Various retinal vessel segmentation methods based on convolutional neural networks were proposed recently, and Dense U-net as a new semantic segmentation network was successfully applied to scene segmentation. Retinal vessel is tiny, and the features of retinal vessel can be learned effectively by the patch-based learning strategy. In this study, we proposed a new retinal vessel segmentation framework based on Dense U-net and the patch-based learning strategy. In the process of training, training patches were obtained by random extraction strategy, Dense U-net was adopted as a training network, and random transformation was used as a data augmentation strategy. In the process of testing, test images were divided into image patches, test patches were predicted by training model, and the segmentation result can be reconstructed by overlapping-patches sequential reconstruction strategy. This proposed method was applied to public datasets DRIVE and STARE, and retinal vessel segmentation was performed. Sensitivity (Se), specificity (Sp), accuracy (Acc), and area under each curve (AUC) were adopted as evaluation metrics to verify the effectiveness of proposed method. Compared with state-of-the-art methods including the unsupervised, supervised, and convolutional neural network (CNN) methods, the result demonstrated that our approach is competitive in these evaluation metrics. This method can obtain a better segmentation result than specialists, and has clinical application value.

Fusion process is known to be the initial step of viral infection and hence targeting the entry process is a promising strategy to design antiviral therapy. The self-inhibitory peptides derived from the enveloped (E) proteins function to inhibit the protein-protein interactions in the membrane fusion step mediated by the viral E protein. Thus, they have the potential to be developed into effective antiviral therapy. Herein, we have developed a Monte Carlo-based computational method with the aim to identify and optimize potential peptide hits from the E proteins. The stability of the peptides, which indicates their potential to bind in situ to the E proteins, was evaluated by two different scoring functions, dipolar distance-scaled, finite, ideal-gas reference state and residue-specific all-atom probability discriminatory function. The method was applied to α-helical Class I HIV-1 gp41, β-sheet Class II Dengue virus (DENV) type 2 E proteins, as well as Class III Herpes Simplex virus-1 (HSV-1) glycoprotein, a E protein with a mixture of α-helix and β-sheet structural fold. The peptide hits identified are in line with the druggable regions where the self-inhibitory peptide inhibitors for the three classes of viral fusion proteins were derived. Several novel peptides were identified from either the hydrophobic regions or the functionally important regions on Class II DENV-2 E protein and Class III HSV-1 gB. They have potential to disrupt the protein-protein interaction in the fusion process and may serve as starting points for the development of novel inhibitors for viral E proteins.

Microsatellites are found in taxonomically different organisms, and such repeats are related with genomic structure, function and certain diseases. To characterize microsatellites for macaques, we searched and compared SSRs with 1-6 bp nucleotide motifs in rhesus, cynomolgus and pigtailed macaque. A total of 1395671, 1284929 and 1266348 perfect SSRs were mined, respectively. The most frequent perfect SSRs were mononucleotide SSRs. The most GC-content was in dinucleotide SSRs and the least was in the mononucleotide SSRs. Chromosome size was positively correlated with SSR number and negatively correlated with the relative frequency and density of SSRs. The GC content of chromosome SSRs were negatively correlated with relative frequency of SSRs and GC content of chromosome sequences. The features of microsatellite distribution in assembled genomes of the three species were greatly similar, which revealed that the distributional pattern of microsatellites is probably conservative in genus Macaca. The degenerated number of repeat motifs was found to be different in pentanucleotide and hexanucleotide repeats. Species-specific motifs for each macaque were significantly underrepresented. Overall, SSR frequencies of each chromosome in rhesus macaque were higher than in cynomolgus macaque. The maximum repeat times of mono- to pentanucleotide repeats in cynomolgus macaque was more than other two macaques. These results emphasize the genetic diversity and phylogenetic relationship of genus Macaca species. Our data will be beneficial for comparative genome mapping, understanding the distribution of SSRs and genome structure between these animal models, and provide a foundation for further development and identification of more macaque-specific SSRs.

Metasurfaces are investigated intensively for biophotonics applications due to their resonant wavelength flexibly tuned in the near infrared region specially matching biological tissues. Here, we present numerically a metasurface structure combining dielectric resonance with surface plasmon mode of a metal plane, which is a perfect absorber with a narrow linewidth 10 nm wide and quality factor 120 in the near infrared regime. As a sensor, its bulk sensitivity and bulk figure of merit reach respectively 840 nm/RIU and 84/RIU, while its surface sensitivity and surface figure of merit are respectively 1 and 0.1/nm. For different types of adsorbate layers with the same thickness of 8 nm, its surface sensitivity and figure of merit are respectively 32.3 and 3.2/RIU. The enhanced electric field is concentrated on top of dielectric patch ends and in the patch ends simultaneously. Results show that the presented structure has high surface (and bulk) sensing capability in sensing applications due to its narrow linewidth and deep modulation depth. This could pave a new route toward dielectric-metal metasurface in biosensing applications, such as early disease detections and designs of neural stem cell sensing platforms.

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A novel and efficient entrance to the pyrimidine skeleton has been presented via the α,β-dehydrogenation and deamination of tertiary alkylamines. This I2-catalyzed dehydrogenative multicomponent procedure utilizes simple aldehydes to trap the hidden enamine intermediates and suspend generation of azadienes from amidines, enabling the difunctionalization of a vicinal C­(sp3)–H bond. These studies provide valuable possibilities for the introduction of aliphatic substituents and show how to switch to a new reactive modality.

At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either a univariate measure or a single type of brain connectivity, which may not fully capture the abnormal brain changes of SZ patients. In this paper, event-related potentials were collected from 45 SZ patients and 30 healthy controls (HCs) during a learning task, and then a combination of partial directed coherence (PDC) effective and phase lag index (PLI) functional connectivity were used as features to train a support vector machine classifier with leave-one-out cross-validation for classification of SZ from HCs. Our results indicated that an excellent classification performance (accuracy = 95.16%, specificity = 94.44%, and sensitivity = 96.15%) was obtained when the combination of functional and effective connectivity features was used, and the corresponding optimal feature number was 15, which included 12 PDC and three PLI connectivity features. The selected effective connectivity features were mainly located between the frontal/temporal/central and visual/parietal lobes, and the selected functional connectivity features were mainly located between the frontal/temporal and visual cortexes of the right hemisphere. In addition, most of the selected effective connectivity abnormally enhanced in SZ patients compared with HCs, whereas all the selected functional connectivity features decreased in SZ patients. The above results showed that our proposed method has great potential to become a tool for the auxiliary diagnosis of SZ.

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