Characterization of pathway specific inhibitor response in pancreatic ductal adenocarcinoma in vitro models with different genetic backgrounds

Key proteins such as CK2, CDK, PI3K, AKT and KRAS are often considered as promising target molecules for therapeutic intervention strategies due to their important roles in the occurrence, development and prognosis of PDAC. The aim of this doctoral thesis was to explore the different effects of the above-mentioned key protein inhibitors on various PDAC cell lines, and to explore the effect of aberrations in the target genes of the inhibitors on the efficacy of the corresponding inhibitors by applying whole-exome sequencing (WES) and RNA sequencing (RNA-seq)

A Deep Choice Model for Hiring Outcome Prediction in Online Labor Markets

A key challenge faced by online labor market researchers and practitioners is to understand how employers make hiring decisions from many job bidders with distinct attributes. This study investigates employer hiring behavior in one of the largest online labor markets by building a datadriven hiring decision prediction model. With the limitation of traditional discrete choice model (conditional logit model), we develop a novel deep choice model to simulate the hiring behavior from 722,339 job posts. The deep choice model extends the classical conditional logit model by learning a non-linear utility function identically for each bidder within of the job posts via a pointwise convolutional neural network. This non-linear mapping can be straightforwardly optimized using stochastic gradient approach. We test the model on 12 categories of job posts in the dataset. Results show that our deep choice model outperforms the linear-utility conditional logit model in predicting hiring preferences. By analyzing the model using dimensionality reduction and sensitivity analysis, we highlight the nonlinear combination of bidders’ features in impacting employers’ hiring decisions

中国の地域高齢者では身体活動量の低下がosteosarcopenic obesity(骨粗鬆症性少筋性肥満)と関連する

Tohoku University上月正博課

中国の地域高齢者では身体活動量の低下がosteosarcopenic obesity(骨粗鬆症性少筋性肥満)と関連する

Tohoku University上月正博課

Cyclic voltammetry: A simple method for determining contents of total and free iron ions in sodium ferric gluconate complex

Sodium ferric gluconate complex (SFGC) is the third generation of iron supplement of polysaccharide iron (III) complex (PIC). For evaluation of technological level and application value of the prepared SFGC, it would be of great significance to determine the iron content in SFGC in a simple but effective way. This paper introduces the cyclic voltammetry (CV) method for determination of iron content in SFGC. Under established optimal experimental conditions, the content of free iron ions can be directly scanned and calculated, while the total iron content can also be determined by completely acidifying SFGC into Fe3+ ions. After optimizing and screening, the optimal scanning conditions are determined as pH 3 and 0.05 V/s of the scanning rate. Prior CV measurements, 0.4 V of the enrichment potential, and 3 min of the enrichment time are found optimal. It has also been verified that CO32- ions present in the solution show little interference in the system within the experimental range of investigation. The contents of free Fe3+, Fe2+ ions and the total iron determined after acid-hydrolysis of SFGC can be accurately calculated according to the corresponding linear relationships between peak currents and iron concentrations. In this paper, the repeatability and accuracy of the method are verified, and its feasibility as a convenient and effective method to determine the iron supplements is confirmed

Potential bioactive compounds and mechanisms of Fibraurea recisa Pierre for the treatment of Alzheimer’s disease analyzed by network pharmacology and molecular docking prediction

IntroductionHeat-clearing and detoxifying Chinese medicines have been documented to have anti-Alzheimer’s disease (AD) activities according to the accumulated clinical experience and pharmacological research results in recent decades. In this study, Fibraurea recisa Pierre (FRP), the classic type of Heat-clearing and detoxifying Chinese medicine, was selected as the object of research.Methods12 components with anti-AD activities were identified in FRP by a variety of methods, including silica gel column chromatography, multiple databases, and literature searches. Then, network pharmacology and molecular docking were adopted to systematically study the potential anti-AD mechanism of these compounds. Consequently, it was found that these 12 compounds could act on 235 anti-AD targets, of which AKT and other targets were the core targets. Meanwhile, among these 235 targets, 71 targets were identified to be significantly correlated with the pathology of amyloid beta (Aβ) and Tau.Results and discussionIn view of the analysis results of the network of active ingredients and targets, it was observed that palmatine, berberine, and other alkaloids in FRP were the key active ingredients for the treatment of AD. Further, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis revealed that the neuroactive ligand-receptor interaction pathway and PI3K-Akt signaling pathway were the most significant signaling pathways for FRP to play an anti-AD role. Findings in our study suggest that multiple primary active ingredients in FRP can play a multitarget anti-AD effect by regulating key physiological processes such as neurotransmitter transmission and anti-inflammation. Besides, key ingredients such as palmatine and berberine in FRP are expected to be excellent leading compounds of multitarget anti-AD drugs

Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization

We study the multi-agent Bayesian optimization (BO) problem, where multiple agents maximize a black-box function via iterative queries. We focus on Entropy Search (ES), a sample-efficient BO algorithm that selects queries to maximize the mutual information about the maximum of the black-box function. One of the main challenges of ES is that calculating the mutual information requires computationally-costly approximation techniques. For multi-agent BO problems, the computational cost of ES is exponential in the number of agents. To address this challenge, we propose the Gaussian Max-value Entropy Search, a multi-agent BO algorithm with favorable sample and computational efficiency. The key to our idea is to use a normal distribution to approximate the function maximum and calculate its mutual information accordingly. The resulting approximation allows queries to be cast as the solution of a closed-form optimization problem which, in turn, can be solved via a modified gradient ascent algorithm and scaled to a large number of agents. We demonstrate the effectiveness of Gaussian max-value Entropy Search through numerical experiments on standard test functions and real-robot experiments on the source-seeking problem. Results show that the proposed algorithm outperforms the multi-agent BO baselines in the numerical experiments and can stably seek the source with a limited number of noisy observations on real robots.Comment: 10 pages, 9 figure