Individual risk and prognostic value prediction by machine learning for distant metastasis in pulmonary sarcomatoid carcinoma: a large cohort study based on the SEER database and the Chinese population

BackgroundThis study aimed to develop diagnostic and prognostic models for patients with pulmonary sarcomatoid carcinoma (PSC) and distant metastasis (DM).MethodsPatients from the Surveillance, Epidemiology, and End Results (SEER) database were divided into a training set and internal test set at a ratio of 7 to 3, while those from the Chinese hospital were assigned to the external test set, to develop the diagnostic model for DM. Univariate logistic regression was employed in the training set to screen for DM-related risk factors, which were included into six machine learning (ML) models. Furthermore, patients from the SEER database were randomly divided into a training set and validation set at a ratio of 7 to 3 to develop the prognostic model which predicts survival of patients PSC with DM. Univariate and multivariate Cox regression analyses have also been performed in the training set to identify independent factors, and a prognostic nomogram for cancer-specific survival (CSS) for PSC patients with DM.ResultsFor the diagnostic model for DM, 589 patients with PSC in the training set, 255 patients in the internal and 94 patients in the external test set were eventually enrolled. The extreme gradient boosting (XGB) algorithm performed best on the external test set with an area under the curve (AUC) of 0.821. For the prognostic model, 270 PSC patients with DM in the training and 117 patients in the test set were enrolled. The nomogram displayed precise accuracy with AUC of 0.803 for 3-month CSS and 0.869 for 6-month CSS in the test set.ConclusionThe ML model accurately identified individuals at high risk for DM who needed more careful follow-up, including appropriate preventative therapeutic strategies. The prognostic nomogram accurately predicted CSS in PSC patients with DM

Density Matrix in Quantum Mechanics and Distinctness of Ensembles Having the Same Compressed Density Matrix

We clarify different definitions of the density matrix by proposing the use of different names, the full density matrix for a single-closed quantum system, the compressed density matrix for the averaged single molecule state from an ensemble of molecules, and the reduced density matrix for a part of an entangled quantum system, respectively. We show that ensembles with the same compressed density matrix can be physically distinguished by observing fluctuations of various observables. This is in contrast to a general belief that ensembles with the same compressed density matrix are identical. Explicit expression for the fluctuation of an observable in a specified ensemble is given. We have discussed the nature of nuclear magnetic resonance quantum computing. We show that the conclusion that there is no quantum entanglement in the current nuclear magnetic resonance quantum computing experiment is based on the unjustified belief that ensembles having the same compressed density matrix are identical physically. Related issues in quantum communication are also discussed.Comment: 26 pages. To appear in Foundations of Physics, 36 (8), 200

Cold Starting Temperature Drift Modeling and Compensation of Micro-Accelerometer Based on High-Order Fourier Transform

The traditional temperature modeling method is based on the full heating of the accelerometer to achieve thermal balance, which is not suitable for the cold start-up phase of the micro-accelerometer. For decreasing the complex temperature drift of the cold start-up phase, a new temperature compensation method based on a high-order Fourier transform combined model is proposed. The system structure and repeatability test of the micro digital quartz flexible accelerometer are provided at first. Additionally, we analyzed where the complex temperature drift of the cold start-up phase comes from based on the system structure and repeatability test. Secondly, a high-order temperature compensation model combined with K-means clustering and the symbiotic organisms search (SOS) algorithm is established with repeatability test data as training data. To verify the proposed temperature compensation model, a test platform was built to transmit the measured values before and after compensation with the proposed Fourier-related model and the other time-related model, which is also a model aiming at temperature compensation in the cold start-up phase. The experimental results indicate that the proposed method achieves better compensation accuracy compared with the traditional temperature compensation methods and the time-related compensation model. Furthermore, the compensation for the cold start-up phase has no effect on the original accuracy over the whole temperature range. The stability of the accelerometer can be significantly improved to about 30 μg in the start-up phase of different temperatures after compensation

Influence of Heterogeneity on Relative Permeability for CO2/Brine: CT Observations and Numerical Modeling

AbstractThe determination of relative permeability of CO2/brine fluids under reservoir condition is critical for the design of CO2 injection strategy and prediction of CO2 behavior underground through reservoir simulation. For some reservoirs only heterogeneous samples are available for measurement. Heterogeneity, such as layering or cross bedding lamination can commonly be seen in sandstone cores. The effects and mechanism of core-scale heterogeneities on macroscopic scale relative permeability must be well-addressed. Here we report two sets of laboratory core flooding experiments using Berea sandstone for steady-state measurement of relative permeability of CO2/Brine at reservoir condition [1]. Berea sandstone is relatively homogeneous but has strong bedding or lamination structures. Two Berea samples were used (cored along the directions parallel to and perpendicular to the bedding, and named Berea-1 and Berea-2 respectively). We recorded the pressure and discharge volume to get the relative permeability curves for both samples; and utilized the X-ray computed tomography to estimate the distributions of porosity and CO2 (or Brine) saturation for Berea-2. The measured phase relative permeability of Berea-2 sample is greatly deviated from Berea-1. To further investigate the effects of core-scale heterogeneity on the measurement of relative permeability, we carried out a series numerical simulation on core scale using Tough2ECO2N code with building models based on X-ray CT scan images. By comparing the numerical results, we found the heterogeneity of capillary pressure field under one injection direction plays a dominant role in CO2/brine saturation patterns, flow regime and apparent relative permeability model

Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models

Abstract Accurate prognostic prediction is crucial for treatment decision-making in lung papillary adenocarcinoma (LPADC). The aim of this study was to predict cancer-specific survival in LPADC using ensemble machine learning and classical Cox regression models. Moreover, models were evaluated to provide recommendations based on quantitative data for personalized treatment of LPADC. Data of patients diagnosed with LPADC (2004–2018) were extracted from the Surveillance, Epidemiology, and End Results database. The set of samples was randomly divided into the training and validation sets at a ratio of 7:3. Three ensemble models were selected, namely gradient boosting survival (GBS), random survival forest (RSF), and extra survival trees (EST). In addition, Cox proportional hazards (CoxPH) regression was used to construct the prognostic models. The Harrell’s concordance index (C-index), integrated Brier score (IBS), and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used to evaluate the performance of the predictive models. A user-friendly web access panel was provided to easily evaluate the model for the prediction of survival and treatment recommendations. A total of 3615 patients were randomly divided into the training and validation cohorts (n = 2530 and 1085, respectively). The extra survival trees, RSF, GBS, and CoxPH models showed good discriminative ability and calibration in both the training and validation cohorts (mean of time-dependent AUC: > 0.84 and > 0.82; C-index: > 0.79 and > 0.77; IBS: < 0.16 and < 0.17, respectively). The RSF and GBS models were more consistent than the CoxPH model in predicting long-term survival. We implemented the developed models as web applications for deployment into clinical practice (accessible through https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/ ). All four prognostic models showed good discriminative ability and calibration. The RSF and GBS models exhibited the highest effectiveness among all models in predicting the long-term cancer-specific survival of patients with LPADC. This approach may facilitate the development of personalized treatment plans and prediction of prognosis for LPADC

A Privacy-Preserving Intelligent Medical Diagnosis System Based on Oblivious Keyword Search

One of the concerns people have is how to get the diagnosis online without privacy being jeopardized. In this paper, we propose a privacy-preserving intelligent medical diagnosis system (IMDS), which can efficiently solve the problem. In IMDS, users submit their health examination parameters to the server in a protected form; this submitting process is based on Paillier cryptosystem and will not reveal any information about their data. And then the server retrieves the most likely disease (or multiple diseases) from the database and returns it to the users. In the above search process, we use the oblivious keyword search (OKS) as a basic framework, which makes the server maintain the computational ability but cannot learn any personal information over the data of users. Besides, this paper also provides a preprocessing method for data stored in the server, to make our protocol more efficient

Prognostic nomogram and epidemiological analysis for lung atypical carcinoid: A SEER database and external validation study

Abstract Purpose Our study aims to delineate the epidemiological distribution of pulmonary carcinoids, including atypical carcinoid (AC) and typical carcinoid (TC), identify independent prognostic factors, develop an integrative nomogram and examine the effects of various surgical modalities on atypical carcinoid‐specific survival (ACSS). Methods Joinpoint regression model and age‐group distribution diagram were applied to determine the epidemiological trend of the pulmonary carcinoids. Univariate and least absolute shrinkage and selection operator (LASSO)‐based Cox regression models were used to identify independent factors, and a nomogram and web‐based predictor were developed to evaluate prognosis of AC patients individually. We performed Kaplan–Meier survival analyses to compare the scope of various surgical interventions, with and without G‐computation adjustment, utilising restricted mean survival time (RMST) to assess survival disparities. Results A total of 1132 patients were recruited from the Surveillance, Epidemiology, and End Results database (SEER) and a separate medical centre in China. The mean age of AC patients was 63.4 years and a smoking history was identified in 79.8% of AC patients. Joinpoint analysis shows rising annual rates of new AC and carcinoid cases among lung cancers. Both the proportion of pulmonary TC and AC within the total lung cancer population exhibits an L‐shaped trend across successive age groups. The nomogram predicted 1, 3 and 5 years of AC with excellent accuracy and discrimination. Kaplan–Meier survival analyses, conducted both pre‐ and post‐adjustment, demonstrated that sublobar resection's survival outcomes were not inferior to those of lobectomy in patients with stage I‐II and stage III disease. Conclusion This study is the first to reveal epidemiological trends in pulmonary carcinoids over the past decade and across various age cohorts. For patients with early‐stage AC, sublobar resection may be a viable surgical recommendation. The established nomogram and web‐based calculator demonstrated decent accuracy and practicality

Seasonal and Diurnal Changes of Air Temperature and Water Vapor Observed with a Microwave Radiometer in Wuhan, China

Based on Microwave Radiometer (MWR) observations in Wuhan over the course of 21 months, we compared the temperature and water vapor levels with those from radiosonde (RS) sounding data at 00:00 and 12:00 UTC, and then analyzed the seasonal and diurnal changes of temperature and water vapor levels from the MWR data. The MWR and RS mean temperatures and dew points are roughly consistent with each other below 2 km, whereas above 2 km, the MWR temperature is slightly lower than the RS temperature. The difference in their water vapor densities decreases quickly with height, and the bias of their relative humidities is generally in the range of −15% to 20%. The MWR observations show that in autumn, the surface temperature is 6.8 K lower during precipitation events than during non-precipitation events, indicating that precipitation in autumn is mainly caused by cold air from the north. The relative humidity during precipitation events exceeds 90% from the ground to 5 km, which is obviously larger than during non-precipitation events. During non-precipitation events, the seasonal mean water vapor density at 0–1.0 km shows an approximately linear increase with the mean temperature; however, their diurnal changes are opposite due to the effect of the boundary layer. At 4.5–5.5 km and 8.5–9.5 km, the mean temperature shows a synchronized diurnal evolution, with the maximum value prior to that at 0–1.0 km, indicating the strong influence of the air–land interaction on the temperature near the ground. Hence, this study is helpful for deepening our understanding of temperature and humidity variabilities over Wuhan

Sensitivity Analysis of Seismic Velocity and Attenuation Variations for Longmaxi Shale during Hydraulic Fracturing Testing in Laboratory

During the hydraulic fracturing procedure in shale-gas exploitation, the poroelastic properties of shale formation can be altered significantly. However, it is difficult to evaluate these variations using microseismic field data. In this study, we conduct a hydro-fracturing experiment using Longmaxi shale, which is a major formation for shale-gas production in China, to simulate the water injection and rock fracturing procedure. The variation of the velocity and attenuation for primary/secondary (P/S) ultrasonic waves was investigated throughout the entire experimental procedure. The results show that the attenuation is more sensitive to sample rupture than the velocity. However, P-wave attenuation loses sensitivity to the water injection after the fractures are saturated with water. In that case, it is preferable to use S-wave attenuation to identify the opening/closing of the fractures. Based on the experimental results, we can conclude that the variation of the attenuation must be considered during microseismic data processing and interpretation

Design of 400 V Miniature DC Solid State Circuit Breaker with SiC MOSFET

Silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) have the advantages of high-frequency switching capability and the capability to withstand high temperatures, which are suitable for switching devices in a direct current (DC) solid state circuit breaker (SSCB). To guarantee fast and reliable action of a 400 V DC SSCB with SiC MOSFET, circuit design and prototype development were carried out. Taking 400V DC microgrid as research background, firstly, the topology of DC SSCB with SiC MOSFET was introduced. Then, the drive circuit of SiC MOSFET, fault detection circuit, energy absorption circuit, and snubber circuit of the SSCB were designed and analyzed. Lastly, a prototype of the DC SSCB with SiC MOSFET was developed, tested, and compared with the SSCB with Silicon (Si) insulated gate bipolar transistor (IGBT). Experimental results show that the designed circuits of SSCB with SiC MOSFET are valid. Also, the developed miniature DC SSCB with the SiC MOSFET exhibits faster reaction to the fault and can reduce short circuit time and fault current in contrast with the SSCB with Si IGBT. Hence, the proposed SSCB can better meet the requirements of DC microgrid protection