Reinforced Mnemonic Reader for Machine Reading Comprehension

In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects. First, a reattention mechanism is proposed to refine current attentions by directly accessing to past attentions that are temporally memorized in a multi-round alignment architecture, so as to avoid the problems of attention redundancy and attention deficiency. Second, a new optimization approach, called dynamic-critical reinforcement learning, is introduced to extend the standard supervised method. It always encourages to predict a more acceptable answer so as to address the convergence suppression problem occurred in traditional reinforcement learning algorithms. Extensive experiments on the Stanford Question Answering Dataset (SQuAD) show that our model achieves state-of-the-art results. Meanwhile, our model outperforms previous systems by over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD datasets.Comment: Published in 27th International Joint Conference on Artificial Intelligence (IJCAI), 201

Photoacoustic computed tomography guided microrobots for targeted navigation in intestines in vivo

Tremendous progress in synthetic micro/nanomotors has been made for potential biomedical applications. However, existing micro/nanomotor platforms are inefficient for deep tissue imaging and motion control in vivo. Here, we present a photoacoustic computed tomography (PACT) guided investigation of micromotors in intestines in vivo. The micromotors enveloped in microcapsules exhibit efficient propulsion in various biofluids once released. PACT has visualized the migration of micromotor capsules toward the targeted regions in real time in vivo. The integration of the developed microrobotic system and PACT enables deep imaging and precise control of the micromotors in vivo

The expression and significance of P-glycoprotein, lung resistance protein and multidrug resistance-associated protein in gastric cancer

<p>Abstract</p> <p>Background</p> <p>To detect the expression of multidrug resistance molecules P-glycoprotein (P-gp), Lung resistnce protein (LRP) and Multidrug resistance-associated protein (MRP) and analyze the relationship between them and the clinico-pathological features.</p> <p>Methods</p> <p>The expressions of P-gp, LRP and MRP in formalin-fixed paraffin-embedded tissue sections from 59 gastric cancer patients were determined by a labbelled Streptavidin-Peroxidase (SP) immunohistochemical technique, and the results were analyzed in correlation with clinicopathological data. None of these patients received chemotherapy prior to surgery.</p> <p>Results</p> <p>The positive rates of P-gp, LRP, MRP were 86.4%, 84.7% and 27.1%, respectively. The difference between the positive rate of P-gp and MRP was significant statistically, as well as the difference between the expression of MRP and LRP. No significant difference was observed between P-gp and LRP, but the positively correlation between the expression of P-gp and LRP had been found. No significant correlation between the expression of P-gp, LRP, MRP and the grade of differentiation were observed. The expression of P-gp was correlated with clinical stages positively (r = 0.742), but the difference with the expression of P-gp in different stages was not significant.</p> <p>Conclusion</p> <p>The expressions of P-gp, LRP and MRP in patients with gastric cancer without prior chemotherapy are high, indicating that innate drug resistance may exist in gastric cancer.</p