Multi-almost periodicity and invariant basins of general neural networks under almost periodic stimuli

In this paper, we investigate convergence dynamics of $2^N$ almost periodic encoded patterns of general neural networks (GNNs) subjected to external almost periodic stimuli, including almost periodic delays. Invariant regions are established for the existence of $2^N$ almost periodic encoded patterns under two classes of activation functions. By employing the property of $\mathscr{M}$-cone and inequality technique, attracting basins are estimated and some criteria are derived for the networks to converge exponentially toward $2^N$ almost periodic encoded patterns. The obtained results are new, they extend and generalize the corresponding results existing in previous literature.Comment: 28 pages, 4 figure

Effect of end-stage renal disease on long-term survival after a first-ever mechanical ventilation: a population-based study

The 30-day, 6-month, and 1-, 2-, 5-, and 10-year survival rate differences in the ESRD Pos and ESRD Neg groups from the beginning. (DOCX 17 kb

Assembling a cellulase cocktail and a cellodextrin transporter into a yeast host for CBP ethanol production

Background: Many microorganisms possess enzymes that can efficiently degrade lignocellulosic materials, but donot have the capability to produce a large amount of ethanol. Thus, attempts have been made to transform suchenzymes into fermentative microbes to serve as hosts for ethanol production. However, an efficient host for aconsolidated bioprocess (CBP) remains to be found. For this purpose, a synthetic biology technique that cantransform multiple genes into a genome is instrumental. Moreover, a strategy to select cellulases that interactsynergistically is needed.Results: To engineer a yeast for CBP bio-ethanol production, a synthetic biology technique, called “promoter-basedgene assembly and simultaneous overexpression” (PGASO), that can simultaneously transform and express multiplegenes in a kefir yeast, Kluyveromyces marxianus KY3, was recently developed. To formulate an efficient cellulasecocktail, a filter-paper-activity assay for selecting heterologous cellulolytic enzymes was established in this study andused to select five cellulase genes, including two cellobiohydrolases, two endo-β-1,4-glucanases and onebeta-glucosidase genes from different fungi. In addition, a fungal cellodextrin transporter gene was chosen totransport cellodextrin into the cytoplasm. These six genes plus a selection marker gene were one-step assembledinto the KY3 genome using PGASO. Our experimental data showed that the recombinant strain KR7 could expressthe five heterologous cellulase genes and that KR7 could convert crystalline cellulose into ethanol.Conclusion: Seven heterologous genes, including five cellulases, a cellodextrin transporter and a selection marker,were simultaneously transformed into the KY3 genome to derive a new strain, KR7, which could directly convertcellulose to ethanol. The present study demonstrates the potential of our strategy of combining a cocktailformulation protocol and a synthetic biology technique to develop a designer yeast host

A powerful and efficient multivariate approach for voxel-level connectome-wide association studies

We describe an approach to multivariate analysis, termed structured kernel principal component regression (sKPCR), to identify associations in voxel-level connectomes using resting-state functional magnetic resonance imaging (rsfMRI) data. This powerful and computationally efficient multivariate method can identify voxel-phenotype associations based on the whole-brain connectivity pattern of voxels, and it can detect linear and non-linear signals in both volume-based and surface-based rsfMRI data. For each voxel, sKPCR first extracts low-dimensional signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations by an adaptive regression model. The method's power is derived from appropriately modelling the spatial structure of the data when performing dimension reduction, and then adaptively choosing an optimal dimension for association testing using the adaptive regression strategy. Simulations based on real connectome data have shown that sKPCR can accurately control the false-positive rate and that it is more powerful than many state-of-the-art approaches, such as the connectivity-wise generalized linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). Moreover, since sKPCR can reduce the computational cost of non-parametric permutation tests, its computation speed is much faster. To demonstrate the utility of sKPCR for real data analysis, we have also compared sKPCR with the above methods based on the identification of voxel-wise differences between schizophrenic patients and healthy controls in four independent rsfMRI datasets. The results showed that sKPCR had better between-sites reproducibility and a larger proportion of overlap with existing schizophrenia meta-analysis findings. Code for our approach can be downloaded from https://github.com/weikanggong/sKPCR. [Abstract copyright: Copyright © 2018 Elsevier Inc. All rights reserved.

Morphological and Molecular Defects in Human Three-Dimensional Retinal Organoid Model of X-Linked Juvenile Retinoschisis

X-linked juvenile retinoschisis (XLRS), linked to mutations in the RS1 gene, is a degenerative retinopathy with a retinal splitting phenotype. We generated human induced pluripotent stem cells (hiPSCs) from patients to study XLRS in a 3D retinal organoid in vitro differentiation system. This model recapitulates key features of XLRS including retinal splitting, defective retinoschisin production, outer-segment defects, abnormal paxillin turnover, and impaired ER-Golgi transportation. RS1 mutation also affects the development of photoreceptor sensory cilia and results in altered expression of other retinopathy-associated genes. CRISPR/Cas9 correction of the disease-associated C625T mutation normalizes the splitting phenotype, outer-segment defects, paxillin dynamics, ciliary marker expression, and transcriptome profiles. Likewise, mutating RS1 in control hiPSCs produces the disease-associated phenotypes. Finally, we show that the C625T mutation can be repaired precisely and efficiently using a base-editing approach. Taken together, our data establish 3D organoids as a valid disease model

Functional connectivity of the human amygdala in health and in depression

To analyze the functioning of the amygdala in depression, we performed the first voxel-level resting state functional-connectivity neuroimaging analysis of depression of voxels in the amygdala with all other voxels in the brain, with 336 patients with major depressive disorder and 350 controls. Amygdala voxels had decreased functional connectivity with the orbitofrontal cortex, temporal lobe areas, including the temporal pole, inferior temporal gyrus, and the parahippocampal gyrus. The reductions in the strengths of the functional connectivity of the amygdala voxels with the medial orbitofrontal cortex and temporal lobe voxels were correlated with increases in the Beck Depression Inventory score and in the duration of illness measures of depression. Parcellation analysis in 350 healthy controls based on voxel-level functional connectivity showed that the basal division of the amygdala has high functional connectivity with medial orbitofrontal cortex areas, and the dorsolateral amygdala has strong functional connectivity with the lateral orbitofrontal cortex and related ventral parts of the inferior frontal gyrus. In depression, the basal amygdala division had especially reduced functional connectivity with the medial orbitofrontal cortex which is involved in reward; and the dorsolateral amygdala subdivision had relatively reduced functional connectivity with the lateral orbitofrontal cortex which is involved in non-reward

Computing Optical Properties of Ultra-thin Crystals

An overview is given of recent advances in experimental and theoretical understanding of optical properties of ultra-thin crystal structures (graphene, phosphorene, silicene, MoS2, MoSe2 , WS2 , WSe2 , h-AlN, h-BN, fluorographene, graphane). Ultra-thin crystals are atomically-thick layered crystals that have unique properties which differ from their 3D counterpart. Because of the difficulties in the synthesis of few-atom-thick crystal structures, which are thought to be the main building blocks of future nanotechnology, reliable theoretical predictions of their electronic, vibrational and optical properties are of great importance. Recent studies revealed the reliable predictive power of existing theoretical approaches based on density functional theory (DFT)