Actin filament assembly by bacterial factors VopL/F: Which end is up?

Competing models have been proposed for actin filament nucleation by the bacterial proteins VopL/F. In this issue, Burke et al. (2017. J. Cell Biol. https://doi.org/10.1083/jcb.201608104) use direct observation to demonstrate that VopL/F bind the barbed and pointed ends of actin filaments but only nucleate new filaments from the pointed end

Autoinhibition of the formin Cappuccino in the absence of canonical autoinhibitory domains.

Formins are a conserved family of proteins known to enhance actin polymerization. Most formins are regulated by an intramolecular interaction. The Drosophila formin, Cappuccino (Capu), was believed to be an exception. Capu does not contain conserved autoinhibitory domains and can be regulated by a second protein, Spire. We report here that Capu is, in fact, autoinhibited. The N-terminal half of Capu (Capu-NT) potently inhibits nucleation and binding to the barbed end of elongating filaments by the C-terminal half of Capu (Capu-CT). Hydrodynamic analysis indicates that Capu-NT is a dimer, similar to the N-termini of other formins. These data, combined with those from circular dichroism, suggest, however, that it is structurally distinct from previously described formin inhibitory domains. Finally, we find that Capu-NT binds to a site within Capu-CT that overlaps with the Spire-binding site, the Capu-tail. We propose models for the interaction between Spire and Capu in light of the fact that Capu can be regulated by autoinhibition

Spire stimulates nucleation by Cappuccino and binds both ends of actin filaments.

The actin nucleators Spire and Cappuccino synergize to promote actin assembly, but the mechanism of their synergy is controversial. Together these proteins promote the formation of actin meshes, which are conserved structures that regulate the establishment of oocyte polarity. Direct interaction between Spire and Cappuccino is required for oogenesis and for in vitro synergistic actin assembly. This synergy is proposed to be driven by elongation and the formation of a ternary complex at filament barbed ends, or by nucleation and interaction at filament pointed ends. To mimic the geometry of Spire and Cappuccino in vivo, we immobilized Spire on beads and added Cappuccino and actin. Barbed ends, protected by Cappuccino, grow away from the beads while pointed ends are retained, as expected for nucleation-driven synergy. We found that Spire is sufficient to bind barbed ends and retain pointed ends of actin filaments near beads and we identified Spire's barbed-end binding domain. Loss of barbed-end binding increases nucleation by Spire and synergy with Cappuccino in bulk pyrene assays and on beads. Importantly, genetic rescue by the loss-of-function mutant indicates that barbed-end binding is not necessary for oogenesis. Thus, increased nucleation is a critical element of synergy both in vitro and in vivo

Drosophila Cappuccino alleles provide insight into formin mechanism and role in oogenesis.

During Drosophila development, the formin actin nucleator Cappuccino (Capu) helps build a cytoplasmic actin mesh throughout the oocyte. Loss of Capu leads to female sterility, presumably because polarity determinants fail to localize properly in the absence of the mesh. To gain deeper insight into how Capu builds this actin mesh, we systematically characterized seven capu alleles, which have missense mutations in Capu's formin homology 2 (FH2) domain. We report that all seven alleles have deleterious effects on fly fertility and the actin mesh in vivo but have strikingly different effects on Capu's biochemical activity in vitro. Using a combination of bulk and single- filament actin-assembly assays, we find that the alleles differentially affect Capu's ability to nucleate and processively elongate actin filaments. We also identify a unique "loop" in the lasso region of Capu's FH2 domain. Removing this loop enhances Capu's nucleation, elongation, and F-actin-bundling activities in vitro. Together our results on the loop and the seven missense mutations provides mechanistic insight into formin function in general and Capu's role in the Drosophila oocyte in particular

Learning a Static Analyzer from Data

To be practically useful, modern static analyzers must precisely model the effect of both, statements in the programming language as well as frameworks used by the program under analysis. While important, manually addressing these challenges is difficult for at least two reasons: (i) the effects on the overall analysis can be non-trivial, and (ii) as the size and complexity of modern libraries increase, so is the number of cases the analysis must handle. In this paper we present a new, automated approach for creating static analyzers: instead of manually providing the various inference rules of the analyzer, the key idea is to learn these rules from a dataset of programs. Our method consists of two ingredients: (i) a synthesis algorithm capable of learning a candidate analyzer from a given dataset, and (ii) a counter-example guided learning procedure which generates new programs beyond those in the initial dataset, critical for discovering corner cases and ensuring the learned analysis generalizes to unseen programs. We implemented and instantiated our approach to the task of learning JavaScript static analysis rules for a subset of points-to analysis and for allocation sites analysis. These are challenging yet important problems that have received significant research attention. We show that our approach is effective: our system automatically discovered practical and useful inference rules for many cases that are tricky to manually identify and are missed by state-of-the-art, manually tuned analyzers

Learning Multi-label Alternating Decision Trees from Texts and Data

International audienceMulti-label decision procedures are the target of the supervised learning algorithm we propose in this paper. Multi-label decision procedures map examples to a finite set of labels. Our learning algorithm extends Schapire and Singer?s Adaboost.MH and produces sets of rules that can be viewed as trees like Alternating Decision Trees (invented by Freund and Mason). Experiments show that we take advantage of both performance and readability using boosting techniques as well as tree representations of large set of rules. Moreover, a key feature of our algorithm is the ability to handle heterogenous input data: discrete and continuous values and text data. Keywords boosting - alternating decision trees - text mining - multi-label problem

The Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes

The measurement of student performance during their progress through university study provides academic leadership with critical information on each student’s likelihood of success. Academics have traditionally used their interactions with individual students through class activities and interim assessments to identify those “at risk” of failure/withdrawal. However, modern university environments, offering easy on-line availability of course material, may see reduced lecture/tutorial attendance, making such identification more challenging. Modern data mining and machine learning techniques provide increasingly accurate predictions of student examination assessment marks, although these approaches have focussed upon large student populations and wide ranges of data attributes per student. However, many university modules comprise relatively small student cohorts, with institutional protocols limiting the student attributes available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. We describe an experiment conducted on a final-year university module student cohort of 23, where individual student data are limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. We found potential for predicting individual student interim and final assessment marks in small student cohorts with very limited attributes and that these predictions could be useful to support module leaders in identifying students potentially “at risk.”.Peer reviewe

Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that these star/galaxy classifications are expected to be reliable for approximately 22 million objects with r < ~20. The general machine learning environment Data-to-Knowledge and supercomputing resources enabled extensive investigation of the decision tree parameter space. This work presents the first public release of objects classified in this way for an entire SDSS data release. The objects are classified as either galaxy, star or nsng (neither star nor galaxy), with an associated probability for each class. To demonstrate how to effectively make use of these classifications, we perform several important tests. First, we detail selection criteria within the probability space defined by the three classes to extract samples of stars and galaxies to a given completeness and efficiency. Second, we investigate the efficacy of the classifications and the effect of extrapolating from the spectroscopic regime by performing blind tests on objects in the SDSS, 2dF Galaxy Redshift and 2dF QSO Redshift (2QZ) surveys. Given the photometric limits of our spectroscopic training data, we effectively begin to extrapolate past our star-galaxy training set at r ~ 18. By comparing the number counts of our training sample with the classified sources, however, we find that our efficiencies appear to remain robust to r ~ 20. As a result, we expect our classifications to be accurate for 900,000 galaxies and 6.7 million stars, and remain robust via extrapolation for a total of 8.0 million galaxies and 13.9 million stars. [Abridged]Comment: 27 pages, 12 figures, to be published in ApJ, uses emulateapj.cl