Critical Role of microRNA-21 in the Pathogenesis of Liver Diseases

MicroRNAs are small non-coding RNAs that range in length from 18 to 24 nucleotides. As one of the most extensively studied microRNAs, microRNA-21 (miR-21) is highly expressed in many mammalian cell types. It regulates multiple biological functions such as proliferation, differentiation, migration, and apoptosis. In this review, we summarized the mechanism of miR-21 in the pathogenesis of various liver diseases. While it is clear that miR-21 plays an important role in different types of liver diseases, its use as a diagnostic marker for specific liver disease or its therapeutic implication are not ready for prime time due to significant variability and heterogeneity in the expression of miR-21 in different types of liver diseases depending on the studies. Additional studies to further define miR-21 functions and its mechanism in association with each type of chronic liver diseases are needed before we can translate the bedside observations into clinical settings

Low loss high index contrast nanoimprinted polysiloxane waveguides

Nanoimprint lithography is gaining rapid acceptance in fields as diverse as microelectronics and microfluidics due to its simplicity high resolution and low cost. These properties are critically important for the fabrication of photonic devices, where cost is often the major inhibiting deployment factor for high volume applications. We report here on the use of nanoimprint technology to fabricate low loss broadband high index contrast waveguides in a Polysiloxane polymer system for the first time

(3-Pyrid­yl)methanaminium 4-nitro­phenolate 4-nitro­phenol solvate

In the crystal structure of the title compound, C6H9N2 +·C6H4NO3 −·C6H5NO3, ions and mol­ecules are connected via inter­molecular N—H⋯O, N—H⋯N, O—H⋯O and C—H⋯O hydrogen bonds into a three-dimensional network

Matching Natural Language Sentences with Hierarchical Sentence Factorization

Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Depending on whether training data is available, prior research has proposed both unsupervised distance-based schemes and supervised deep learning schemes for sentence matching. However, previous approaches either omit or fail to fully utilize the ordered, hierarchical, and flexible structures of language objects, as well as the interactions between them. In this paper, we propose Hierarchical Sentence Factorization---a technique to factorize a sentence into a hierarchical representation, with the components at each different scale reordered into a "predicate-argument" form. The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies. We apply our techniques to text-pair similarity estimation and text-pair relationship classification tasks, based on multiple datasets such as STSbenchmark, the Microsoft Research paraphrase identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments show that the proposed hierarchical sentence factorization can be used to significantly improve the performance of existing unsupervised distance-based metrics as well as multiple supervised deep learning models based on the convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page

Online algorithms for 1-space bounded multi dimensional bin packing and hypercube packing

In this paper, we study 1-space bounded multi-dimensional bin packing and hypercube packing. A sequence of items arrive over time, each item is a d-dimensional hyperbox (in bin packing) or hypercube (in hypercube packing), and the length of each side is no more than 1. These items must be packed without overlapping into d-dimensional hypercubes with unit length on each side. In d-dimensional space, any two dimensions i and j define a space P ij. When an item arrives, we must pack it into an active bin immediately without any knowledge of the future items, and 90 {ring operator}-rotation on any plane P ij is allowed. The objective is to minimize the total number of bins used for packing all these items in the sequence. In the 1-space bounded variant, there is only one active bin for packing the current item. If the active bin does not have enough space to pack the item, it must be closed and a new active bin is opened. For d-dimensional bin packing, an online algorithm with competitive ratio 4 d is given. Moreover, we consider d-dimensional hypercube packing, and give a 2 d+1-competitive algorithm. These two results are the first study on 1-space bounded multi dimensional bin packing and hypercube packing. © 2012 The Author(s).published_or_final_versionSpringer Open Choice, 28 May 201