The most recent discoveries in the biochemical field are highlighting the increasingly important role of lipid droplets(LDs)in several regulatory mechanisms in living cells.LDs are dynamic organelles and therefore the...The most recent discoveries in the biochemical field are highlighting the increasingly important role of lipid droplets(LDs)in several regulatory mechanisms in living cells.LDs are dynamic organelles and therefore their complete characterization in terms of number,size,spatial positioning and relative distribution in the cell volume can shed light on the roles played by LDs.Until now,fluorescence microscopy and transmission electron microscopy are assessed as the gold standard methods for identifying LDs due to their high sensitivity and specificity.However,such methods generally only provide 2D assays and partial measurements.Furthermore,both can be destructive and with low productivity,thus limiting analysis of large cell numbers in a sample.Here we demonstrate for the first time the capability of 3D visualization and the full LD characterization in high-throughput with a tomographic phase-contrast flow-cytometer,by using ovarian cancer cells and monocyte cell lines as models.A strategy for retrieving significant parameters on spatial correlations and LD 3D positioning inside each cell volume is reported.The information gathered by this new method could allow more in depth understanding and lead to new discoveries on how LDs are correlated to cellular functions.展开更多
Background: Several recently published studies suggest that screening for symptoms could improve the early diagnosis of ovarian cancer. This report describes the development of a simple and reliable method of collecti...Background: Several recently published studies suggest that screening for symptoms could improve the early diagnosis of ovarian cancer. This report describes the development of a simple and reliable method of collecting symptom information in a primary care clinic. Methods: 1200 women, ages 40 - 87, completed several versions of a draft symptom index (SI) assessment form during their visits to a primary care clinic. Factors associated with a positive SI result were examined. Providers were surveyed about acceptability of the symptom screening procedures. Findings: Variation in the instructions provided to women influenced the rate at which women indicated having symptoms indicative of a positive SI, 5% had positive results when written instructions emphasized listing only current symptoms. Women coming to the clinic because of a current medical concern or problem did have higher rates of positive SI results, as did non-white women (p < 0.05). Acceptability by providers was high. Patients could independently complete the SI in under 5 minutes. One patient with a positive SI was diagnosed with ovarian cancer and none with a negative SI developed cancer. Interpretation: A quick paper and pencil form can be used to identify women with symptoms potentially indicative of ovarian cancer. Use of such a form for ovarian cancer screening purposes is acceptable to most women and providers in a primary care clinic setting.展开更多
Nuclear fusion holds great potential as a carbon-neutral means of electricity production.However,technical aspects of its implementation remain challenging.The real-time measurement of the fusion power released during...Nuclear fusion holds great potential as a carbon-neutral means of electricity production.However,technical aspects of its implementation remain challenging.The real-time measurement of the fusion power released during Deuterium-Tritium(DT)fusion is one such aspect.The use of tools from artificial intelligence may help to solve this issue.Recently,during experiments performed at the Joint European Torus,a novel method was developed to measure the fusion power in magnetic confinement fusion devices.Said method exploits the fact that gammarays released by the DT fusion reaction can be registered with a gamma-ray spectrometer.Expanding on this work,a machine learning algorithm was developed to estimate DT fusion power at ITER by use of the Radial Gamma-Ray Spectrometer(RGRS)measurements,as well as the magnetic equilibrium as an additional source of information.The algorithm was trained and tested on a set of 75 simulations of ITER DT plasma scenarios.By testing the algorithm by repeated 5-fold cross-validation,the average deviation of the estimated fusion power from the reference was found to be 0.32%,while the relative error had a standard deviation of 0.97%.When statistical fluctuations were included in the analysis,the lowest measurable fusion power resulted to be around 30MW,making the RGRS suitable for the fusion power measurement requirements at ITER.This project demonstrated that a machine learning approach leads to promising results when coupled with prior knowledge and the integration of various kinds of sensor and simulation data.This and related algorithms may eventually contribute to the development of fusion power as a reliable,carbon-neutral source of energy.展开更多
基金funded by the Italian Ministry of University and Research(PRIN 2017-Prot.2017N7R2CJ)Fondazione Cassa di Risparmio in Bologna(Italy)for the financial support to I.K.finalized to the acquisition of EVOS M5000。
文摘The most recent discoveries in the biochemical field are highlighting the increasingly important role of lipid droplets(LDs)in several regulatory mechanisms in living cells.LDs are dynamic organelles and therefore their complete characterization in terms of number,size,spatial positioning and relative distribution in the cell volume can shed light on the roles played by LDs.Until now,fluorescence microscopy and transmission electron microscopy are assessed as the gold standard methods for identifying LDs due to their high sensitivity and specificity.However,such methods generally only provide 2D assays and partial measurements.Furthermore,both can be destructive and with low productivity,thus limiting analysis of large cell numbers in a sample.Here we demonstrate for the first time the capability of 3D visualization and the full LD characterization in high-throughput with a tomographic phase-contrast flow-cytometer,by using ovarian cancer cells and monocyte cell lines as models.A strategy for retrieving significant parameters on spatial correlations and LD 3D positioning inside each cell volume is reported.The information gathered by this new method could allow more in depth understanding and lead to new discoveries on how LDs are correlated to cellular functions.
文摘Background: Several recently published studies suggest that screening for symptoms could improve the early diagnosis of ovarian cancer. This report describes the development of a simple and reliable method of collecting symptom information in a primary care clinic. Methods: 1200 women, ages 40 - 87, completed several versions of a draft symptom index (SI) assessment form during their visits to a primary care clinic. Factors associated with a positive SI result were examined. Providers were surveyed about acceptability of the symptom screening procedures. Findings: Variation in the instructions provided to women influenced the rate at which women indicated having symptoms indicative of a positive SI, 5% had positive results when written instructions emphasized listing only current symptoms. Women coming to the clinic because of a current medical concern or problem did have higher rates of positive SI results, as did non-white women (p < 0.05). Acceptability by providers was high. Patients could independently complete the SI in under 5 minutes. One patient with a positive SI was diagnosed with ovarian cancer and none with a negative SI developed cancer. Interpretation: A quick paper and pencil form can be used to identify women with symptoms potentially indicative of ovarian cancer. Use of such a form for ovarian cancer screening purposes is acceptable to most women and providers in a primary care clinic setting.
文摘Nuclear fusion holds great potential as a carbon-neutral means of electricity production.However,technical aspects of its implementation remain challenging.The real-time measurement of the fusion power released during Deuterium-Tritium(DT)fusion is one such aspect.The use of tools from artificial intelligence may help to solve this issue.Recently,during experiments performed at the Joint European Torus,a novel method was developed to measure the fusion power in magnetic confinement fusion devices.Said method exploits the fact that gammarays released by the DT fusion reaction can be registered with a gamma-ray spectrometer.Expanding on this work,a machine learning algorithm was developed to estimate DT fusion power at ITER by use of the Radial Gamma-Ray Spectrometer(RGRS)measurements,as well as the magnetic equilibrium as an additional source of information.The algorithm was trained and tested on a set of 75 simulations of ITER DT plasma scenarios.By testing the algorithm by repeated 5-fold cross-validation,the average deviation of the estimated fusion power from the reference was found to be 0.32%,while the relative error had a standard deviation of 0.97%.When statistical fluctuations were included in the analysis,the lowest measurable fusion power resulted to be around 30MW,making the RGRS suitable for the fusion power measurement requirements at ITER.This project demonstrated that a machine learning approach leads to promising results when coupled with prior knowledge and the integration of various kinds of sensor and simulation data.This and related algorithms may eventually contribute to the development of fusion power as a reliable,carbon-neutral source of energy.
