Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online)

BACKGROUND: The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. RESULTS: Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO , to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. CONCLUSION: This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment

SirT1—A Sensor for Monitoring Self-Renewal and Aging Process in Retinal Stem Cells

Retinal stem cells bear potency of proliferation, self-renewal, and differentiation into many retinal cells. Utilizing appropriate sensors one can effectively detect the self-renewal and aging process abilities. Silencing information regulator (SirT1), a member of the sirtuin family, is a NAD-dependent histone deacetylase and an essential mediator for longevity in normal cells by calorie restriction. We firstly investigate the SirT1 mRNA expression in retinal stem cells from rats and 19 human eyes of different ages. Results revealed that SirT1 expression was significantly decreased in in vivo aged eyes, associated with poor self-renewal abilities. Additionally, SirT1 mRNA levels were dose-dependently increased in resveratrol- treated retinal stem cells. The expression of SirT1 on oxidative stress-induced damage was significantly decreased, negatively correlated with the level of intracellular reactive oxygen species production. Treatment with resveratrol could effectively further reduce oxidative stress induced by H2O2 treatment in retinal stem cells. Importantly, the anti-oxidant effects of resveratrol in H2O2-treated retinal stem cells were significantly abolished by knockdown of SirT1 expression (sh-SirT1). SirT1 expression provides a feasible sensor in assessing self-renewal and aging process in retinal stem cells. Resveratrol can prevent reactive oxygen species-induced damages via increased retinal SirT1 expression

The Atacama Large Millimeter/submillimeter Array (ALMA) Band-1 Receiver

The Atacama Large Millimeter/submillimeter Array(ALMA) Band 1 receiver covers the 35-50 GHz frequency band. Development of prototype receivers, including the key components and subsystems has been completed and two sets of prototype receivers were fully tested. We will provide an overview of the ALMA Band 1 science goals, and its requirements and design for use on the ALMA. The receiver development status will also be discussed and the infrastructure, integration, evaluation of fully-assembled band 1 receiver system will be covered. Finally, a discussion of the technical and management challenges encountered will be presented

International Validation of the SORG Machine-learning Algorithm for Predicting the Survival of Patients with Extremity Metastases Undergoing Surgical Treatment

Background The Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) estimate 90- day and 1-year survival in patients with long-bone metastases undergoing surgical treatment and have demonstrated good discriminatory ability on internal validation. However, the performance of a prediction model could potentially vary by race or region, and the SORG-MLA must be externally validated in an Asian cohort. Furthermore, the authors of the original developmental study did not consider the Eastern Cooperative Oncology Group (ECOG) performance status, a survival prognosticator repeatedly validated in other studies, in their algorithms because of missing data. Questions/purposes (1) Is the SORG-MLA generalizable to Taiwanese patients for predicting 90-day and 1-year mortality? (2) Is the ECOG score an independent factor associated with 90-day and 1-year mortality while controlling for SORG-MLA predictions? Methods All 356 patients who underwent surgery for long-bone metastases between 2014 and 2019 at one tertiary care center in Taiwan were included. Ninety-eight percent (349 of 356) of patients were of Han Chinese descent. The median (range) patient age was 61 years (25 to 95), 52% (184 of 356) were women, and the median BMI was 23 kg/m2 (13 to 39 kg/m2). The most common primary tumors were lung cancer (33% [116 of 356]) and breast cancer (16% [58 of 356]). Fifty-five percent (195 of 356) of patients presented with a complete pathologic fracture. Intramedullary nailing was the most commonly performed type of surgery (59% [210 of 356]), followed by plate screw fixation (23% [81 of 356]) and endoprosthetic reconstruction (18% [65 of 356]). Six patients were lost to follow-up within 90 days; 30 were lost to follow-up within 1 year. Eighty-five percent (301 of 356) of patients were followed until death or for at least 2 years. Survival was 82% (287 of 350) at 90 days and 49% (159 of 326) at 1 year. The model's performance metrics included discrimination (concordance index [c-index]), calibration (intercept and slope), and Brier score. In general, a c-index of 0.5 indicates random guess and a c-index of 0.8 denotes excellent discrimination. Calibration refers to the agreement between the predicted outcomes and the actual outcomes, with a perfect calibration having an intercept of 0 and a slope of 1. The Brier score of a prediction model must be compared with and ideally should be smaller than the score of the null model. A decision curve analysis was then performed for the 90-day and 1-year prediction models to evaluate their net benefit across a range of different threshold probabilities. A multivariate logistic regression analysis was used to evaluate whether the ECOG score was an independent prognosticator while controlling for the SORG-MLA's predictions. We did not perform retraining/recalibration because we were not trying to update the SORG-MLA algorithm in this study. Results The SORG-MLA had good discriminatory ability at both timepoints, with a c-index of 0.80 (95% confidence interval 0.74 to 0.86) for 90-day survival prediction and a c-index of 0.84 (95% CI 0.80 to 0.89) for 1-year survival prediction. However, the calibration analysis showed that the SORG-MLAs tended to underestimate Taiwanese patients' survival (90-day survival prediction: Calibration intercept 0.78 [95% CI 0.46 to 1.10], calibration slope 0.74 [95% CI 0.53 to 0.96]; 1-year survival prediction: Calibration intercept 0.75 [95% CI 0.49 to 1.00], calibration slope 1.22 [95% CI 0.95 to 1.49]). The Brier score of the 90-day and 1-year SORG-MLA prediction models was lower than their respective null model (0.12 versus 0.16 for 90-day prediction; 0.16 versus 0.25 for 1-year prediction), indicating good overall performance of SORG-MLAs at these two timepoints. Decision curve analysis showed SORG-MLAs provided net benefits when threshold probabilities ranged from 0.40 to 0.95 for 90-day survival prediction and from 0.15 to 1.0 for 1-year prediction. The ECOG score was an independent factor associated with 90-day mortality (odds ratio 1.94 [95% CI 1.01 to 3.73]) but not 1-year mortality (OR 1.07 [95% CI 0.53 to 2.17]) after controlling for SORG-MLA predictions for 90-day and 1- year survival, respectively. Conclusion SORG-MLAs retained good discriminatory ability in Taiwanese patients with long-bone metastases, although their actual survival time was slightly underestimated. More international validation and incremental value studies that address factors such as the ECOG score are warranted to refine the algorithms, which can be freely accessed online at https://sorg-apps.shinyapps. io/extremitymetssurvival/

Synthesis and applications of porous non-silica metal oxide submicrospheres

© 2016 Royal Society of Chemistry. Nowadays the development of submicroscale products of specific size and morphology that feature a high surface area to volume ratio, well-developed and accessible porosity for adsorbates and reactants, and are non-toxic, biocompatible, thermally stable and suitable as synergetic supports for precious metal catalysts is of great importance for many advanced applications. Complex porous non-silica metal oxide submicrospheres constitute an important class of materials that fulfill all these qualities and in addition, they are relatively easy to synthesize. This review presents a comprehensive appraisal of the methods used for the synthesis of a wide range of porous non-silica metal oxide particles of spherical morphology such as porous solid spheres, core-shell and yolk-shell particles as well as single-shell and multi-shell particles. In particular, hydrothermal and low temperature solution precipitation methods, which both include various structure developing strategies such as hard templating, soft templating, hydrolysis, or those taking advantage of Ostwald ripening and the Kirkendall effect, are reviewed. In addition, a critical assessment of the effects of different experimental parameters such as reaction time, reaction temperature, calcination, pH and the type of reactants and solvents on the structure of the final products is presented. Finally, the practical usefulness of complex porous non-silica metal oxide submicrospheres in sensing, catalysis, biomedical, environmental and energy-related applications is presented

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe