Research on waste gas treatment technology and comprehensive environmental performance evaluation for collaborative management of pollution and carbon in China's pharmaceutical industry based on life cycle assessment (LCA).

China is the largest industrial and pharmaceutical country in the world. The pharmaceutical industry, being a highly polluting sector, is the primary target of environmental regulation in the industry. The rapid development of pharmaceutical industry has posed a severe challenge to the environmental protection strategy of "carbon reduction and carbon neutrality" and the goal of "synergizing the reduction of pollution and carbon emissions" in China's "14th Five-Year Plan". Therefore, this paper starts from the whole industry, takes the life cycle of the whole production process of the pharmaceutical industry as the guidance, and selects a pharmaceutical company in Tianjin as the research object. Then using Life Cycle Assessment (LCA) to Characterization, Standardization, and Weighting the environmental impact of the waste gas treatment process before and after improvement based on waste gas emission characteristics from the pharmaceutical factory. LCA results show that GWP and AP are the most important environmental impact types, which account for >85 % of the total characterization value. I and II - Chemical Pharmaceutical Stage is the critical life cycle stage, accounting for over 80 % of the total characteristic values. This research proposes emission reduction countermeasures based on LCA results and simulates Emission reduction scenarios and economic evolution. If effectively implementing emission reduction countermeasures, reducing the environmental characterization value by 80 to 90 %, and generating economic benefit of 2.66 × 108 RMB/year. This research could guide improvement plans and emission reduction countermeasures of waste gas treatment in the pharmaceutical industry, which realizes collaborative management about efficient reduction of pollution and carbon and generates significant environmental, technological, economic, and social benefits.

Automated tooth crown design with optimized shape and biomechanics properties.

Despite the large demand for dental restoration each year, the design of crown restorations is mainly performed via manual software operation, which is tedious and subjective. Moreover, the current design process lacks biomechanics optimization, leading to localized stress concentration and reduced working life. To tackle these challenges, we develop a fully automated algorithm for crown restoration based on deformable model fitting and biomechanical optimization. From a library of dental oral scans, a conditional shape model (CSM) is constructed to represent the inter-teeth shape correlation. By matching the CSM to the patient's oral scan, the optimal crown shape is estimated to coincide with the surrounding teeth. Next, the crown is seamlessly integrated into the finish line of preparation via a surface warping step. Finally, porous internal supporting structures of the crown are generated to avoid excessive localized stresses. This algorithm is validated on clinical oral scan data and achieved less than 2 mm mean surface distance as compared to the manual designs of experienced human operators. The mechanical simulation was conducted to prove that the internal supporting structures lead to uniform stress distribution all over the model.

Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images.

PURPOSE: Training deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training process, the strategy of annotation by iterative deep learning recently becomes popular in the research community. However, due to the lack of domain knowledge or efficient human-interaction tools, the current AID methods still suffer from long training time and high annotation burden. METHODS: We develop a contour-based annotation by iterative deep learning (AID) algorithm which uses boundary representation instead of voxel labels to incorporate high-level organ shape knowledge. We propose a contour segmentation network with a multi-scale feature extraction backbone to improve the boundary detection accuracy. We also developed a contour-based human-intervention method to facilitate easy adjustments of organ boundaries. By combining the contour-based segmentation network and the contour-adjustment intervention method, our algorithm achieves fast few-shot learning and efficient human proofreading. RESULTS: For validation, two human operators independently annotated four abdominal organs in computed tomography (CT) images using our method and two compared methods, i.e. a traditional contour-interpolation method and a state-of-the-art (SOTA) convolutional network (CNN) method based on voxel label representation. Compared to these methods, our approach considerably saved annotation time and reduced inter-rater variabilities. Our contour detection network also outperforms the SOTA nnU-Net in producing anatomically plausible organ shape with only a small training set. CONCLUSION: Taking advantage of the boundary shape prior and the contour representation, our method is more efficient, more accurate and less prone to inter-operator variability than the SOTA AID methods for organ segmentation from volumetric medical images. The good shape learning ability and flexible boundary adjustment function make it suitable for fast annotation of organ structures with regular shape.

AnatomySketch: An Extensible Open-Source Software Platform for Medical Image Analysis Algorithm Development.

The development of medical image analysis algorithm is a complex process including the multiple sub-steps of model training, data visualization, human-computer interaction and graphical user interface (GUI) construction. To accelerate the development process, algorithm developers need a software tool to assist with all the sub-steps so that they can focus on the core function implementation. Especially, for the development of deep learning (DL) algorithms, a software tool supporting training data annotation and GUI construction is highly desired. In this work, we constructed AnatomySketch, an extensible open-source software platform with a friendly GUI and a flexible plugin interface for integrating user-developed algorithm modules. Through the plugin interface, algorithm developers can quickly create a GUI-based software prototype for clinical validation. AnatomySketch supports image annotation using the stylus and multi-touch screen. It also provides efficient tools to facilitate the collaboration between human experts and artificial intelligent (AI) algorithms. We demonstrate four exemplar applications including customized MRI image diagnosis, interactive lung lobe segmentation, human-AI collaborated spine disc segmentation and Annotation-by-iterative-Deep-Learning (AID) for DL model training. Using AnatomySketch, the gap between laboratory prototyping and clinical testing is bridged and the development of MIA algorithms is accelerated. The software is opened at https://github.com/DlutMedimgGroup/AnatomySketch-Software .

Study on quantitative diagnosis model of TCM syndromes of post-stroke depression based on combination of disease and syndrome.

BACKGROUND: Post-stroke depression (PSD) is one of the most common stroke complications with high morbidity. Researchers have done much clinical research on Traditional Chinese Medicine (TCM) treatment, but very little research on diagnosis. Based on the thought of combination of disease and syndrome, this study will establish a unified and objective quantitative diagnosis model of TCM syndromes of PSD, so as to improve the clinical diagnosis and treatment of PSD. OBJECTIVE: First: To establish a unified and objective quantitative diagnosis model of TCM syndromes in PSD under different disease courses, and identify the corresponding main, secondary, and concurrent symptoms, which are based on the weighting factor of each TCM symptom. Second: To find out the relationship between different stages of PSD and TCM syndromes. Clarify the main syndrome types of PSD under different stages of disease. Reveal the evolution and progression mechanism of TCM syndromes of PSD. METHODS AND ANALYSIS: This is a retrospective study of PSD TCM diagnosis. Three hundred patients who were hospitalized in the First Teaching Hospital of Tianjin University of TCM with complete cases from January 2014 to January 2019 are planned to be recruited. The study will mainly collect the diagnostic information from the cases, find the related indicators of TCM and Western medicine in PSD, and clarify the relationship between different disease stages and TCM syndromes. Finally, the PSD TCM syndrome quantitative diagnosis model will be established based on the operation principle of Back Propagation (BP) artificial neural network. CONCLUSION: To collect sufficient medical records and establish models to speed up the process of TCM diagnosis.

[Clinical observation of Alzheimer's disease treated with acupuncture].

OBJECTIVE: To compare the differences in the clinical efficacy on Alzheimer's disease between acupuncture and medicine. METHODS: One hundred and forty-one patients were randomized into an acupuncture group (72 cases) and a medicine group (69 cases). In the acupuncture group, the needling technique for benefiting qi, promoting blood circulation, regulating mind and improving intelligence was used at Shenting (GV 24), Baihui (GV 20), Fengchi (GB 20), Wangu (GB 12), Danzhong (CV 17), Zhangwan (CV 12), Qihai (CV 6), Xuehai (SP 10) and Zusanli (ST 36). The supplementary acupoints were selected according to the symptoms and physical signs. Acupuncture was given once a day and 6 treatments were required for a week. In the medicine group, the choline sterase inhibitor, donepezil (aricept) was prescribed for oral administration, 1 tablet (5 mg) each time, once every night. Four weeks later, the dose was increased to 2 tablets (10 mg) each time. In the two groups, the treatment of 4 weeks made one session and 4 sessions were required. The changes of scores before and after treatment in the minimum mental state examination (MMSE), the activity of daily living scale (ADL), Alzheimer's disease assessment scale-cognition (ADAS-cog) and the digit span (DS) were observed. RESULTS: After treatment, scores of MMSE and DS were increased as compared with those before treatment (both P < 0.05) and scores of ADL and ADAS-cog were reduced as compared with those before treatment. The score differences in MMSE, ADL, ADAS-cog and DS before and after treatment were significant in the two groups (all P < 0.01). CONCLUSION: The needling technique for benefiting qi, promoting blood circulation, regulating mind and improving intelligence significantly improves the overall function, cognition and activity of daily life in the patients of Alzheimer's disease and the efficacy is better than donepezil.

[Application of DPLS-based LDA in corn qualitative near infrared spectroscopy analysis].

NIR technology is a rapid, nondestructive and user-friendly method ideally suited for Qualitative analysis. In this paper the authors present the use of discriminant partial least Squares (DPLS)-based linear discriminant analysis (LDA) in corn qualitative near infrared spectroscopy analysis. Firstly, a training set including 30 corn varieties (each variety has 20 samples) was used to build the DPLS regression model, and 28 principal components (DPLS-PCs) were obtained from original spectrum. Secondly, the DPLS-PCs scores of the training set were extracted as DPLS features. Thirdly, LDA was applied to the DPLS features, determining 26 principal components (LDA-PCs). A test sample was first projected onto the DPLS-PCs and then onto the LDA-PCs, and finally 26 DPLS+LDA features were obtained. The recognition results were obtained by minimum distance classifier. DPLS+LDA method achieved 96.18% recognition rate, while traditional DPLS regression method and DPLS feature extraction method only achieved 85.38% and 95.76% recognition rate respectively. The experiment results indicated that DPLS +LDA method is with better generalization ability compared with traditional DPLS regression method and NIRS analysis by DPLS+LDA method is an efficient way to discriminate corn species.