Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Frequency-dependent conspecific attraction to food patches

In many ecological situations, resources are difficult to find but become more apparent to nearby searchers after one of their numbers discovers and begins to exploit them. If the discoverer cannot monopolize the resources, then others may benefit from joining the discoverer and sharing their discovery. Existing theories for this type of conspecific attraction have often used very simple rules for how the decision to join a discovered resource patch should be influenced by the number of individuals already exploiting that patch. We use a mechanistic, spatially explicit model to demonstrate that individuals should not necessarily simply join patches more often as the number of individuals exploiting the patch increases, because those patches are likely to be exhausted soon or joining them will intensify future local competition. Furthermore, we show that this decision should be sensitive to the nature of the resource patches, with individuals being more responsive to discoveries in general and more tolerant of larger numbers of existing exploiters on a patch when patches are resource-rich and challenging to locate alone. As such, we argue that this greater focus on underlying joining mechanisms suggests that conspecific attraction is a more sophisticated and flexible tactic than currently appreciated.     

一种复合神经元网络结构的并行动力学及快速学习算法

提出两种功能互相不同的神经细胞组成的复合神经元网络(CNN)模型;导出一种特殊结构的CNN的并行动力学;而且证明了它的稳定性。在这些结果基础上,得到快速的假逆矩阵学习算法。计算机仿真试验证实学习算法与动力学稳定性的正确性,并表现出良好的容错性能与存储容量。     

Comparison of RAPD and RFLP genetic markers in determining genetic similarity among Brassica oleracea L. genotypes

Genetic similarity among 45 Brassica Oleracea genotypes was compared using two molecular markers, random amplified polymorphic DNA (RAPD) and restriction fragment length polymorphisms (RFLPs). The genotypes included 37 broccolis (var. italica), five cauliflowers (var. botrytis) and three cabbages (var. capitata) which represented a wide range of commercially-available germplasm, and included open-pollinated cultivars, commercial hybrids, and inbred parents of hybrid cultivars. Fifty-six polymorphic RFLP bands and 181 polymorphic RAPD bands were generated using 15 random cDNA probes and 62 10-mer primers, respectively. The objectives were to compare RFLP and RAPD markers with regard to their (1) sampling variance, (2) rank correlations of genetic distance among sub-samples, and (3) inheritance. A bootstrap procedure was used to generate 200 random samples of size n (n=2,3,5,... 55) independently from the RAPD and RFLP data sets. The coefficient of variance (CV) was estimated for each sample. Pooled regressions of the coefficient of variance on bootstrap sample size indicated that the rate of decrease in CV with increasing sample size was the same for RFLPs and RAPDs. The rank correlation between the Nei-Li genetic similarity values for all pairs of genotypes (990) based on RFLP and RAPD data was 0.745. Differences were observed between the RFLP and RAPD dendrograms of the 45 genotypes. Overlap in the distributions of rank correlations between independent sub-samples from the RAPD data set, compared to correlations between RFLP and RAPD sub-samples, suggest that observed differences in estimation of genetic similarity between RAPDs and RFLPs is largely due to sampling error rather than due to DNA-based differences in how RAPDs and RFLPs reveal polymorphisms. A crossing algorithm was used to generate hypothetical banding patterns of hybrids based on the genotypes of the parents. The results of this study indicate that RAPDs provide a level of resolution equivalent to RFLPs for detemination of the genetic relationships among genotypes.