Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer’s Disease

Recently, transfer learning has been successfully applied in early diagnosis of Alzheimer’s Disease (AD) based on multi-domain data. However, most of existing methods only use data from a single auxiliary domain, and thus cannot utilize the intrinsic useful correlation information from multiple domains. Accordingly, in this paper, we consider the joint learning of tasks in multi-auxiliary domains and the target domain, and propose a novel Multi-Domain Transfer Learning (MDTL) framework for early diagnosis of AD. Specifically, the proposed MDTL framework consists of two key components: 1) a multi-domain transfer feature selection (MDTFS) model that selects the most informative feature subset from multi-domain data, and 2) a multidomain transfer classification (MDTC) model that can identify disease status for early AD detection. We evaluate our method on 807 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline magnetic resonance imaging (MRI) data. The experimental results show that the proposed MDTL method can effectively utilize multi-auxiliary domain data for improving the learning performance in the target domain, compared with several state-of-the-art methods

Amylose-Derived Macrohollow Core and Microporous Shell Carbon Spheres as Sulfur Host for Superior Lithium–Sulfur Battery Cathodes

Porous carbon can be tailored to great effect for electrochemical energy storage. In this study, we propose a novel structured spherical carbon with a macrohollow core and a microporous shell derived from a sustainable biomass, amylose, by a multistep pyrolysis route without chemical etching. This hierarchically porous carbon shows a particle distribution of 2–10 μm and a surface area of 672 m2 g–1. The structure is an effective host of sulfur for lithium–sulfur battery cathodes, which reduces the dissolution of polysulfides in the electrolyte and offers high electrical conductivity during discharge/charge cycling. The hierarchically porous carbon can hold 48 wt % sulfur in its porous structure. The S@C hybrid shows an initial capacity of 1490 mAh g–1 and retains a capacity of 798 mAh g–1 after 200 cycles at a discharge/charge rate of 0.1 C. A capacity of 487 mAh g–1 is obtained at a rate of 3 C. Both a one-step pyrolysis and a chemical-reagent-assisted pyrolysis are also assessed to obtain porous carbon from amylose, but the obtained carbon shows structures inferior for sulfur cathodes. The multistep pyrolysis and the resulting hierarchically porous carbon offer an effective approach to the engineering of biomass for energy storage. The micrometer-sized spherical S@C hybrid with different sizes is also favorable for high-tap density and hence the volumetric density of the batteries, opening up a wide scope for practical applications

Amylose-Derived Macrohollow Core and Microporous Shell Carbon Spheres as Sulfur Host for Superior Lithium–Sulfur Battery Cathodes

Porous carbon can be tailored to great effect for electrochemical energy storage. In this study, we propose a novel structured spherical carbon with a macrohollow core and a microporous shell derived from a sustainable biomass, amylose, by a multistep pyrolysis route without chemical etching. This hierarchically porous carbon shows a particle distribution of 2–10 μm and a surface area of 672 m2 g–1. The structure is an effective host of sulfur for lithium–sulfur battery cathodes, which reduces the dissolution of polysulfides in the electrolyte and offers high electrical conductivity during discharge/charge cycling. The hierarchically porous carbon can hold 48 wt % sulfur in its porous structure. The S@C hybrid shows an initial capacity of 1490 mAh g–1 and retains a capacity of 798 mAh g–1 after 200 cycles at a discharge/charge rate of 0.1 C. A capacity of 487 mAh g–1 is obtained at a rate of 3 C. Both a one-step pyrolysis and a chemical-reagent-assisted pyrolysis are also assessed to obtain porous carbon from amylose, but the obtained carbon shows structures inferior for sulfur cathodes. The multistep pyrolysis and the resulting hierarchically porous carbon offer an effective approach to the engineering of biomass for energy storage. The micrometer-sized spherical S@C hybrid with different sizes is also favorable for high-tap density and hence the volumetric density of the batteries, opening up a wide scope for practical applications

Domain Transfer Learning for MCI Conversion Prediction

Machine learning methods have been increasingly used to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD), by classifying MCI converters (MCI-C) from MCI non-converters (MCI-NC). However, most of existing methods construct classifiers using only data from one particular target domain (e.g., MCI), and ignore data in the other related domains (e.g., AD and normal control (NC)) that could provide valuable information to promote the performance of MCI conversion prediction. To this end, we develop a novel domain transfer learning method for MCI conversion prediction, which can use data from both the target domain (i.e., MCI) and the auxiliary domains (i.e., AD and NC). Specifically, the proposed method consists of three key components: 1) a domain transfer feature selection (DTFS) component that selects the most informative feature-subset from both target domain and auxiliary domains with different imaging modalities, 2) a domain transfer sample selection (DTSS) component that selects the most informative sample-subset from the same target and auxiliary domains with different data modalities, and 3) a domain transfer support vector machine (DTSVM) classification component that fuses the selected features and samples to separate MCI-C and MCI-NC patients. We evaluate our method on 202 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with MRI, FDG-PET and CSF data. The experimental results show that the proposed method can classify MCI-C patients from MCI-NC patients with an accuracy of 79.4%, with the aid of additional domain knowledge learned from AD and NC

MicroRNA-483 amelioration of experimental pulmonary hypertension.

Endothelial dysfunction is critically involved in the pathogenesis of pulmonary arterial hypertension (PAH) and that exogenously administered microRNA may be of therapeutic benefit. Lower levels of miR-483 were found in serum from patients with idiopathic pulmonary arterial hypertension (IPAH), particularly those with more severe disease. RNA-seq and bioinformatics analyses showed that miR-483 targets several PAH-related genes, including transforming growth factor-β (TGF-β), TGF-β receptor 2 (TGFBR2), β-catenin, connective tissue growth factor (CTGF), interleukin-1β (IL-1β), and endothelin-1 (ET-1). Overexpression of miR-483 in ECs inhibited inflammatory and fibrogenic responses, revealed by the decreased expression of TGF-β, TGFBR2, β-catenin, CTGF, IL-1β, and ET-1. In contrast, inhibition of miR-483 increased these genes in ECs. Rats with EC-specific miR-483 overexpression exhibited ameliorated pulmonary hypertension (PH) and reduced right ventricular hypertrophy on challenge with monocrotaline (MCT) or Sugen + hypoxia. A reversal effect was observed in rats that received MCT with inhaled lentivirus overexpressing miR-483. These results indicate that PAH is associated with a reduced level of miR-483 and that miR-483 might reduce experimental PH by inhibition of multiple adverse responses

Multimodal manifold-regularized transfer learning for MCI conversion prediction

As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods

Industrial steam consumption analysis and prediction based on multi-source sensing data for sustainable energy development

Centralized heating is an energy-saving and environmentally friendly way that is strongly promoted by the state. It can improve energy utilization and reduce carbon emissions. However, Centralized heating depends on accurate heat demand forecasting. On the one hand, it is impossible to save energy if over producing, while on the other hand, it is impossible to meet the heat demand of enterprises if there is not enough capacity. Therefore, it is necessary to forecast the future trend of heat consumption, so as to provide a reliable basis for enterprises to reasonably deploy fuel stocks and boiler power. At the same time, it is also necessary to analyze and monitor the steam consumption of enterprises for abnormalities in order to monitor pipeline leakage and enterprise gas theft. Due to the nonlinear characteristics of heat load, it is difficult for traditional forecasting methods to capture data trend. Therefore, it is necessary to study the characteristics of heat loads and explore suitable heat load prediction models. In this paper, industrial steam consumption of a paper manufacturer is used as an example, and steam consumption data are periodically analyzed to study its time series characteristics; then steam consumption prediction models are established based on ARIMA model and LSTM neural network, respectively. The prediction work was carried out in minutes and hours, respectively. The experimental results show that the LSTM neural network has greater advantages in this steam consumption load prediction and can meet the needs of heat load prediction

Caulobacter and Novosphingobium in tumor tissues are associated with colorectal cancer outcomes

Diversity and composition of the gut microbiome are associated with cancer patient outcomes including colorectal cancer (CRC). A growing number of evidence indicates that Fusobacterium nucleatum (Fn) in CRC tissue is associated with worse survival. However, few studies have further analyzed the differences in bacteria in tumor tissues of different patients depending on the survival time of CRC patients. Therefore, there is a need to further explore the bacterial differences in tumor tissues of patients with different prognoses and to identify key bacteria for analysis. Here, we sought to compare the differences in tumor microbiome between patients with long-term survival (LS) longer than 3 years or 4 and 5 years and patients with short-term survival (SS) in the present study cohort. We found that there were significant differences in tumor microbiome between the LS and SS and two bacteria—Caulobacter and Novosphingobium—that are present in all of the three groups. Furthermore, by analyzing bacteria in different clinical features, we also found that lower levels of microbiome (Caulobacter and Novosphingobium) have long-term survival and modulating microbiome in tumor tissue may provide an alternative way to predict the prognosis of CRC patients

Chromosome-level genome assembly of a regenerable maize inbred line A188.

BACKGROUND The maize inbred line A188 is an attractive model for elucidation of gene function and improvement due to its high embryogenic capacity and many contrasting traits to the first maize reference genome, B73, and other elite lines. The lack of a genome assembly of A188 limits its use as a model for functional studies. RESULTS Here, we present a chromosome-level genome assembly of A188 using long reads and optical maps. Comparison of A188 with B73 using both whole-genome alignments and read depths from sequencing reads identify approximately 1.1 Gb of syntenic sequences as well as extensive structural variation, including a 1.8-Mb duplication containing the Gametophyte factor1 locus for unilateral cross-incompatibility, and six inversions of 0.7 Mb or greater. Increased copy number of carotenoid cleavage dioxygenase 1 (ccd1) in A188 is associated with elevated expression during seed development. High ccd1 expression in seeds together with low expression of yellow endosperm 1 (y1) reduces carotenoid accumulation, accounting for the white seed phenotype of A188. Furthermore, transcriptome and epigenome analyses reveal enhanced expression of defense pathways and altered DNA methylation patterns of the embryonic callus. CONCLUSIONS The A188 genome assembly provides a high-resolution sequence for a complex genome species and a foundational resource for analyses of genome variation and gene function in maize. The genome, in comparison to B73, contains extensive intra-species structural variations and other genetic differences. Expression and network analyses identify discrete profiles for embryonic callus and other tissues