Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.

Mesenchymal stem cells (MSCs) secrete a variety of cytokines and growth factors in addition to self-renewal and multiple forms of differentiation. Some of these secreted bioactive factors could improve meiotic maturation in vitro and subsequent embryo developmental potential. The aim of the present study was to determine whether in vitro maturation (IVM) of mouse oocyte with or without cumulus cells could be improved by contact with conditioned medium (CM) of MSCs as well as the efficiency of CM to support follicular growth and oocyte maturation in the ovarian organ of mice cultured on soft agar. The developmental potential of matured oocyte was assessed by blastocyst formation after in vitro fertilization (IVF). Germinal vesicle stage oocytes with or without cumulus cells were subjected to IVM in either CM, Dulbecco's modified Eagle's medium (DMEM), α-minimum essential medium (α-MEM) or human tubal fluid (HTF). Approximately 120 oocytes were studied for each medium. CM produced a higher maturation rate (91.2%) than DMEM (54.7%), α-MEM (63.5%) and HTF (27.1%). Moreover, CM improved embryo development to blastocyst stage significantly more than DMEM and HTF (85 vs 7% and 41.7%, respectively) but there was no significant difference compared with α-MEM (85 vs 80.3%). The behavior of cortical granules of IVM oocytes cultured in CM revealed cytoplasmic maturation. Moreover, CM also supported preantral follicles growth well in organotypic culture on soft agar resulting in the maturation of 60% of them to developmentally competent oocytes. The production of estrogen progressively increased approximately 1-fold every other day during organ culture, while a dramatic 10-fold increase in progesterone was observed 17 h after human chorionic gonadotropin stimulus at the end of culture. Thus, CM is an effective medium for preantral follicle growth, oocyte maturation, and sequential embryo development.

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We present full atomistic calculations of the spin-flip time (T1) of electrons and holes mediated by acoustic phonons in self-assembled In1−xGaxAs/GaAs quantum dots at zero magnetic field. At low magnetic field, the first-order process is suppressed, and the second-order process becomes dominant. We find that the spin-phonon-interaction induced spin relaxation time is 40 -80 s for electrons, and 1 -20 ms for holes at 4.2 K. The calculated hole-spin relaxation times are in good agreement with recent experiments, which suggests that the two-phonon process is the main relaxation mechanism for hole-spin relaxation in the self-assembled quantum dots at zero field. We further clarify the structural and alloy composition effects on the spin relaxation in the quantum dots. The electron/hole spin in semiconductor quantum dots (QDs) is believed to be a promising candidate for solid state quantum computations [1]. Recently years, huge progress have been made experimentally in initialization, manipulation and controlling of the spins in QDs [2][3][4][5]. However, the short spin lifetime is still a major obstacle to realize the quantum computation. Until now the mechanisms for spin relaxation in QDs are still not well understood both in theory and in experiments. Spinphonon interaction due to the spin-orbit coupling (SOC) effects is one of the main mechanisms lead to spin relaxation in the QDs [6,7], which depends strongly on the geometries and compositions of the QDs. An accurate description of SOC is crucial to understand the spin relaxation. Unfortunately, the understanding of the spin relaxation from the atomistic level is still unavailable.Electrons were expected to have very long spin lifetime, because they have small SOC in QDs. However, because of hyperfine interactions with nuclear spins, the electron spin coherence time is greatly reduced (∼ 500 ps) [3]. The electron spin lifetime can be prolonged by applying an external magnetic field to polarize the nuclear spin [2,3]. Unfortunately, at the same time, the electron spin lifetimes (T 1 ) decrease fast with the magnetic field, being proportional to B At very low magnetic field, the first-order spin-phonon interaction is greatly suppressed, and the multi-phonon process becomes dominate. The T 1 ∝ B −5 law breaks down at low magnetic field [2,9]. Most previous theoretical works focus on spin relaxation in a large magnetic field [6,7,10], and there are only few studies of spin relaxation in QDs at low magnetic field [9]. These studies are all based on effective mass approximations or k · p theory [6,10,11], in which the effective SOC are added in by hand in the form of Dresselhaus interactions and Rashba interactions. These continuum theories treat poorly the local strain and alloy composition effects especially for hole, which may play an extremely important role to the effective SOC.In this letter, we present the first atomistic calculations [12] of the spin relaxation of electrons and holes at zero magnetic field in self-assembled In 1−x Ga x As/GaAs QDs. In this me...

Accumulated researches have revealed that Piwi-interacting RNAs (piRNAs) are regulating the development of germ and stem cells, and they are closely associated with the progression of many diseases. As the number of the detected piRNAs is increasing rapidly, it is important to computationally identify new piRNA-disease associations with low cost and provide candidate piRNA targets for disease treatment. However, it is a challenging problem to learn effective association patterns from the positive piRNA-disease associations and the large amount of unknown piRNA-disease pairs. In this study, we proposed a computational predictor called iPiDi-PUL to identify the piRNA-disease associations. iPiDi-PUL extracted the features of piRNA-disease associations from three biological data sources, including piRNA sequence information, disease semantic terms and the available piRNA-disease association network. Principal component analysis (PCA) was then performed on these features to extract the key features. The training datasets were constructed based on known positive associations and the negative associations selected from the unknown pairs. Various random forest classifiers trained with these different training sets were merged to give the predictive results via an ensemble learning approach. Finally, the web server of iPiDi-PUL was established at http://bliulab.net/iPiDi-PUL to help the researchers to explore the associated diseases for newly discovered piRNAs.