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.     

基于指标自动筛选的新疆开孔河流域生态健康评价

生态健康评价对了解区域生态健康状况和促进区域可持续发展具有重要意义,如何自动筛选出能反映生态系统特性的重要指标,是生态健康定量评估的关键问题。基于压力-状态-响应(PSR,Press-State-Response)框架和生态等级网络框架(EHN,Ecological Hierarchy Network),通过文献调研和因果分析建立要素层与指标层之间的交叉联系,构建了生态健康评价"网状"指标体系;在保证指标体系完备性基础上,通过结合主成分分析和熵权法的候选指标权重的客观计算,基于目标优化理论构建了评价指标的自动筛选模型,并基于中选指标计算了新疆开孔河流域2001—2017年生态健康指数(EHCI,Ecological Health Comprehensive Indexes),分析其空间分异和时间变化特征。结果表明:利用所建立的评价指标自动筛选模型,开孔河流域生态健康评价指标由31个候选指标自动筛选出了17个中选指标,用54.8%的指标表达了85.98%的信息,中选的17个指标在干旱/半干旱区域有关文献中应用较多,使用频次比例都在20%以上,其中归一化植被指数(NDVI,Normalized Difference Vegetation Index)、年降水量和植被覆盖度(FVC,Fractional Vegetation Coverage)3个指标的使用频次百分比均超过了50%,说明指标自动筛选模型的合理性;开孔河流域空间分布差异显著,总体上西北高、东南低,东南部和中部绿洲区外围生态健康状况较差,西北部河谷地带和中部两大绿洲区生态健康状况较好;17年来,流域生态质量整体趋于改善,显著改善区域占10.26%,远高于显著退化的1.61%,显著改善区域以孔雀河绿洲最为明显。开孔河流域生态健康的总体好转趋势说明区域生态综合治理取得一定成效。     

Ecological and social determinants of association and proximity patterns in the fission–fusion society of spider monkeys (Ateles geoffroyi)

Some social species exhibit high levels of fission–fusion dynamics (FFD) that improve foraging efficiency. In this study, we shed light on the way that FFD allows animal groups to cope with fluctuations in fruit availability. We explore the relative contribution of fruit availability and social factors like sex in determining association and proximity patterns in spider monkeys. We tested the influence of fruit availability and social factors on the association and proximity patterns using three-year data from a group of spider monkeys in the Yucatan Peninsula of Mexico. We identified subgroup members and estimated their Interindividual distances through instantaneous scan sampling. We evaluated fruit availability by monitoring the phenology of the 10 most important food tree species for spider monkeys in the study site. Social network analyses allowed us to evaluate association and proximity patterns in subgroups. We showed that association patterns vary between seasons, respond to changes in fruit availability, and are influenced by the sex of individuals, likely reflecting biological and behavioral differences between sexes and the interplay between ecological and social factors. In contrast, proximity patterns were minimally affected by changes in fruit availability, suggesting that social factors are more important than food availability in determining cohesion within subgroups.     

Biogeographical network analysis of plant species distribution in the Mediterranean region

The delimitation of bioregions helps to understand historical and ecological drivers of species distribution. In this work, we performed a network analysis of the spatial distribution patterns of plants in south of France (Languedoc‐Roussillon and Provence‐Alpes‐Côte d'Azur) to analyze the biogeographical structure of the French Mediterranean flora at different scales. We used a network approach to identify and characterize biogeographical regions, based on a large database containing 2.5 million of geolocalized plant records corresponding to more than 3,500 plant species. This methodology is performed following five steps, from the biogeographical bipartite network construction to the identification of biogeographical regions under the form of spatial network communities, the analysis of their interactions, and the identification of clusters of plant species based on the species contribution to the biogeographical regions. First, we identified two sub‐networks that distinguish Mediterranean and temperate biota. Then, we separated eight statistically significant bioregions that present a complex spatial structure. Some of them are spatially well delimited and match with particular geological entities. On the other hand, fuzzy transitions arise between adjacent bioregions that share a common geological setting, but are spread along a climatic gradient. The proposed network approach illustrates the biogeographical structure of the flora in southern France and provides precise insights into the relationships between bioregions. This approach sheds light on ecological drivers shaping the distribution of Mediterranean biota: The interplay between a climatic gradient and geological substrate shapes biodiversity patterns. Finally, this work exemplifies why fragmented distributions are common in the Mediterranean region, isolating groups of species that share a similar eco‐evolutionary history.     

Detecting early-warning signals for influenza A pandemic based on protein dynamical network biomarkers

The outbreak of influenza A comes from a relatively stable state is a critical phenomenon on epidemic. In this paper, influenza A varying from different states is studied in the method of dynamical network biomarkers (DNB). Through studying DNB of influenza A virus protein, we can detect the warning signals of outbreak for influenza A and obtain a composite index. The composite index varies along with the state of pandemic influenza, which gives a clue showing the turn point of outbreak. The low value (<1) steady state of the composite index means influenza A is normally in the relatively steady stage. Meanwhile, if the composite index of a certain year increases by more than 0.8 relative to the previous year and it is less than 1 and it increases sharply and reaches a peak being larger than 1 in next year, it means the year is normal in the critical state before outbreak and the next year is normally in the outbreak state. Therefore, we can predict the outbreak of influenza A and identify the critical state before influenza A outbreak or outbreak state by observing the variation of index value.     

Historical demographic profiles and genetic variation of the East African Butana and Kenana indigenous dairy zebu cattle

Butana and Kenana breeds from Sudan are part of the East African zebu Bos indicus type of cattle. Unlike other indigenous zebu cattle in Africa, they are unique due to their reputation for high milk production and are regarded as dairy cattle, the only ones of their kind on the African continent. In this study, we sequenced the complete mitochondrial DNA (mtDNA) D‐loop of 70 animals to understand the maternal genetic variation, demographic profiles and history of the two breeds in relation to the history of cattle pastoralism on the African continent. Only taurine mtDNA sequences were identified. We found very high mtDNA diversity but low level of maternal genetic structure within and between the two breeds. Bayesian coalescent‐based analysis revealed different historical and demographic profiles for the two breeds, with an earlier population expansion in the Butana vis a vis the Kenana. The maternal ancestral populations of the two breeds may have diverged prior to their introduction into the African continent, with first the arrival of the ancestral Butana population. We also reveal distinct demographic history between the two breeds with the Butana showing a decline in its effective population size (N_e) in the recent past ~590 years. Our results provide new insights on the early history of cattle pastoralism in Sudan indicative of a large ancient effective population size.     

基于多尺度协同的长沙市生态网络构建与层级优化

城市化背景下人类与自然环境的矛盾呈现出多尺度、层级化特征，而传统生态网络的构建方式较少考虑不同尺度下生态要素的关系，无法从区域落实到中心城区，难以形成系统性的解决方案。研究在综合梳理各尺度生态网络构建方法的基础上，以长沙市为例，基于形态学空间格局分析（Morphological Spatial Pattern Analysis，MSPA）、景观连通性原理和生态斑块重要性评价识别生态源地，并通过多层级生态阻力面的确定，综合运用最小费用路径（Least-cost path method，LCP）、电路理论、层级传导理论、尺度嵌套等方法对市域、都市区、中心城区的生态网络进行了协同构建和层级优化，最后基于不同尺度生态网络的特点应用并落实到多层级的国土空间规划体系中。研究结果表明：（1）长沙市域生态网络和都市区生态网络具有较好的层级嵌套特征；共识别两尺度生态叠合源地14个、生态叠合廊道15条，主要通过中心城区内的湘江、浏阳河和捞刀河部分河段与外围生态绿圈相衔接，形成"外环内楔"的空间格局。（2）确定市域重要廊道、市域潜在廊道、生态叠合廊道、都市区重要廊道、都市区潜在廊道的核心保护面积共501.14 km²，并提取位于生态廊道核心保护区范围内的生态夹点和生态障碍点，以进一步落实生态保护修复策略。（3）得到具有重要生态连通功能的中心城区生态绿道长度441.2 km，生态修复单元56个，并结合生态阻力值划分为5级进行针对性修复。（4）基于不同尺度生态网络的衔接、嵌套，最终构建"市域总体生态安全格局-都市区城市生态空间发展格局-以城市绿道为基础的中心城区生态修复单元"，并与不同层级的国土空间规划体系相对应。研究结果将为以大城市为中心的跨尺度生态系统修复和生态安全格局构建提供科学参考。     

Energetics and protomer communication in the dynamical structure of S100A13 in free and protein-bound states

S100A13 is S100 family of EF-hand-containing calcium-binding protein involved in the secretion of some growth factors and pro-inflammatory cytokines lacking signal peptides. The involvement of S100A13 in cancer progression and inflammatory diseases has been reported. In this study, structures generated during atomistic molecular dynamics simulation were studied. Dynamical network analysis data revealed that native inter-protomer communication network driven principally by vdW interaction (~550 kj/mol) is altered (Receptor for advanced glycation end products (RAGE) C2- and Fibroblast growth factor (FGF)-1-bound S100A13) or completely abolished (interleukin-1 (IL1)-α- and C2A-p40Syt1-bound S100A13) in protein-bound S100A13 homodimer. Bulk water density (weighted atomic density) around exposed S100A13 homodimer surface explored tends to follow the dynamical network lead as S100A13 homodimer appeared densely solvated in C2A-p40Syt1- and IL1)-α-bound states but not in RAGE C2- and FGF-1-bound biosystems. Furthermore, projection of radius of gyration and root mean square deviation (from native structure) variables of the generated structures along the 3D-free energy surface showed anti-parallel β-sheet proximal to Ca²⁺-binding loops-I/II in most metastable complexes retrieved from energy minima state with strong indications for β-sheet network formation during protein binding. Interaction between S100A13 homodimer and ligand–proteins may be dictated by the strength of vdW and electrostatic interaction with possible involvement of bulk water desolvation in some complexes. All these results strongly suggest that disruption of multiprotein receptor complex can be achieved by designing specific compounds targeting a specific aspect of S100A13/protein interaction; such drugs may have clinical usefulness in blocking angiogenesis, reversing cell proliferation and attenuating inflammatory processes.