Functional brain network identification and fMRI augmentation using a VAE-GAN framework.

Recently, deep learning models have achieved superior performance for mapping functional brain networks from functional magnetic resonance imaging (fMRI) data compared with traditional methods. However, due to the lack of sufficient data and the high dimensionality of brain volume, deep learning models of fMRI tend to suffer from overfitting. In addition, existing methods rarely studied fMRI data augmentation and its application.

A deep learning method for autism spectrum disorder identification based on interactions of hierarchical brain networks.

Background: It has been recently shown that deep learning models exhibited remarkable performance of representing functional Magnetic Resonance Imaging (fMRI) data for the understanding of brain functional activities. With hierarchical structure, deep learning models can infer hierarchical functional brain networks (FBN) from fMRI. However, the applications of the hierarchical FBNs have been rarely studied.

Isolation and identification of the alga-symbiotic bacterium and characterisation of its exopolysaccharide.

An exopolysaccharide (EPS)-producing bacterium TD18, isolated from the culture broth of green alga , was identified as based on the 100% identity of 16S rRNA sequences and designated TD18. The results of compositional and structural analyses and physiochemical tests show that (1) the exopolysaccharide produced by TD18 (TD18-EPS) is an acidic hetero-polysaccharide with a molecular weight of 23 kDa, consisting of glucose, mannose, galactose and glucuronic acid, and (2) TD18-EPS is of high thermal stability with a degradation temperature of 308 °C, the solution of which is non-Newtonian pseudoplastic fluid exhibiting good emulsifying properties over a wide range of temperatures, pH and NaCl concentrations. Hence, TD18 is the first alga-symbiotic strain identified thus far, while TD18-EPS is unique in terms of composition and structure, different from the known EPS, with excellent physiochemical properties and thus has potential applications in industry.

A comparative study of thumb reconstruction through the transplant of the first toe compound free flap between emergency surgery and elective surgery.

This study compared emergency surgery with elective surgery for thumb reconstruction to explore the advantages, safety, and clinical value of emergency reconstruction. By comparing the advantages and disadvantages of thumb reconstruction in emergency surgery and elective surgery, it provides data support for optimizing the treatment process and methods. In this study, 22 patients who underwent thumb reconstruction in Rizhao people's Hospital from January 2018 to December 2020 were randomly divided into emergency operation group and elective operation group.

Analysis of clinical characteristics, pathogen infection, and drug sensitivity of Marine injury patients: A cross-sectional study.

The infection rate is high in patients injured at sea, and because of the unique distribution of marine microorganisms, the infection is often not easily controlled effectively with the empirical application of antibiotics. This study aims to consider the clinical characteristics and pathogen infection and drug susceptibility of patients injured at sea. From 2019 to 2021, there were 635 patients injured at sea in Rizhao People's Hospital.

NEAT1 silencing alleviates pulmonary arterial smooth muscle cell migration and proliferation under hypoxia through regulation of miR‑34a‑5p/KLF4 .

Pulmonary arterial hypertension (PAH) is a severe vascular disease that adversely affects patient health and can be life threatening. The present study aimed to investigate the detailed role of nuclear paraspeckle assembly transcript 1 (NEAT1) in PAH. Using RT‑qPCR, the expression levels of NEAT1, microRNA (miR)‑34a‑5p, and Krüppel‑like factor 4 (KLF4) were detected in both hypoxia‑treated pulmonary arterial smooth muscle cells (PASMCs) and serum from PAH patients.

Modeling and augmenting of fMRI data using deep recurrent variational auto-encoder.

. Recently, deep learning models have been successfully applied in functional magnetic resonance imaging (fMRI) modeling and associated applications. However, there still exist at least two challenges.

Separation, quantification and characterisation of the pigment produced by TD16.

Fungal pigments are important natural products with a wide range of applications. In this study, the purple-red pigment produced by the fungus TD16 (TD16 pigment) was separated with acidulated ethyl acetate and purified by silica gel column chromatography. Results of UV-visible spectrum and HPLC analyses showed that TD16 pigment is a new polyketide pigment with three absorption peaks at 228, 272 and 527 nm and a retention time of 11.

Simultaneous spatial-temporal decomposition for connectome-scale brain networks by deep sparse recurrent auto-encoder.

Exploring the spatial patterns and temporal dynamics of human brain activity has been of great interest, in the quest to better understand connectome-scale brain networks. Though modeling spatial and temporal patterns of functional brain networks have been researched for a long time, the development of a unified and simultaneous spatial-temporal model has yet to be realized. For instance, although some deep learning methods have been proposed recently in order to model functional brain networks, most of them can only represent either spatial or temporal perspective of functional Magnetic Resonance Imaging (fMRI) data and rarely model both domains simultaneously.

Long-read transcriptomic analysis of orb-weaving spider Araneus ventricosus indicates transcriptional diversity of spidroins.

Spider silk, which is composed of diverse silk proteins (spidroin), is a kind of natural high-mass biomaterial with great potential. However, due to the complexity of both the structure and the composition of the spidroins in natural spider silk, application of this valuable biomass is still limited to date. There are diverse kinds of spider silk in the orb-weaving spider with different mechanical and structural characteristics.

NR4A2 Exacerbates Cerebral Ischemic Brain Injury via Modulating microRNA-652/Mul1 Pathway.

Background: Nuclear receptor subfamily group A member 2 (NR4A2), a transcription factor, was suggested to be involved in the pathogenesis of ischemic stroke. Nevertheless, the specific role of NR4A2 in ischemic brain injury has yet to be elucidated. Our aim was to probe the mechanisms behind the repression of microRNA (miRNA) expression resulting from NR4A2 regulation in ischemic brain injury.

Colorless Silk/Copper Sulfide Hybrid Fiber and Fabric with Spontaneous Heating Property under Sunlight.

With the increasing demand for comfort, thinness, and warmth of fabrics, various functional fibers have emerged. However, natural silkworm silk, as one of the most widely used natural fibers in textile, faces the issue that it cannot be modified during the spinning process like synthetic fibers. Herein, copper sulfide nanoparticles (CuS NPs) with a near-infrared (NIR) absorption property were first prepared by using regenerated silk fibroin (RSF) as the biological template.

Hierarchical Organization of Functional Brain Networks Revealed by Hybrid Spatiotemporal Deep Learning.

Hierarchical organization of brain function has been an established concept in the neuroscience field for a long time, however, it has been rarely demonstrated how such hierarchical macroscale functional networks are actually organized in the human brain. In this study, to answer this question, we propose a novel methodology to provide an evidence of hierarchical organization of functional brain networks. This article introduces the hybrid spatiotemporal deep learning (HSDL), by jointly using deep belief networks (DBNs) and deep least absolute shrinkage and selection operator (LASSO) to reveal the temporal hierarchical features and spatial hierarchical maps of brain networks based on the Human Connectome Project 900 functional magnetic resonance imaging (fMRI) data sets.

Artificial ligament made from silk protein/Laponite hybrid fibers.

With developments in tissue engineering, artificial ligaments are expected to be future materials for anterior cruciate ligament (ACL) reconstruction. However, poor healing of the intraosseous part after ACL reconstruction significantly hinders their applications in this field. In this study, a bioactive clay Laponite (LAP) was introduced into the regenerated silk fibroin (RSF) spinning dope to produce functional RSF/LAP hybrid fibers by wet-spinning.

Effect of stress on the molecular structure and mechanical properties of supercontracted spider dragline silks.

Supercontraction is one of the most interesting properties of spider dragline silks. In this study, changes in the secondary structures of the Nephila edulis spider dragline silk after it was subjected to different supercontraction processes were investigated by integrating synchrotron Fourier transform infrared (S-FTIR) microspectroscopy and mechanical characterization. The results showed that after free supercontraction, the β-sheet lost most of its orientation, while the helix and random coils were almost totally disordered.

Modeling Hierarchical Brain Networks via Volumetric Sparse Deep Belief Network.

It has been recently shown that deep learning models such as convolutional neural networks (CNN), deep belief networks (DBN) and recurrent neural networks (RNN), exhibited remarkable ability in modeling and representing fMRI data for the understanding of functional activities and networks because of their superior data representation capability and wide availability of effective deep learning tools. For example, spatial and/or temporal patterns of functional brain activities embedded in fMRI data can be effectively characterized and modeled by a variety of CNN/DBN/RNN deep learning models as shown in recent studies. However, it has been rarely investigated whether it is possible to directly infer hierarchical brain networks from volumetric fMRI data using deep learning models such as DBN.

Discovering hierarchical common brain networks via multimodal deep belief network.

Studying a common architecture reflecting both brain's structural and functional organizations across individuals and populations in a hierarchical way has been of significant interest in the brain mapping field. Recently, deep learning models exhibited ability in extracting meaningful hierarchical structures from brain imaging data, e.g.

Cancer cells arise from bacteria.

Background: The origin of cancer cells is the most fundamental yet unresolved problem in cancer research. Cancer cells are thought to be transformed from the normal cells. However, recent studies reveal that the primary cancer cells (PCCs) for cancer initiation and secondary cancer cells (SCCs) for cancer progression are formed in but not transformed from the senescent normal and cancer cells, respectively.

Recognizing Brain States Using Deep Sparse Recurrent Neural Network.

Brain activity is a dynamic combination of different sensory responses and thus brain activity/state is continuously changing over time. However, the brain's dynamical functional states recognition at fast time-scales in task fMRI data have been rarely explored. In this paper, we propose a novel 5-layer deep sparse recurrent neural network (DSRNN) model to accurately recognize the brain states across the whole scan session.

Silk fibroin and hydroxyapatite segmented coating enhances graft ligamentization and osseointegration processes of the polyethylene terephthalate artificial ligament in vitro and in vivo.

The inferior biocompatibility of the polyethylene terephthalate (PET) artificial ligament may lead to poor healing in both the intra-articular part (IAP) and the intraosseous part (IOP) after anterior cruciate ligament (ACL) reconstruction. This study aimed to systematically investigate the effect of silk fibroin (SF) and hydroxyapatite (HA) segmented coating on graft ligamentization and osseointegration processes of the PET ligament. Several techniques including scanning electron microscopy (SEM) and attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, X-ray diffraction (XRD) and water contact angle (WCA) measurements were carried out to validate the introduction of SF and HA.

Mass spider silk production through targeted gene replacement in .

Spider silk is one of the best natural fibers and has superior mechanical properties. However, the large-scale harvesting of spider silk by rearing spiders is not feasible, due to their territorial and cannibalistic behaviors. The silkworm, , has been the most well known silk producer for thousands of years and has been considered an ideal bioreactor for producing exogenous proteins, including spider silk.

Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs.

Many recent literature studies have revealed interesting dynamics patterns of functional brain networks derived from fMRI data. However, it has been rarely explored how functional networks spatially overlap (or interact) and how such connectome-scale network interactions temporally evolve. To explore these unanswered questions, this paper presents a novel framework for spatio-temporal modeling of connectome-scale functional brain network interactions via two main effective computational methodologies.

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases.