稻茬麦根系构型的定向分型分析

为探索稻茬麦根构型的方向性,使用田间数字化仪实现稻茬麦根系的数值化,将根系数据导入Pro-E重构出根系的空间状态图,然后将根构型每隔10°进行各向投影,计算根系构型在18个维度的分形维数与分形丰度.结果表明:小麦苗期根构型在各维度的分形特征具有较强的规律性,表明根系在土体中的分布具有明显的方向性.在苗期到返青期,根构型在18个维度的分形指标波动性大,表明这一时期内根系生长处于持续的动态变化过程.在拔节期,根构型在各维度的分形再次呈现出一定的规律性,表明根系在土体中的分布重新表现出明显的方向性.该研究方法可以精准描述和分析植物根系在田间环境中的分布状况.     

Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach

The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing opportunities for scientific discoveries related to health and diseases. However, working with cytometry data often imposes complex computational challenges due to high-dimensionality, large size, and nonlinearity of the data structure. In addition, cytometry data frequently exhibit diverse patterns across biomarkers and suffer from substantial class imbalances which can further complicate the problem. The existing methods of cytometry data analysis either predict cell population or perform feature selection. Through this study, we propose a “wisdom of the crowd” approach to simultaneously predict rare cell populations and perform feature selection by integrating a pool of modern machine learning (ML) algorithms. Given that our approach integrates superior performing ML models across different normalization techniques based on entropy and rank, our method can detect diverse patterns existing across the model features. Furthermore, the method identifies a dynamic biomarker structure that divides the features into persistently selected, unselected, and fluctuating assemblies indicating the role of each biomarker in rare cell prediction, which can subsequently aid in studies of disease progression.     

Mesoscopic Models of Plants Composed of Metallic Nanowires

Various metallic structures of complex shape resembling living plant organisms (biomimetics) are produced as a result of self-assembly of nanowires growing on porous membranes in the course of pulse current electrodeposition. These structures occur if the electroplating is continued after the nanowires appear on the membrane surface. By varying the membrane geometry, pulse current electroplating parameters, and alternating electrodeposition from two baths composed of a variety of electrolytes, diverse models were fabricated, including a hollow container with a wall thickness of 10 nm – 20 nm. This biomimetic method suggests an analogy between the shape-forming processes of plants and their metallic models. Nanostructured mesostructures of various metals (Ag, Pd, Ni), alloys (PdNi, PbIn) and hybrid structures (PdNi/Pb, PdNi/PbIn) were obtained. They can be of interest for fundamental research (self-assembly, morphogenesis) as well as for applications in nanotechnology (catalysis, nanoplasmonics, medicine, superhydrophobic surfaces).     

Rainforest and savanna landscape dynamics in New Caledonia: Towards a mosaic of stable rainforest and savanna states?

Stable forested environments can be converted to savanna in response to changes in environmental disturbances. New Caledonia is a biodiversity hotspot; significant ecological and economic resources would be lost if forests were turned into savanna by anthropogenic environmental changes. On the landscape scale, systems that have undergone shifts of this kind are characterized by sharp forest–savanna boundaries and mosaic‐like distributions of savanna and forest. Understanding the locations and the dynamics of such boundaries is a challenge for ecologists and is critical for landscape management and biodiversity conservation. Using a time series of aerial photographs (1955–2000) and a forest habitat suitability map, we tested the hypothesis that topography and spatial processes, especially those relating to fire spread and seed dispersal, are the main determinants of the spatial distribution of rainforest and savanna in a New Caledonian landscape covering 24 km². Within the studied landscape, the overall forest coverage decreased by 24% between 1976 and 2000. This was primarily due to the contraction of forests on west‐facing slopes, which accounted for about 90% of the total loss. Conversely, the east‐facing forests seemed to have contracted extensively prior to the studied period, and were confined to refuges. A habitat suitability index calculated from the landscape's topographical features using generalized additive models accurately predicted both the presence of forests and the probability of forest expansion/contraction. We also provide evidence that spatial processes such as fire spread and seed dispersal limit the expansion and contraction of forests. Our results suggest that rainforests on west‐facing slopes in New Caledonia will be progressively destroyed by fire until they are restricted to refuges along thalwegs and creeks, as appears to have already happened for their east‐facing counterparts.     

Machine learning methods for optimal prediction of motor outcome in Parkinson’s disease

PurposeIt is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson’s disease (PD). Thus, it is critical to improve prediction of outcome in PD patients.MethodsWe systematically probed a range of robust predictor algorithms, aiming to find best combinations of features for significantly improved prediction of motor outcome (MDS-UPDRS-III) in PD. We analyzed 204 PD patients with 18 features (clinical measures; dopamine-transporter (DAT) SPECT imaging measures), performing different randomized arrangements and utilizing data from 64%/6%/30% of patients in each arrangement for training/training validation/final testing. We pursued 3 approaches: i) 10 predictor algorithms (accompanied with automated machine learning hyperparameter tuning) were first applied on 32 experimentally created combinations of 18 features, ii) we utilized Feature Subset Selector Algorithms (FSSAs) for more systematic initial feature selection, and iii) considered all possible combinations between 18 features (262,143 states) to assess contributions of individual features.ResultsA specific set (set 18) applied to the LOLIMOT (Local Linear Model Trees) predictor machine resulted in the lowest absolute error 4.32 ± 0.19, when we firstly experimentally created 32 combinations of 18 features. Subsequently, 2 FSSAs (Genetic Algorithm (GA) and Ant Colony Optimization (ACO)) selecting 5 features, combined with LOLIMOT, reached an error of 4.15 ± 0.46. Our final analysis indicated that longitudinal motor measures (MDS-UPDRS-III years 0 and 1) were highly significant predictors of motor outcome.ConclusionsWe demonstrate excellent prediction of motor outcome in PD patients by employing automated hyperparameter tuning and optimal utilization of FSSAs and predictor algorithms.     

The spatial ecology of adult Labeobarbus marequensis and their response to flow and habitat variability in the Crocodile River,Kruger National Park

The spatial movement of Labeobarbus marequensis was evaluated by attaching radio transmitters to 16 adult fish and tracking them from August 2009 to July 2012 in the Crocodile River, Kruger National Park, South Africa. Monthly monitoring surveys acquired 1 620 manual and 64 499 remote observations. Results showed adult L. marequensis did not participate in any migrations. Habitat preferences for L. marequensis were deep runs and fast glides, and there was disproportionately high use of submerged boulders as a cover feature. Tagged individuals also made use of rapids and pools, however, avoided shallow sandy slow glides. There was a reduced activity response of tagged individuals to rapid changes in water discharge. Continued changes in flow and instream habitat might threaten the Crocodile River population. Additional investigation into the effect of altered flows and associated habitat availability to the movement of L. marequensis is needed to better understand the effect of anthropogenic water resource use on L. marequensis in the Crocodile River.     

Association between competition and facilitation processes and vegetation spatial patterns in alpha steppes

In semiarid ecosystems, the self-organized spatial patterns of plants associated with catastrophic shifts can emerge from a variety of processes. In this study, on moderate slopes where Stipa tenacissima cover was high, the self-organization of some of the typical species of semiarid Mediterranean matorral ( Phlomis purpurea , Sideritis oxteosylla, Helianthemum almeriense , and Brachypodium retusum ) was negatively correlated with Stipa cover. The extent of Stipa cover did not affect desert pioneer species, such as Artemisia herba-alba , Fagonia cretica , and Launaea lanifera . On pronounced slopes, the self-organizing structure of brushwood vegetation did not vary predictably with the amount of Stipa cover. We examined the competition/facilitation processes associated with self-organizing patterns in the dwarf shrub ( Phl. purpurea ) and the half shrub ( H. almeriense ). The developmental stability of H. almeriense was positively correlated with Stipa cover, which was expected because they are associated species in this seral thyme brushwood community. Indeed, facilitation processes were manifested by the developmental stability increases under the Stipa canopy, particularly on high slope areas, where Stipa is less competitive. In Phl. purpurea , negative feedback processes from competition with Stipa were manifested where Stipa cover was high and on low slopes (developmental instability increased). In general, competition with Stipa on low slopes tended to decrease plant self-organization.  © 2006 The Linnean Society of London, Biological Journal of the Linnean Society , 2006, 87 , 103–113.     

An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets

In this paper, a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection (ACO-S) is proposed for high-dimensional datasets. Because microarray datasets comprise tens of thousands of features (genes), they are usually used to test the dimension reduction techniques. ACO-S consists of two stages in which two well-known ACO algorithms, namely ant system and ant colony system, are utilized to seek for genes, respectively. In the first stage, a modified ant system is used to filter the nonsignificant genes from high-dimensional space, and a number of promising genes are reserved in the next step. In the second stage, an improved ant colony system is applied to gene selection. In order to enhance the search ability of ACOs, we propose a method for calculating priori available heuristic information and design a fuzzy logic controller to dynamically adjust the number of ants in ant colony system. Furthermore, we devise another fuzzy logic controller to tune the parameter (q₀) in ant colony system. We evaluate the performance of ACO-S on five microarray datasets, which have dimensions varying from 7129 to 12000. We also compare the performance of ACO-S with the results obtained from four existing well-known bionic optimization algorithms. The comparison results show that ACO-S has a notable ability to generate a gene subset with the smallest size and salient features while yielding high classification accuracy. The comparative results generated by ACO-S adopting different classifiers are also given. The proposed method is shown to be a promising and effective tool for mining high-dimension data and mobile robot navigation.