东北地区草本植物1个新记录科和3个新记录属

东北地区位于我国东北部,共有维管束植物164科928属3 103种。根据野外调查结果及相关资料,报道了东北地区草本植物1个新记录科—粟米草科(Molluginaceae)和3个新记录属—粟米草属(Mollugo L.)、牛膝属(Achyranthes L.)和秃疮花属(Dicranostigma Hook. f. et Thoms.),以及对应的3个新纪录种—粟米草(Mollugo stricta L.)、牛膝(Achyranthes bidentata Blume)和秃疮花[Dicranostigma leptopodum(Maxim.) Fedde]。新记录种凭证标本保存于大连自然博物馆植物标本室。这些新记录草本植物的发现,对东北地区生物多样性的研究具有重要意义,为其在我国的地理分布研究提供了新资料。     

Patellofemoral cartilage stresses are most sensitive to variations in vastus medialis muscle forces

The purpose of this study was to evaluate the effects of variations in quadriceps muscle forces on patellofemoral stress. We created subject-specific finite element models for 21 individuals with chronic patellofemoral pain and 16 pain-free control subjects. We extracted three-dimensional geometries from high resolution magnetic resonance images and registered the geometries to magnetic resonance images from an upright weight bearing squat with the knees flexed at 60°. We estimated quadriceps muscle forces corresponding to 60° knee flexion during a stair climb task from motion analysis and electromyography-driven musculoskeletal modelling. We applied the quadriceps muscle forces to our finite element models and evaluated patellofemoral cartilage stress. We quantified cartilage stress using an energy-based effective stress, a scalar quantity representing the local stress intensity in the tissue. We used probabilistic methods to evaluate the effects of variations in quadriceps muscle forces from five trials of the stair climb task for each subject. Patellofemoral effective stress was most sensitive to variations in forces in the two branches of the vastus medialis muscle. Femur cartilage effective stress was most sensitive to variations in vastus medialis forces in 29/37 (78%) subjects, and patella cartilage effective stress was most sensitive to variations in vastus medialis forces in 21/37 (57%) subjects. Femur cartilage effective stress was more sensitive to variations in vastus medialis longus forces in subjects classified as maltrackers compared to normal tracking subjects (p?=?0.006). This study provides new evidence of the importance of the vastus medialis muscle in the treatment of patellofemoral pain.     

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.