Toward Noise-Resistant Graph Embedding With Subspace Clustering Information.

Most existing approaches of attributed network embedding often combine topology and attribute information based on the homophily assumption. In many real-world networks, such an assumption does not hold since the nodes are usually associated with many noisy or irrelevant attributes. To tackle this issue, we propose a noise-resistant graph embedding method, called NGE, by leveraging the subspace clustering information (i.e., the formation of communities is driven by different latent features in distinct subspaces). Specifically, we first construct a tensor to represent a given attributed network and then map it into different feature subspaces to capture community structure via tensor decomposition. For structure embedding, the link-level and community-level constraints are imposed. For attribute embedding, the feature-selection constraint is used to reinforce the relationship between topology and noise-removal attributes. By learning structure and attribute embedding with subspace clustering information, NGE can benefit both community detection, link prediction, and node classification. Extensive experimental results have demonstrated the superiority of NGE over many state-of-the-art approaches.

Reducing variations in multi-center Alzheimer's disease classification with convolutional adversarial autoencoder.

Based on brain magnetic resonance imaging (MRI), multiple variations ranging from MRI scanners to center-specific parameter settings, imaging protocols, and brain region-of-interest (ROI) definitions pose a big challenge for multi-center Alzheimer's disease characterization and classification. Existing approaches to reduce such variations require intricate multi-step, often manual preprocessing pipelines, including skull stripping, segmentation, registration, cortical reconstruction, and ROI outlining. Such procedures are time-consuming, and more importantly, tend to be user biased. Contrasting costly and biased preprocessing pipelines, the question arises whether we can design a deep learning model to automatically reduce these variations from multiple centers for Alzheimer's disease classification? In this study, we used T1 and T2-weighted structural MRI from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset based on three groups with 375 subjects, respectively: patients with Alzheimer's disease (AD) dementia, with mild cognitive impairment (MCI), and healthy controls (HC); to test our approach, we defined AD classification as classifying an individual's structural image to one of the three group labels. We first introduced a convolutional adversarial autoencoder (CAAE) to reduce the variations existing in multi-center raw MRI scans by automatically registering them into a common aligned space. Afterward, a convolutional residual soft attention network (CRAT) was further proposed for AD classification. Canonical classification procedures demonstrated that our model achieved classification accuracies of 91.8%, 90.05%, and 88.10% for the 2-way classification tasks using the RAW aligned MRI scans, including AD vs. HC, AD vs. MCI, and MCI vs. HC, respectively. Thus, our automated approach achieves comparable or even better classification performance by comparing it with many baselines with dedicated conventional preprocessing pipelines. Furthermore, the uncovered brain hotpots, i.e., hippocampus, amygdala, and temporal pole, are consistent with previous studies.

Cotton genes GhMML1 and GhMML2 control trichome branching when ectopically expressed in tobacco.

Trichomes exhibit extraordinary diversity in shape, ultrastructure, distribution, secretion capability, biological functions, and morphological differences, which are strongly associated with their multifunction. Previous researches showed MIXTA-like transcription factors involved in regulating trichome initiation and patterning via forming MYB-bHLH-WD40 transcriptional activator complex to induce the expression of downstream genes. Here, we report the characteristics and role of GhMML1 and GhMML2, members of subgroup 9 of the R2R3-type MYB TFs. GhMML1 and GhMML2 were preferentially targeted to the nucleus and prominently expressed in the early stage during fiber development. Ectopic expression of GhMML1 and GhMML2 respectively in the transgenic tobacco plants changed the morphological characteristics of leaf trichomes; that is, the unbranched trichomes turned into multiple branched, and in the meantime, the density of trichomes was reduced on the surface of the leaf. Y2H and LCI assay revealed that both GhMML1 and GhMML2 could physically interact with a bZIP transcription factor family protein (GhbZIP) in vivo and in vitro. It has been reported that GhbZIP's homolog TAG3 in Arabidopsis is involved in the asymmetric growth of leaves and flowers via direct interaction with BOP1. Taken together, our results demonstrated that two MYB MIXTA-like proteins, GhMML1 and GhMML2, together with GhbZIP might form a multimeric complex to involve in trichome development. This study highlights the importance of MIXTA-like genes from TF subgroup 9 and will help to uncover the molecular mechanism underlying differential trichomes and their development.

An Online Semantic-Enhanced Graphical Model for Evolving Short Text Stream Clustering.

Due to the popularity of social media and online fora, such as Twitter, Reddit, Facebook, and Wechat, short text stream clustering has gained significant attention in recent years. However, most existing short text stream clustering approaches usually work on static data and tend to cause a "term ambiguity" problem due to the sparse word representation. Beyond, they often exploit short text streams in a batch way and are difficult to find evolving topics in term-changing subspaces. In this article, we propose an online semantic-enhanced graphical model for evolving short text stream clustering (OSGM), by exploiting the word-occurrence semantic information and dynamically maintaining evolving active topics in term-changing subspaces in an online way. Compared to the existing approaches, our online model is not only free of determining the optimal batch size but also lends itself to handling large-scale data streams efficiently. It is also able to handle the "term ambiguity" problem without incorporating features from external resources. More importantly, to the best of our knowledge, it is the first work to extract evolving topics in term-changing subspaces automatically in an online way. Extensive experiments demonstrate that the proposed model yields better performance compared to many state-of-the-art algorithms on both synthetic and real-world datasets.

Evaluating the contribution of different environmental drivers to changes in evapotranspiration and soil moisture, a case study of the Wudaogou Experimental Station.

Evapotranspiration and soil moisture content (SMC) are key elements of the hydrological cycle. Accurate prediction of the dynamic processes of evapotranspiration and soil water is essential for irrigation and water management. Here, the boosted regression tree (BRT) method was employed to quantify environmental controls on actual evapotranspiration (ETa), potential evapotranspiration (ET0), and SMC using monitoring data from the Wudaogou hydrological experimental station. The results indicated that: (1) the BRT algorithm was effective in predicting the relative control of different environmental factors on ETa, ET0, and SMC; and (2) vapor pressure deficit (VPD) was the most important factor affecting daily ET0, and sunshine duration (SSD) also played a nonnegligible role. The results further explained the phenomenon of the "evaporation paradox" in the study area. SSD could be a leading control on daily ETa, followed by VPD, leaf area index (LAI). (3) Among the underground factors, groundwater level (GL) and LAI played a dominant role in the relative contribution to SMC. Among the aboveground factors, relative humidity (RH) and soil temperature (TS) have a relatively large influence on SMC. (4) The differences in SMC at different depths were determined by multiple influencing factors, including LAI, VPD, and precipitation (P). This study also underscores the importance of vegetation variations to hydrological cycle processes. In general, climate warming and an increase in extreme rainfall events will increase the control of temperature on SMC and weaken the control of P on SMC in the future.

The transcription factor MML4_D12 regulates fiber development through interplay with the WD40-repeat protein WDR in cotton.

In planta, a vital regulatory complex, MYB-basic helix-loop-helix (bHLH)-WD40 (MBW), is involved in trichome development and synthesis of anthocyanin and proanthocyanin in Arabidopsis. Usually, WD40 proteins provide a scaffold for protein-protein interaction between MYB and bHLH proteins. Members of subgroup 9 of the R2R3 MYB transcription factors, which includes MYBMIXTA-Like (MML) genes important for plant cell differentiation, are unable to interact with bHLH. In this study, we report that a cotton (Gossypium hirsutum) seed trichome or lint fiber-related GhMML factor, GhMML4_D12, interacts with a diverged WD40 protein (GhWDR) in a process similar to but different from that of the MBW ternary complex involved in Arabidopsis trichome development. Amino acids 250-267 of GhMML4_D12 and the first and third WD40 repeat domains of GhWDR determine their interaction. GhWDR could rescue Arabidopsis ttg1 to its wild type, confirming its orthologous function in trichome development. Our findings shed more light towards understanding the key role of the MML and WD40 families in plants and in the improvement of cotton fiber production.

Assessing climate impact on forest cover in areas undergoing substantial land cover change using Landsat imagery.

In this study, we propose to assess climate impact on forest cover (represented by EVI) at multiple scales in areas undergoing substantial land cover change, using Landsat imagery with human-induced land cover change effect excluded. Taking the Qingliu River catchment located in a subtropical humid monsoon area in China as a case study, the results indicate that EVI increases significantly (p < 0.01) during 1989-2014 with a magnitude of 0.026/decade. Spatial distribution of EVI is distinct in summer and growing season. Temperature and precipitation show high partial correlations with EVI, with better partial correlation found between EVI and temperature. Their partial correlations with EVI on monthly scale are higher than those on annual scale. Besides, precipitation and pan evaporation show accumulative lag effects (4 months) on forest EVI, while temperature has no lag effect. Finally, an empirical formula is established to quantify the relationship among EVI and its main driving factors (temperature and precipitation) by considering the precipitation threshold (200 mm). The findings should provide scientific supports for local forest management and ecosystem services, and should also support the hydrological effect assessment of vegetation cover change under climate change for the study area.

Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder.

Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN), salience network (SN) and central executive network (CEN)) are consistently altered in schizophrenia. However, similar changes have also been found in patients with major depressive disorder, prompting the question of specific triple network signatures for the two disorders. In this study, we proposed Supervised Convex Nonnegative Matrix Factorization (SCNMF) to extract distributed multi-modal brain patterns. These patterns distinguish schizophrenia and major depressive disorder in a latent low-dimensional space of the triple brain network. Specifically, 21 patients of schizophrenia and 25 patients of major depressive disorder were assessed by T1-weighted, diffusion-weighted, and resting-state functional MRIs. Individual structural and functional connectivity networks, based on pre-defined regions of the triple network were constructed, respectively. Afterwards, SCNMF was employed to extract the discriminative patterns. Experiments indicate that SCNMF allows extracting the low-rank discriminative patterns between the two disorders, achieving a classification accuracy of 82.6% based on the extracted functional and structural abnormalities with support vector machine. Experimental results show the specific brain patterns for schizophrenia and major depressive disorder that are multi-modal, complex, and distributed in the triple network. Parts of the prefrontal cortex including superior frontal gyri showed variation between patients with schizophrenia and major depression due to structural properties. In terms of functional properties, the middle cingulate cortex, inferior parietal lobule, and cingulate cortex were the most discriminative regions.

Common and distinct changes of default mode and salience network in schizophrenia and major depression.

Brain imaging reveals schizophrenia as a disorder of macroscopic brain networks. In particular, default mode and salience network (DMN, SN) show highly consistent alterations in both interacting brain activity and underlying brain structure. However, the same networks are also altered in major depression. This overlap in network alterations induces the question whether DMN and SN changes are different across both disorders, potentially indicating distinct underlying pathophysiological mechanisms. To address this question, we acquired T1-weighted, diffusion-weighted, and resting-state functional MRI in patients with schizophrenia, patients with major depression, and healthy controls. We measured regional gray matter volume, inter-regional structural and intrinsic functional connectivity of DMN and SN, and compared these measures across groups by generalized Wilcoxon rank tests, while controlling for symptoms and medication. When comparing patients with controls, we found in each patient group SN volume loss, impaired DMN structural connectivity, and aberrant DMN and SN functional connectivity. When comparing patient groups, SN gray matter volume loss and DMN structural connectivity reduction did not differ between groups, but in schizophrenic patients, functional hyperconnectivity between DMN and SN was less in comparison to depressed patients. Results provide evidence for distinct functional hyperconnectivity between DMN and SN in schizophrenia and major depression, while structural changes in DMN and SN were similar. Distinct hyperconnectivity suggests different pathophysiological mechanism underlying aberrant DMN-SN interactions in schizophrenia and depression.

Prediction of Alzheimer's disease using individual structural connectivity networks.

Alzheimer's disease (AD) progressively degrades the brain's gray and white matter. Changes in white matter reflect changes in the brain's structural connectivity pattern. Here, we established individual structural connectivity networks (ISCNs) to distinguish predementia and dementia AD from healthy aging in individual scans. Diffusion tractography was used to construct ISCNs with a fully automated procedure for 21 healthy control subjects (HC), 23 patients with mild cognitive impairment and conversion to AD dementia within 3 years (AD-MCI), and 17 patients with mild AD dementia. Three typical pattern classifiers were used for AD prediction. Patients with AD and AD-MCI were separated from HC with accuracies greater than 95% and 90%, respectively, irrespective of prediction approach and specific fiber properties. Most informative connections involved medial prefrontal, posterior parietal, and insular cortex. Patients with mild AD were separated from those with AD-MCI with an accuracy of approximately 85%. Our finding provides evidence that ISCNs are sensitive to the impact of earliest stages of AD. ISCNs may be useful as a white matter-based imaging biomarker to distinguish healthy aging from AD.

Feature selection methods for characterizing and classifying adaptive Sustainable Flood Retention Basins.

The European Union's Flood Directive 2007/60/EC requires member states to produce flood risk maps for all river basins and coastal areas at risk of flooding by 2013. As a result, flood risk assessments have become an urgent challenge requiring a range of rapid and effective tools and approaches. The Sustainable Flood Retention Basin (SFRB) concept has evolved to provide a rapid assessment technique for impoundments, which have a pre-defined or potential role in flood defense and diffuse pollution control. A previous version of the SFRB survey method developed by the co-author Scholz in 2006 recommends gathering of over 40 variables to characterize an SFRB. Collecting all these variables is relatively time-consuming and more importantly, these variables are often correlated with each other. Therefore, the objective is to explore the correlation among these variables and find the most important variables to represent an SFRB. Three feature selection techniques (Information Gain, Mutual Information and Relief) were applied on the SFRB data set to identify the importance of the variables in terms of classification accuracy. Four benchmark classifiers (Support Vector Machine, K-Nearest Neighbours, C4.5 Decision Tree and Naïve Bayes) were subsequently used to verify the effectiveness of the classification with the selected variables and automatically identify the optimal number of variables. Experimental results indicate that our proposed approach provides a simple, rapid and effective framework for variable selection and SFRB classification. Only nine important variables are sufficient to accurately classify SFRB. Finally, six typical cases were studied to verify the performance of the identified nine variables on different SFRB types. The findings provide a rapid scientific tool for SFRB assessment in practice. Moreover, the generic value of this tool allows also for its wide application in other areas.

Classification and assessment of water bodies as adaptive structural measures for flood risk management planning.

Severe rainfall events have become increasingly common in Europe. Flood defence engineering works are highly capital intensive and can be limited by land availability, leaving land and communities exposed to repeated flooding. Any adaptive drainage structure must have engineered inlets and outlets that control the water level and the rate of release. In Scotland, there are a relatively high number of drinking water reservoirs (operated by Scottish Water), which fall within this defined category and could contribute to flood management control. Reducing the rate of runoff from the upper reaches of a catchment will reduce the volume and peak flows of flood events downstream, thus allowing flood defences to be reduced in size, decreasing the corresponding capital costs. A database of retention basins with flood control potential has been developed for Scotland. The research shows that the majority of small and former drinking water reservoirs are kept full and their spillways are continuously in operation. Utilising some of the available capacity to contribute to flood control could reduce the costs of complying with the EU Flood Directive. Furthermore, the application of a previously developed classification model for Baden in Germany for the Scottish data set showed a lower diversity for basins in Scotland due to less developed infrastructure. The principle value of this approach is a clear and unambiguous categorisation, based on standard variables, which can help to promote communication and understanding between stakeholders.