Novel artificial intelligence machine learning approaches to precisely predict survival and site-specific recurrence in cervical cancer: A multi-institutional study

BackgroundMachine learning (ML) has been gradually integrated into oncologic research but seldom applied to predict cervical cancer (CC), and no model has been reported to predict survival and site-specific recurrence simultaneously. Thus, we aimed to develop ML models to predict survival and site-specific recurrence in CC and to guide individual surveillance.MethodsWe retrospectively collected data on CC patients from 2006 to 2017 in four hospitals. The survival or recurrence predictive value of the variables was analyzed using multivariate Cox, principal component, and K-means clustering analyses. The predictive performances of eight ML models were compared with logistic or Cox models. A novel web-based predictive calculator was developed based on the ML algorithms.ResultsThis study included 5112 women for analysis (268 deaths, 343 recurrences): (1) For site-specific recurrence, larger tumor size was associated with local recurrence, while positive lymph nodes were associated with distant recurrence. (2) The ML models exhibited better prognostic predictive performance than traditional models. (3) The ML models were superior to traditional models when multiple variables were used. (4) A novel predictive web-based calculator was developed and externally validated to predict survival and site-specific recurrence.ConclusionML models might be a better analytic approach in CC prognostic prediction than traditional models as they can predict survival and site-specific recurrence simultaneously, especially when using multiple variables. Moreover, our novel web-based calculator may provide clinicians with useful information and help them make individual postoperative follow-up plans and further treatment strategies.     

Genetic alterations associated with 18F-fluorodeoxyglucose positron emission tomography/computed tomography in head and neck squamous cell carcinoma

BackgroundWe investigated the relationship between genetic alterations and ¹⁸F-FDG PET/CT findings in head and neck squamous cell carcinoma (HNSC).MethodsUsing mRNA-sequences of HNSC samples (480 patients) from the Cancer Genome Atlas (TCGA) portal, gene coexpression networks were constructed via a weighted correlation network analysis (WGCNA) algorithm, and their association with the tumor-to-blood signal ratio on ¹⁸F-FDG PET/CT data (21 patients) was explored. An elastic-net regression model was developed to estimate the PET tumor-to-blood ratio from the gene networks and to derive an FDG signature score (FDG_SS). The FDG_SS was evaluated with regard to clinical variables and general mutational profiles, as well as alterations to oncogenic signaling pathways.FindingsThe FDG_SS values differed across clinical stages (p = 0.027), HPV-status (p< 0.001), and molecular subtypes of HNSC (p< 0.001). Multivariate Cox regression demonstrated that FDG_SS was an independent predictor for overall (p = 0.019) and progression-free survival (p = 0.024). FDG_SS positively correlated with total mutation rate (p = 0.016), aneuploidy (p < 0.001), and somatic copy number alteration scores (p < 0.001). CDKN2A in the cell cycle pathway (q = 0.014) and the TP53 gene in the TP53 pathway (q = 0.005) showed significant differences between high and low FDG_SS patients.ConclusionFDG_SS based on the gene coexpression network was associated with the mutational landscape of HNSC. ¹⁸F-FDG PET/CT is therefore a valuable tool for the in vivo imaging of these cancers, being able to visualize the glucose metabolism of the tumor and allow inferences to be made on the underlying genetic alterations in the tumor.     

Deep Learning Using Havrda-Charvat Entropy for Classification of Pulmonary Optical Endomicroscopy

1) ObjectivePulmonary optical endomicroscopy (POE) is an imaging technology in real time. It allows to examine pulmonary alveoli at a microscopic level. Acquired in clinical settings, a POE image sequence can have as much as 25% of the sequence being uninformative frames (i.e. pure-noise and motion artifacts). For future data analysis, these uninformative frames must be first removed from the sequence. Therefore, the objective of our work is to develop an automatic detection method of uninformative images in endomicroscopy images.2) Material and methodsWe propose to take the detection problem as a classification one. Considering advantages of deep learning methods, a classifier based on CNN (Convolutional Neural Network) is designed with a new loss function based on Havrda-Charvat entropy which is a parametrical generalization of the Shannon entropy. We propose to use this formula to get a better hold on all sorts of data since it provides a model more stable than the Shannon entropy.3) ResultsOur method is tested on one POE dataset including 3895 distinct images and is showing better results than using Shannon entropy and behaves better with regard to the problem of overfitting. We obtain 70% of accuracy with Shannon entropy versus 77 to 79% with Havrda-Charvat.4) ConclusionWe can conclude that Havrda-Charvat entropy is better suited for restricted and or noisy datasets due to its generalized nature. It is also more suitable for classification in endomicroscopy datasets.     

Modified Fuzzy Q Learning Based Classifier for Pneumonia and Tuberculosis

This work proposes reinforcement learning for correctly identifying pneumonia and tuberculosis (TB) using a repository of X ray images. To our knowledge, this is a first attempt at employing reinforcement learning for pneumonia and TB classification. In particular, modified fuzzy Q learning (MFQL) algorithm in conjunction with wavelet based pre-processing has been used to build a classifier for identifying pneumonia and tuberculosis's severity. Proposed classifier is a self-learning one and uses pneumonia dataset (no pneumonia, mild pneumonia and severe pneumonia) and tuberculosis dataset (TB present, TB absent) samples to classify X ray images of subjects. Results indicate that MFQL based approach achieves high accuracy and fares much better over contemporary Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) classifiers. Proposed classifier can be a useful tool for pneumonia and tuberculosis diagnosis in a practical setting.     

Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.     

Coccinellid immigration to infested host patches influences suppression of Aphis glycines in soybean

Generalist natural enemies may be well adapted to annual crop systems in which pests and natural enemies re-colonize fields each year. In addition, for patchily-distributed pests, a natural enemy must disperse within a crop field to arrive at infested host patches. As they typically have longer generation times than their prey, theory suggests that generalist natural enemies need high immigration rates to and within fields to effectively suppress pest populations. The soybean aphid, Aphis glycines Matsumura, is a pest of an annual crop and is predominantly controlled by coccinellids. To test if rates of coccinellid arrival at aphid-infested patches are crucial for soybean aphid control, we experimentally varied coccinellid immigration to 1 m² soybean patches using selective barriers and measured effects on A. glycines populations. In a year with low ambient aphid pressure, naturally-occurring levels of coccinellid immigration to host patches were sufficient to suppress aphid populations, while decreasing coccinellid immigration rates resulted in large increases in soybean aphid populations within infested patches. Activity of other predators was low in this year, suggesting that most of the differences in aphid population growth were due to changes in coccinellid immigration. Alternatively, in a year in which alate aphids continually colonized plots, aphid suppression was incomplete and increased activity of other predatory taxa contributed to adult coccinellid predation of A. glycines. Our results suggest that in a system in which natural enemy populations cannot track pest populations through reproduction, immigration of natural enemies to infested patches can compensate and result in pest control.     

Potential Distribution and Risk Assessment of an Invasive Plant Species: A Case Study of Hymenachne amplexicaulis in Australia

Given the limited resources available for weed management, a strategic approach is required to give the “best bang for your buck.” The current study incorporates: (1) a model ensemble approach to identify areas of uncertainty and commonality regarding a species invasive potential, (2) current distribution of the invaded species, and (3) connectivity of systems to identify target regions and focus efforts for more effective management. Uncertainty in the prediction of suitable habitat for H. amplexicaulis (study species) in Australia was addressed in an ensemble-forecasting approach to compare distributional scenarios from four models (CLIMATCH; CLIMEX; boosted regression trees [BRT]; maximum entropy [Maxent]). Models were built using subsets of occurrence and environmental data. Catchment risk was determined through incorporating habitat suitability, the current abundance and distribution of H. amplexicaulis, and catchment connectivity. Our results indicate geographic differences between predictions of different approaches. Despite these differences a number of catchments in northern, central, and southern Australia were identified as high risk of invasion or further spread by all models suggesting they should be given priority for the management of H. amplexicaulis. The study also highlighted the utility of ensemble approaches in indentifying areas of uncertainty and commonality regarding the species’ invasive potential.     

Variation in hippocampal glial cell numbers in food‐caching birds from different climates

Enhancements to memory are associated with enhanced neural structures that support those capabilities. A great deal of work has examined this relationship in the context of natural variation in spatial memory capability and hippocampal (Hp) structure. Most studies have focused on volumetric and neuron measures, but have seldom examined the role of glial cells. Once considered involved only in supportive functions associated with neurons, the importance of glial cells in cognitive processes, including memory, is gaining more attention. Building upon our previous study on the relationship between the brain, memory, and environmental severity in food‐caching birds, we compared the total number of Hp glial cells in wild‐sampled and in lab‐reared (common garden) black‐capped chickadees (Poecile atricapillus) originating from two different environmental extremes. We found that birds from more harsh climate tended to have significantly more Hp glial cells than those from more mild climate and that lab‐reared chickadees had significantly fewer Hp glial cells compared to the wild‐sampled birds. These results suggest that population differences in glial numbers may be controlled, at least in part, by heritable mechanisms, but glial numbers appear to be additionally regulated by an individual's environment. The pattern of Hp glial cell abundance among our treatment groups closely followed that of the Hp volume, suggesting that Hp glial cell number may be associated with the Hp volume. Unlike Hp neurons, however, the number of Hp glial cells may be, at least in part, affected by an individual's experiences and environment. © 2013 Wiley Periodicals, Inc. Develop Neurobiol 73: 480–485, 2013     

Automated analysis of courtship suppression learning and memory in Drosophila melanogaster

Study of the fruit fly, Drosophila melanogaster, has yielded important insights into the underlying molecular mechanisms of learning and memory. Courtship conditioning is a well-established behavioral assay used to study Drosophila learning and memory. Here, we describe the development of software to analyze courtship suppression assay data that correctly identifies normal or abnormal learning and memory traits of individual flies. Development of this automated analysis software will significantly enhance our ability to use this assay in large-scale genetic screens and disease modeling. The software increases the consistency, objectivity, and types of data generated.     

实验诊断学PBL教学案例的设计原则

由临床检验专业人员承担的《实验诊断学》教学应以临床检验学为教学重点,案例的教学目标及学习重点应围绕临床检验基本原理,检验结果质量保证和检验结果影响因素三方面进行。案例选择应遵循以下原则：①选择质量保证的过程中容易出现问题的环节;②选择物质代谢过程清楚,相关疾病的病理生理改变明确的检验项目,如血糖;③选择在,临床检验中应用较多的方法,比如酶的连续监测法。