Generalized Linear Splitting Rules in Decision Forests

Random forests (RFs) is one of the most widely employed machine learning algorithms for general classification tasks due to its speed, ease-of-use, and excellent empirical performance. Recent large-scale comparisons of classification algorithms have concluded that RFs outperform many other classifiers on a variety of datasets. However, the trees in a RF are constructed via a series of recursive axis-aligned splits, rendering the learning procedure sensitive to the orientation of the data. Several studies have proposed ``oblique'' decision forest methods to address this limitation, which search for good splits that aren't constrained to be axis-aligned. In this work, we explore how properties of the split selection procedure relate to empirical and theoretical performance. We then establish a generalized decision forest framework called Randomer Forests (RerFs), which encompasses RFs and many previously proposed decision forest algorithms as particular instantiations. With this framework in mind, we propose a default instantiation and provide theoretical and experimental evidence motivating its use. Additionally, we demonstrate how our framework can exploit prior domain knowledge to boost performance. Last, we use RerF to identify important biomarkers for ovarian cancer classification and learn a classifier with high sensitivity and specificity

Myosin II activity dependent and independent vinculin recruitment to the sites of E-cadherin-mediated cell-cell adhesion

<p>Abstract</p> <p>Background</p> <p>Maintaining proper adhesion between neighboring cells depends on the ability of cells to mechanically respond to tension at cell-cell junctions through the actin cytoskeleton. Thus, identifying the molecules involved in responding to cell tension would provide insight into the maintenance, regulation, and breakdown of cell-cell junctions during various biological processes. Vinculin, an actin-binding protein that associates with the cadherin complex, is recruited to cell-cell contacts under increased tension in a myosin II-dependent manner. However, the precise role of vinculin at force-bearing cell-cell junctions and how myosin II activity alters the recruitment of vinculin at quiescent cell-cell contacts have not been demonstrated.</p> <p>Results</p> <p>We generated vinculin knockdown cells using shRNA specific to vinculin and MDCK epithelial cells. These vinculin-deficient MDCK cells form smaller cell clusters in a suspension than wild-type cells. In wound healing assays, GFP-vinculin accumulated at cell-cell junctions along the wound edge while vinculin-deficient cells displayed a slower wound closure rate compared to vinculin-expressing cells. In the presence of blebbistatin (myosin II inhibitor), vinculin localization at quiescent cell-cell contacts was unaffected while in the presence of jasplakinolide (F-actin stabilizer), vinculin recruitment increased in mature MDCK cell monolayers.</p> <p>Conclusion</p> <p>These results demonstrate that vinculin plays an active role at adherens junctions under increased tension at cell-cell contacts where vinculin recruitment occurs in a myosin II activity-dependent manner, whereas vinculin recruitment to the quiescent cell-cell junctions depends on F-actin stabilization.</p

Manifold Forests: Closing the Gap on Neural Networks

Decision forests (DFs), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learning scenarios. In particular, DFs dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to permuting feature indices. However, in structured data lying on a manifold---such as images, text, and speech---deep networks (DNs), specifically convolutional deep networks (ConvNets), tend to outperform DFs. We conjecture that at least part of the reason for this is that the input to DNs is not simply the feature magnitudes, but also their indices (for example, the convolution operation uses feature locality). In contrast, naive DF implementations fail to explicitly consider feature indices. A recently proposed DF approach demonstrates that DFs, for each node, implicitly sample a random matrix from some specific distribution. These DFs, like some classes of DNs, learn by partitioning the feature space into convex polytopes corresponding to linear functions. We build on that approach and show that one can choose distributions in a manifold-aware fashion to incorporate feature locality. We demonstrate the empirical performance on data whose features live on three different manifolds: a torus, images, and time-series. In all simulations, our Manifold Oblique Random Forest (MORF) algorithm empirically dominates other state-of-the-art approaches that ignore feature space structure and challenges the performance of ConvNets. Moreover, MORF runs significantly faster than ConvNets and maintains interpretability and theoretical justification. This approach, therefore, has promise to enable DFs and other machine learning methods to close the gap to deep networks on manifold-valued data.Comment: 12 pages, 4 figure

Postsynaptic BDNF-TrkB Signaling in Synapse Maturation, Plasticity, and Disease

Brain-derived neurotrophic factor (BDNF) is a prototypic neurotrophin that regulates diverse developmental events from the selection of neural progenitors to the terminal dendritic differentiation and connectivity of neurons. We focus here on activity-dependent synaptic regulation by BDNF and its receptor, full length TrkB. BDNF-TrkB signaling is involved in transcription, translation, and trafficking of proteins during various phases of synaptic development and has been implicated in several forms of synaptic plasticity. These functions are carried out by a combination of the three signaling cascades triggered when BDNF binds TrkB: The mitogen-activated protein kinase (MAPK), the phospholipase Cγ (PLC PLCγ), and the phosphatidylinositol 3-kinase (PI3K) pathways. MAPK and PI3K play crucial roles in both translation and/or trafficking of proteins induced by synaptic activity, whereas PLCγ regulates intracellular Ca2+ that can drive transcription via cyclic AMP and a protein kinase C. Conversely, the abnormal regulation of BDNF is implicated in various developmental and neurodegenerative diseases that perturb neural development and function. We will discuss the current state of understanding BDNF signaling in the context of synaptic development and plasticity with a focus on the postsynaptic cell and close with the evidence that basic mechanisms of BDNF function still need to be understood to effectively treat genetic disruptions of these pathways that cause devastating neurodevelopmental diseases.United States. Dept. of Defense (contract grant number: TS080074)National Institutes of Health (U.S.) (Contract grant number: R01EY014074)National Institutes of Health (U.S.) (Contract grant number: R01EY006039

Suppression of Lung Adenocarcinoma Progression by Nkx2-1

Despite the high prevalence and poor outcome of patients with metastatic lung cancer the mechanisms of tumour progression and metastasis remain largely uncharacterized. Here we modelled human lung adenocarcinoma, which frequently harbours activating point mutations in KRAS and inactivation of the p53 pathway, using conditional alleles in mice. Lentiviral-mediated somatic activation of oncogenic Kras and deletion of p53 in the lung epithelial cells of Kras[superscript LSL-G12D/+];p53[superscript flox/flox] mice initiates lung adenocarcinoma development4. Although tumours are initiated synchronously by defined genetic alterations, only a subset becomes malignant, indicating that disease progression requires additional alterations. Identification of the lentiviral integration sites allowed us to distinguish metastatic from non-metastatic tumours and determine the gene expression alterations that distinguish these tumour types. Cross-species analysis identified the NK2-related homeobox transcription factor Nkx2-1 (also called Ttf-1 or Titf1) as a candidate suppressor of malignant progression. In this mouse model, Nkx2-1 negativity is pathognomonic of high-grade poorly differentiated tumours. Gain- and loss-of-function experiments in cells derived from metastatic and non-metastatic tumours demonstrated that Nkx2-1 controls tumour differentiation and limitsmetastatic potential in vivo. Interrogation of Nkx2-1-regulated genes, analysis of tumours at defined developmental stages, and functional complementation experiments indicate that Nkx2-1 constrains tumours in part by repressing the embryonically restricted chromatin regulator Hmga2. Whereas focal amplification of NKX2-1 in a fraction of human lung adenocarcinomas has focused attention on its oncogenic function, our data specifically link Nkx2-1 downregulation to loss of differentiation, enhanced tumour seeding ability and increased metastatic proclivity. Thus, the oncogenic and suppressive functions ofNkx2-1 in the sametumourNational Institutes of Health (U.S.) (grant U01-CA84306 )National Institutes of Health (U.S.) (grant K99-CA151968)Howard Hughes Medical InstituteLudwig Center for Molecular OncologyNational Cancer Institute (U.S.) (Cancer Center Support (core) grant P30-CA14051

Reflexion and reflection: A social cognitive neuroscience approach to attributional inference

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Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial

Background: The EMPA KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population. Methods: EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110. Findings: Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5–2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62–0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16–1·59), representing a 50% (42–58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all &gt;0·1). Interpretation: In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease. Funding: Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council