Tectonic Controls on Gas Hydrate Distribution off SW Taiwan

The northern part of the South China Sea is characterized by widespread occurrence of bottom simulating reflectors (BSR) indicating the presence of marine gas hydrate. Because the area covers both a tectonically inactive passive margin and the termination of a subduction zone, the influence of tectonism on the dynamics of gas hydrate systems can be studied in this region. Geophysical data show that there are multiple thrust faults on the active margin while much fewer and smaller faults exist in the passive margin. This tectonic difference matches with a difference in the geophysical characteristics of the gas hydrate systems. High hydrate saturation derived from ocean bottom seismometer data and controlled source electromagnetic data and conspicuous high‐amplitude reflections in P‐Cable 3D seismic data above the BSR are found in the anticlinal ridges of the active margin. In contrast all geophysical evidence for the passive margin points to normal to low hydrate saturations. Geochemical analyses of gas samples collected at seep sites on the active margin show methane with heavy δ13C isotope composition, while gas collected at the passive margin shows light carbon isotope composition. Thus, we interpret the passive margin as a typical gas hydrate province fuelled by biogenic production of methane and the active margin gas hydrate system as a system that is fuelled not only by biogenic gas production but also by additional advection of thermogenic methane from the subduction system

A rapid and sensitive assay for quantification of siRNA efficiency and specificity

RNA Interference has rapidly emerged as an efficient procedure for knocking down gene expression in model systems. However, cross-reactivity, whereby multiple genes may be simultaneously targeted by a single short interfering RNA (siRNA), can potentially jeopardize correct interpretation of gene function. As such, it is essential to test the specificity of a siRNA prior to a full phenotypic analysis. To this end, we have adapted a reporter-based assay harnessing the sensitivity of luciferase activity to provide a quantitative readout of relative RNAi efficacy and specificity. We have tested different siRNAs directed against Thymosin β4 (Tβ4); determined their effectiveness at silencing Tβ4 and have both excluded off-target silencing of the Tβ4 homologue Thymosin β10 (Tβ10) and demonstrated partial knockdown of Tβ10 despite significant (12/23; 52%) sequence mismatch. This assay system is applicable to any RNAi study where there is a risk of targeting homologous genes and to the monitoring of off-target effects at the genome level following microarray expression profiling

Establishment of LIF-Dependent Human iPS Cells Closely Related to Basic FGF-Dependent Authentic iPS Cells

Human induced pluripotent stem cells (iPSCs) can be divided into a leukemia inhibitory factor (LIF)-dependent naïve type and a basic fibroblast growth factor (bFGF)-dependent primed type. Although the former are more undifferentiated than the latter, they require signal transduction inhibitors and sustained expression of the transgenes used for iPSC production. We used a transcriptionally enhanced version of OCT4 to establish LIF-dependent human iPSCs without the use of inhibitors and sustained transgene expression. These cells belong to the primed type of pluripotent stem cell, similar to bFGF-dependent iPSCs. Thus, the particular cytokine required for iPSC production does not necessarily define stem cell phenotypes as previously thought. It is likely that the bFGF and LIF signaling pathways converge on unidentified OCT4 target genes. These findings suggest that our LIF-dependent human iPSCs could provide a novel model to investigate the role of cytokine signaling in cellular reprogramming

Prediction of metastatic pheochromocytoma and paraganglioma:a machine learning modelling study using data from a cross-sectional cohort

BACKGROUND: Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field.METHODS: In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets.FINDINGS: Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p&lt;0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%.INTERPRETATION: Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up.FUNDING: Deutsche Forschungsgemeinschaft.</p

Prediction of metastatic pheochromocytoma and paraganglioma:a machine learning modelling study using data from a cross-sectional cohort

BACKGROUND: Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field.METHODS: In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets.FINDINGS: Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p&lt;0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%.INTERPRETATION: Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up.FUNDING: Deutsche Forschungsgemeinschaft.</p

Prediction of metastatic pheochromocytoma and paraganglioma:a machine learning modelling study using data from a cross-sectional cohort

BACKGROUND: Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field.METHODS: In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets.FINDINGS: Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p&lt;0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%.INTERPRETATION: Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up.FUNDING: Deutsche Forschungsgemeinschaft.</p

Prediction of metastatic pheochromocytoma and paraganglioma:a machine learning modelling study using data from a cross-sectional cohort

BACKGROUND: Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field.METHODS: In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets.FINDINGS: Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p&lt;0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%.INTERPRETATION: Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up.FUNDING: Deutsche Forschungsgemeinschaft.</p

Prediction of metastatic pheochromocytoma and paraganglioma:a machine learning modelling study using data from a cross-sectional cohort

BACKGROUND: Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field.METHODS: In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets.FINDINGS: Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p&lt;0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%.INTERPRETATION: Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up.FUNDING: Deutsche Forschungsgemeinschaft.</p

Prediction of metastatic pheochromocytoma and paraganglioma:a machine learning modelling study using data from a cross-sectional cohort

BACKGROUND: Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field.METHODS: In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets.FINDINGS: Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p&lt;0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%.INTERPRETATION: Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up.FUNDING: Deutsche Forschungsgemeinschaft.</p

Virus-free induction of pluripotency and subsequent excision of reprogramming factors

Reprogramming of somatic cells to pluripotency, thereby creating induced pluripotent stem (iPS) cells, promises to transform regenerative medicine. Most instances of direct reprogramming have been achieved by forced expression of defined factors using multiple viral vectors1-7. However, such iPS cells contain a large number of viral vector integrations1,8, any one of which could cause unpredictable genetic dysfunction. While c-Myc is dispensable for reprogramming9,10, complete elimination of the other exogenous factors is also desired since ectopic expression of either Oct4 or Klf4 can induce dysplasia11,12. Two transient transfection reprogramming methods have been published to address this issue13,14. However, the efficiency of either approach is extremely low, and neither has thus far been applied successfully to human cells. Here we show that non-viral transfection of a single multiprotein expression vector, which comprises the coding sequences of c-Myc​,​ Klf4​,​ Oct4 and Sox2 linked with 2A peptides, can reprogram both mouse and human fibroblasts. Moreover, the transgene can be removed once reprogramming has been achieved. iPS cells produced with this non-viral vector show robust expression of pluripotency markers, indicating a reprogrammed state confirmed functionally by in vitro differentiation assays and formation of adult chimeric mice. When the single vector reprogramming system was combined with a piggyBac transposon15,16 we succeeded in establishing reprogrammed human cell lines from embryonic fibroblasts with robust expression of pluripotency markers. This system minimizes genome modification in iPS cells and enables complete elimination of exogenous reprogramming factors, efficiently providing iPS cells that are applicable to regenerative medicine, drug screening and the establishment of disease models