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.     

Modelling biomass of mountainous grasslands by including a species composition map

High mountain grasslands offer multiple goods and services to society but are severely threatened by improper land use practices such as abandonment or rapid intensification. In order to reduce abandonment and strengthen the common extensive agricultural practice a sustainable land use management of high mountain grasslands is needed. A spatially detailed yield assessment helps to identify possible meadows or, on the contrary, areas with a low carrying capacity in a region, making it easier to manage these sites. Such assessments are rarely available for remote and inaccessible areas. Remotely sensed vegetation indices are able to provide valuable information on grassland properties. These indices tend, however, to saturate for high biomass. This affects their applicability to assessments of high-yield grasslands.The main aim of this study was to model a spatially explicit grassland yield map and to test whether saturation issues can be tackled by consideration of plant species composition in the modelling process. The high mountain grassland of the subalpine belt (1800 – 2500 m a.s.l.) in the Kazbegi region, Greater Caucasus, Georgia, was chosen as test site for its strong species composition and yield gradients.We first modelled the species composition of the grassland described as metrically scaled gradients in the form of ordination axes by random forest regression. We then derived vegetation indices from Rapid Eye imagery, and topographic variables from a digital elevation model, which we used together with the multispectral bands as predictive variables. For comparison, we performed two yield models, one excluding the species composition maps and one including the species composition map as predictors. Moreover, we performed a third individual model, with species composition as predictors and a split dataset, to produce the final yield map.Three main grassland types were found in the vegetation analysis: Hordeum violaceum-meadows, Gentianella caucasea-grassland and Astragalus captiosus-grassland. The three random forest regression models for the ordination axes explained 64%, 33% and 46% of the variance in species composition. Independent validation of modelled ordination scores against a validation data set resulted in an R² of 0.64, 0.32 and 0.46 for the first, second and third axes, respectively. The model based on species composition resulted in a R² = 0.55, whereas the benchmark model showed weaker relationships between yield and the multispectral reflectance, vegetation indices, and topographical parameters (R² = 0.42). The final random forest yield model used to derive the yield map resulted in 62% variance explained and an R² = 0.64 between predicted and observed biomass. The results further indicate that high yields are generally difficult to predict with both models.The benefit of including a species composition map as a predictor variable for grassland yield lies in the preservation of ecologically meaningful features, especially the occurrence of high yielding vegetation type of Hordeum violaceum meadows is depicted accurately in the map. Even though we used a gradient based design, sharp boundaries or immediate changes in productivity were visible, especially in small structures such as arable fields or roads (Fig. 6b), making it a valuable tool for sustainable land use management. The saturation effect however, was mitigated by using species composition as predictor variables but is still present at high yields.     

Phyletic and evolutionary relationships ofBrachyscome lineariloba (Compositae)

A comparison of karyotypes ofBrachyscome breviscapis (2n = 8),B. lineariloba cytodemes E (2n = 10), B (2n = 12) and C (2n = 16) suggests that these species have a homoelogous basic set of four chromosome pairs, two large pairs and two small, and that theB. lineariloba cytodemes E, B and C are related toB. breviscapis by successive additions of small chromosomes. A pronounced asynchrony of chromosome condensation between these large and small chromosomes has been observed. In the artificial hybrids betweenB. dichromosomatica (2n = 4) ×B. breviscapis, and theB. lineariloba cytodemes, theB. dichromosomatica chromosomes are similar in size and condensation behaviour to the small chromosomes ofB. breviscapis and ofB. lineariloba cytodemes E, B and C. Meiotic pairing in these hybrids also demonstrates the strong affinities between these chromosomes. It is suggested thatB. breviscapis may be of amphidiploid origin between a species with two large early condensing chromosome pairs and another,B. dichromosomatica-like species with two small late condensing pairs. It seems most likely that the additional small and late condensing chromosomes inB. lineariloba cytodemes E, B and C are derived from theB. dichromosomatica-like parent, and that each addition increases vigour, fecundity and drought tolerance, allowing these cytodemes to colonize more open and arid environments. Transmission of the univalents in the quasidiploidB. lineariloba cytodeme E was verified as being via the pollen, and not via the embryo sacs.The cytology ofBrachyscome lineariloba (Compositae, Asteroidae), 10.     

猪肺炎支原体膜上ATP酶生化性质的研究

猪肺炎支原体膜上ATP酶为Mg~(2+)激活,乌巴因不抑制。DCCD和寡霉素对该酶也无抑制作用,只有NBD与Quercetin才有一定的抑制效果。用梯度凝胶电泳可获均一的具有活性的酶蛋白带。     

Protonmotive force in freshwater sulfate-reducing bacteria,and its role in sulfate accumulation in Desulfobulbus propionicus

The protonmotive force in several sulfate-reducing bacteria has been determined by means of radiolabelled membrane-permeant probes (tetraphenyl-phosphonium cation, TPP⁺, for , and benzoate for pH). In six of ten freshwater strains tested only the pH gradient could be determine, while the membrane potential was not accessible due to nonspecific binding of TPP⁺. The protonmotive force of the other four strains was between –110 and –155 mV, composed of a membrane potential of –80 to –140 mV and a pH gradient between 0.25 and 0.8 (inside alkaline) at pH_out=7. In Desulfobulbus propionicus the pH gradient decreased with rising external pH values. This decrease, however, was compensated by an increasing membrane potential. Sulfate, which can be highly accumulated by the cells, did not affect the protonmotive force, if added in concentrations of up to 4 mM. The highest sulfate accumulation observed (2500-fold), which occurred at external sulfate concentrations below 5 M, could be explained by a symport of three protons per sulfate, if equilibrium with the protonmotive force was assumed. At higher sulfate concentrations the accumulation decreased and suggested an electroneutral symport of two protons per sulfate. At sulfate concentrations above 500 M, the cells stopped sulfate uptake before reaching an equilibrium with the protonmotive force.Abbreviations CCCP carbonyl cyanide m-chlorophenylhydrazone - MOPS morpholinopropanesulfonic acid - TPP⁺ tetraphenylphosphonium cation - EDTA ethylenediaminetetraacetic acid - pH transmembrane pH gradient (pH_in-pH_out) - transmembrane electrical potential difference     

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。