A genetically tractable jellyfish model for systems and evolutionary neuroscience.

Jellyfish are radially symmetric organisms without a brain that arose more than 500 million years ago. They achieve organismal behaviors through coordinated interactions between autonomously functioning body parts. Jellyfish neurons have been studied electrophysiologically, but not at the systems level. We introduce Clytia hemisphaerica as a transparent, genetically tractable jellyfish model for systems and evolutionary neuroscience. We generate stable F1 transgenic lines for cell-type-specific conditional ablation and whole-organism GCaMP imaging. Using these tools and computational analyses, we find that an apparently diffuse network of RFamide-expressing umbrellar neurons is functionally subdivided into a series of spatially localized subassemblies whose synchronous activation controls directional food transfer from the tentacles to the mouth. These data reveal an unanticipated degree of structured neural organization in this species. Clytia affords a platform for systems-level studies of neural function, behavior, and evolution within a clade of marine organisms with growing ecological and economic importance.

The Ground State and Evolution of Promoter Region Directionality.

Eukaryotic promoter regions are frequently divergently transcribed in vivo, but it is unknown whether the resultant antisense RNAs are a mechanistic by-product of RNA polymerase II (Pol II) transcription or biologically meaningful. Here, we use a functional evolutionary approach that involves nascent transcript mapping in S. cerevisiae strains containing foreign yeast DNA. Promoter regions in foreign environments lose the directionality they have in their native species. Strikingly, fortuitous promoter regions arising in foreign DNA produce equal transcription in both directions, indicating that divergent transcription is a mechanistic feature that does not imply a function for these transcripts. Fortuitous promoter regions arising during evolution promote bidirectional transcription and over time are purged through mutation or retained to enable new functionality. Similarly, human transcription is more bidirectional at newly evolved enhancers and promoter regions. Thus, promoter regions are intrinsically bidirectional and are shaped by evolution to bias transcription toward coding versus non-coding RNAs.

mRNA location and translation rate determine protein targeting to dual destinations.

Numerous proteins are targeted to two or multiple subcellular destinations where they exert distinct functional consequences. The balance between such differential targeting is thought to be determined post-translationally, relying on protein sorting mechanisms. Here, we show that mRNA location and translation rate can also determine protein targeting by modulating protein binding to specific interacting partners. Peripheral localization of the NET1 mRNA and fast translation lead to higher cytosolic retention of the NET1 protein by promoting its binding to the membrane-associated scaffold protein CASK. By contrast, perinuclear mRNA location and/or slower translation rate favor nuclear targeting by promoting binding to importins. This mRNA location-dependent mechanism is modulated by physiological stimuli and profoundly impacts NET1 function in cell motility. These results reveal that the location of protein synthesis and the rate of translation elongation act in coordination as a "partner-selection" mechanism that robustly influences protein distribution and function.

The pausing zone and control of RNA polymerase II elongation by Spt5: Implications for the pause-release model.

The pause-release model of transcription proposes that 40-100 bases from the start site RNA Pol II pauses, followed by release into productive elongation. Pause release is facilitated by the PTEFb phosphorylation of the RNA Pol II elongation factor, Spt5. We mapped paused polymerases by eNET-seq and found frequent pausing in zones that extend ∼0.3-3 kb into genes even when PTEFb is inhibited. The fraction of paused polymerases or pausing propensity declines gradually over several kb and not abruptly as predicted for a discrete pause-release event. Spt5 depletion extends pausing zones, suggesting that it promotes the maturation of elongation complexes to a low-pausing state. The expression of mutants after Spt5 depletion showed that phosphomimetic substitutions in the CTR1 domain diminished pausing throughout genes. By contrast, mutants that prevent the phosphorylation of the Spt5 RNA-binding domain strengthened pausing. Thus, distinct Spt5 phospho-isoforms set the balance between pausing and elongation.

Extreme drought impacts have been underestimated in grasslands and shrublands globally.

Climate change is increasing the frequency and severity of short-term (~1 y) drought events-the most common duration of drought-globally. Yet the impact of this intensification of drought on ecosystem functioning remains poorly resolved. This is due in part to the widely disparate approaches ecologists have employed to study drought, variation in the severity and duration of drought studied, and differences among ecosystems in vegetation, edaphic and climatic attributes that can mediate drought impacts. To overcome these problems and better identify the factors that modulate drought responses, we used a coordinated distributed experiment to quantify the impact of short-term drought on grassland and shrubland ecosystems. With a standardized approach, we imposed ~a single year of drought at 100 sites on six continents. Here we show that loss of a foundational ecosystem function-aboveground net primary production (ANPP)-was 60% greater at sites that experienced statistically extreme drought (1-in-100-y event) vs. those sites where drought was nominal (historically more common) in magnitude (35% vs. 21%, respectively). This reduction in a key carbon cycle process with a single year of extreme drought greatly exceeds previously reported losses for grasslands and shrublands. Our global experiment also revealed high variability in drought response but that relative reductions in ANPP were greater in drier ecosystems and those with fewer plant species. Overall, our results demonstrate with unprecedented rigor that the global impacts of projected increases in drought severity have been significantly underestimated and that drier and less diverse sites are likely to be most vulnerable to extreme drought.

Modeling direct air carbon capture and storage in a 1.5 °C climate future using historical analogs.

Limiting the rise in global temperature to 1.5 °C will rely, in part, on technologies to remove CO2 from the atmosphere. However, many carbon dioxide removal (CDR) technologies are in the early stages of development, and there is limited data to inform predictions of their future adoption. Here, we present an approach to model adoption of early-stage technologies such as CDR and apply it to direct air carbon capture and storage (DACCS). Our approach combines empirical data on historical technology analogs and early adoption indicators to model a range of feasible growth pathways. We use these pathways as inputs to an integrated assessment model (the Global Change Analysis Model, GCAM) and evaluate their effects under an emissions policy to limit end-of-century temperature change to 1.5 °C. Adoption varies widely across analogs, which share different strategic similarities with DACCS. If DACCS growth mirrors high-growth analogs (e.g., solar photovoltaics), it can reach up to 4.9 GtCO2 removal by midcentury, compared to as low as 0.2 GtCO2 for low-growth analogs (e.g., natural gas pipelines). For these slower growing analogs, unabated fossil fuel generation in 2050 is reduced by 44% compared to high-growth analogs, with implications for energy investments and stranded assets. Residual emissions at the end of the century are also substantially lower (by up to 43% and 34% in transportation and industry) under lower DACCS scenarios. The large variation in growth rates observed for different analogs can also point to policy takeaways for enabling DACCS.

Power of inclusion: Enhancing polygenic prediction with admixed individuals.

Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals for developing more equitable PGS models.

CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net.

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence-based image segmentation model such as Meta's Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet.

Governing the net-zero transition: Strategy, policy, and politics.

This paper applies insights from the literature on transitions in major consumption-production systems to clarify the nature of the challenge of moving to a net-zero greenhouse gas (GHG) emission society. It highlights critical features of transitions including their multiactor/multicausal logic, phased development, and distributive impacts. Because current systems are so dependent on fossil energy resources, and on GHG-emitting industrial processes and agricultural practices, multiple transitions across a range of distinct consumption-production systems will be required for net zero. The transformation of each system faces different barriers and enabling conditions and is influenced by varied nonclimate-related disruptions. Important policy implications follow, including the need to focus on sector and regional transitions, link climate policy to other societal goals, and adopt policy mixes appropriate to the transition phase. The article discusses recent policy and politics-related findings from the transitions literatures including those dealing with policy mixes, transition intermediaries, and green industrial policy.

The expanding family of neutrophil-derived extracellular vesicles.

Neutrophils are immune cells involved in several inflammatory and homeostatic processes. Their capacity to release cargo can be classified based on whether the cargo is released on its own, or in conjunction with plasma membrane structures. Examples of plasma membrane-free secretion modes are degranulation, neutrophil extracellular trap (NET) release, and cytokine release through inflammasome formation. The most studied membrane-covered neutrophil-derived structures are exosomes and ectosomes that are collectively called extracellular vesicles (EV). Apoptotic vesicles are another recognized EV subtype. Over the last decade, additional membrane-covered neutrophil-derived structures were characterized: migratory cytoplasts, migrasomes, and elongated neutrophil-derived structures (ENDS). All these structures are smaller than the neutrophils, cannot reproduce themselves, and thus meet the latest consensus definition of EVs. In this review, we focus on the less well-studied neutrophil EVs: apoptotic vesicles, cytoplasts, migrasomes, and ENDS.

Exogenous serpin B1 restricts immune complex-mediated NET formation via inhibition of a chymotrypsin-like protease and enhances microbial phagocytosis.

Immune complex (IC)-driven formation of neutrophil extracellular traps (NETs) is a major contributing factor to the pathogenesis of autoimmune diseases including systemic lupus erythematosus (SLE). Exogenous recombinant human serpin B1 (rhsB1) can regulate NET formation; however, its mechanism(s) of action is currently unknown as is its ability to regulate IC-mediated NET formation and other neutrophil effector functions. To investigate this, we engineered or post-translationally modified rhsB1 proteins that possessed specific neutrophil protease inhibitory activities and pretreated isolated neutrophils with them prior to inducing NET formation with ICs derived from patients with SLE, PMA, or the calcium ionophore A23187. Neutrophil activation and phagocytosis assays were also performed with rhsB1 pretreated and IC-activated neutrophils. rhsB1 dose-dependently inhibited NET formation by all three agents in a process dependent on its chymotrypsin-like inhibitory activity, most likely cathepsin G. Only one variant (rhsB1 C344A) increased surface levels of neutrophil adhesion/activation markers on IC-activated neutrophils and boosted intracellular ROS production. Further, rhsB1 enhanced complement-mediated neutrophil phagocytosis of opsonized bacteria but not ICs. In conclusion, we have identified a novel mechanism of action by which exogenously administered rhsB1 inhibits IC, PMA, and A2138-mediated NET formation. Cathepsin G is a well-known contributor to autoimmune disease but to our knowledge, this is the first report implicating it as a potential driver of NET formation. We identified the rhsB1 C334A variant as a candidate protein that can suppress IC-mediated NET formation, boost microbial phagocytosis, and potentially impact additional neutrophil effector functions including ROS-mediated microbial killing in phagolysosomes.

Trehalose-6-phosphate synthase 8 increases photosynthesis and seed yield in Brassica napus.

Trehalose-6-phosphate (T6P) functions as a vital proxy for assessing carbohydrate status in plants. While class II T6P synthases (TPS) do not exhibit TPS activity, they are believed to play pivotal regulatory roles in trehalose metabolism. However, their precise functions in carbon metabolism and crop yield have remained largely unknown. Here, BnaC02.TPS8, a class II TPS gene, is shown to be specifically expressed in mature leaves and the developing pod walls of Brassica napus. Overexpression of BnaC02.TPS8 increased photosynthesis and the accumulation of sugars, starch, and biomass compared to wild type. Metabolomic analysis of BnaC02.TPS8 overexpressing lines and CRISPR/Cas9 mutants indicated that BnaC02.TPS8 enhanced the partitioning of photoassimilate into starch and sucrose, as opposed to glycolytic intermediates and organic acids, which might be associated with TPS activity. Furthermore, the overexpression of BnaC02.TPS8 not only increased seed yield but also enhanced seed oil accumulation and improved the oil fatty acid composition in B. napus under both high nitrogen (N) and low N conditions in the field. These results highlight the role of class II TPS in impacting photosynthesis and seed yield of B. napus, and BnaC02.TPS8 emerges as a promising target for improving B. napus seed yield.

Gastroenteropancreatic Neuroendocrine Tumors.

Neuroendocrine tumors (NETs) are heterogeneous malignancies arising from the diffuse neuroendocrine system. They frequently originate in the gastroenteropancreatic (GEP) tract and the bronchopulmonary tree, and their incidence has steadily increased in the last 3 decades. Fundamental biologic and genomic differences underlie the clinical heterogeneity of NETs, and distinct molecular features characterize NETs of different grades and different primary sites. Although surgery remains the cornerstone of treatment for localized tumors, systemic treatment options for patients with metastatic NETs have expanded considerably. Somatostatin analogs have demonstrated both antisecretory and antitumor efficacy. Peptide receptor radionuclide therapy with lutetium-177 dotatate (177 Lu-DOTATATE) has been approved for advanced GEP-NETs. The antitumor activity of everolimus has been demonstrated across a wide spectrum of NETs, and the antiangiogenic agent sunitinib has been approved for pancreatic NETs (pNETs). Chemotherapy with temozolomide and capecitabine has recently demonstrated an unprecedented prolongation of progression-free survival in a randomized trial of pNETs. Multiple retrospective series have reported the efficacy of liver-directed therapies both for palliating symptoms of hormone excess and for controlling tumor growth. Telotristat, an oral inhibitor of tryptophan hydroxylase, has been shown to reduce diarrhea in patients with carcinoid syndrome. Defining the therapeutic algorithm and identifying biomarkers predictive of response to treatments are among the main priorities for the next decade of research in the NET field.

ARAP3 protects from excessive formylated peptide-induced microvascular leakage by acting on endothelial cells and neutrophils.

Vascular permeability is temporarily heightened during inflammation, but excessive inflammation-associated microvascular leakage can be detrimental, as evidenced in the inflamed lung. Formylated peptides regulate vascular leakage indirectly via formylated peptide receptor-1 (FPR1)-mediated recruitment and activation of neutrophils. Here we identify how the GTPase-activating protein ARAP3 protects against formylated peptide-induced microvascular permeability via endothelial cells and neutrophils. In vitro, Arap3-/- endothelial monolayers were characterised by enhanced formylated peptide-induced permeability due to upregulated endothelial FPR1 and enhanced vascular endothelial cadherin internalisation. In vivo, enhanced inflammation-associated microvascular leakage was observed in Arap3-/- mice. Leakage of plasma protein into the lungs of Arap3-/- mice increased within hours of formylated peptide administration. Adoptive transfer experiments indicated this was dependent upon ARAP3 deficiency in both immune and non-immune cells. Bronchoalveolar lavages of formylated peptide-challenged Arap3-/- mice contained neutrophil extracellular traps (NETs). Pharmacological inhibition of NET formation abrogated excessive microvascular leakage, indicating a critical function of NETs in this context. The observation that Arap3-/- mice developed more severe influenza suggests these findings are pertinent to pathological situations characterised by abundant formylated peptides. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

m6Aexpress-enet: Predicting the regulatory expression m6A sites by an enet-regularization negative binomial regression model.

As the most abundant mRNA modification, m6A controls and influences many aspects of mRNA metabolism including the mRNA stability and degradation. However, the role of specific m6A sites in regulating gene expression still remains unclear. In additional, the multicollinearity problem caused by the correlation of methylation level of multiple m6A sites in each gene could influence the prediction performance. To address the above challenges, we propose an elastic-net regularized negative binomial regression model (called m6Aexpress-enet) to predict which m6A site could potentially regulate its gene expression. Comprehensive evaluations on simulated datasets demonstrate that m6Aexpress-enet could achieve the top prediction performance. Applying m6Aexpress-enet on real MeRIP-seq data from human lymphoblastoid cell lines, we have uncovered the complex regulatory pattern of predicted m6A sites and their unique enrichment pathway of the constructed co-methylation modules. m6Aexpress-enet proves itself as a powerful tool to enable biologists to discover the mechanism of m6A regulatory gene expression. Furthermore, the source code and the step-by-step implementation of m6Aexpress-enet is freely accessed at https://github.com/tengzhangs/m6Aexpress-enet.

Prediction of individual brain age using movie and resting-state fMRI.

Brain age is a promising biomarker for predicting chronological age based on brain imaging data. Although movie and resting-state functional MRI techniques have attracted much research interest for the investigation of brain function, whether the 2 different imaging paradigms show similarities and differences in terms of their capabilities and properties for predicting brain age remains largely unexplored. Here, we used movie and resting-state functional MRI data from 528 participants aged from 18 to 87 years old in the Cambridge Centre for Ageing and Neuroscience data set for functional network construction and further used elastic net for age prediction model building. The connectivity properties of movie and resting-state functional MRI were evaluated based on the connections supporting predictive model building. We found comparable predictive abilities of movie and resting-state connectivity in estimating brain age of individuals, as evidenced by correlation coefficients of 0.868 and 0.862 between actual and predicted age, respectively. Despite some similarities, notable differences in connectivity properties were observed between the predictive models using movie and resting-state functional MRI data, primarily involving components of the default mode network. Our results highlight that both movie and resting-state functional MRI are effective and promising techniques for predicting brain age. Leveraging its data acquisition advantages, such as improved child and patient compliance resulting in reduced motion artifacts, movie functional MRI is emerging as an important paradigm for studying brain function in pediatric and clinical populations.

Paf1 Has Distinct Roles in Transcription Elongation and Differential Transcript Fate.

RNA polymerase II (Pol2) movement through chromatin and the co-transcriptional processing and fate of nascent transcripts is coordinated by transcription elongation factors (TEFs) such as polymerase-associated factor 1 (Paf1), but it is not known whether TEFs have gene-specific functions. Using strand-specific nucleotide resolution techniques, we show that levels of Paf1 on Pol2 vary between genes, are controlled dynamically by environmental factors via promoters, and reflect levels of processing and export factors on the encoded transcript. High levels of Paf1 on Pol2 promote transcript nuclear export, whereas low levels reflect nuclear retention. Strains lacking Paf1 show marked elongation defects, although low levels of Paf1 on Pol2 are sufficient for transcription elongation. Our findings support distinct Paf1 functions: a core general function in transcription elongation, satisfied by the lowest Paf1 levels, and a regulatory function in determining differential transcript fate by varying the level of Paf1 on Pol2.

Oligomerization of Monoamine Transporters.

Transporters of the monoamine transporter (MAT) family regulate the uptake of important neurotransmitters like dopamine, serotonin, and norepinephrine. The MAT family functions using the electrochemical gradient of ions across the membrane and comprises three transporters, dopamine transporter (DAT), serotonin transporter (SERT), and norepinephrine transporter (NET). MAT transporters have been observed to exist in monomeric states to higher-order oligomeric states. Structural features, allosteric modulation, and lipid environment regulate the oligomerization of MAT transporters. NET and SERT oligomerization are regulated by levels of PIP2 present in the membrane. The kink present in TM12 in the MAT family is crucial for dimer interface formation. Allosteric modulation in the dimer interface hinders dimer formation. Oligomerization also influences the transporters' function, trafficking, and regulation. This chapter will focus on recent studies on monoamine transporters and discuss the factors affecting their oligomerization and its impact on their function.

expHRD: an individualized, transcriptome-based prediction model for homologous recombination deficiency assessment in cancer.

BACKGROUND: Homologous recombination deficiency (HRD) stands as a clinical indicator for discerning responsive outcomes to platinum-based chemotherapy and poly ADP-ribose polymerase (PARP) inhibitors. One of the conventional approaches to HRD prognostication has generally centered on identifying deleterious mutations within the BRCA1/2 genes, along with quantifying the genomic scars, such as Genomic Instability Score (GIS) estimation with scarHRD. However, the scarHRD method has limitations in scenarios involving tumors bereft of corresponding germline data. Although several RNA-seq-based HRD prediction algorithms have been developed, they mainly support cohort-wise classification, thereby yielding HRD status without furnishing an analogous quantitative metric akin to scarHRD. This study introduces the expHRD method, which operates as a novel transcriptome-based framework tailored to n-of-1-style HRD scoring. RESULTS: The prediction model has been established using the elastic net regression method in the Cancer Genome Atlas (TCGA) pan-cancer training set. The bootstrap technique derived the HRD geneset for applying the expHRD calculation. The expHRD demonstrated a notable correlation with scarHRD and superior performance in predicting HRD-high samples. We also performed intra- and extra-cohort evaluations for clinical feasibility in the TCGA-OV and the Genomic Data Commons (GDC) ovarian cancer cohort, respectively. The innovative web service designed for ease of use is poised to extend the realms of HRD prediction across diverse malignancies, with ovarian cancer standing as an emblematic example. CONCLUSIONS: Our novel approach leverages the transcriptome data, enabling the prediction of HRD status with remarkable precision. This innovative method addresses the challenges associated with limited available data, opening new avenues for utilizing transcriptomics to inform clinical decisions.

Multivariate pattern analysis: a method and software to reveal, quantify, and visualize predictive association patterns in multicollinear data.

BACKGROUND: Strongly multicollinear covariates, such as those typically represented in metabolomics applications, represent a challenge for multivariate regression analysis. These challenges are commonly circumvented by reducing the number of covariates to a subset of linearly independent variables, but this strategy may lead to loss of resolution and thus produce models with poorer interpretative potential. The aim of this work was to implement and illustrate a method, multivariate pattern analysis (MVPA), which can handle multivariate covariates without compromising resolution or model quality. RESULTS: MVPA has been implemented in an open-source R package of the same name, mvpa. To facilitate the usage and interpretation of complex association patterns, mvpa has also been integrated into an R shiny app, mvpaShiny, which can be accessed on www.mvpashiny.org . MVPA utilizes a general projection algorithm that embraces a diversity of possible models. The method handles multicollinear and even linear dependent covariates. MVPA separates the variance in the data into orthogonal parts within the frame of a single joint model: one part describing the relations between covariates, outcome, and explanatory variables and another part describing the "net" predictive association pattern between outcome and explanatory variables. These patterns are visualized and interpreted in variance plots and plots for pattern analysis and ranking according to variable importance. Adjustment for a linear dependent covariate is performed in three steps. First, partial least squares regression with repeated Monte Carlo resampling is used to determine the number of predictive PLS components for a model relating the covariate to the outcome. Second, postprocessing of this PLS model by target projection provided a single component expressing the predictive association pattern between the outcome and the covariate. Third, the outcome and the explanatory variables were adjusted for the covariate by using the target score in the projection algorithm to obtain "net" data. We illustrate the main features of MVPA by investigating the partial mediation of a linearly dependent metabolomics descriptor on the association pattern between a measure of insulin resistance and lifestyle-related factors. CONCLUSIONS: Our method and implementation in R extend the range of possible analyses and visualizations that can be performed for complex multivariate data structures. The R packages are available on github.com/liningtonlab/mvpa and github.com/liningtonlab/mvpaShiny.