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Lung cancer is one of the most common types of carcinoma worldwide. Cigarette smoking is considered the leading cause of lung cancer. Aberrant expression of several YT521-B homology (YTH) family proteins has been reported to be closely associated with multiple cancer types. The present study aims to evaluate the function and regulatory mechanisms of the N6-methyladenosine (m6A) reader protein YTH domain containing 2 (YTHDC2) by in vitro, in vivo and bioinformatics analyses. The results revealed that YTHDC2 was reduced in lung cancer and cigarette smoke-exposed cells. Notably, bioinformatics and tissue arrays analysis demonstrated that decreased YTHDC2 was highly associated with smoking history, pathological stage, invasion depth, lymph node metastasis and poor outcomes. The in vivo and in vitro studies revealed that YTHDC2 overexpression inhibited the proliferation and migration of lung cancer cells as well as tumor growth in nude mice. Furthermore, YTHDC2 decreased expression was modulated by copy number deletion in lung cancer. Importantly, the cylindromatosis (CYLD)/NF-κB pathways were confirmed as the downstream signaling of YTHDC2, and this axis was mediated by m6A modification. The present results indicated that smoking-related downregulation of YTHDC2 was associated with enhanced proliferation and migration in lung cancer cells, and appeared to be regulated by DNA copy number variation. Importantly, YTHDC2 functions as a tumor suppressor through the CYLD/NF-κB signaling pathway, which is mediated by m6A modification.

As environmental pollutants and possible carcinogens, carbon nanotubes (CNTs) have recently been found to induce carcinogenesis and tumor metastasis after long-term pulmonary exposure. However, whether CNT-induced carcinogenesis can be inherited and last for generations remains unclear. Herein, postchronic single-walled carbon nanotubes (SWCNTs) exposed human lung cell model (BEAS-2B cells) are established to investigate SWCNT-induced carcinogenesis. At a tolerated sublethal dose level, postchronic SWCNT exposure significantly increases the migration and invasion abilities of BEAS-2B cells, leading to malignant cell transformation. Notably, the malignant transformation of BEAS-2B cells is irreversible within a 60 day recovery period after SWCNT exposure, and the malignant transformation activities of cells gradually increase during the recovery period. Moreover, these transformed cells promote carcinogenesis in vivo, accompanied by a raised level of biomarkers of lung adenocarcinoma. Further mechanism analyses reveal that postchronic exposure to SWCNTs causes substantial DNA methylation and transcriptome dysregulation of BEAS-2B cells. Subsequent enrichment and clinical database analyses reveal that differentially expressed/methylated genes of BEAS-2B cells are enriched in cancer-related biological pathways. These results not only demonstrate that postchronic SWCNT-exposure-induced carcinogenesis is heritable but also uncover a mechanism from the perspective of DNA methylation.

Bisphenols are important industrial materials for example for the production of plastics, but are also well known for their adverse health effects, in particular bisphenol A (BPA) is an endocrine disruptor. The widespread use of plastics has raised concerns. Therefore, the removal of bisphenols from wastewater has sparked the interest of the scientific community. This work introduces a novel hybrid technique of micro-nano bubbles assisted laccase (MNB-Lac) to degrade bisphenols in water. The feasibility of MNB-Lac using BPA as a model contaminant was evaluated by comparing with MNB, Lac, ultrasound (UL), UL-Lac, and UL-MNB-Lac. Comprehensive investigations were carried out to understand the specific influences of key process parameters including the initial pollutant concentration, temperature, air intake, pH, outlet pipe length, and Lac concentration on BPA degradation. The alkaline environment and extended length of outlet pipe could improve the degradation efficiency further. MNB-Lac exhibited 2.3–6.2 folds higher BPA degradation and less time than the other above process under the optimal parameters. The mechanism of MNB-Lac revealed that the generation of hydroxyl radical, high O2 solubility, and high mass transfer efficiency induced by MNB play important roles on enhancing the degradation catalyzed by Lac. MNB-Lac was successfully used for treating bisphenol B, bisphenol C, and the mixture of three bisphenols with high removal efficiency. Subsequently, these degradation products were analyzed by GC–MS. MNB-Lac potentially represents an innovative technology with considerable advantages in contaminant cleanup and time efficiency for treating phenolic contaminated water. Furthermore, the findings provide new insights into the enhancement of the performance of an oxidizing enzyme by introducing MNB technology.

Cigarette smoking is the leading cause of all histological types of lung cancer, and the role that microRNAs (miRNAs) serve in its pathogenesis is being increasingly recognized. The aim of the present study was to investigate the role of miR‑200b on migration in cigarette smoke‑induced malignant transformed cells. In the present study, miR‑200b expression was found to be increased in cigarette smoke (CS)‑exposed BEAS‑2B cells, lung cancer cell lines and tumor tissue samples. Using wound healing and Transwell migration assays, the migratory ability was shown to be increased in miR‑200b‑overexpressing cells, whereas miR‑200b knockdown resulted in reduced migration. Additionally, the expression of E‑Cadherin was downregulated, whereas that of N‑Cadherin was upregulated in miR‑200b mimic‑transfected cells, suggesting an increase in epithelial‑mesenchymal transition. Downstream, using four target gene prediction tools, six target genes of miR‑200b were predicted, amongst which, ETS proto‑oncogene 1 transcription factor (ETS1) was shown to be significantly associated with tumor invasion depth and negatively associated with miR‑200b expression. The interaction between miR‑200b and ETS1 was confirmed using a dual‑luciferase reporter assay. Using rescue experiments, the increased migratory ability of the miR‑200b‑overexpressing cells was reversed by ETS1 overexpression. In summary, this study showed that miR‑200b overexpression serves a carcinogenic role and promotes the migration of BEAS‑2B cells following long‑term exposure to CS by targeting ETS1.

Objective This study aimed to elucidate the possible mechanism of Gest-Aid Plus Oral Liquid (GAP) on healthcare function. Method Ultrahigh-performance liquid chromatography–quadrupole time-of-flight mass spectrometry-based metabolomics and 16S rDNA sequencing of gut microbiota were performed on serum and fecal samples of GAP and control rats. Additionally, short-chain fatty acids (SCFAs) and inflammatory cytokines in fecal samples were determined through gas chromatography–mass spectrometry and enzyme-linked immunosorbent assay kits. Result Metabolomics discovered 41 metabolites, which mainly involved amino acid metabolism, lipid metabolism, coenzyme factors, and vitamin metabolism. Administration of GAP increased abundance of Prevotella_9, Alloprevotella, Blautia, Phascolarctobacterium, Parabacteroides, and Fusicatenibacter, and six SCFAs were increased in the GAP group. Measurement of inflammatory cytokines showed that GAP had an anti-inflammatory effect in rats. Conclusion Administration of GAP greatly affects the aspartate metabolism and microecology of rats, enhances intestinal motility and gut barrier integrity and anti-inflammation. These findings not only have possible implications for further application of GAP, but also provide a link between the gut microbiome, SCFAs, inflammation and serum metabolites in rats.

Background: Lung cancer is one of the most common carcinomas worldwide. Cigarette smoking is considered as the leading cause of lung cancer. The aberrant expression of several YTH family proteins has been reported to be closely associated with several cancers. This study aims to evaluate the expression profile and function of YTHDC2, an RNA m6 A methylation reader protein, by in vitro and animal experiments and bioinformatics analysis. Methods: The expression of YTHDC2 gene and protein was analyzed by the GEPIA online tool and Gene Expression Omnibus (GEO) database, as well as immunohistochemistry of tissue arrays. The YTHDC2 expression in cigarette smoke-exposed cells was examined by RT-qPCR and western blot assays. The TCGA datasets and proteomic analysis of YTHDC2 knockdown cells were used to predict the clinical significance and biological function of YTHDC2. Functional assays, including wound healing, transwell migration, cell cycle and cell proliferation, were used to determine the biological role of YTHDC2 in lung cancer. The xenograft animal model further validated the tumor suppressor effect of YTHDC2 in vivo. The copy number variation of YTHDC2 was analyzed by TCGA datasets and TaqMan copy number assay. Results: YTHDC2 was significantly low-expressed in lung cancer and cigarette smoke-exposed cells. Notably, decreased YTHDC2 was found highly associated with smoking history, pathological stage, invasion depth, and lymph node metastasis, as well as poor outcomes. The YTHDC2 related genes and proteins were enriched in several cancer-related pathways, and several tumor suppressor genes were found positively correlated with YTHDC2. The in vivo and in vitro study revealed that overexpression of YTHDC2 inhibited the proliferation and migration ability of lung cancer cells, as well as the tumor growth in nude mice. Besides, a significant positive correlation between the copy number and expression level of YTHDC2 was found in lung cancer. Conclusions: Our results indicated that the smoking-related down-regulation of YTHDC2 was associated with enhanced proliferation and migration of lung cancer cells and appeared to be regulated by DNA copy number variation. Importantly, YTHDC2 may serve as a tumor suppressor gene and a favorable prognostic indicator in lung cancer patients.

The trends in cochlear implantation candidacy and benefit have changed rapidly in the last two decades. It is now widely accepted that early implantation leads to better postimplant outcomes. Although some generalizations can be made about postimplant auditory and language performance, neural mechanisms need to be studied to predict individual prognosis.The aim of this study was to use functional magnetic resonance imaging (fMRI) to identify preimplant neuroimaging biomarkers that predict children’s postimplant auditory and language outcomes as measured by parental observation/reports.This is a pre–post correlational measures study.Twelve possible cochlear implant candidates with bilateral severe to profound hearing loss were recruited via referrals for a clinical magnetic resonance imaging to ensure structural integrity of the auditory nerve for implantation.Participants underwent cochlear implantation at a mean age of 19.4 mo. All children used the advanced combination encoder strategy (ACE, Cochlear Corporation™, Nucleus® Freedom cochlear implants). Three participants received an implant in the right ear; one in the left ear whereas eight participants received bilateral implants. Participants’ preimplant neuronal activation in response to two auditory stimuli was studied using an event-related fMRI method.Blood oxygen level dependent contrast maps were calculated for speech and noise stimuli. The general linear model was used to create z-maps. The Auditory Skills Checklist (ASC) and the SKI-HI Language Development Scale (SKI-HI LDS) were administered to the parents 2 yr after implantation. A nonparametric correlation analysis was implemented between preimplant fMRI activation and postimplant auditory and language outcomes based on ASC and SKI-HI LDS. Statistical Parametric Mapping software was used to create regression maps between fMRI activation and scores on the aforementioned tests. Regression maps were overlaid on the Imaging Research Center infant template and visualized in MRIcro.Regression maps revealed two clusters of brain activation for the speech versus silence contrast and five clusters for the noise versus silence contrast that were significantly correlated with the parental reports. These clusters included auditory and extra-auditory regions such as the middle temporal gyrus, supramarginal gyrus, precuneus, cingulate gyrus, middle frontal gyrus, subgyral, and middle occipital gyrus. Both positive and negative correlations were observed. Correlation values for the different clusters ranged from −0.90 to 0.95 and were significant at a corrected p value of The results of the present study suggest that (1) fMRI can be used to identify neuroimaging biomarkers of auditory and language performance before implantation and (2) activation in certain brain regions may be predictive of postimplant auditory and language performance as measured by parental observation/reports.

Additional file 1. Additional tables.

OBJECTIVES Despite the positive effects of cochlear implantation, postimplant variability in speech perception and oral language outcomes is still difficult to predict. The aim of this study was to identify neuroimaging biomarkers of postimplant speech perception and oral language performance in children with hearing loss who receive a cochlear implant. The authors hypothesized positive correlations between blood oxygen level-dependent functional magnetic resonance imaging (fMRI) activation in brain regions related to auditory language processing and attention and scores on the Clinical Evaluation of Language Fundamentals-Preschool, Second Edition (CELF-P2) and the Early Speech Perception Test for Profoundly Hearing-Impaired Children (ESP), in children with congenital hearing loss. DESIGN Eleven children with congenital hearing loss were recruited for the present study based on referral for clinical MRI and other inclusion criteria. All participants were

Near-infrared (NIR) spectroscopy was applied to reagent-free quantitative analysis of polysaccharide of a brand product of proprietary Chinese medicine (PCM) oral solution samples. A novel method, called absorbance upper optimization partial least squares (AUO-PLS), was proposed and successfully applied to the wavelength selection. Based on varied partitioning of the calibration and prediction sample sets, the parameter optimization was performed to achieve stability. On the basis of the AUO-PLS method, the selected upper bound of appropriate absorbance was 1.53 and the corresponding wavebands combination was 400 - 1880 & 2088 - 2346 nm. With the use of random validation samples excluded from the modeling process, the root-mean-square error and correlation coefficient of prediction for polysaccharide were 27.09 mg·L-1 and 0.888, respectively. The results indicate that the NIR prediction values are close to those of the measured values. NIR spectroscopy combined with AUO-PLS method provided a promising tool for quantification of the polysaccharide for PCM oral solution and this technique is rapid and simple when compared with conventional methods.

Psoriasis is an immune-mediated chronic inflammatory skin disease. Although its pathogenesis is not fully understood, Th17 cells and the cytokines they produce, such as IL-17, IL-22 and IL-23, play critical roles in the pathogenesis of psoriasis. Evidence has demonstrated that psoriasis has some common features, including immune responses (due to Th17 cells) and inflammatory cytokine profiles, with systematic diseases including inflammatory bowel diseases (IBDs) and obesity. Recently, studies have demonstrated that the gut microbiota plays a crucial role in host homoeostasis and immune response, particular in Th17 cells, but the role of the gut microbiota in psoriasis remains unclear. To study the relationship between gut microbiota and psoriasis, we analysed microbiota profiles in psoriasis using a 16S rDNA sequencing platform, and we found that the abundance of Akkermansia muciniphila was significantly reduced in patients with psoriasis. A. muciniphila is believed to have an important function in the pathogenesis of IBD and obesity; therefore, A. muciniphila, which is an indicator of health status, may be a key node for psoriasis as well as IBD and obesity. Taken together, our study identified that gut microbiota signature and function are significantly altered in the gut of patients with psoriasis, which provides a novel angle to understanding the pathogenesis of psoriasis.

Accurately predicting essential genes is important in many aspects of biology, medicine and bioengineering. In previous research, we have developed a machine learning based integrative algorithm to predict essential genes in bacterial species. This algorithm lends itself to two approaches for predicting essential genes: learning the traits from known essential genes in the target organism, or transferring essential gene annotations from a closely related model organism. However, for an understudied microbe, each approach has its potential limitations. The first is constricted by the often small number of known essential genes. The second is limited by the availability of model organisms and by evolutionary distance. In this study, we aim to determine the optimal strategy for predicting essential genes by examining four microbes with well-characterized essential genes. Our results suggest that, unless the known essential genes are few, learning from the known essential genes in the target organism usually outperforms transferring essential gene annotations from a related model organism. In fact, the required number of known essential genes is surprisingly small to make accurate predictions. In prokaryotes, when the number of known essential genes is greater than 2% of total genes, this approach already comes close to its optimal performance. In eukaryotes, achieving the same best performance requires over 4% of total genes, reflecting the increased complexity of eukaryotic organisms. Combining the two approaches resulted in an increased performance when the known essential genes are few. Our investigation thus provides key information on accurately predicting essential genes and will greatly facilitate annotations of microbial genomes.

Protein phosphorylation is an important step in many biological processes, such as cell cycles, membrane transport, apoptosis, etc. In order to obtain more useful information about protein phosphorylation, it is necessary to develop a robust, stable, and accurate approach to predict phosphorylation sites. Although there exist a number of approaches to predict phosphorylation sites, such as those based on neural network and the support vector machine, they only use a single classifier. In general, the prediction results obtained by these approaches are not very stable and robust. In this paper, we design a new classifier ensemble approach called Bagging-AdaBoost ensemble (BAE) for the prediction of eukaryotic protein phosphorylation sites, which incorporates the bagging technique and the AdaBoost technique into the classifier framework to improve the accuracy, stability, and robustness of the final result. To our knowledge, this is the first time in which a combined bagging and boosting ensemble approach is applied to predict phosphorylation sites. Our prediction system based on BAE focuses on six kinase families: CDK, CK2, MAPK, PKA, PKC, and SRC. BAE achieves good performance in these six families, and the accuracies of the prediction system for these families are 0.8634, 0.8721, 0.8542 , 0.8537, 0.8052, and 0.7432, respectively.

Knowledge of influencing factors of industrial carbon emissions (ICE) is crucial to the efforts of reducing anthropogenic greenhouse gas emissions. In this paper, main factors responsible for the ICE in Shanghai between 1996 and 2007 were identified and quantitatively analyzed using the Log-Mean Divisia Index method. It was found that the industrial output was the main driving force of ICE. The decline in energy intensity and the adjustment of energy and industrial structure are major determinants for reduction of ICE, with the former alone accounting for 90% of the reduction. To better investigate the relative contribution of different industrial sectors and their changes over time, we divided the study period into two equal time intervals and analyzed some high-carbon emission sectors. The results suggested that the intensity of energy use should be reduced further, for it was far higher than the world average. Adjustment of industrial structure by developing low-carbon emission industries is more crucial than energy mix.

Introduction We developed a machine learning model to predict whether or not a cochlear implant (CI) candidate will develop effective language skills within 2 years after the CI surgery by using the pre-implant brain fMRI data from the candidate. Methods The language performance was measured 2 years after the CI surgery by the Clinical Evaluation of Language Fundamentals-Preschool, Second Edition (CELF-P2). Based on the CELF-P2 scores, the CI recipients were designated as either effective or ineffective CI users. For feature extraction from the fMRI data, we constructed contrast maps using the general linear model, and then utilized the Bag-of-Words (BoW) approach that we previously published to convert the contrast maps into feature vectors. We trained both supervised models and semi-supervised models to classify CI users as effective or ineffective. Results Compared with the conventional feature extraction approach, which used each single voxel as a feature, our BoW approach gave rise to much better performance for the classification of effective versus ineffective CI users. The semi-supervised model with the feature set extracted by the BoW approach from the contrast of speech versus silence achieved a leave-one-out cross-validation AUC as high as 0.97. Recursive feature elimination unexpectedly revealed that two features were sufficient to provide highly accurate classification of effective versus ineffective CI users based on our current dataset. Conclusion We have validated the hypothesis that pre-implant cortical activation patterns revealed by fMRI during infancy correlate with language performance 2 years after cochlear implantation. The two brain regions highlighted by our classifier are potential biomarkers for the prediction of CI outcomes. Our study also demonstrated the superiority of the semi-supervised model over the supervised model. It is always worthwhile to try a semi-supervised model when unlabeled data are available.

Detecting brain structural changes from magnetic resonance (MR) images can facilitate early diagnosis and treatment of neurological and psychiatric diseases. Many existing methods require an accurate deformation registration, which is difficult to achieve and therefore prevents them from obtaining high accuracy. We develop a novel local feature based support vector machine (SVM) approach to detect brain structural changes as potential biomarkers. This approach does not require deformation registration and thus is less influenced by artifacts such as image distortion. We represent the anatomical structures based on scale invariant feature transform (SIFT). Likelihood scores calculated using feature-based morphometry is used as the criterion to categorize image features into three classes (healthy, patient and noise). Regional SVMs are trained to classify the three types of image features in different brain regions. Only healthy and patient features are used to predict the disease status of new brain images. An ensemble classifier is built from the regional SVMs to obtain better prediction accuracy. We apply this approach to 3D MR images of Alzheimer's disease, Parkinson's disease and bipolar disorder. The classification accuracy ranges between 70% and 87%. The highly predictive disease-related regions, which represent significant anatomical differences between the healthy and diseased, are shown in heat maps. The common and disease-specific brain regions are identified by comparing the highly predictive regions in each disease. All of the top-ranked regions are supported by literature. Thus, this approach will be a promising tool for assisting automatic diagnosis and advancing mechanism studies of neurological and psychiatric diseases.

Networks, where nodes/vertices denote entities and links/edges denote associations, provide a unified representation for a variety of complex systems, from social relationships to molecular interactions. In general, network analysis has proved useful in applications such as prediction of protein function, guiding the design of wet-lab experiments, and discovery of biomarkers of disease. Driven by the availability of large-scale data sets and appropriate informatics’ tools, the research community has begun to apply network analysis of these data to define underlying causes of pediatric diseases. This will almost certainly lead to more effective strategies for prevention and treatment. In this chapter, we will introduce classic and the state-of-the-art network analysis methodologies, approaches and their applications. We then provide three examples of recent research, where network analysis is being applied in pediatrics. These include identification of targets for development of new drugs to treat Pseudomonas infections, protein interactions in high-density lipoprotein particles that underlie development of cardiovascular disease, and analysis of networks in brain that may underlie conditions such as Attention Deficit Hyperactivity Disorder.

Storm surge disaster is very serious in China's coastal areas. Firstly, storm surge disaster vulnerability index (Storm Surge Vulnerability Index, SSVI) is built to assess the physical vulnerability of storm surge disasters of China's coastal provinces by learning form the assessment method of Coastal Vulnerability Index (CVI). Then, the physical vulnerability of storm surge disasters is assessed using additive model and the level of SSVI is divided, according to the storm surge disaster loss data during 1990 to 2009 of eleven provinces in coastal region. The results show that there is a large inter-annual and phase variation of SSVI in eleven provinces of China's coastal region, and generally the level of SSVI in southeastern provinces is greater than the northern.

Given the essential role of protein in life processes, computational assignment of protein functions has become one of the most important tasks in the area of bioinformatics. While Gene Ontology (GO) has been widely used in functional annotation, new approaches to address the problem of annotation incompleteness, which can leverage the support of the GO framework, are imminently required. In this paper, two new models are proposed to predict GO terms from domain content: a Correlation Coefficient based model (CC-M) and a Support Vector Machine (SVM) based model (SVM-M). We have developed our models in the form of predictors for all GO terms with manually curated annotations. In comparison with the Bayesian probabilistic approach published previously [Forslund et al., 2008], our methods are demonstrated to have better capability in dealing with incomplete training data. In particular, the CC-M method is suitable for GO terms with extremely low occurrence frequency, and the SVM-M method for the remaining GO terms. Therefore, CC-M and SVM-M are subsequently integrated into a single model (CC-SVM), with their respective advantages combined.

Within the framework of credibility theory, several fuzzy portfolio selection models have been researched such as mean-variance model, chance constrained programming model, entropy optimization model and so on. However, all of them are proposed in the forms of single-objective programming, and there is no investigation on the transaction costs between the new portfolio and the existing one. In this paper, a fuzzy multi-objective mean-variance-skewness model with transaction costs is presented. In order to solve this model, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, relevance vector machine and fuzzy simulation techniques, where the relevance vector machine is used to approximate the expected value, variance and skewness of portfolio returns and the fuzzy simulation is used to generate the training data for relevance vector machine.

In this research, we developed a robust two-layer classifier that can accurately classify normal hearing (NH) from hearing impaired (HI) infants with congenital sensori-neural hearing loss (SNHL) based on their Magnetic Resonance (MR) images. Unlike traditional methods that examine the intensity of each single voxel, we extracted high-level features to characterize the structural MR images (sMRI) and functional MR images (fMRI). The Scale Invariant Feature Transform (SIFT) algorithm was employed to detect and describe the local features in sMRI. For fMRI, we constructed contrast maps and detected the most activated/de-activated regions in each individual. Based on those salient regions occurring across individuals, the bag-of-words strategy was introduced to vectorize the contrast maps. We then used a two-layer model to integrate these two types of features together. With the leave-one-out cross-validation approach, this integrated model achieved an AUC score of 0.90. Additionally, our algorithm highlighted several important brain regions that differentiated between NH and HI children. Some of these regions, e.g. planum temporale and angular gyrus, were well known auditory and visual language association regions. Others, e.g. the anterior cingulate cortex (ACC), were not necessarily expected to play a role in differentiating HI from NH children and provided a new understanding of brain function and of the disorder itself. These important brain regions provided clues about neuroimaging markers that may be relevant to the future use of functional neuroimaging to guide predictions about speech and language outcomes in HI infants who receive a cochlear implant. This type of prognostic information could be extremely useful and is currently not available to clinicians by any other means., Highlights • We probe brain structural and functional changes in hearing impaired (HI) infants. • We build a robust two-layer classifier that integrates sMRI and fMRI data. • This integrated model accurately separates HI from normal infants (AUC 0.9). • Our method detects important brain regions different between HI and normal infants. • Our method can include diverse types of data and be applied to other diseases.