Studies have explored the influence of DNA damage in assisted reproductive technology (ART), but the outcome remains controversial. To determine whether sperm DNA fragmentation index (DFI) has any effect on ART outcomes, we collected detailed data regarding 1,333 IVF cycles performed at our centre, and the data of our retrospective cohort study were extracted for this meta‐analysis. We searched PubMed, Web of Science, EMBASE and Google Scholar and performed a systemic review and meta‐analysis. Primary meta‐analysis of 10 studies comprising 1,785 couples showed that live birth rate was no significantly different between low‐DFI group and high‐DFI group (p > 0.05). Secondary meta‐analysis of 25 studies comprising 3,992 couples showed a higher miscarriage rate in high‐DFI group than in low‐DFI group (RR=1.57 [1.18, 2.09], p < 0.01). Meta‐analysis of eight studies comprising 17,879 embryos revealed a lower good‐quality embryo rate (RR=0.65 [0.62, 0.68], p < 0.01). Meta‐analysis of 23 studies comprising 6,771 cycles showed that the high‐DFI group had a lower clinical pregnancy rate than low‐DFI group (RR=0.85 [0.75, 0.96], p < 0.01). Heterogeneity of included studies weakened our conclusions. Our study showed that DFI has adverse effects on ART outcome. More well‐designed studies exploring the association between DFI and ART outcome are desired.

No abstract

The accurate diagnosis of Alzheimer's disease (AD) and its early stage, e.g., mild cognitive impairment (MCI), is essential for timely treatment or possible intervention to slow down AD progression. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis and prognosis. Therefore, information fusion strategies with multi-modal neuroimaging data, such as voxel-based measures extracted from structural MRI (VBM-MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET), have shown their effectiveness for AD diagnosis. However, most existing methods are proposed to simply integrate the multi-modal data, but do not make full use of structure information across the different modalities. In this paper, we propose a novel multi-modal neuroimaging feature selection method with consistent metric constraint (MFCC) for AD analysis. First, the similarity is calculated for each modality (i.e. VBM-MRI or FDG-PET) individually by random forest strategy, which can extract pairwise similarity measures for multiple modalities. Then the group sparsity regularization term and the sample similarity constraint regularization term are used to constrain the objective function to conduct feature selection from multiple modalities. Finally, the multi-kernel support vector machine (MK-SVM) is used to fuse the features selected from different models for final classification. The experimental results on the *

ObjectiveTo investigate the Interaction between chronic endometritis (CE) caused endometrial microbiota disorder and endometrial immune environment change in recurrent implantation failure (RIF).MethodTranscriptome sequencing analysis of the endometrial of 112 patients was preform by using High-Throughput Sequencing. The endometrial microbiota of 43 patients was analyzed by using 16s rRNA sequencing technology.ResultIn host endometrium, CD4 T cell and macrophage exhibited significant differences abundance between CE and non-CE patients. The enrichment analysis indicated differentially expressed genes mainly enriched in immune-related functional terms. Phyllobacterium and Sphingomonas were significantly high infiltration in CE patients, and active in pathways related to carbohydrate metabolism and/or fat metabolism. The increased synthesis of lipopolysaccharide, an important immunomodulator, was the result of microbial disorders in the endometrium.ConclusionThe composition of endometrial microorganisms in CE and non-CE patients were significantly different. Phyllobacterium and Sphingomonas mainly regulated immune cells by interfering with the process of carbohydrate metabolism and/or fat metabolism in the endometrium. CE endometrial microorganisms might regulate Th17 response and the ratio of Th1 to Th17 through lipopolysaccharide (LPS).

We study nonsequential double ionization (NSDI) processes of an atom by applying the frequency-domain theory based on the nonperturbative quantum electrodynamics. We obtain the transition formulas that describe the NSDI processes caused by the collision ionization (CI) and the collision-excitation ionization (CEI) mechanisms. By analyzing the NSDI results of each above-threshold ionization (ATI) channel, we investigate the contributions to the NSDI from the backward and forward collisions. In particular, for the CI process, the backward collision makes a major contribution to the NSDI probability, whereas for the CEI process, it depends on the characteristics of the laser-atom system: if the energy that the recolliding electron needs to excite a bound electron is much larger than the laser photon energy, such as for the case of helium in this work, the backward collision dominates the contribution; otherwise, the forward collision dominates the contribution. We also discuss the source of interference fringes in the NSDI momentum spectra due to the CI mechanism and find that the fringes can be predicted by using a simple cosine function. This work can be regarded as a development of the frequency-domain theory, which may shed light on the study of multiparticle dynamics in intense laser fields.

Using a frequency-domain theory, we demonstrate that an angle-resolved high-order above-threshold ionization ͑HATI͒ spectrum carries three pieces of important information: the fingerprint of the molecular wave function in the direct above-threshold-ionization amplitude, the geometrical structure of the molecule in the potential scattering between two plane waves, and the interaction between the ionized electron and the laser field, manifested in a phase factor associated with laser-assisted collisions. As a result all main interference features in the HATI spectrum can be physically explained. As an application it is pointed out that the skeleton structure of a molecule can be better imaged using lasers of higher frequencies.

Micro-expression recognition has been an active research area in recent years, it plays an important role in psychology and public security. Due to the aspects of short duration and subtle movement, it is challenging to extract spatiotemporal features of micro-expressions. The existing methods only extract features in the three-dimensional orthogonal plane and fail to make full use of that information. To solve this problem, we propose a new Local Cubes Binary Patterns (LCBP) method for micro-expression recognition. LCBP is cascaded by the motion information LCBP direction , the amplitude information LCBP amplitudes , and the spatial information LCBP 3D to obtain the spatiotemporal features. The advantage of LCBP is its ability to preserve the spatiotemporal information and the low feature dimension. Furthermore, to increase the discrimination of features in micro-expression sequences, we apply a differential calculation energy map to find regions of interest (ROI) for getting a weighted energy map. The final micro-expression feature acquired by fusing the LCBP features and the weighted energy map are classified through the Support Vector Machine (SVM). We evaluate the proposed method on four published micro-expression databases including SMIC, CASME, CASME2, SAMM. Experimental results demonstrate that our proposed method achieves promising performance for micro-expression recognition. INDEX TERMS Micro-expression recognition, differential energy map, local cubes binary patterns, SVM.

No abstract