Multi-angle lensless ptychographic imaging via adaptive correction and the Nesterov method.

Lensless systems based on ptychographic imaging can simultaneously achieve a large field of view and high resolution while having the advantages of small size, portability, and low cost compared to traditional lensed imaging. However, lensless imaging systems are susceptible to environmental noise and have a lower resolution of individual images than lens-based imaging systems, which means that they require a longer time to obtain a good result. Therefore, in this paper, to improve the convergence rate and robustness of noise in lensless ptychographic imaging, we propose an adaptive correction method, in which we add an adaptive error term and noise correction term in lensless ptychographic algorithms to reach convergence faster and create a better suppression effect on both Gaussian noise and Poisson noise. The Wirtinger flow and the Nesterov algorithms are used in our method to reduce computational complexity and improve the convergence rate. We applied the method to phase reconstruction for lensless imaging and demonstrated the effectiveness of the method by simulation and experiment. The method can be easily applied to other ptychographic iterative algorithms.

Efficient multiplexed illumination and imaging approach for Fourier ptychographic microscopy.

A Fourier ptychographic microscope (FPM) can obtain images with high resolution and a wide field of view (FOV). However, the time-consuming process of image acquisition and computation leads to low reconstruction efficiency. Therefore, we propose a state-multiplexed method through an optimized illumination pattern to accelerate FPM. First, to reduce the redundancy of the spectrum and analyze the impact of missing a certain sub-spectrum on overall spectrum reconstruction, we use an image quality evaluation method to obtain the differential expression between missing a certain LED lighting and all LED lighting. Second, we use the difference expression to select the important LEDs and obtain an optimized illumination pattern that lights up only the LEDs in the central area and the edge LEDs. Then, we update the multiplexing method with the new, to the best of our knowledge, illumination pattern and obtain satisfactorily reconstructed images. Finally, we validate the effectiveness and efficiency of our method with both simulation and experiments. Compared with the traditional method, our method accelerates the reconstruction speed of FPM while ensuring a large FOV and high resolution, saving about 73% of time.

Neuronal HMGB1 in nucleus accumbens regulates cocaine reward memory.

Cocaine is a common abused drug that can induce abnormal synaptic and immune responses in the central nervous system (CNS). High mobility group box 1 (HMGB1) is one kind of inflammatory molecules that is expressed both on neurons and immune cells. Previous studies of HMGB1 in the CNS have largely focused on immune function, and the role of HMGB1 in neurons and cocaine addiction remains unknown. Here, we show that cocaine exposure induced the translocation and release of HMGB1 in the nucleus accumbens (NAc) neurons. Gain and loss of HMGB1 in the NAc bidirectionally regulate cocaine-induced conditioned place preference. From the nucleus to the cytosol, HMGB1 binds to glutamate receptor subunits (GluA2/GluN2B) on the membrane, which regulates cocaine-induced synaptic adaptation and the formation of cocaine-related memory. These data unveil the role of HMGB1 in neurons and provide the evidence for the HMGB1 involvement in drug addiction.

Periodic and Spatial Spreading of Alkanes and Alcanivorax Bacteria in Deep Waters of the Mariana Trench.

In subduction zones, serpentinization and biological processes may release alkanes to the deep waters, which would probably result in the rapid spread of Alcanivorax However, the timing and area of the alkane distribution and associated enrichment of alkane-degrading microbes in the dark world of the deep ocean have not been explored. In this study, we report the richness (up to 17.8%) of alkane-degrading bacteria, represented by Alcanivorax jadensis, in deep water samples obtained at 3,000 to 6,000 m in the Mariana Trench in two cruises. The relative abundance of A. jadensis correlated with copy numbers of functional almA and alkB genes, which are involved in alkane degradation. In these water samples, we detected a high flux of alkanes, which probably resulted in the prevalence of A. jadensis in the deep waters. Contigs of A. jadensis were binned from the metagenomes for examination of alkane degradation pathways and deep sea-specific pathways, which revealed a lack of nitrate and nitrite dissimilatory reduction in our A. jadensis strains. Comparing the results for the two cruises conducted close to each other, we suggest periodic release of alkanes that may spread widely but periodically in the trench. Distribution of alkane-degrading bacteria in the world's oceans suggests the periodic and remarkable contributions of Alcanivorax to the deep sea organic carbon and nitrogen sources.IMPORTANCE In the oligotrophic environment of the Mariana Trench, alkanes as carbohydrates are important for the ecosystem, but their spatial and periodic spreading in deep waters has never been reported. Alkane-degrading bacteria such as Alcanivorax spp. are biological signals of the alkane distribution. In the present study, Alcanivorax was abundant in some waters, at depths of up to 6,000 m, in the Mariana Trench. Genomic, transcriptomic, and chemical analyses provide evidence for the presence and activities of Alcanivorax jadensis in deep sea zones. The periodic spreading of alkanes, probably from the subductive plates, might have fundamentally modified the local microbial communities, as well as perhaps the deep sea microenvironment.

The Enigmatic Genome of an Obligate Ancient Spiroplasma Symbiont in a Hadal Holothurian.

Protective symbiosis has been reported in many organisms, but the molecular mechanisms of the mutualistic interactions between the symbionts and their hosts are unclear. Here, we sequenced the 424-kbp genome of "Candidatus Spiroplasma holothuricola," which dominated the hindgut microbiome of a sea cucumber, a major scavenger captured in the Mariana Trench (6,140 m depth). Phylogenetic relationships indicated that the dominant bacterium in the hindgut was derived from a basal group of Spiroplasma species. In this organism, the genes responsible for the biosynthesis of amino acids, glycolysis, and sugar transporters were lost, strongly suggesting endosymbiosis. The highly decayed genome consists of two chromosomes and harbors genes coding for proteolysis, microbial toxin, restriction-methylation systems, and clustered regularly interspaced short palindromic repeats (CRISPRs), composed of three cas genes and 76 CRISPR spacers. The holothurian host is probably protected against invading viruses from sediments by the CRISPRs/Cas and restriction systems of the endosymbiotic spiroplasma. The protective endosymbiosis indicates the important ecological role of the ancient Spiroplasma symbiont in the maintenance of hadal ecosystems.IMPORTANCE Sea cucumbers are major inhabitants in hadal trenches. They collect microbes in surface sediment and remain tolerant against potential pathogenic bacteria and viruses. This study presents the genome of endosymbiotic spiroplasmas in the gut of a sea cucumber captured in the Mariana Trench. The extreme reduction of the genome and loss of essential metabolic pathways strongly support its endosymbiotic lifestyle. Moreover, a considerable part of the genome was occupied by a CRISPR/Cas system to provide immunity against viruses and antimicrobial toxin-encoding genes for the degradation of microbes. This novel species of Spiroplasma is probably an important protective symbiont for the sea cucumbers in the hadal zone.

Classification of cancers based on copy number variation landscapes.

Genomic alterations in DNA can cause human cancer. DNA copy number variants (CNV), as one of the types of DNA mutations, have been considered to be associated with various human cancers. CNVs vary in size from 1bp up to one complete chromosome arm. In order to understand the difference between different human cancers on CNVs, in this study, we developed a method to computationally classify six human cancer types by using only CNV level values. The CNVs of 23,082 genes were used as features to construct the classifier. Then the features are carefully selected by mRMR (minimum Redundancy Maximum Relevance Feature Selection) and IFS (Incremental Feature Selection) methods. An accuracy of over 0.75 was reached by using only the CNVs of 19 genes based on Dagging method in 10-fold cross validation. It was indicated that these 19 genes may play important roles in differentiating cancer types. We also analyzed the biological functions of several top genes within the 19 gene list. The statistical results and biological analysis of these genes from this work might further help understand different human cancer types and provide guidance for related validation experiments. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Alternative splicing at GYNNGY 5' splice sites: more noise, less regulation.

Numerous eukaryotic genes are alternatively spliced. Recently, deep transcriptome sequencing has skyrocketed proportion of alternatively spliced genes; over 95% human multi-exon genes are alternatively spliced. One fundamental question is: are all these alternative splicing (AS) events functional? To look into this issue, we studied the most common form of alternative 5' splice sites-GYNNGYs (Y = C/T), where both GYs can function as splice sites. Global analyses suggest that splicing noise (due to stochasticity of splicing process) can cause AS at GYNNGYs, evidenced by higher AS frequency in non-coding than in coding regions, in non-conserved than in conserved genes and in lowly expressed than in highly expressed genes. However, ∼20% AS GYNNGYs in humans and ∼3% in mice exhibit tissue-dependent regulation. Consistent with being functional, regulated GYNNGYs are more conserved than unregulated ones. And regulated GYNNGYs have distinctive sequence features which may confer regulation. Particularly, each regulated GYNNGY comprises two splice sites more resembling each other than unregulated GYNNGYs, and has more conserved downstream flanking intron. Intriguingly, most regulated GYNNGYs may tune gene expression through coupling with nonsense-mediated mRNA decay, rather than encode different proteins. In summary, AS at GYNNGY 5' splice sites is primarily splicing noise, and secondarily a way of regulation.

Acid-sensing ion channels promote the inflammation and migration of cultured rat microglia.

Microglia, the major immune cells in central nervous system, act as the surveillance and scavenger of immune defense and inflammatory response. Previous studies suggest that there might be close relationship between acid-sensing ion channels (ASICs) and inflammation, however, the exact role of ASICs in microglia during inflammation remains elusive. In the present study, we identified the existence of ASICs in the primary cultured rat microglia and explored their functions. By using reverse transcriptase polymerase chain reaction (RT-PCR), quantitative real-time PCR (qPCR), western blotting, and immunofluorescence experiments, we demonstrated that ASIC1, ASIC2a, and ASIC3 were existed in cultured and in situ rat microglia. After lipopolysaccharide (LPS) stimulation, the expressions of microglial ASIC1 and ASIC2a were upregulated. Meanwhile, ASIC-like currents and acid-induced elevation of intracellular calcium were increased, which could be inhibited by the nonspecific ASICs antagonist amiloride and specific homomeric ASIC1a blocker PcTx1. In addition, both inhibitors reduced the expression of inflammatory cytokines, including inducible nitric oxide synthase and cyclooxygenase 2 stimulated by LPS. Furthermore, we also observed significant increase in the expression of ASIC1 and ASIC2a in scrape-stimulated microglial migration. Amiloride and PcTx1 prevented the migration by inhibiting ERK phosphorylation. Taken together, these results suggest that ASICs participate in neuroinflammatory response, which will provide a novel therapeutic strategy for controlling the inflammation-relevant neuronal diseases.

Dynamic Tensile Behavior of Woven C/SiC Composite: Experiments and Strain-Rate-Dependent Yield Criterion.

This paper studies the yield behavior of a woven carbon-fiber-reinforced silicon-matrix (C/SiC) composite under dynamic tensile loading. Experiments were carried out to obtain the tensile properties of the C/SiC composite at a strain rate range of 2 × 10-5/s to 99.4/s. A strain-rate-dependent yield criterion based on the distortional strain energy density theory is established to describe the yield behavior. The interval uncertainty is considered for a more reliable yield prediction. Experimental results show that the yield stress, elastic modulus, and yield strain of the C/SiC composite grow with the increasing strain rate. The failure mode transitions from progressive crack extension to uneven fiber bundle breakage. The predicted results by the yield criterion match well with experimental data. Experimental results are enveloped within the uncertainty level of 45% in the critical distortional energy density, corresponding to an uncertainty of 14% and 11% in the yield stress and yield strain, respectively. With the support of the proposed strain-rate-dependent yield criterion, the yield behavior of the C/SiC composite under dynamic loading conditions can be predicted with reasonable accuracy.

A Multi-Scale Submodel Method for Fatigue Analysis of Braided Composite Structures.

A multi-scale fatigue analysis method for braided ceramic matrix composites (CMCs) based on sub-models is developed in this paper. The finite element shape function is used as the interpolation function for transferring the displacement information between the macro-scale and meso-scale models. The fatigue failure criterion based on the shear lag theory is used to implement the coupling calculation of the meso-scale and micro-scale. Combining the meso-scale cell model and the fatigue failure criterion based on the shear lag theory, the fatigue life of 2D SiC/SiC is analyzed. The analysis results are in good agreement with the experimental results, which proves the accuracy of the meso-scale cell model and the fatigue life calculation method. A multi-scale sub-model fatigue analysis method is used to study the fatigue damage of 2D SiC/SiC stiffened plates under random tension-tension loads. The influence of the sub-models at different positions in the macro-model element on the analysis results was analyzed. The results shows that the fatigue analysis method proposed in this paper takes into account the damage condition of the meso-structured of composite material, and at the same time has high calculation efficiency, and has low requirements for modeling of the macro finite element model, which can be better applied to the fatigue analysis of CMCs structure.

Assessing the activity of nonsense-mediated mRNA decay in lung cancer.

BACKGROUND: Inhibition of nonsense-mediated mRNA decay (NMD) in tumor cells can suppress tumor growth through expressing new antigens whose mRNAs otherwise are degraded by NMD. Thus NMD inhibition is a promising approach for developing cancer therapies. Apparently, the success of this approach relies on the basal NMD activity in cancer cells. If NMD is already strongly inhibited in tumors, the approach would not work. Therefore, it is crucial to assess NMD activity in cancers to forecast the efficacy of NMD-inhibition based therapy. METHODS: Here we develop three metrics using RNA-seq data to measure NMD activity, and apply them to a dataset consisting of 72 lung cancer (adenocarcinoma) patients. RESULTS: We show that these metrics have good correlations, and that the NMD activities in adenocarcinoma samples vary among patients: some cancerous samples show significantly stronger NMD activities than the normal tissues while some others show the opposite pattern. The variation of NMD activities among these samples may be partly explained by the varying expression of NMD effectors. CONCLUSIONS: In sum, NMD activity varies among lung cancerous samples, which forecasts varying efficacies of NMD-inhibition based therapy. The developed metrics can be further used in other cancer types to assess NMD activity.

The Use of Protein-Protein Interactions for the Analysis of the Associations between PM2.5 and Some Diseases.

Nowadays, pollution levels are rapidly increasing all over the world. One of the most important pollutants is PM2.5. It is known that the pollution environment may cause several problems, such as greenhouse effect and acid rain. Among them, the most important problem is that pollutants can induce a number of serious diseases. Some studies have reported that PM2.5 is an important etiologic factor for lung cancer. In this study, we extensively investigate the associations between PM2.5 and 22 disease classes recommended by Goh et al., such as respiratory diseases, cardiovascular diseases, and gastrointestinal diseases. The protein-protein interactions were used to measure the linkage between disease genes and genes that have been reported to be modulated by PM2.5. The results suggest that some diseases, such as diseases related to ear, nose, and throat and gastrointestinal, nutritional, renal, and cardiovascular diseases, are influenced by PM2.5 and some evidences were provided to confirm our results. For example, a total of 18 genes related to cardiovascular diseases are identified to be closely related to PM2.5, and cardiovascular disease relevant gene DSP is significantly related to PM2.5 gene JUP.

Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithm.

Thyroid cancer is a typical endocrine malignancy. In the past three decades, the continued growth of its incidence has made it urgent to design effective treatments to treat this disease. To this end, it is necessary to uncover the mechanism underlying this disease. Identification of thyroid cancer-related genes and chemicals is helpful to understand the mechanism of thyroid cancer. In this study, we generalized some previous methods to discover both disease genes and chemicals. The method was based on shortest path algorithm and applied to discover novel thyroid cancer-related genes and chemicals. The analysis of the final obtained genes and chemicals suggests that some of them are crucial to the formation and development of thyroid cancer. It is indicated that the proposed method is effective for the discovery of novel disease genes and chemicals.

Classifying ten types of major cancers based on reverse phase protein array profiles.

Gathering vast data sets of cancer genomes requires more efficient and autonomous procedures to classify cancer types and to discover a few essential genes to distinguish different cancers. Because protein expression is more stable than gene expression, we chose reverse phase protein array (RPPA) data, a powerful and robust antibody-based high-throughput approach for targeted proteomics, to perform our research. In this study, we proposed a computational framework to classify the patient samples into ten major cancer types based on the RPPA data using the SMO (Sequential minimal optimization) method. A careful feature selection procedure was employed to select 23 important proteins from the total of 187 proteins by mRMR (minimum Redundancy Maximum Relevance Feature Selection) and IFS (Incremental Feature Selection) on the training set. By using the 23 proteins, we successfully classified the ten cancer types with an MCC (Matthews Correlation Coefficient) of 0.904 on the training set, evaluated by 10-fold cross-validation, and an MCC of 0.936 on an independent test set. Further analysis of these 23 proteins was performed. Most of these proteins can present the hallmarks of cancer; Chk2, for example, plays an important role in the proliferation of cancer cells. Our analysis of these 23 proteins lends credence to the importance of these genes as indicators of cancer classification. We also believe our methods and findings may shed light on the discoveries of specific biomarkers of different types of cancers.

Integrative analysis reveals enhanced regulatory effects of human long intergenic non-coding RNAs in lung adenocarcinoma.

Although there is an accumulating appreciation of the key roles that long intergenic non-coding RNAs (lincRNAs) play in diverse cellular processes, our knowledge of how lincRNAs function in cancer remains sparse. Here, we present a comprehensive landscape of RNA-seq transcriptome profiles of lung adenocarcinomas and their paired normal counterparts to unravel gene regulation rules of lincRNAs. Consistent with previous findings of co-expression between neighboring protein-coding genes, lincRNAs were typically co-expressed with their neighboring genes, which was found in both cancerous and normal tissues. By building a mathematical model based on correlated gene expression, we distinguished an additional subset of lincRNAs termed "regulatory lincRNAs", representing their dominant roles in gene regulation. The number of regulatory lincRNAs was significantly higher in cancerous compared to normal tissues, and most of them positively regulated protein-coding genes in trans. Functional validation, using knockdown, determined that regulatory lincRNA, GAS5, affected its predicted protein-coding targets. Moreover, we discovered hundreds of differentially expressed regulatory lincRNAs with inclusion of some cancer-associated lincRNAs. Our integrated analysis reveals enhanced regulatory effects of lincRNAs and provides a resource for the study of regulatory lincRNAs that play critical roles in lung adenocarcinoma.