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Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.MLL3 is a histone H3K4 methyltransferase that is frequently mutated in cancer, but the underlying molecular mechanisms remain elusive. Here, we found that MLL3 depletion by CRISPR/sgRNA significantly enhanced cell migration, but did not elevate the proliferation rate of cancer cells. Through RNA-Seq and ChIP-Seq approaches, we identified TNS3 as the potential target gene for MLL3. MLL3 depletion caused downregulation of H3K4me1 and H3K27ac on an enhancer ~ 7 kb ahead of TNS3. 3C assay indicated the identified enhancer interacts with TNS3 promoter and repression of enhancer activity by dCas9-KRAB system impaired TNS3 expression. Exogenous expression of TNS3 in MLL3 deficient cells completely blocked the enhanced cell migration phenotype. Taken together, our study revealed a novel mechanism for MLL3 in suppressing cancer, which may provide novel targets for diagnosis or drug development.The oxygenation of early Earth's atmosphere during the Great Oxidation Event, is generally accepted to have been caused by oceanic Cyanobacterial oxygenic photosynthesis. Recent studies suggest that Fe(II) toxicity delayed the Cyanobacterial expansion necessary for the GOE. This study investigates the effects of Fe(II) on two Cyanobacteria, Pseudanabaena sp. PCC7367 and Synechococcus sp. PCC7336, in a simulated shallow-water marine Archean environment. A similar Fe(II) toxicity response was observed as reported for closed batch cultures. This toxicity was not observed in cultures provided with continuous gaseous exchange that showed significantly shorter doubling times than the closed-culture system, even with repeated nocturnal addition of Fe(II) for 12 days. The green rust (GR) formed under high Fe(II) conditions, was not found to be directly toxic to Pseudanabaena sp. PCC7367. In summary, we present evidence of diurnal Fe cycling in a simulated shallow-water marine environment for two ancestral strains of Cyanobacteria, with increased O2 production under anoxic conditions.Steel production is a difficult-to-mitigate sector that challenges climate mitigation commitments. Efforts for future decarbonization can benefit from understanding its progress to date. Here we report on greenhouse gas emissions from global steel production over the past century (1900-2015) by combining material flow analysis and life cycle assessment. We find that ~45 Gt steel was produced in this period leading to emissions of ~147 Gt CO2-eq. Significant improvement in process efficiency (~67%) was achieved, but was offset by a 44-fold increase in annual steel production, resulting in a 17-fold net increase in annual emissions. Despite some regional technical improvements, the industry's decarbonization progress at the global scale has largely stagnated since 1995 mainly due to expanded production in emerging countries with high carbon intensity. Our analysis of future scenarios indicates that the expected demand expansion in these countries may jeopardize steel industry's prospects for following 1.5 °C emission reduction pathways. To achieve the Paris climate goals, there is an urgent need for rapid implementation of joint supply- and demand-side mitigation measures around the world in consideration of regional conditions.Recent research suggests that climate variability and change significantly affect forced migration, within and across borders. Yet, migration is also informed by a range of non-climatic factors, and current assessments are impeded by a poor understanding of the relative importance of these determinants. Here, we evaluate the eligibility of climatic conditions relative to economic, political, and contextual factors for predicting bilateral asylum migration to the European Union-form of forced migration that has been causally linked to climate variability. Results from a machine-learning prediction framework reveal that drought and temperature anomalies are weak predictors of asylum migration, challenging simplistic notions of climate-driven refugee flows. Instead, core contextual characteristics shape latent migration potential whereas political violence and repression are the most powerful predictors of time-varying migration flows. Future asylum migration flows are likely to respond much more to political changes in vulnerable societies than to climate change.Despite a century of research, it remains unclear whether human intelligence should be studied as one dominant, several major, or many distinct abilities, and how such abilities relate to the functional organisation of the brain. Here, we combine psychometric and machine learning methods to examine in a data-driven manner how factor structure and individual variability in cognitive-task performance relate to dynamic-network connectomics. We report that 12 sub-tasks from an established intelligence test can be accurately multi-way classified (74%, chance 8.3%) based on the network states that they evoke. The proximities of the tasks in behavioural-psychometric space correlate with the similarities of their network states. https://www.selleckchem.com/products/pmx-53.html Furthermore, the network states were more accurately classified for higher relative to lower performing individuals. These results suggest that the human brain uses a high-dimensional network-sampling mechanism to flexibly code for diverse cognitive tasks. Population variability in intelligence test performance relates to the fidelity of expression of these task-optimised network states.Methods that enable site selective acylation of sp3 C-H bonds in complex organic molecules are not well explored, particularly if compared with analogous transformations of aromatic and vinylic sp2 C-H bonds. We report herein a direct acylation of benzylic C-H bonds by merging N-heterocyclic carbene (NHC) and photoredox catalysis. The method allows the preparation of a diverse range of benzylic ketones with good functional group tolerance under mild conditions. The reaction can be used to install acyl groups on highly functionalized natural product derived compounds and the C-H functionalization works with excellent site selectivity. The combination of NHC and photoredox catalysis offers options in preparing benzyl aryl ketones.Autophagy defection contributes to inflammation dysregulation, which plays an important role in gastric cancer (GC) progression. Various studies have demonstrated that long noncoding RNA could function as novel regulators of autophagy. Previously, long noncoding RNA MALAT1 was reported upregulated in GC cells and could positively regulate autophagy in various cancers. Here, we for the first time found that MALAT1 could promote interleukin-6 (IL-6) secretion in GC cells by blocking autophagic flux. Moreover, IL-6 induced by MALAT1 could activate normal to cancer-associated fibroblast conversion. The interaction between GC cells and cancer-associated fibroblasts in the tumour microenvironment could facilitate cancer progression. Mechanistically, MALAT1 overexpression destabilized the PTEN mRNA in GC cells by competitively interacting with the RNA-binding protein ELAVL1 to activate the AKT/mTOR pathway for impairing autophagic flux. As a consequence of autophagy inhibition, SQSTM1 accumulation promotes NF-κB translocation to elevate IL-6 expression. Overall, these results demonstrated that intercellular interaction between GC cells and fibroblasts was mediated by autophagy inhibition caused by increased MALAT1 that promotes GC progression, providing novel prevention and therapeutic strategies for GC.Variable number tandem repeats (VNTRs) account for significant genetic variation in many organisms. In humans, VNTRs have been implicated in both Mendelian and complex disorders, but are largely ignored by genomic pipelines due to the complexity of genotyping and the computational expense. We describe adVNTR-NN, a method that uses shallow neural networks to genotype a VNTR in 18 seconds on 55X whole genome data, while maintaining high accuracy. We use adVNTR-NN to genotype 10,264 VNTRs in 652 GTEx individuals. Associating VNTR length with gene expression in 46 tissues, we identify 163 "eVNTRs". Of the 22 eVNTRs in blood where independent data is available, 21 (95%) are replicated in terms of significance and direction of association. 49% of the eVNTR loci show a strong and likely causal impact on the expression of genes and 80% have maximum effect size at least 0.3. The impacted genes are involved in diseases including Alzheimer's, obesity and familial cancers, highlighting the importance of VNTRs for understanding the genetic basis of complex diseases.Neuron death in spinal cords is caused primarily by apoptosis after spinal cord injury (SCI). Autophagy can act as a cellular response to maintain neuron homeostasis that can reduce apoptosis. Although more studies have shown that an epigenetic enzyme called Lysine-specific demethylase 1 (LSD1) can negatively regulate autophagy during cancer research, existing research does not focus on impacts related to LSD1 in nerve injury diseases. This study was designed to determine whether inhibiting LSD1 could enhance autophagy against apoptosis and provide effective neuroprotection in vitro and vivo after SCI. The results showed that LSD1 inhibition treatment significantly reduced spinal cord damage in SCI rat models and was characterized by upregulated autophagy and downregulated apoptosis. Further research demonstrated that using both pharmacological inhibition and gene knockdown could enhance autophagy and reduce apoptosis for in vitro simulation of SCI-caused damage models. Additionally, 3-methyladenine (3-MA) could partially eliminate the effect of autophagy enhancement and apoptosis suppression. These findings demonstrated that LSD1 inhibition could protect against SCI by activating autophagy and hindering apoptosis, suggesting a potential candidate for SCI therapy.Gastrointestinal stromal tumors (GISTs) are common neoplasms of the gastrointestinal tract that can be treated successfully using C-kit target therapy and surgery; however, imatinib chemoresistance is a major barrier to success in therapy. The present study aimed to discover alternative pathways in imatinib-resistant GISTs. Long noncoding RNAs (lncRNAs) are newly discovered regulators of chemoresistance. Previously, we showed that the lncRNA HOTAIR was upregulated in recurrent GISTs. In this study, we analyzed differentially expressed lncRNAs after imatinib treatment and found that HOTAIR displayed the largest increase. The distribution of HOTAIR in GISTs was shifted from nucleus to cytoplasm after imatinib treatments. The expression of HOTAIR was validated as related to drug sensitivity through Cell Counting Kit-8 assays. Moreover, HOTAIR was associated strongly with cell autophagy and regulated drug sensitivity via autophagy. Mechanistically, HOTAIR correlated negatively with miRNA-130a in GISTs. The downregulation of miRNA-130a reversed HOTAIR-small interfering RNA-induced suppression of autophagy and imatinib sensitivity.