Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes

We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients.     

On‐line yeast propagation process monitoring and control using an intelligent automatic control system

The monitoring and control of bioprocesses is a challenging task. This applies particularly if the actions to the process have to be carried out in real‐time. This work presents a system for on‐line monitoring and control of batch yeast propagation under limiting conditions based on a virtual plant operator, which uses the concept of intelligent control algorithms by means of fuzzy logic theory. Process information is provided on‐line using a sensor array comprising the measurement of OD, operating temperature, pressure, density, dissolved oxygen, and pH value. In this context practical problems arising through on‐line sensing and signal processing are addressed. The preprocessed sensor data are fed to a neural network for on‐line biomass estimation. The root mean squared error of prediction is 4 × 10⁶ cells/mL. The proposed system then triggers temperature and aeration by usage of a temperature dependent metabolic growth model and sensor data. The deviation of the predicted biomass from that of the reference trajectory as modeled by the metabolic growth model and its temporal derivative are used as inputs for the fuzzy temperature controller. The inputs used by the fuzzy aeration controller are the deviation of measured extract from that of the reference trajectory, the predicted cell count, and the dissolved oxygen concentration. The fuzzy‐based expert system allows to provide the desired yeast cell concentration of 100–120 × 10⁶ cells/mL at a minimum residual extract limit of 6.0 g/100 g at the required point of time. Thus, a dynamic adjustment of the propagation process to the overall production schedule is possible in order to produce the required amount of biomass at the right time.     

Computerized classification of Mediterranean vegetation using panchromatic aerial photographs

Abstract. Historical aerial photographs are an important source for data on medium- to long-term (10 - 50 yr) vegetation changes. Older photographs are panchromatic, and manual interpretation has traditionally been used to derive vegetation data from such photographs. We present a method for computerized analysis of panchromatic aerial photographs, which enables one to create high resolution, accurate vegetation maps. Our approach is exemplified using two aerial photographs (from 1964 and 1992) of a test area on Mt. Meron, Israel. Spatial resolution (pixel size) of the geo-rectified photos was 0.30 m and spatial accuracy (RMS error) ca. 1 m. An illumination adjustment prior to classification was found to be essential in reducing misclassification error rates. Two classification approaches were employed: a standard maximum-likelihood supervised classifier, and a modification of a supervised classification, which takes into account spectral properties of individual pixels as well as their neighbourhood characteristics. Accuracy of the maximum likelihood classification was 81 % in the 1992 image and 54 % in the 1964 image. The neighbour classifier increased accuracy to 89 % and 82 % respectively. The overall results suggest that computerized analysis of sequences of panchromatic aerial photographs may serve as a valuable tool for the quantification of medium-term vegetation changes.     

Developing an indicator system for local governments to evaluate transport sustainability strategies

This study proposed an indicator system for measuring and monitoring transport sustainability at the county (or city) level. Twenty-one indicators were grouped into economy, environment, society, and energy aspects. A committee comprised of government officials from Taipei City and New Taipei City proposed transport solutions to improve the transport sustainability of the Taipei metropolitan area. Ten key indicators were selected to measure the sustainable transport strategies. This study applied Fuzzy Cognitive Maps (FCMs) and the Analytic Hierarchy Process (AHP) to construct the cause–effect relationships between these key indicators and to evaluate sustainable transport strategies. The evaluation results showed that the strategy of expanding mass rapid transit (MRT) lines was predicted to produce the most significant improvements; the strategy of integrating bus exclusive lanes would provide the least improvement; and the strategies of promoting cleaner vehicles and integrating Fu-Kang bus resources would perform similarly to each other in improving transport sustainability.     

北缘地区柑桔生态区划模糊决策系统

以年平均极端最低气温、活动积温、最冷月平均气温、年平均气温等作为区划决策的重要指标,综合年降水量、最低气温≤-9℃年平均日数及地貌与海拔、水体调节、冷风屏障和土壤营养丰度、PH值等立地条件,建立了北缘地区柑桔生态区划决策系统．引入L-R型模糊数,确立了模糊识别模式,并把模糊推理模型放在知识库里,将精确推理与模糊推理置于同一系统中进行复合推理,使决策系统具有更广泛的实用性．以安徽柑桔生态区划为例,对系统进行了验证．本系统的开发为北缘地区发展柑桔生产提供了决策工具．     

韶关国家级自然保护区生态旅游资源及其定量化评价

韶关市是广东省自然保护区最集中的地区.近几年来,在可持续发展发展理念的指导下,自然保护区生态旅游开发蓬勃发展.论文介绍了韶关市三个国家级自然保护区的生态旅游资源特点,探讨利用模糊数学评价指标体系进行定量评价.通过对车八岭、南岭、丹霞山三个国家级自然保护区生态旅游自然景观资源比较分析,运用三种运算方法得出了较一致的结论.结果显示南岭自然保护区资源评价最好,车八岭其次,丹霞山则相对较低.根据评价结果提出了建立生态网络、加强环境建设以及社区参与等可持续发展建议.     

棉蚜种群动态预报模型

对1980-1989年河南棉区棉蚜种群动态分析表明,棉蚜为害可分为苗蚜和伏蚜两个时期,根据环境因子(气温、相对湿度、降雨量)和不同危害期,建立了2个模糊预报模型,并取得了较满意的预测效果。     

Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series

In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper features and remove the redundancy from the primary feature vector. Finally, the extracted features are applied to the K-nearest neighbor (KNN) and support vector machine (SVM) classifiers separately to determine the normal image or disease type. Experimental results indicate that the proposed algorithm achieves high classification rate and outperforms recently introduced methods while it needs less number of features for classification.     

Prediction of C-to-U RNA editing sites in plant mitochondria using both biochemical and evolutionary information

Although cytidine-to-uridine conversions in plant mitochondria were discovered 18 years ago, it was still an enigmatic process. Since the sequencing projects of plant mitochondrial genomes are providing more and more available sequences, the requirements of computationally identifying C-to-U RNA editing sites are also increasing. By incorporating both evolutionary and biochemical information, we developed a novel algorithm for predicting C-to-U RNA editing sites in plant mitochondria. The algorithm has been implemented as an online service called CURE (Cytidine-to-Uridine Recognizing Editor). CURE performs better than other methods that are based on only biochemical or only evolutionary information. CURE also provides the ability of predicting C-to-U RNA editing sites in non-coding regions and the synonymous C-to-U RNA editing sites in coding regions that are impossible for other methods. Furthermore, CURE can carry out prediction directly on the entire mitochondria genome sequence. The prediction results of CURE suggest the functional importance of synonymous RNA editing sites, which was neglected before. The CURE service can be accessed at http://bioinfo.au.tsinghua.edu.cn/cure.     

An analytical study of the factors that influence COVID-19 spread

The outbreak of the coronavirus disease 2019 (COVID-19) continues to constitute an international public health emergency. Seasonality is a long-recognized attribute of many viral infections of humans. Nevertheless, the relationship between environmental factors and the spread of infection, particularly for person-to-person communicable diseases, remains poorly understood. This study explores the relationship between environmental factors and the incidence of COVID-19 in 188 countries with reported COVID-19 cases as of April 13, 2020. Here we show that COVID-19 growth rates peaked in temperate zones in the Northern Hemisphere during the outbreak period, while they were lower in tropical zones. The relationships between COVID-19 and environmental factors were resistant to the potentially confounding effects of air pollution, sea level, and population. To prove the effect of those factors, study, and analysis of the prevalence of COVID-19 in Italy, Spain, and China was undertaken. A fuzzy logic system was designed to predict the effects of that variables on the rate of viral spread of COVID-19.