Application of Statistical Classification Technique to the Forecasting of Outcomes of Diseases

A probabilistic statistical model based on statistical classification is proposed for disease outcome forecasting. In particular, cardiac infarction outcome is forecasted and a table of the forecasting results is given. A conclusion of theoretical character is made about the preferable use of a non-uniform band width in the construction of a general histogram for unknown distribution density estimation.     

Forecasting model of bacterial leaf spot disease of mulberry caused by Xanthomonas campestris pv. mori

In order to develop weather-based forecasting model of bacterial leaf spot (BLS) disease of mulberry caused by Xanthomonas campestris pv. mori, weekly disease severity data were recorded for three years on the ruling cultivar S-1. Daily meteorological data viz. maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, rainfall and number of rainy days were also recorded. It was observed that BLS appeared in April/May and continued up to November with maximum severity in July. The correlation coefficient between disease severity and meteorological parameters revealed that the BLS disease severity has significant positive correlation with minimum temperatures, maximum and minimum relative humidity, rainfall and number of rainy days and negative correlation with maximum temperature. Multiple regressions analysis revealed that average of maximum temperature, minimum temperature and rainfall of preceding seven days and maximum relative humidity, minimum relative humidity of previous 9–15 days was found to maximally influence BLS disease severity. The contribution of the meteorological factors was found to be highest of minimum temperature (40.65%) followed by maximum temperature (24.20%), maximum relative humidity (16.41%), minimum relative humidity (8.07%), rainfall (5.29%) and number of rainy days (5.38%).     

Development of a pest threshold decision support system for minimising damage to winter wheat from wheat bulb fly,Delia coarctata

Wheat bulb fly, Delia coarctata, is an important pest of winter wheat in the UK, causing significant damage of up to 4 t/ha. Accepted population thresholds for D. coarctata are 250 eggs/m² for crops sown up to the end of October and 100 eggs/m² for crops sown from November. Fields with populations of D. coarctata that exceed the thresholds are at higher risk of experiencing economically damaging infestations. In the UK, recent withdrawal of insecticides means that only a seed treatment (Signal 300 ES) is available for chemical control of D. coarctata; however, this is only effective for late-sown crops and accurate estimations of annual population levels are required to ensure a seed treatment is applied if needed. As a result of the lack of postdrilling control strategies, the management of D. coarctata is becoming reliant on nonchemical methods of control. Control strategies that are effective in managing similar stem-boring pests of wheat include sowing earlier and using higher seed rates to produce crops with greater pest tolerance. In this study, we develop two predictive models that can be used for integrated D. coarctata management. The first is an updated pest level prediction model that predicts D. coarctata populations from meteorological parameters with a predictive accuracy of 70%, a significant improvement on previous prediction models. Our second model predicts the maximum number of shoots for a winter wheat crop that would be expected at the terminal spikelet development stage. This shoot number model uses information about the thermal time from plant emergence to terminal spikelet, leaf phyllochron length, plant population and sowing date to predict the degree of tolerance a crop will have against D. coarctata. The shoot number model was calibrated against data collected from five field experiments and tested against data from four experiments. Model testing demonstrated that the shoot number model has a predictive accuracy of 65.7%. The foundation for a future decision support system using these models for the sustainable management of D. coarcata risk is described. It should be noted that these models represent a stepping-stone towards a decision support system and that further model validation over a wider geographic range is required.     

Determining the geographic origin of the brown planthopper,Nilaparvata lugens,using trace element content

Abstract A chemometric study using pattern recognition technology was carried out to characterize the geographic origins of the brown planthopper, Nilaparvata lugens. The concentrations of 23 trace elements (Mn, Mo, Cd, Ce, V, Th, Cs, Be, Tl, Fe, Nd, Pr, Se, Tm, Lu, Eu, Ho, Br, Dy, Gd, U, Sm and Er) in 53 samples from seven regions in southern China were determined using inductively coupled plasma mass spectrometry. The data obtained were successively evaluated using a multivariate statistical approach, namely, linear discriminant analysis, which allowed classification and discrimination of the N. lugens samples from Fuqing, Shaoguan, Hepu, Yongfu, Hengnan, Wan‐an and Yongkang with high accuracy and a clear separation among the seven regions. The results show that pattern identification on the basis of trace elements in the bodies of N. lugens is feasible for determining the geographic origins of individuals.     

Temperature-dependent dynamics of the villin headpiece helical subdomain,an unusually small thermostable protein

(15)N spin relaxation experiments were used to measure the temperature-dependence of protein backbone conformational fluctuations in the thermostable helical subdomain, HP36, of the F-actin-binding headpiece domain of chicken villin. HP36 is the smallest domain of a naturally occurring protein that folds cooperatively to a compact native state. Spin-lattice, spin-spin, and heteronuclear nuclear Overhauser effect relaxation data for backbone amide (15)N spins were collected at five temperatures in the range of 275-305 K. The data were analyzed using a model-free formalism to determine generalized order parameters, S, that describe the distribution of N-H bond vector orientations in a molecular reference frame. A novel parameter, Lambda=dln(1-S)/dln T is introduced to characterize the temperature-dependence of S. An average value of Lambda=4.5 is obtained for residues in helical conformations in HP36. This value of Lambda is not reproduced by model potential energy functions commonly used to parameterize S. The maximum entropy principle was used to derive a new model potential function that reproduces both S and Lambda. Contributions to the entropy, S(r), and heat capacity, C(r)(p), from reorientational conformational fluctuations were analyzed using this potential energy function. Values of S(r) show a qualitative dependence on S similar to that obtained for the diffusion-in-a-cone model; however, quantitative differences of up to 0.5k, in which k is the Boltzmann constant, are observed. Values of C(r)(p) approach zero for small values of S and approach k for large values of S; the largest values of C(r)(p) are predicted to occur for intermediate values of S. The results suggest that backbone dynamics, as probed by relaxation measurements, make very little contribution to the heat capacity difference between folded and unfolded states for HP36.     

A hierarchical spatiotemporal analog forecasting model for count data

Analog forecasting is a mechanism‐free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous applications of analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes, but it has typically been presented in an empirical or heuristic procedure, rather than as a formal statistical model. The methodology presented here extends the model‐based analog method of McDermott and Wikle (Environmetrics, 27, 2016, 70) by placing analog forecasting within a fully hierarchical statistical framework that can accommodate count observations. Using a Bayesian approach, the hierarchical analog model is able to quantify rigorously the uncertainty associated with forecasts. Forecasting waterfowl settling patterns in the northwestern United States and Canada is conducted by applying the hierarchical analog model to a breeding population survey dataset. Sea surface temperature (SST) in the Pacific Ocean is used to help identify potential analogs for the waterfowl settling patterns.     

Model-free conditional screening for ultrahigh-dimensional survival data via conditional distance correlation

How to select the active variables that have significant impact on the event of interest is a very important and meaningful problem in the statistical analysis of ultrahigh-dimensional data. In many applications, researchers often know that a certain set of covariates are active variables from some previous investigations and experiences. With the knowledge of the important prior knowledge of active variables, we propose a model-free conditional screening procedure for ultrahigh dimensional survival data based on conditional distance correlation. The proposed procedure can effectively detect the hidden active variables that are jointly important but are weakly correlated with the response. Moreover, it performs well when covariates are strongly correlated with each other. We establish the sure screening property and the ranking consistency of the proposed method and conduct extensive simulation studies, which suggests that the proposed procedure works well for practical situations. Then, we illustrate the new approach through a real dataset from the diffuse large-B-cell lymphoma study S1 .     

Escaping from prison: an economic reassessment of Rutland Water

Recently in the water industry interest has been growing in solutions to widening demand-supply gaps. These include demand-management elements. The paper examines the 1970 decision by Parliament to build Rutland Water and concludes that, despite over-predictions of water demands, the very few hard facts then available concerning water economy measures would probably not have prevented the reservoir being authorised, had they been taken into account. By 1981, however, dramatic reductions in expected population and per capita public water supply growth had occurred, with much more evidence available concerning the scope of demand-management. It is suggested a similar decision on economic grounds would probably have been a negative one.     

数学判别模型在预测害虫种群动态上的应用

根据两个总体的Fisher判别准则,建立了预测害虫种群动态的数学判别模型,对山东省惠民县1967～1977年共11年二代棉铃虫发生程度的两类资料进行了数量分析,建立了数学模型：y＝0．0127x1－0．023X2,对历史资料的回代验证与独立样本的预测,符合率在90％以上。     

A HAB warning system for shellfish harvesting in Portugal

The development of sustainable shellfish aquaculture is highly dependent on the provision of reliable monitoring and predictive information on the occurrence of harmful algal blooms (HABs). The Portuguese HAB early warning system and shellfish closures presented here is a prototype, developed in the ASIMUTH project. It relies on weekly monitoring data composed of observations of HAB species and toxin concentrations within shellfish, and ocean circulation forecasts generated by an operational oceanographic model. The shellfish harvesting areas comprise coastal areas, estuaries + rías and coastal lagoons. The weekly bulletin characterizes the current shellfish closure situation and next week's forecasts for potentially impacted areas. The period analyzed ranged from 27 July 2013 to 17 March 2014, and describes the first skill assessment of the warning system. The forecast accuracy was evaluated, considering the number of forecasts that were verified to be correct the following week (85%) as well as the number of events not forecasted (false negatives, 12%) and those expected but did not occur (false positives, 3%). Variations were most visible in the first weeks of bulletin implementation and during autumn–winter months. The complementary use of field data, remote sensing and operational models led to more accurate predictions of blooms and range of the event.