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Gastro-esophageal (GE) cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of GE cancers, to yield predictive response to the available therapies. Our study aims to identify leading genes that are differentially regulated in patients with these cancers. We explored the expression data for those genes whose protein products can be detected in the plasma using the Cancer Genome Atlas to identify leading genes that are differentially regulated in patients with GE cancers. Our work predicted several candidates as potential biomarkers for distinct stages of GE cancers, including previously identified CST1, INHBA, STMN1, whose expression correlated with cancer recurrence, or resistance to adjuvant therapies or surgery. To define the predictive accuracy of these genes as possible biomarkers, we constructed a co-expression network and performed complex network analysis to measure the importance of the genes in terms of a ratio of closeness centrality (RCC). Furthermore, to measure the significance of these differentially regulated genes, we constructed an SVM classifier using machine learning approach and verified these genes by using receiver operator characteristic (ROC) curve as an evaluation metric. The area under the curve measure was > 0.9 for both the overexpressed and downregulated genes suggesting the potential use and reliability of these candidates as biomarkers. In summary, we identified leading differentially expressed genes in GE cancers that can be detected in the plasma proteome. These genes have potential to become diagnostic and therapeutic biomarkers for early detection of cancer, recurrence following surgery and for development of targeted treatment.In this article, we propose SNG (single negative) metamaterial fabricated on Mg-Zn ferrite-based flexible microwave composites. Firstly, the flexible composites are synthesized by the sol-gel method having four different molecular compositions of MgxZn(1-x)Fe2O4, which are denoted as Mg20, Mg40, Mg60, and Mg80. The structural, morphological, and microwave properties of the synthesized flexible composites are analyzed using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and conventional dielectric assessment kit (DAK) to justify their possible application as dielectric substrate at microwave frequency regime. Eeyarestatin 1 Thus the average grain size is found from 20 to 24 nm, and the dielectric constants are 6.01, 5.10, 4.19, and 3.28, as well as loss tangents, are 0.002, 0.004, 0.006, and 0.008 for the prepared Mg-Zn ferrites, i.e., Mg20, Mg40, Mg60, and Mg80 respectively. Besides, the prepared low-cost Mg-Zn ferrite composites exhibit high flexibility and lightweight, which makes them a potential candidate as a metamaterial substrate. Furthermore, a single negative (SNG) metamaterial unit cell is fabricated on the prepared, flexible microwave composites, and their essential electromagnetic behaviors are observed. Very good effective medium ratios (EMR) vales are obtained from 14.65 to 18.47, which ensure the compactness of the fabricated prototypes with a physical dimension of 8 × 6.5 mm2. Also, the proposed materials have shown better performances comparing with conventional FR4 and RO4533 materials, and they have covered S-, C-, X-, Ku-, and K-band of microwave frequency region. Thus, the prepared, flexible SNG metamaterials on MgxZn(1-x)Fe2O4 composites are suitable for microwave and flexible technologies.Pathogenic genetic variants in the ATP7B gene cause Wilson disease, a recessive disorder of copper metabolism showing a significant variability in clinical phenotype. Promoter mutations have been rarely reported, and controversial data exist on the site of transcription initiation (the core promoter). We quantitatively investigated transcription initiation and found it to be located in immediate proximity of the translational start. The effects human single-nucleotide alterations of conserved bases in the core promoter on transcriptional activity were moderate, explaining why clearly pathogenic mutations within the core promoter have not been reported. Furthermore, the core promoter contains two frequent polymorphisms (rs148013251 and rs2277448) that could contribute to phenotypical variability in Wilson disease patients with incompletely inactivating mutations. However, neither polymorphism significantly modulated ATP7B expression in vitro, nor were copper household parameters in healthy probands affected. In summary, the investigations allowed to determine the biologically relevant site of ATP7B transcription initiation and demonstrated that genetic variations in this site, although being the focus of transcriptional activity, do not contribute significantly to Wilson disease pathogenesis.Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA's feature at the inter-residue level, we added an attention layer to the deep neural network. We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features. A further improvement was observed when an attention layer was used and even more when additional prediction tasks of bond angle predictions were added. The improvement of distance predictions were successfully transferred to achieve better protein tertiary structure modeling.Central Taiwan is among the most heavily polluted regions in Taiwan because of a complex mixing of local emissions from intense anthropogenic activities with natural dust. Long-range transport (LRT) of pollutants from outside Taiwan also contributes critically to the deterioration of air quality, especially during the northeast monsoon season. To identify the sources of particulate matter  less then  10 μm (PM10) in central Taiwan, this study performed several sampling campaigns, including three local events, one LRT event, and one dust storm event, during the northeast monsoon season of 2018/2019. The PM10 samples were analyzed for water-soluble ion and trace metal concentrations as well as Pb isotope ratios. Local sediments were also collected and analyzed to constrain chemical/isotopic signatures of natural sources. The Pb isotope data were interpreted together with the enrichment factors and elemental ratios of trace metals in PM10, and reanalysis data sets were used to delineate the sources of PM10 in central Taiwan.