Determining the quality and complexity of next-generation sequencing data without a reference genome

Genomics, epigenetics, population genetics and bioinformatic

Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing

In this paper, we study the effects of several Monte Carlo Tree Search (MCTS) modifications for video game testing. Although MCTS modifications are highly studied in game playing, their impacts on finding bugs are blank. We focused on bug finding in our previous study where we introduced synthetic and human-like test goals and we used these test goals in Sarsa and MCTS agents to find bugs. In this study, we extend the MCTS agent with several modifications for game testing purposes. Furthermore, we present a novel tree reuse strategy. We experiment with these modifications by testing them on three testbed games, four levels each, that contain 45 bugs in total. We use the General Video Game Artificial Intelligence (GVG-AI) framework to create the testbed games and collect 427 human tester trajectories using the GVG-AI framework. We analyze the proposed modifications in three parts: we evaluate their effects on bug finding performances of agents, we measure their success under two different computational budgets, and we assess their effects on human-likeness of the human-like agent. Our results show that MCTS modifications improve the bug finding performance of the agents

Playtesting: What is Beyond Personas

We present two approaches to improve automated playtesting. First, we propose developing persona, which allows a persona to progress to different goals. In contrast, the procedural persona is fixed to a single goal. Second, a human playtester knows which paths she has tested before, and during the consequent tests, she may test different paths. However, Reinforcement Learning (RL) agents disregard these previous paths. We propose a novel methodology that we refer to as Alternative Path Finder (APF). We train APF with previous paths and employ APF during the training of a RL agent. APF modulates the reward structure of the environment while preserving the agent's goal. When evaluated, the agent generates a different trajectory that achieves the same goal. We use the General Video Game Artificial Intelligence and VizDoom frameworks to test our proposed methodologies. We use Proximal Policy Optimization RL agent during experiments. First, we compare the playtest data generated by developing and procedural persona. Our experiments show that developing persona provides better insight into the game and how different players would play. Second, we present the alternative paths found using APF and argue why traditional RL agents cannot learn those paths

Automated Video Game Testing Using Synthetic and Human-Like Agents

In this paper, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of agents -synthetic and human-like- and two distinct approaches to create them. Our agents are derived from Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) agents, but focus on finding defects. The synthetic agent uses test goals generated from game scenarios, and these goals are further modified to examine the effects of unintended game transitions. The human-like agent uses test goals extracted by our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL) algorithm from tester trajectories. MGPIRL captures multiple policies executed by human testers. These testers' aims are finding defects while interacting with the game to break it, which is considerably different from game playing. We present interaction states to model such interactions. We use our agents to produce test sequences, run the game with these sequences, and check the game for each run with an automated test oracle. We analyze the proposed method in two parts: we compare the success of human-like and synthetic agents in bug finding, and we evaluate the similarity between humanlike agents and human testers. We collected 427 trajectories from human testers using the General Video Game Artificial Intelligence (GVG-AI) framework and created three games with 12 levels that contain 45 bugs. Our experiments reveal that human-like and synthetic agents compete with human testers' bug finding performances. Moreover, we show that MGP-IRL increases the human-likeness of agents while improving the bug finding performance

Birt-Hogg-Dubé sendromunda görülen kistik akciğer hastalığı: Üç hastalık olgu serisi

Birt-Hogg-Dubé syndrome is characterized by clinical manifestations such as hamartomas of the skin, renal tumors and lung cysts with spontaneous pneumothoraces. Patients with Birt-Hogg-Dubé syndrome may present with only multiple lung cysts. We report the chest computerized tomography (CT) features of three patients with Birt- Hogg-Dubé syndrome. Each patient had multiple lung cysts of various sizes according to chest CT evaluation, most of which were located in lower lobes and related to pleura. The identification of unique characteristics in the chest CT of patients with Birt-Hogg-Dubé syndrome may provide an efficient mechanism for diagnosis. © 2014 by the Atatürk University School of Medicine

New methods for next generation sequencing based microRNA expression profiling

<p>Abstract</p> <p>Background</p> <p>MicroRNAs are small non-coding RNA transcripts that regulate post-transcriptional gene expression. The millions of short sequence reads generated by next generation sequencing technologies make this technique explicitly suitable for profiling of known and novel microRNAs. A modification to the small-RNA expression kit (SREK, Ambion) library preparation method for the SOLiD sequencing platform is described to generate microRNA sequencing libraries that are compatible with the Illumina Genome Analyzer.</p> <p>Results</p> <p>High quality sequencing libraries can successfully be prepared from as little as 100 ng small RNA enriched RNA. An easy to use perl-based analysis pipeline called E-miR was developed to handle the sequencing data in several automated steps including data format conversion, 3' adapter removal, genome alignment and annotation to non-coding RNA transcripts. The sample preparation and E-miR pipeline were used to identify 37 cardiac enriched microRNAs in stage 16 chicken embryos. Isomir expression profiles between the heart and embryo were highly correlated for all miRNAs suggesting that tissue or cell specific miRNA modifications do not occur.</p> <p>Conclusions</p> <p>In conclusion, our alternative sample preparation method can successfully be applied to generate high quality miRNA sequencing libraries for the Illumina genome analyzer.</p

The Complete Chloroplast Genome of 17 Individuals of Pest Species Jacobaea vulgaris: SNPs, Microsatellites and Barcoding Markers for Population and Phylogenetic Studies

Invasive individuals from the pest species Jacobaea vulgaris show different allocation patterns in defence and growth compared with native individuals. To examine if these changes are caused by fast evolution, it is necessary to identify native source populations and compare these with invasive populations. For this purpose, we are in need of intraspecific polymorphic markers. We therefore sequenced the complete chloroplast genomes of 12 native and 5 invasive individuals of J. vulgaris with next generation sequencing and discovered single-nucleotide polymorphisms (SNPs) and microsatellites. This is the first study in which the chloroplast genome of that many individuals within a single species was sequenced. Thirty-two SNPs and 34 microsatellite regions were found. For none of the individuals, differences were found between the inverted repeats. Furthermore, being the first chloroplast genome sequenced in the Senecioneae clade, we compared it with four other members of the Asteraceae family to identify new regions for phylogentic inference within this clade and also within the Asteraceae family. Five markers (ndhC-trnV, ndhC-atpE, rps18-rpl20, clpP and psbM-trnD) contained parsimony-informative characters higher than 2%. Finally, we compared two procedures of preparing chloroplast DNA for next generation sequencing

Iron accumulation induces oxidative stress, while depressing inflammatory polarization in human iPSC-derived microglia

Iron accumulation in microglia has been observed in Alzheimer’s disease and other neurodegenerative disorders and is thought to contribute to disease progression through various mechanisms, including neuroinflammation. To study this interaction, we treated human induced pluripotent stem cell-derived microglia (iPSC-MG) with iron, in combination with inflammatory stimuli such as interferon gamma (IFN-γ) and amyloid β. Both IFN-γ and iron treatment increased labile iron levels, but only iron treatment led to a consistent increase of ferritin levels, reflecting long-term iron storage. Therefore, in iPSC-MG, ferritin appeared to be regulated by iron revels rather than inflammation. Further investigation showed that while IFN-γ induced pro-inflammatory activation, iron treatment dampened both classic pro- and anti-inflammatory activation on a transcriptomic level. Notably, iron-loaded microglia showed strong upregulation of cellular stress response pathways, the NRF2 pathway, and other oxidative stress pathways. Functionally, iPSC-MG exhibited altered phagocytosis and impaired mitochondrial metabolism following iron treatment. Collectively, these data suggest that in MG, in contrast to current hypotheses, iron treatment does not result in pro-inflammatory activation, but rather dampens it and induces oxidative stress