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Background Conditional survival (CS) provides dynamic prognostic estimates by considering the patients existing survival time. Since CS for endemic nasopharyngeal carcinoma (NPC) is lacking, we aimed to assess the CS of endemic NPC and establish a web‐based calculator to predict individualized, conditional site‐specific recurrence risk. Methods Using an NPC‐specific database with a big‐data intelligence platform, 10,058 endemic patients with non‐metastatic stage I–IVA NPC receiving intensity‐modulated radiotherapy with or without chemotherapy between April 2009 and December 2015 were investigated. Crude CS estimates of conditional overall survival (COS), conditional disease‐free survival (CDFS), conditional locoregional relapse‐free survival (CLRRFS), conditional distant metastasis‐free survival (CDMFS), and conditional NPC‐specific survival (CNPC‐SS) were calculated. Covariate‐adjusted CS estimates were generated using inverse probability weighting. A prediction model was established using competing risk models and was externally validated with an independent, non‐metastatic stage I–IVA NPC cohort undergoing intensity‐modulated radiotherapy with or without chemotherapy (n = 601) at another institution. Results The median follow‐up of the primary cohort was 67.2 months. The 5‐year COS, CDFS, CLRRFS, CDMFS, and CNPC‐SS increased from 86.2%, 78.1%, 89.8%, 87.3%, and 87.6% at diagnosis to 87.3%, 87.7%, 94.4%, 96.0%, and 90.1%, respectively, for an existing survival time of 3 years since diagnosis. Differences in CS estimates between prognostic factor subgroups of each endpoint were noticeable at diagnosis but diminished with time, whereas an ever‐increasing disparity in CS between different age subgroups was observed over time. Notably, the prognoses of patients that were poor at diagnosis improved greatly as patients survived longer. For individualized CS predictions, we developed a web‐based model to estimate the conditional risk of local (C‐index, 0.656), regional (0.667), bone (0.742), lung (0.681), and liver (0.711) recurrence, which significantly outperformed the current staging system (P < 0.001). The performance of this web‐based model was further validated using an external validation cohort (median follow‐up, 61.3 months), with C‐indices of 0.672, 0.736, 0.754, 0.663, and 0.721, respectively. Conclusions We characterized the CS of endemic NPC in the largest cohort to date. Moreover, we established a web‐based calculator to predict the CS of site‐specific recurrence, which may help to tailor individualized, risk‐based, time‐adapted follow‐up strategies.

Traditional Chinese medicine (TCM) has wide application and important functions in curing many diseases, but a great number of herb residues are usually generated after its manufacture and usage. Without proper and timely treatment, these traditional Chinese medicinal herb (TCMH) residues will cause some environmental pollution. In addition to treatment, bioresources utilization of TCMH residues is also important for its great potential as a suitable feedstock for the production of energy, materials, and chemicals. In this situation, advanced and well-designed solid waste management is important to make the TCM industry environmentally friendly and economically attractive. In this review article, the recent progress focusing on various methods for TCMH residues treatment and bioresources utilization are introduced in detail. In particular, the technologies for thermochemical conversion and biochemical conversion of TCMH residues are mainly focused on in order to show how to fulfill effective and efficient bioresources utilization. Besides, some other technologies which are suitable for the treatment and bioresources utilization of TCMH residues are presented as well. Finally, some industrial prospects are given from the economic, operational, and environmental aspects for the further development of treatment and bioresources utilization of TCMH residues. Overall, this work can provide some systematical and comprehensive information for the development of technologies that help sustainably manage the herb residues generated in the TCM industry.

Purpose To develop predictive models with dosimetric and clinical variables for temporal lobe injury (TLI) in nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). Materials and methods Data of 8194 NPC patients who received IMRT-based treatment were retrospectively reviewed. TLI was diagnosed by magnetic resonance imaging. Dosimetric factors were selected by penalized regression and machine learning, with area under the receiver operating curve (AUC) calculated. Cox proportional hazards models containing the most predictive dosimetric factor with/without clinical variables were performed. A nomogram was generated as a visualization of Cox regression for predicting TLI-free survival. Results During median follow-up of 66.8 months (interquartile range [IQR] 54.2–82.2 months), 12.1% of patients (989/8194) developed TLI. Median latency from IMRT to TLI was 36 months (IQR 28–47 months). D0.5cc (dose delivered to 0.5-cm3 temporal-lobe volume) was the most predictive dosimetric factor (AUC: 0.799). Tolerance dose for 5% and 50% probabilities to develop TLI in 5 years were 65.06 Gy (95% confidence interval [CI]: 64.19–65.92) and 89.75 Gy (95% CI: 87.39–92.11), respectively. A nomogram comprising age, T stage, and D0.5cc significantly outperformed the model with only D0.5cc in predicting TLI (C-index: 0.78 vs. 0.737 in train set; 0.775 vs. 0.73 in test set; both P Conclusions D0.5cc of 65.06 Gy was the tolerance dose of the temporal lobe. Reducing D0.5cc decreased risk of TLI, especially in older patients with advanced T stage. The nomogram could predict TLI precisely and allow individualized follow-up management.

Objectives To screen subgroup potentially benefiting from cumulative cisplatin dose (CCD) ≥ 200 mg/m2 during concurrent chemoradiotherapy (CCRT) of patients with locoregionally-advanced nasopharyngeal carcinoma (LA-NPC) receiving induction chemotherapy (IC) and CCRT. Materials and Methods In total, 2 063 patients with non-disseminated LA-NPC diagnosed from 2009 to 2015 receiving IC plus CCRT were enrolled. Patients were restaged based on proposed stage groupings and risk groupings was established. After propensity score matching, survival outcomes were compared within different risk groupings with 200 mg/m2 CCD. Post-IC gross primary tumor (GTVp) and lymph node (GTVnd) volumes were calculated from planning computed tomography. The role of risk groupings and post-IC tumor volume to CCD was explored. Results Compared with the low-risk group, the high-risk group showed poor survival outcomes in terms of 5-year progression-free survival (PFS), overall survival (OS), distant metastasis-free survival (DMFS), and locoregional relapse-free survival (LRRFS). CCD ≥ 200 mg/m2 improved survival in terms of 5-year PFS, OS and DMFS in the high-risk group but not in the low-risk group. High-risk patients with unfavorable response to IC benefited from CCD ≥ 200 mg/m2 with respect to PFS and DMFS; while those in low-risk group or with favorable response to IC didn’t. Conclusions Risk groupings was effective for risk stratification. Combining risk groupings and post-IC tumor volume is a simple and useful method to guide individualized CCD treatment of CCRT for patients with LA-NPC receiving IC and CCRT. CCD ≥ 200 mg/m2 may be indicated for high-risk patients with unfavorable response to IC.

BACKGROUND The current study was performed to investigate whether circulating cell-free Epstein-Barr virus DNA (cfEBV DNA) would be useful for posttreatment surveillance in patients with nasopharyngeal carcinoma (NPC). METHODS The authors identified a total of 1984 nondisseminated NPC patients from an institutional big-data research platform. Blood samples were collected within 3 months of the completion of radiotherapy and every 3 to 12 months thereafter for cfEBV DNA analysis. Patients were followed until disease recurrence was detected or for a median of 60 months. Diagnostic performance was assessed by calculating the sensitivity, specificity, and accuracy based on the clinical detection of disease recurrence by conventional surveillance modalities (imaging scans and pathological examination). RESULTS During follow-up, a total of 767 patients (38.7%) had detectable cfEBV DNA. The recurrence rate among these patients was 63.8% (489 of 767 patients), which was significantly higher than that in patients with undetectable cfEBV DNA (8.6%; 105 of 1217 patients). cfEBV DNA sensitivity, specificity, and accuracy were 68.8%, 80.0%, and 78.2%, respectively, for local recurrence; 80.2%, 80.0%, and 85.9%, respectively, for regional recurrence; and 91.1%, 80.0%, and 92.8%, respectively, for distant metastasis. cfEBV DNA was found to have higher sensitivity for the detection of extrapulmonary metastases (94.9%-96.5%) compared with pulmonary metastases (78.4%). It is interesting to note that among the patients with disease recurrence with detectable cfEBV DNA, positive cfEBV DNA results preceded radiological and/or clinical evidence of disease recurrence by a median of 2.3 months (interquartile range, 0.1-9.5 months). In addition, of the 278 cfEBV DNA-positive patients who did not develop disease recurrence, 227 (81.7%) had transiently positive cfEBV DNA that fell to undetectable levels during long-term monitoring. CONCLUSIONS Plasma cfEBV DNA in patients with NPC appears to be an early sign of tumor recurrence, especially extrapulmonary metastases.

The diagnostic accuracy of existing transfer learning-based bearing fault diagnosis methods is high in the source condition, but accuracy in the target condition is not guaranteed. These methods mainly focus on the whole distribution of bearing source domain data and target condition data, ignoring the transfer learning of each kind of bearing fault data, which may lead to lower diagnostic accuracy. To overcome these limitations, we propose a transfer learning fault diagnosis model based on a deep Fully Convolutional Conditional Wasserstein Adversarial Network (FCWAN). The proposed model addresses the described problems separately: (1) A random-sampling map classification and difference classifier are used to handle the first limitation. (2) A label is introduced into the domain of adversarial learning to strengthen the supervision of the learning process and the effect of category field alignment, thus overcoming the second limitation. Experimental results demonstrate the superiority of this method over existing methods.

Nowadays, traditional Chinese medicine plays an important role in curing many diseases, and its market expands quickly due to the potential great profits it can bring. To be an attractive industry, both economic and environmental aspects should be considered. As wastewater is usually generated from traditional Chinese medicine processing and utilization, its treatment is one of the hot research topics in environmental engineering currently. Many technologies can be applied for the treatment of this wastewater, but the technical background and performance vary greatly especially for different kinds of wastewater. In this situation, a systematical and comprehensive summary for the development of these technologies is necessary. In this review article, the sources of wastewater from the processing and utilization of traditional Chinese medicine are introduced firstly to show where this wastewater comes from. And then, the recent advances on the technologies for the treatment of this wastewater are introduced in detail to show what technologies can be applied. At last, the future outlook for the development of these treatment technologies is given to discuss how to fulfill their industrial application. Overall, this review article can present some important information for building a clean and sustainable traditional Chinese medicine industry.

Aiming to deal with thermal error of NC machine tool which can cause reduce of machining accuracy, this paper uses an external error compensation which interacts with NC controllers and PMAC multi-axis and then revises the tool path by adding the error tested in real-time by PMAC card. The processing accuracy is improved eventually. This method can compensate machine geometric errors and thermal errors in real-time. Comparing with other methods of error preventing, this method is more effective and affordable.

The total reflection of the wave will occur twice and the lightning over-voltage will be increased by 2 times when the lightning wave along the line reached in the disconnect switch are derived through the analysis of the accident 66kV transformer insulation breakdown. This will lead to the switch breakdown and discharge to be grounded. In this case the breaker is between the arrester and the transformer and the transformer will withstand 2 times arrester residual voltage result in the transformer breakdown. Finally, effective preventive measures combined with the practical experience are proposed.

A wearable-optimized implementation of a sleep stage classification algorithm that has low detection latency, high detection accuracy and low resource consumption is developed and successfully implemented on a low-power FPGA microsystem for closed-loop electrical brain stimulation. This implementation uses EEG and EMG signals as inputs and classifies stages of sleep. By structurally merging multichannel FIR and window averaging filters into one reconfigurable, multipurpose filter, the new implementation maintains a sleep detection accuracy of 79.7%, a REM detection sensitivity of 98.2%, a REM detection specificity of 89.2% and a detection latency of 0.982 ms, while consuming 6.8 times fewer logic elements and 96.28% less power compared with the current state of the art implementation. With its high performance and low resource usage, this implementation enables a low-power wearable microsystem to perform neural recording, real-time REM sleep stage detection, and closed-loop responsive brain stimulation as a tool to study the mechanisms of neurodegenerative diseases.

A control strategy which based on minimum instantaneous equivalent fuel consumption was proposed in this paper. The important parameters that affected the calculation of instantaneous fuel consumption were analyzed. The important parameters include battery equivalent fuel consumption, penalty function to maintain the battery SOC and the revise of the regenerative braking energy. More precise expression of minimum instantaneous fuel consumption was deduced. Under the simulation platform of ADVISOR software, orthogonal optimization of parameters was performed and the range of important parameters in the optimization of expression was determined.

This paper discusses the application research on high-tech tax preference policy of manufacturing. Analyzing high-tech tax preference policy of the current manufacturing;The disadvantages of high-tech tax preference policy in production manufacturing;Suggestions for further implementing the high-tech tax preference policy of manufacturing.