Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of major interest in AD research. A large number of potential predictors have been proposed, with most investigations tending to examine one or a set of related predictors. In this study, we simultaneously examined multiple features from different modalities of data, including structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid (CSF) biomarkers and neuropsychological and functional measures (NMs), to explore an optimal set of predictors of conversion from MCI to AD in an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. After FreeSurfer-derived MRI feature extraction, CSF and NM feature collection, feature selection was employed to choose optimal subsets of features from each modality. Support vector machine (SVM) classifiers were then trained on normal control (NC) and AD participants. Testing was conducted on MCIc (MCI individuals who have converted to AD within 24 months) and MCInc (MCI individuals who have not converted to AD within 24 months) groups. Classification results demonstrated that NMs outperformed CSF and MRI features. The combination of selected NM, MRI and CSF features attained an accuracy of 67.13%, a sensitivity of 96.43%, a specificity of 48.28%, and an AUC (area under curve) of 0.796. Analysis of the predictive values of MCIc who converted at different follow-up evaluations showed that the predictive values were significantly different between individuals who converted within 12 months and after 12 months. This study establishes meaningful multivariate predictors composed of selected NM, MRI and CSF measures which may be useful and practical for clinical diagnosis.

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners' interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in.

Mutations in MECP2 cause Rett syndrome (RTT), an X-linked neurological disorder characterized by regressive loss of neurodevelopmental milestones and acquired psychomotor deficits. However, the cellular heterogeneity of the brain impedes an understanding of how MECP2 mutations contribute to RTT. Here we developed a Cre-inducible method for cell type-specific biotin tagging of MeCP2 in mice. Combining this approach with an allelic series of knockin mice carrying frequent RTT mutations (T158M and R106W) enabled the selective profiling of RTT-associated nuclear transcriptomes in excitatory and inhibitory cortical neurons. We found that most gene expression changes are largely specific to each RTT mutation and cell type. Lowly expressed cell type-enriched genes are preferentially disrupted by MeCP2 mutations, with upregulated and downregulated genes reflecting distinct functional categories. Subcellular RNA analysis in MeCP2 mutant neurons further reveals reductions in the nascent transcription of long genes and uncovers widespread post-transcriptional compensation at the cellular level. Finally, we overcame X-linked cellular mosaicism in female RTT models and identified distinct gene expression changes between neighboring wild-type and mutant neurons, altogether providing contextual insights into RTT etiology that support personalized therapeutic interventions.

Alpha-naphthylisothiocyanate (ANIT) causes intrahepatic cholestasis by injuring biliary epithelial cells. Adaptive regulation of hepatobiliary transporter expression has been proposed to reduce liver injury during cholestasis. Recently, the oxidative stress transcription factor Nrf2 (nf-e2-related factor 2) was shown to regulate expression of hepatobiliary transporters. The purpose of this study was to determine whether ANIT-induced hepatotoxicity and regulation of hepatobiliary transporters are altered in the absence of Nrf2. For this purpose, wild-type and Nrf2-null mice were administered ANIT (75 mg/kg po). Surprisingly, ANIT-induced hepatotoxicity was similar in both genotypes at 48 h. Accumulation of bile acids in serum and liver was lower in Nrf2-null mice compared with wild-types treated with ANIT. Transporter mRNA profiles differed between wild-type and Nrf2-null mice after ANIT. Bsep (bile salt export pump), Mdr2 (multidrug resistance gene), and Mrp3 (multidrug resistance-associated protein) efflux transporters were increased by ANIT in wild-type, but not in Nrf2-null mice. In contrast, mRNA expression of two hepatic uptake transporters, Ntcp (sodium-taurocholate cotransporting polypeptide) and Oatp1b2 (organic anion transporting peptide), were decreased in both genotypes after ANIT, with larger declines in Nrf2-null mice. mRNA expression of the transcriptional repressor of Ntcp, small heterodimeric partner (SHP), was increased in Nrf2-null mice after ANIT. Furthermore, hepatocyte nuclear factor 1alpha (HNF1alpha), which regulates Oatp1b2, was downregulated in ANIT-treated Nrf2-null mice. Preferential upregulation of SHP and downregulation of HNF1alpha and uptake transporters likely explains why Nrf2-null mice exhibited similar injury to wild-types after ANIT. A subsequent study revealed that treatment of mice with the Nrf2 activator oltipraz protects against ANIT-induced histological injury. Despite compensatory changes in Nrf2-null mice to limit ANIT toxicity, pharmacological activation of Nrf2 may represent a therapeutic option for intrahepatic cholestasis.

BackgroundCurrent fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information.MethodsMotivated by the ability of recurrent neural networks (RNN) in capturing dynamic information of time sequences, we propose a multi-scale RNN model, which enables classification between 558 schizophrenia and 542 healthy controls by using time courses of fMRI independent components (ICs) directly. To increase interpretability, we also propose a leave-one-IC-out looping strategy for estimating the top contributing ICs.FindingsAccuracies of 83·2% and 80·2% were obtained respectively for the multi-site pooling and leave-one-site-out transfer classification. Subsequently, dorsal striatum and cerebellum components contribute the top two group-discriminative time courses, which is true even when adopting different brain atlases to extract time series.InterpretationThis is the first attempt to apply a multi-scale RNN model directly on fMRI time courses for classification of mental disorders, and shows the potential for multi-scale RNN-based neuroimaging classifications.FundNatural Science Foundation of China, the Strategic Priority Research Program of the Chinese Academy of Sciences, National Institutes of Health Grants, National Science Foundation.