Recently, electrical stimulation as a physical stimulus draws lots of attention. It shows great potential in disease treatment, wound healing, and mechanism study because of significant experimental performance. Electrical stimulation can activate many intracellular signaling pathways, and influence intracellular microenvironment, as a result, affect cell migration, cell proliferation, and cell differentiation. Electrical stimulation is using in tissue engineering as a novel type of tool in regeneration medicine. Besides, with the advantages of biocompatible conductive materials coming into view, the combination of electrical stimulation with suitable tissue engineered scaffolds can well combine the benefits of both and is ideal for the field of regenerative medicine. In this review, we summarize the various materials and latest technologies to deliver electrical stimulation. The influences of electrical stimulation on cell alignment, migration and its underlying mechanisms are discussed. Then the effect of electrical stimulation on cell proliferation and differentiation are also discussed.

Recent studies on Question Answering (QA) and Conversational QA (ConvQA) emphasize the role of retrieval: a system first retrieves evidence from a large collection and then extracts answers. This open-retrieval ConvQA setting typically assumes that each question is answerable by a single span of text within a particular passage (a span answer). The supervision signal is thus derived from whether or not the system can recover an exact match of this ground-truth answer span from the retrieved passages. This method is referred to as spanmatch weak supervision. However, information-seeking conversations are challenging for this span-match method since long answers, especially freeform answers, are not necessarily strict spans of any passage. Therefore, we introduce a learned weak supervision approach that can identify a paraphrased span of the known answer in a passage. Our experiments on QuAC and CoQA datasets show that the span-match weak supervisor can only handle conversations with span answers, and has less satisfactory results for freeform answers generated by people. Our method is more flexible as it can handle both span answers and freeform answers. Moreover, our method can be more powerful when combined with the span-match method which shows it is complementary to the span-match method. We also conduct in-depth analyses to show more insights on open-retrieval ConvQA under a weak supervision setting.

Conversational question answering (ConvQA) is a simplified but concrete setting of conversational search [24]. One of its major challenges is to leverage the conversation history to understand and answer the current question. In this work, we propose a novel solution for ConvQA that involves three aspects. First, we propose a positional history answer embedding method to encode conversation history with position information using BERT [6] in a natural way. BERT is a powerful technique for text representation. Second, we design a history attention mechanism (HAM) to conduct a "soft selection" for conversation histories. This method attends to history turns with different weights based on how helpful they are on answering the current question. Third, in addition to handling conversation history, we take advantage of multi-task learning (MTL) to do answer prediction along with another essential conversation task (dialog act prediction) using a uniform model architecture. MTL is able to learn more expressive and generic representations to improve the performance of ConvQA. We demonstrate the effectiveness of our model with extensive experimental evaluations on QuAC, a large-scale ConvQA dataset. We show that position information plays an important role in conversation history modeling. We also visualize the history attention and provide new insights into conversation history understanding.

Traffic prediction is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, such as spatial dependency of complicated road networks and temporal dynamics, and many more. The factors make traffic prediction a challenging task due to the uncertainty and complexity of traffic states. In the literature, many research works have applied deep learning methods on traffic prediction problems combining convolutional neural networks (CNNs) with recurrent neural networks (RNNs), which CNNs are utilized for spatial dependency and RNNs for temporal dynamics. However, such combinations cannot capture the connectivity and globality of traffic networks. In this paper, we first propose to adopt residual recurrent graph neural networks (Res-RGNN) that can capture graph-based spatial dependencies and temporal dynamics jointly. Due to gradient vanishing, RNNs are hard to capture periodic temporal correlations. Hence, we further propose a novel hop scheme into Res-RGNN to utilize the periodic temporal dependencies. Based on Res-RGNN and hop Res-RGNN, we finally propose a novel end-to-end multiple Res-RGNNs framework, referred to as “MRes-RGNN”, for traffic prediction. Experimental results on two traffic datasets have demonstrated that the proposed MRes-RGNN outperforms state-of-the-art methods significantly.

Bacteriophages (phages) are the most abundant and widely distributed organisms on Earth, constituting a virtually unlimited resource to explore the development of biomedical therapies. The therapeutic use of phages to treat bacterial infections (“phage therapy”) was conceived by Felix d’Herelle nearly a century ago. However, its power has been realized only recently, largely due to the emergence of multi-antibiotic resistant bacterial pathogens. Progress in technologies, such as high-throughput sequencing, genome editing, and synthetic biology, further opened doors to explore this vast treasure trove. Here, we review some of the emerging themes on the use of phages against infectious diseases. In addition to phage therapy, phages have also been developed as vaccine platforms to deliver antigens as part of virus-like nanoparticles that can stimulate immune responses and prevent pathogen infections. Phage engineering promises to generate phage variants with unique properties for prophylactic and therapeutic applications. These approaches have created momentum to accelerate basic as well as translational phage research and potential development of therapeutics in the near future.

Diagnosis and treatment at an early stage may improve survival of non-small-cell lung cancer (NSCLC). Previous studies have found that long noncoding RNA growth arrestspecific transcript 5 (GAS5) is essential to cancer progression. However, the expression and diagnostic value of GAS5 in exosomes (Exo-GAS5) remain unclear. One hundred and four participants were enrolled, including subjects with NSCLC (n = 64) and healthy subjects (n = 40). The total Exosome Isolation Kit was applied to isolate exosomes from serum. Total RNA was extracted and the AS5 expression was analyzed using quantitative reverse transcription polymerase chain reaction. Receiver operating characteristic (ROC) curve analysis was applied to evaluate the diagnostic value of Exo-GAS5 in NSCLC. Our data indicated that the Exo-GAS5 was downregulated in patients with NSCLC compared with healthy controls (p < 0.001). Furthermore, patients with NSCLC with larger tumor size (p = 0.025) and advanced TNM (T: extent of the primary tumor; N: lymph node involvement; M: metastatic disease) classification (p = 0.047) showed lower Exo-GAS5 expression. ROC curve analysis using Exo-GAS5 combined with carcinoembryonic antigen showed an area under curve (AUC) of 0.929. Exo-GAS5 could be used to distinguish patients with Stage I NSCLC with an AUC of 0.822. In conclusion, Exo-GAS5 may function as an ideal noninvasive serum-based marker for identifying patients with early NSCLC. K E Y W O R D Sbiomarker, exosomes, GAS5, NSCLC

. (2011) 'Stable polymorphs crystallized directly under thermodynamic control in three-dimensional nanocon nement : a generic methodology.', Crystal growth design., 11 (2). pp. 363-366. Further information on publisher's website:https://doi.org/10.1021/cg101200fPublisher's copyright statement:This document is the Accepted Manuscript version of a Published Work that appeared in nal form in Crystal growth design, copyright c American Chemical Society after peer review and technical editing by the publisher. To access the nal edited and published work see https://doi.org/10.1021/cg101200fAdditional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThermodynamic control of crystallization has been achieved to produce stable polymorphs directly by using 3D nano-confinement in microemulsions. The theoretical basis for thermodynamic control of crystallization using 3D nano-confinement is outlined. Our approach leap-frogs the usual metastable polymorph pathway because crystallization becomes governed by the ability to form stable nuclei, rather than critical nuclei. The generality of this approach is demonstrated by crystallizing the stable polymorph of three 'problem' compounds from microemulsions under conditions yielding metastable forms in bulk solution. The polymorphic compounds are mefenamic acid (2-[(2,3-(dimethylphenyl)amino] benzoic acid), glycine (aminoethanoic acid) and the highly polymorphic 5-methyl-2-[(2-nitrophenyl) amino]-3-thiophenecarbonitrile, commonly known as ROY because of its red, orange and yellow polymorphs. Application of this methodology should prevent another Ritonavir-type disaster, whereby a marketed drug transforms into a more stable form, reducing its bioavailability and effectiveness. The lowest energy nuclei selectively grow in our approach. Consequently this also provides a generic method for producing higher crystallinity materials, which may prove beneficial for crystallizing proteins and inorganic nanocrystals.Statement of urgency and brief summary of significant findings. We believe the paper fulfills the requirements of urgency for a Communication because it details for the first time a generic method to obtain thermodynamic control of crystallization. This enables stable polymorphs to be crystallized directly, to prevent another Ritonavir-type disaster. The methodology used selectively grows the lowest energy crystal nuclei, so it can also produce materials with higher crystallinity, which may prove of use for a wide range of crystalline materials, including potential...

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