A pharmacogenetic study of perampanel: association between rare variants of glutamate receptor genes and outcomes

Introduction: The selection of antiseizure medication usually requires a trial-and-error process. Our goal is to investigate whether genetic markers can predict the outcome of perampanel (PER) use in patients with epilepsy.Method: The studied participants were selected from our previous epilepsy genetics studies where whole exome sequencing was available. We reviewed the medical records of epilepsy patients older than 20 years old treated with PER. The outcome of PER treatment included the response to PER, the occurrence of any adverse drug reaction (ADR), the presence of behavior ADR, and the ability to adhere to PER for more than 1 year. We investigated the association between the rare variants of the glutamate receptor genes and the outcomes of PER use.Result: A total of 83 patients were collected. The gene group burden analysis showed that enriched genetic variants of the glutamate receptor gene group were statistically significantly associated with the occurrence of ADR, while the glutamate ionotropic receptor delta type subunit had a nominal association with the occurrence of ADR. The gene collapse analysis found that GRID1 had a nominal association with the occurrence of ADR and GRIN3A had a nominal association with the occurrence of behavior ADR. However, these nominal associations did not remain statistically significant once adjusted for multiple testing.Discussion: We found that enriched rare genetic variants of the glutamate receptor genes were associated with the occurrence of ADR in patients taking PER. In the future, combining the results of various pharmacogenetic studies may lead to the development of prediction tools for the outcome of antiseizure medications

MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games

This paper presents MiniZero, a zero-knowledge learning framework that supports four state-of-the-art algorithms, including AlphaZero, MuZero, Gumbel AlphaZero, and Gumbel MuZero. While these algorithms have demonstrated super-human performance in many games, it remains unclear which among them is most suitable or efficient for specific tasks. Through MiniZero, we systematically evaluate the performance of each algorithm in two board games, 9x9 Go and 8x8 Othello, as well as 57 Atari games. For two board games, using more simulations generally results in higher performance. However, the choice of AlphaZero and MuZero may differ based on game properties. For Atari games, both MuZero and Gumbel MuZero are worth considering. Since each game has unique characteristics, different algorithms and simulations yield varying results. In addition, we introduce an approach, called progressive simulation, which progressively increases the simulation budget during training to allocate computation more efficiently. Our empirical results demonstrate that progressive simulation achieves significantly superior performance in two board games. By making our framework and trained models publicly available, this paper contributes a benchmark for future research on zero-knowledge learning algorithms, assisting researchers in algorithm selection and comparison against these zero-knowledge learning baselines. Our code and data are available at https://rlg.iis.sinica.edu.tw/papers/minizero.Comment: Submitted to IEEE Transactions on Games, under revie

Effect of membrane fusion protein AdeT1 on the antimicrobial resistance of Escherichia coli

Acinetobacter baumannii is a prevalent pathogen that can rapidly acquire resistance to antibiotics. Indeed, multidrug-resistant A. baumannii is a major cause of hospital-acquired infections and has been recognised by the World Health Organization as one of the most threatening bacteria to our society. Resistance-nodulation-division (RND) type multidrug efflux pumps have been demonstrated to convey antibiotic resistance to a wide range of pathogens and are the primary resistance mechanism employed by A. baumannii. A component of an RND pump in A. baumannii, AdeT1, was previously demonstrated to enhance the antimicrobial resistance of Escherichia coli. Here, we report the results of experiments which demonstrate that wild-type AdeT1 does not confer antimicrobial resistance in E. coli, highlighting the importance of verifying protein production when determining minimum inhibitory concentrations (MICs) especially by broth dilution. Nevertheless, using an agar-based MIC assay, we found that propionylation of Lys280 on AdeT1 renders E. coli cells more resistant to erythromycin

Various criteria in the evaluation of biomedical named entity recognition

BACKGROUND: Text mining in the biomedical domain is receiving increasing attention. A key component of this process is named entity recognition (NER). Generally speaking, two annotated corpora, GENIA and GENETAG, are most frequently used for training and testing biomedical named entity recognition (Bio-NER) systems. JNLPBA and BioCreAtIvE are two major Bio-NER tasks using these corpora. Both tasks take different approaches to corpus annotation and use different matching criteria to evaluate system performance. This paper details these differences and describes alternative criteria. We then examine the impact of different criteria and annotation schemes on system performance by retesting systems participated in the above two tasks. RESULTS: To analyze the difference between JNLPBA's and BioCreAtIvE's evaluation, we conduct Experiment 1 to evaluate the top four JNLPBA systems using BioCreAtIvE's classification scheme. We then compare them with the top four BioCreAtIvE systems. Among them, three systems participated in both tasks, and each has an F-score lower on JNLPBA than on BioCreAtIvE. In Experiment 2, we apply hypothesis testing and correlation coefficient to find alternatives to BioCreAtIvE's evaluation scheme. It shows that right-match and left-match criteria have no significant difference with BioCreAtIvE. In Experiment 3, we propose a customized relaxed-match criterion that uses right match and merges JNLPBA's five NE classes into two, which achieves an F-score of 81.5%. In Experiment 4, we evaluate a range of five matching criteria from loose to strict on the top JNLPBA system and examine the percentage of false negatives. Our experiment gives the relative change in precision, recall and F-score as matching criteria are relaxed. CONCLUSION: In many applications, biomedical NEs could have several acceptable tags, which might just differ in their left or right boundaries. However, most corpora annotate only one of them. In our experiment, we found that right match and left match can be appropriate alternatives to JNLPBA and BioCreAtIvE's matching criteria. In addition, our relaxed-match criterion demonstrates that users can define their own relaxed criteria that correspond more realistically to their application requirements

A model-based circular binary segmentation algorithm for the analysis of array CGH data

<p>Abstract</p> <p>Background</p> <p>Circular Binary Segmentation (CBS) is a permutation-based algorithm for array Comparative Genomic Hybridization (aCGH) data analysis. CBS accurately segments data by detecting change-points using a maximal-<it>t </it>test; but extensive computational burden is involved for evaluating the significance of change-points using permutations. A recent implementation utilizing a hybrid method and early stopping rules (hybrid CBS) to improve the performance in speed was subsequently proposed. However, a time analysis revealed that a major portion of computation time of the hybrid CBS was still spent on permutation. In addition, what the hybrid method provides is an approximation of the significance upper bound or lower bound, not an approximation of the significance of change-points itself.</p> <p>Results</p> <p>We developed a novel model-based algorithm, extreme-value based CBS (eCBS), which limits permutations and provides robust results without loss of accuracy. Thousands of aCGH data under null hypothesis were simulated in advance based on a variety of non-normal assumptions, and the corresponding maximal-<it>t </it>distribution was modeled by the Generalized Extreme Value (GEV) distribution. The modeling results, which associate characteristics of aCGH data to the GEV parameters, constitute lookup tables (eXtreme model). Using the eXtreme model, the significance of change-points could be evaluated in a constant time complexity through a table lookup process.</p> <p>Conclusions</p> <p>A novel algorithm, eCBS, was developed in this study. The current implementation of eCBS consistently outperforms the hybrid CBS 4× to 20× in computation time without loss of accuracy. Source codes, supplementary materials, supplementary figures, and supplementary tables can be found at <url>http://ntumaps.cgm.ntu.edu.tw/eCBSsupplementary</url>.</p

The Different Clinical Features Between Autoimmune and Infectious Status Epilepticus

Objective: The prognosis of status epilepticus (SE) is highly related to the underlying etiology. Inflammation of the central nervous system (CNS), including infection and autoimmune encephalitis, is one of the treatable conditions causing SE. The initial presentation of infectious and autoimmune CNS disorders can be quite similar, which may be difficult to differentiate at the beginning. However, treatment for these entities can be quite different. In this study, we aim to identify the differences in clinical features among patients with infectious and autoimmune SE, which could help the clinicians to select initial investigation and ensuing therapies that may improve overall outcomes.Methods: This was a retrospective study that included 501 patients with SE within a period of 10.5-years. Patients with inflammatory etiology were collected and separated into infectious and autoimmune SE. The symptoms at onset, SE semiology, status epilepticus severity score, and END-IT score at admission, treatment for SE, and outcome (modified Rankin Scale) on discharge and last follow-up were recorded. Data on the first cerebrospinal fluid, electroencephalography, and magnetic resonance imaging were also collected.Results: Forty-six (9.2%) of the 501 patients had SE with inflammatory etiology. Twenty-five (5%) patients were autoimmune SE and 21 (4.2%) were infectious SE. Patients with autoimmune SE have younger age and female predominance. As for clinical presentations, psychosis, non-convulsive SE, and super refractory SE were more common in patients with autoimmune SE. Nevertheless, the prognosis showed no difference between the two groups.Conclusion: The different initial clinical presentations and patient characteristics may provide some clues about the underlying etiology of SE. When inflammatory etiology is suspected in patients with SE, younger age, female sex, psychosis, non-convulsive SE, and super refractory SE are clinical features that suggest an autoimmune etiology

Acetylome of acinetobacter baumannii SK17 reveals a highly-conserved modification of histone-like protein HU

Lysine acetylation is a prevalent post-translational modification in both eukaryotes and prokaryotes. Whereas this modification is known to play pivotal roles in eukaryotes, the function and extent of this modification in prokaryotic cells remain largely unexplored. Here we report the acetylome of a pair of antibiotic-sensitive and -resistant nosocomial pathogen Acinetobacter baumannii SK17-S and SK17-R. A total of 145 lysine acetylation sites on 125 proteins was identified, and there are 23 acetylated proteins found in both strains, including histone-like protein HU which was found to be acetylated at Lys13. HU is a dimeric DNA-binding protein critical for maintaining chromosomal architecture and other DNA-dependent functions. To analyze the effects of site-specific acetylation, homogenously Lys13-acetylated HU protein, HU(K13ac) was prepared by genetic code expansion. Whilst not exerting an obvious effect on the oligomeric state, Lys13 acetylation alters both the thermal stability and DNA binding kinetics of HU. Accordingly, this modification likely destabilizes the chromosome structure and regulates bacterial gene transcription. This work indicates that acetyllysine plays an important role in bacterial epigenetics