain networks and their relationship with cognitive development and neurodevelopmental disorders.

The Combined Effects of Propionic Acid and a Mixture of Bacillus spp. Probiotic in a Plant Protein-Rich Diet on Growth, Digestive Enzyme Activities, Antioxidant Capacity, and Immune-Related Genes mRNA Transcript Abundance in Lates calcarifer Fry.

A 7-week feeding trial was conducted to evaluate the combined effects of propionic acid (PA, 5 or 10 g/kg) and a multi-strain Bacillus spp. (Bacillus subtilis IS02 (accession no. JN856456) and B. licheniformis IBRC-M 11,319) (1.7 × 107 CFU/g) probiotic in a plant protein source (PP)-rich diet (∼70% of dietary protein derived from PP sources) on performance of Asian sea bass (Lates calcarifer) fry (initial body weight 2.97 ± 0.11 g). In this regard, six isoproteic (∼48%) diets were formulated as follows: a control (without supplementation of the additives); probiotic (only contained Bacillus spp. mixture); 5 g PA/kg diet; 10 g PA/kg diet; probiotic + 5 g PA/kg diet, and probiotic + 10 g PA/kg diet. Specific growth rate in fish fed with 10 g PA/kg (2.84 ± 0.1%) and diets contained blends of probiotic and PA (2.76 ± 0.19% in probiotic + 5 g PA, and 2.79 ± 0.04% in probiotic + 10 g PA) was better than in the control (2.45 ± 0.1%) (P < 0.05). Feed conversion ratio in fish fed with 10 g PA/kg (0.92 ± 0.12) and diets contained blends of probiotic and PA (0.94 ± 0.06 in probiotic + 5 g PA and 0.91 ± 0.02 in probiotic + 10 g PA) was better than in the control (1.24 ± 0.08) (P < 0.05). Digestive enzymes including α-amylase, total alkaline proteases, and bile salt dependent lipase activities improved in fish fed diets contained additives. The activity of glutathione-S-transferase and glutathione reductase enhanced in the liver of fish fed diets contained additives. The relative abundance of lysozyme, interleukin 1ß, and insulin-like growth factor-1 genes mRNA transcript showed multifold increase in the liver of fish fed with the 10 g PA/kg and diets contained blends of probiotic and PA (P < 0.05). By considering the above mentioned results, supplementing a PP-rich diet with 10 g PA/kg diet or combination of PA and a mixture of Bacillus spp. probiotic recommended for L. calcarifer.

GANBOT: a GAN-based framework for social bot detection.

Nowadays, a massive number of people are involved in various social media. This fact enables organizations and institutions to more easily access their audiences across the globe. Some of them use social bots as an automatic entity to gain intangible access and influence on their users by faster content propagation. Thereby, malicious social bots are populating more and more to fool humans with their unrealistic behavior and content. Hence, that's necessary to distinguish these fake social accounts from real ones. Multiple approaches have been investigated in the literature to answer this problem. Statistical machine learning methods are one of them focusing on handcrafted features to represent characteristics of social bots. Although they reached successful results in some cases, they relied on the bot's behavior and failed in the behavioral change patterns of bots. On the other hands, more advanced deep neural network-based methods aim to overcome this limitation. Generative adversarial network (GAN) as new technology from this domain is a semi-supervised method that demonstrates to extract the behavioral pattern of the data. In this work, we use GAN to leak more information of bot samples for state-of-the-art textual bot detection method (Contextual LSTM). Although GAN augments low labeled data, original textual GAN (Sequence Generative Adversarial Net (SeqGAN)) has the known limitation of convergence. In this paper, we invested this limitation and customized the GAN idea in a new framework called GANBOT, in which the generator and classifier connect by an LSTM layer as a shared channel between them. Our experimental results on a bench-marked dataset of Twitter social bot show our proposed framework outperforms the existing contextual LSTM method by increasing bot detection probabilities.

Predicting and Correcting Missing Data on Diffusion Processes in Multiplex Networks.

The diffusion process in networks is studied with the objective of identifying the dynamics and for predicting the behavior of network entities. Social media plays an important role in people's lives. Diffusion processes, as one of the most important branches of social media analysis, have their presence in various domains such as information spreading, diffusion of innovation, idea dissemination, and product acceptance to identify user's pattern and their behavior in social media networks. Users are not limited to one social network and are engaged in multiple social media such as Twitter, Instagram, Telegram, and Facebook. This fact has created new phenomena in social network analysis, called multiplex network analysis. Thus, the scope of diffusion process analysis has been transferred from single layer networks to multiplex networks. Diffusion process analysis can be studied at both infrastructure-level and diffusion-level; at infrastructure-level, the structural network's properties such as clustering coefficient and degree centrality are being studied; and in diffusion-level the diffusion network's properties such as diffusion depth and seed nodes are being studied. On the other hand, a reliable analysis requires complete information on both infrastructure and diffusion networks. However, complete data is not accessible forever, this fact is due to some limitations such as crawling big data, gathering social media policies, and user privacy. Incomplete data can lead to poor analysis, so in this work we, first of all, investigate the impact of missing data in both infrastructure and diffusion networks, the impact of random and non-random missing infrastructure data on nine diffusion network's properties such as number of infected nodes, number of infected edges, diffusion length and number of seed nodes. Secondly, based on the multiplex diffusion tree, we introduce a new model named as MLC-tree for an incomplete diffusion network. Finally, we evaluate our model on both synthetic and real social networks; these results show that the MLC-tree can decrease the relative error more than 50 percent while missing 20 to 80 percent of complete data.

Information Spread and Topic Diffusion in Heterogeneous Information Networks.

Diffusion of information in complex networks largely depends on the network structure. Recent studies have mainly addressed information diffusion in homogeneous networks where there is only a single type of nodes and edges. However, some real-world networks consist of heterogeneous types of nodes and edges. In this manuscript, we model information diffusion in heterogeneous information networks, and use interactions of different meta-paths to predict the diffusion process. A meta-path is a path between nodes across different layers of a heterogeneous network. As its most important feature the proposed method is capable of determining the influence of all meta-paths on the diffusion process. A conditional probability is used assuming interdependent relations between the nodes to calculate the activation probability of each node. As independent cascade models, we consider linear threshold and independent cascade models. Applying the proposed method on two real heterogeneous networks reveals its effectiveness and superior performance over state-of-the-art methods.

Shortest Paths in Multiplex Networks.

The shortest path problem is one of the most fundamental networks optimization problems. Nowadays, individuals interact in extraordinarily numerous ways through their offline and online life (e.g., co-authorship, co-workership, or retweet relation in Twitter). These interactions have two key features. First, they have a heterogeneous nature, and second, they have different strengths that are weighted based on their degree of intimacy, trustworthiness, service exchange or influence among individuals. These networks are known as multiplex networks. To our knowledge, none of the previous shortest path definitions on social interactions have properly reflected these features. In this work, we introduce a new distance measure in multiplex networks based on the concept of Pareto efficiency taking both heterogeneity and weighted nature of relations into account. We then model the problem of finding the whole set of paths as a form of multiple objective decision making and propose an exact algorithm for that. The method is evaluated on five real-world datasets to test the impact of considering weights and multiplexity in the resulting shortest paths. As an application to find the most influential nodes, we redefine the concept of betweenness centrality based on the proposed shortest paths and evaluate it on a real-world dataset from two-layer trade relation among countries between years 2000 and 2015.

The Effects of Fifa 2015 Computer Games on Changes in Cognitive, Hormonal and Brain Waves Functions of Young Men Volunteers.

INTRODUCTION: Computer games have attracted remarkable attentions in general publics with different cultures and their effects are subject of research by cognitive neuroscientists. In the present study, possible effects of the game Fifa 2015 on cognitive performance, hormonal levels, and electroencephalographic (EEG) signals were evaluated in young male volunteers. METHODS: Thirty two subjects aged 20 years on average participated mutually in playing computer game Fifa 2015. Identification information and general knowledge about the game were collected. Saliva samples from the contestants were obtained before and after the competition. Perceptive and cognitive performance including the general cognitive health, response delay, attention maintenance, and mental fatigue were measured using PASAT test. EEG were recorded during the play using EEG device and analyzed later using QEEG. Simultaneously, the players' behavior were recorded using a video camera. Saliva cortisol levels were assessed by ELISA kit. Data were analyzed by SPSS program. RESULTS: The impact of playing computer games on cortisol concentration of saliva before and after the game showed that the amount of saliva plasma after playing the game has dropped significantly. Also the impact of playing computer games on mental health, before and after the game indicated that the number of correct answers has not changed significantly. This indicates that sustained attention has increased in participants after the game in comparison with before that. Also it is shown that mental fatigue measured by PASAT test, did not changed significantly after the game in comparison to before that. The impact of game on changes in brain waves showed that the subjects in high activity state during playing the game had higher power of the EEG signals in most of the channels in lower frequency bands in compared to normal state. DISCUSSION: The present study showed that computer games can positively affect the stress system and the perceptual-cognitive system. Even though this impact was not significant in most cases, the changes in cognitive and hormonal test and also in brain waves were visible. Hence, due to the importance of this matter, it is necessary to create control systems in selecting the types of games for playing.

Sampling from complex networks with high community structures.

In this paper, we propose a novel link-tracing sampling algorithm, based on the concepts from PageRank vectors, to sample from networks with high community structures. Our method has two phases; (1) Sampling the closest nodes to the initial nodes by approximating personalized PageRank vectors and (2) Jumping to a new community by using PageRank vectors and unknown neighbors. Empirical studies on several synthetic and real-world networks show that the proposed method improves the performance of network sampling compared to the popular link-based sampling methods in terms of accuracy and visited communities.