A SEQUENTIAL TESTING PROGRAM FOR PREDICTING AND IDENTIFICATING CARCINOGENS AND ITS APPLICATION

In this paper our studies about the sequential testing program for predicting and identificating carcinogens, sequential discriminant method and cost- effectiveness analysis are summarized. The analysis of our database of carcinogeniclty and genotoxicity of chemicals demonstrates the uncertainty . of short- term tests ( STTs ) to predict carcinogens and the results of most routine STTs are statistically dependent. We recommend the sequential testing program combining STTs and carclnogenicity assay, the optimal STT batteries, the rules of the sequential discrimination and the preferal choices of STTs tor specific chemical class. For illustrative pmposes the carclnogenicity prediction of several sample chamicals is presented. The results of cost-effectiveness analysis suggest that this program has vast social-economic effectiveness.

Mach Number Prediction for a Wind Tunnel Based on the CNN-LSTM-Attention Method

The test section’s Mach number in wind tunnel testing is a significant metric for evaluating system performance.The quality of the flow field in the wind tunnel is contingent upon the system's capacity to maintain stability across various working conditions.The process flow in wind tunnel testing is inherently complex,resulting in a system characterized by nonlinearity,time lag,and multiple working conditions.To implement the predictive control algorithm,a precise Mach number prediction model must be created.Therefore,this report studies the method for Mach number prediction modelling in wind tunnel flow fields with various working conditions.Firstly,this paper introduces a continuous transonic wind tunnel.The key physical quantities affecting the flow field of the wind tunnel are determined by analyzing its structure and blowing process.Secondly,considering the nonlinear and time-lag characteristics of the wind tunnel system,a CNN-LSTM model is employed to establish the Mach number prediction model by combining the 1D-CNN algorithm with the LSTM model,which has long and short-term memory functions.Then,the attention mechanism is incorporated into the CNN-LSTM prediction model to enable the model to focus more on data with greater information importance,thereby enhancing the model's training effectiveness.The application results ultimately demonstrate the efficacy of the proposed approach.

External validation of the Prostate Cancer Prevention Trial and the European Randomized Study of Screening for Prostate Cancer risk calculators in a Chinese cohort

Several prediction models have been developed to estimate the outcomes of prostate biopsies. Most of these tools were designed for use with Western populations and have not been validated across different ethnic groups. Therefore, we evaluated the predictive value of the Prostate Cancer Prevention Trial （PCPT） and the European Randomized Study of Screening for Prostate Cancer （ERSPC） risk calculators in a Chinese cohort. Clinicopathological information was obtained from 495 Chinese men who had undergone extended prostate biopsies between January 2009 and March 2011. The estimated probabilities of prostate cancer and high-grade disease （Gleason 〉6） were calculated using the PCPT and ERSPC risk calculators. Overall measures, discrimination, calibration and clinical usefulness were assessed for the model evaluation. Of these patients, 28.7% were diagnosed with prostate cancer and 19.4% had high-grade disease. Compared to the PCPT model and the prostate-specific antigen （PSA） threshold of 4 ng m1-1, the ERSPC risk calculator exhibited better discriminative ability for predicting positive biopsies and high-grade disease （the area under the curve was 0.831 and 0.852, respectively, P〈O.01 for both）. Decision curve analysis also suggested the favourable clinical utility of the ERSPC calculator in the validation dataset. Both prediction models demonstrated miscalibration： the risk of prostate cancer and high-grade disease was overestimated by approximately 20% for a wide range of predicted probabilities. In conclusion, the ERSPC risk calculator outperformed both the PCPT model and the PSA threshold of 4 ng ml- z in predicting prostate cancer and high-grade disease in Chinese patients. However, the prediction tools derived from Western men significantly overestimated the probability of prostate cancer and high-grade disease compared to the outcomes of biopsies in a Chinese cohort.

Fast determination of meso-level mechanical parameters of PFC models

To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.

Comments on the release Laboratory Manual for the Human Semen of the 5th edition of the WHO Examination and Processing of

The authors of the World Health Organization Semen Analysis Manual are to be congratulated on producing a new edition; it is an essential tool to disseminate good practice in andrology. However, the tests described have poor prognostic power to predict a man＇s fertility and show little about the underlying causes of sub-fertility. This commentary urges a revival of research into the diagnosis of male fertility. It suggests that fertility should be regarded as a continuum and that the artificial binary division between fertile and infertile should be abandoned. Models to predict a sub-fertile couple＇s chance of conception in a year should be developed on the basis of prospective data. These models would allow for sophisticated decision making about management. The future lies in the identification of tests to detect underlying pathologies open to specific treatment. Leads such as oxidative stress, defects in the intracellular regulation and the developing field ofproteomics should be explored.

Gravitational effect of water circulation in the northwest Yunnan

Water circulation is the main disturbance source against precise gravimetry measurement which is one of the principal means of geodynamic study. Some scientists studied the disturbance of water level changes in lakes andrivers and groundwater activities on gravity field.Taking water circulation as a whole and combining it with thehydrogeological conditions in northwest Yunnan mountainous area and the measured gravity data,this paperstudies the features, connections of water circulation in atmosphere,on surface and under ground and its effecton gravimetric data. The main conclusions are as follows: 1) The water circulation in atmosphere has little directdisturbance on gravity field 1 2 ) The change of lake water level may cause gravitational effect of (10- 20) × 10-8m. s-2 ; 3) In the NW Yunnan, types of the groundwater are various,and its changes are complicated, it isnecessary to study point by point. In general, its disturbance on gravity field in this region is about 10 × 10- 8m. s- 2, less than that in plain area.

Investigation into the automatic recognition of time series precursor of earthquakes

In this paper, a new method of quantitative description of earthquake precursors is proposed; by this method, the precursory pattern of time series can be quantitatively described with a two-dimensional matrix. On this basis, a method of automatic recognition or automatic acquirement of precursory pattern, called simply the AA method, is put forward. Then, taking North China region as an example, various seismological precursors such as the frequency, energy, b -value, etc . and various nonlinear parameter precursors such as the capacity dimension, information dimension, correlation dimension, Hurst index and its difference, etc. are analyzed and the 8 time series so obtained are recognized automatically using the proposed precursory pattern and AA method. Besides, C-method tests and very rigorous HF (history and future) tests are made. The result shows that the R-value of prediction efficacy assessment is fairly high.

Prediction and test of cavity＇s hydrodynamic self-noise induced by turbulent boundary layer

The self-noise in cavity is tested in the circling tank, prediction method of cavity＇s self-noise induced by turbulent boundary layer is established. The window＇s vibration is using the simply supported boundary condition, the sound wave in the cavity is expanded using the rigid wall boundary condition, the modal coupling vibration equation between them is established using the radiation boundary condition. The turbulent boundary layer pulsating pressure is random, the self-noise power spectrum in the cavity is solved. Test of self-noise and turbulent pressure is carried out in the circling tank when the flow velocity is 5 m/s and 8 m/s, the result verifies that the theoretical method can predict the real cavity＇s hydrodynamic noise approximately, the trends are similar, this provides one analytical method for sonar dome＇s material selection and noise control.

A Predictive Model for Estimation Risk of Proliferative Lupus Nephritis

Background： Lupus nephritis （LN） is classified by renal biopsy into proliferative and nonproliferative forms, with distinct prognoses, but renal biopsy is not available for every LN patient. The present study aimed to establish an alternate tool by building a predictive model to evaluate the probability of proliferative LN. Methods： In this retrospective cohort with biopsy-proven LN, 382 patients in development cohort, 193 in internal validation cohort, and 164 newly diagnosed patients in external validation cohort were selected. Logistic regression model was established, and the concordance statistics （C-statistics）, Akaike information criterion （AIC）, integrated discrimination improvement, Hosmer-Lemeshow test, and net reclassification improvement were calculated to evaluate the performance and validation of models. Results： The prevalence of proliferative LN was 77.7% in the whole cohort. A model, including age, gender, systolic blood pressure, hemoglobin, proteinuria, hematuria, and serum C3, performed well on good-of-fit and discrimination in the development chohort to predict the risk of proliferative LN （291 for AIC and 0.84 for C-statistics）. In the internal and external validation cohorts, this model showed good capability for discrimination and calibration （0.84 and 0.82 for C-statistics, and 0.99 and 0.75 for P values, respectively）. Conclusion： This study developed and validated a model including demographic and clinical indices to evaluate the probability of presenting proliferative LN to guide therapeutic decisions and outcomes.