Physiological model using diffuse reflectance spectroscopy for nonmelanoma skin cancer diagnosis

Diffuse reflectance spectroscopy (DRS) is a noninvasive, fast, and low‐cost technology with potential to assist cancer diagnosis. The goal of this study was to test the capability of our physiological model, a computational Monte Carlo lookup table inverse model, for nonmelanoma skin cancer diagnosis. We applied this model on a clinical DRS dataset to extract scattering parameters, blood volume fraction, oxygen saturation and vessel radius. We found that the model was able to capture physiological information relevant to skin cancer. We used the extracted parameters to classify (basal cell carcinoma [BCC], squamous cell carcinoma [SCC]) vs actinic keratosis (AK) and (BCC, SCC, AK) vs normal. The area under the receiver operating characteristic curve achieved by the classifiers trained on the parameters extracted using the physiological model is comparable to that of classifiers trained on features extracted via Principal Component Analysis. Our findings suggest that DRS can reveal physiologic characteristics of skin and this physiologic model offers greater flexibility for diagnosing skin cancer than a pure statistical analysis. Physiological parameters extracted from diffuse reflectance spectra data for nonmelanoma skin cancer diagnosis.     

Identification of gravid mosquitoes from changes in spectral and polarimetric backscatter cross sections

Improving the survey of mosquito populations is of the utmost importance to further enhance mitigation techniques that protect human populations from mosquito‐borne diseases. While mosquito populations are generally studied using physical traps, stand‐off optical sensors allow to study insect ecosystems with potentially better spatial and temporal resolution. This can be greatly beneficial to eco‐epidemiological models and various mosquito control programs. In this contribution, we demonstrate that the gravidity of female mosquitoes can be identified from changes in their spectral and polarimetric backscatter cross sections. Among other predictive variables, the wing beat frequency and the depolarization ratio of the mosquito body allows for the identification of gravid females with a precision and recall of 86% and 87%, respectively. Since female mosquitoes need a blood meal to become gravid, statistics on gravidity is of prime importance as only females that have been gravid might carry infectious diseases. In addition, it allows to detect possible breeding habitat, predict a potential increase in the mosquito population and provide a better overall understanding of the ecosystem dynamics. As a result, targeted and localized mitigation techniques can be used, reducing the cost and improving the efficiency of mosquito population control.     

Spectral histopathology of the lung: A review of two large studies

This paper summarizes results from two large lung cancer studies comprising over 700 samples that demonstrate the ability of spectral histopathology (SHP) to distinguish cancerous tissue regions from normal tissue, to differentiate benign lesions from normal tissue and cancerous lesions, and to classify lung cancer types. Furthermore, malignancy‐associated changes can be identified in cancer‐adjacent normal tissue. The ability to differentiate a multitude of normal cells and tissue types allow SHP to identify tumor margins and immune cell infiltration. Finally, SHP easily distinguishes small cell lung cancer (SCLC) from non‐SCLC (NSCLC) and provides a further differentiation of NSCLC into adenocarcinomas and squamous cell carcinomas with an accuracy comparable of classical histopathology combined with immunohistochemistry. Case studies are presented that demonstrates that SHP can resolve interobserver discrepancies in standard histopathology.     

A biogeographical analysis of Muhlenbergia (Poaceae: Chloridoideae: Cynodonteae: Muhlenbergiinae)

A phylogeny based on the analysis of six DNA sequence markers (ITS, ndhA intron, rpl32-trnL, rps3, rps16 intron, and rps16-trnK) is used to infer ancestral areas and divergence times, and reconstruct the biogeographical history and evolution of 150 of the 183 (82%) species of Muhlenbergia. Our results suggest that the genus originated 9.3 mya in the Sierra Madre (Occidental and Oriental) in Mexico, splitting into six lineages: M. ramulosa diverging 8.2 mya, M. subg. Muhlenbergia at 5.9 mya, M. subg. Pseudosporobolus at 5.9 mya, M. subg. Clomena at 5.4 mya, M. subg. Bealia at 4.3 mya, and M. subg. Trichochloa at 1 mya, each of these with a high probability of Sierra Madrean origin. Our results further suggest that founder-event speciation from Sierra Madre to South America occurred independently multiple times in all five subgenera during the Pleistocene and late Pliocene. One long-distance dispersal event most likely originating from Central or Eastern North America to East and Central Asia occurred 1.6–1 mya in M. subg. Muhlenbergia. In our cladogram, members of M. subg. Trichochloa show little genetic resolution, suggesting very low levels of divergence among the species, and this may be a consequence of rapid radiation.     

VPMCD: variable interaction modeling approach for class discrimination in biological systems

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.     

Selection of relevant features for EEG signal classification of schizophrenic patients

In this paper, EEG signals of 20 schizophrenic patients and 20 age-matched control participants are analyzed with the objective of determining the more informative channels and finally distinguishing the two groups. For each case, 22 channels of EEG were recorded. A two-stage feature selection algorithm is designed, such that, the more informative channels are first selected to enhance the discriminative information. Two methods, bidirectional search and plus-L minus-R (LRS) techniques are employed to select these informative channels. The interesting point is that most of selected channels are located in the temporal lobes (containing the limbic system) that confirm the neuro-phychological differences in these areas between the schizophrenic and normal participants. After channel selection, genetic algorithm (GA) is employed to select the best features from the selected channels. In this case, in addition to elimination of the less informative channels, the redundant and less discriminant features are also eliminated. A computationally fast algorithm with excellent classification results is obtained. Implementation of this efficient approach involves several features including autoregressive (AR) model parameters, band power, fractal dimension and wavelet energy. To test the performance of the final subset of features, classifiers including linear discriminant analysis (LDA) and support vector machine (SVM) are employed to classify the reduced feature set of the two groups. Using the bidirectional search for channel selection, a classification accuracy of 84.62% and 99.38% is obtained for LDA and SVM, respectively. Using the LRS technique for channel selection, a classification accuracy of 88.23% and 99.54% is also obtained for LDA and SVM, respectively. Finally, the results are compared and contrasted with two well-known methods namely, the single-stage feature selection (evolutionary feature selection) and principal component analysis (PCA)-based feature selection. The results show improved accuracy of classification in relatively low computational time with the two-stage feature selection.     

Diversity of tropical forest landscape types in Hainan Island, China

A novel landscape classification system was proposed in this study based on landscape ecological theory and the differentiation in climate, topography, soil, vegetation and land use mode. Five basic units (zone, tract, province, region and type) and two assistant units (sub and group) were used in this system. The tropical forest landscape in Hainan Island was regarded as a landscape province, belonging to global tropical forest landscape zone, Asiatic (oriental) tropical forest landscape tract and Chinese tropical forest landscape subtract. Based on the grade system of region, sub-region, type-group and type, this landscape province (Hainan Island) is divided into 6 landscape regions (east moist forest landscape, west semi-arid forest landscape, central-south mountainous forest landscape, tropical evergreen needle-leaved forest landscape, tropical bamboo landscape and tropical plantation landscape), 11 tropical forest landscape sub-regions represented by tropical lowland valley rain forest landscape, 26 tropical forest landscape-type groups represented by tropical lowland valley Dipterocarpaceae forest landscape, and 54 forest landscape types (FLT) represented by lowland valley Vatica mangachapoi forest. Generally, this classification system represents the landscape diversity of the tropical forest in Hainan Island. Further studies are needed to better understand the landscape diversity of Hainan Island.     

Seeking causes: Classifying and evaluating congenital heart defects in etiologic studies

BACKGROUND: Classification and analysis of congenital heart defects (CHD) in etiologic studies is particularly challenging because of diversity of cardiac phenotypes and underlying developmental mechanisms. We describe an approach to classification for risk assessment of CHD based on developmental and epidemiologic considerations, and apply it to data from the National Birth Defect Prevention Study (NBDPS). METHODS: The classification system incorporated the three dimensions of cardiac phenotype, cardiac complexity, and extracardiac anomalies. The system was designed to facilitate the assessment of simple isolated defects and common associations. A team with cardiologic expertise applied the system to a large sample from the NBDPS. RESULTS: Of the 4,703 cases of CHDs in the NBDPS with birth years 1997 through 2002, 63.6% were simple, isolated cases. Specific associations of CHDs represented the majority of the remainder. The mapping strategy generated relatively large samples for most cardiac phenotypes and provided enough detail to isolate important subgroups of CHDs that may differ by etiology or mechanism. CONCLUSIONS: Classification of CHDs that considers cardiac and extracardiac phenotypes is practically feasible, and yields manageable groups of well-characterized phenotypes. Although best suited for large studies, this approach to classification and analysis can be a flexible and powerful tool in many types of etiologic studies of heart defects.     

一种基于数码相机图像和群落冠层结构调查的草地地上生物量估算方法

草地地上生物量是影响其生态系统功能最重要的因素之一, 也是草地生态学研究中不可或缺的监测指标。草地地上生物量监测多采用收割法进行, 但这种破坏性取样方法会对研究区域带来巨大干扰, 尤其是面积较小的长期定位监测或者控制实验研究样地, 从而使得地上生物量监测的频次受到很大限制。因此, 通过获取某些原位易测变量, 建立地上生物量的估算方法具有重要意义。该研究依托内蒙古典型草地刈割控制实验平台, 通过数码照片获取不同土地利用方式下的植被覆盖度, 并对样方内的叶面积指数、植被高度、物种多样性等参数进行了测定, 最后利用一元回归模型、逐步回归模型和随机森林模型对地上生物量进行估算。结果表明, 植被覆盖度、叶面积指数、植被平均高度、植被最大高度和物种丰富度是影响地上生物量的主要驱动因素。通过构建适宜于本地的逐步回归模型, 可以实现草地地上生物量的准确预测。在该研究区域中, 预测模型的决定系数(R²) = 0.91, 均方根误差(RMSE) = 35.60 g·m^-2。该研究提供了一种快速、准确且非破坏性测定草地地上生物量的方法, 可作为传统收割法的有效补充。     

《中国植被志》研编规范的若干说明、补充与修订

前期编制的《中国植被志》研编技术规范为《中国植被志》的研编工作提供了标准和指南。但在后续研编过程中发现存在一些具体问题和不足。针对这些问题和不足, 该文主要就《中国植被志》研编中的“植被类型”及志书卷册划分、中低级植被分类单元群落数量特征的记述以及文献记载的群落类型的收录等内容进行了补充和修订, 并对植被分类方法做了进一步说明。这些补充和修订内容与前期的技术规范共同成为《中国植被志》研编的依据和标准。