Integrating Horizontal Gene Transfer and Common Descent to Depict Evolution and Contrast It with ‘‘Common Design

Horizontal gene transfer (HGT) and common descent interact in space and time. Because events of HGT co-occur with phylogenetic evolution, it is difficult to depict evolutionary patterns graphically. Tree-like representations of life’s diversification are useful, but they ignore the significance of HGT in evolutionary history, particularly of unicellular organisms, ancestors of multicellular life. Here we integrate the reticulated-tree model, ring of life, symbiogenesis whole-organism model, and eliminative pattern pluralism to represent evolution. Using Entamoeba histolytica alcohol dehydrogenase 2 (EhADH2), a bifunctional enzyme in the glycolytic pathway of amoeba, we illustrate how EhADH2 could be the product of both horizontally acquired features from ancestral prokaryotes (i.e. aldehyde dehydrogenase [ALDH] and alcohol dehydrogenase [ADH]), and subsequent functional integration of these enzymes into EhADH2, which is now inherited by amoeba via common descent. Natural selection has driven the evolution of EhADH2 active sites, which require specific amino acids (cysteine 252 in the ALDH domain; histidine 754 in the ADH domain), iron- and NAD1 as cofactors, and the substrates acetyl-CoA for ALDH and acetaldehyde for ADH. Alternative views invoking ‘‘common design’’ (i.e. the non-naturalistic emergence of major taxa independent from ancestry) to explain the interaction between horizontal and vertical evolution are unfounded

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification

Person re-identification (re-id) aims to match pedestrians observed by disjoint camera views. It attracts increasing attention in computer vision due to its importance to surveillance system. To combat the major challenge of cross-view visual variations, deep embedding approaches are proposed by learning a compact feature space from images such that the Euclidean distances correspond to their cross-view similarity metric. However, the global Euclidean distance cannot faithfully characterize the ideal similarity in a complex visual feature space because features of pedestrian images exhibit unknown distributions due to large variations in poses, illumination and occlusion. Moreover, intra-personal training samples within a local range are robust to guide deep embedding against uncontrolled variations, which however, cannot be captured by a global Euclidean distance. In this paper, we study the problem of person re-id by proposing a novel sampling to mine suitable \textit{positives} (i.e. intra-class) within a local range to improve the deep embedding in the context of large intra-class variations. Our method is capable of learning a deep similarity metric adaptive to local sample structure by minimizing each sample's local distances while propagating through the relationship between samples to attain the whole intra-class minimization. To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep embedding. This yields local discriminations by selecting local-ranged positive samples, and the learned features are robust to dramatic intra-class variations. Experiments on benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio

Multimodal Grounding for Language Processing

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference of Computational Linguistics. Please refer to this version for citations: https://www.aclweb.org/anthology/papers/C/C18/C18-1197

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Production and analysis of synthetic Cascade variants

CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR assoziiert) ist ein adaptives Immunsystem in Archaeen und Bakterien, das fremdes genetisches Material mit Hilfe von Ribonukleoprotein-Komplexen erkennt und zerstört. Diese Komplexe bestehen aus einer CRISPR RNA (crRNA) und Cas Proteinen. CRISPR-Cas Systeme sind in zwei Hauptklassen und mehrere Typen unterteilt, abhängig von den beteiligten Cas Proteinen. In Typ I Systemen sucht ein Komplex namens Cascade (CRISPR associated complex for antiviral defence) nach eingedrungener viraler DNA während einer Folgeinfektion und bindet die zu der eingebauten crRNA komplementäre Sequenz. Anschließend wird die Nuklease/Helikase Cas3 rekrutiert, welche die virale DNA degradiert (Interferenz). Das Typ I System wird in mehrere Subtypen unterteilt, die Unterschiede im Aufbau von Cascade vorweisen. Im Fokus dieser Arbeit steht eine minimale Cascade-Variante aus Shewanella putrefaciens CN-32. Im Vergleich zur gut untersuchten Typ I-E Cascade aus Escherichia coli fehlen in diesem Komplex zwei Untereinheiten, die gewöhnlicher Weise für die Zielerkennung benötigt werden. Dennoch ist der Komplex aktiv. Rekombinante I-Fv Cascade wurde bereits aus E. coli aufgereinigt und es war möglich, den Komplex zu modifizieren, indem das Rückgrat entweder verlängert oder verkürzt wurde. Dadurch wurden synthetische Varianten mit veränderter Protein-Stöchiometrie erzeugt. In der vorliegenden Arbeit wurde I-Fv Cascade weiter mit in vitro Methoden untersucht. So wurde die Bindung von Ziel-DNA beobachtet und die 3D Struktur zeigt, dass strukturelle Veränderungen im Komplex die fehlenden Untereinheiten ersetzen, möglicherweise um viralen Anti-CRISPR Proteinen zu entgehen. Die Nuklease/Helikase dieses Systems, Cas2/3fv, ist eine Fusion des Cas3 Proteins mit dem Interferenz-unabhängigen Protein Cas2. Ein unabhängiges Cas3fv ohne Cas2 Untereinheit wurde aufgereinigt und in vitro Assays zeigten, dass dieses Protein sowohl freie ssDNA als auch Cascadegebundene Substrate degradiert. Das komplette Cas2/3fv Protein bildet einen Komplex mit dem Protein Cas1 und zeigt eine reduzierte Aktivität gegenüber freier ssDNA, möglicherweise als Regulationsmechanismus zur Vermeidung von unspezifischer Aktivität. Weiterhin wurde ein Prozess namens „RNA wrapping“ etabliert. Synthetische Cascade-Komplexe wurden erzeugt, in denen die grundlegende RNA-Bindung des charakteristischen Cas7fv RückgratProteins auf eine ausgewählte RNA gelenkt wird. Diese spezifische Komplexbildung kann in vivo durch eine Repeat-Sequenz der crRNA stromaufwärts der Zielsequenz und durch Bindung des Cas5fv Proteins initiiert werden. Die erzeugten Komplexe beinhalten die ersten 100 nt der markierten RNA, die anschließend isoliert werden kann. Innerhalb der Komplexe ist die RNA stabilisiert und geschützt vor Degradation durch RNasen. Komplexbildung kann außerdem genutzt werden, um ReportergenTranskripte stillzulegen. Zusätzlich wurden erste Hinweise geliefert, dass das Rückgrat der synthetischen Komplexe durch Fusion mit weiteren Reporterproteinen modifiziert werden kann.CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR associated) is an adaptive immune system of Archaea and Bacteria. It is able to target and destroy foreign genetic material with ribonucleoprotein complexes consisting of CRISPR RNAs (crRNAs) and certain Cas proteins. CRISPR-Cas systems are classified in two major classes and multiple types, according to the involved Cas proteins. In type I systems, a ribonucleoprotein complex called Cascade (CRISPR associated complex for antiviral defence) scans for invading viral DNA during a recurring infection and binds the sequence complementary to the incorporated crRNA. After target recognition, the nuclease/helicase Cas3 is recruited and subsequently destroys the viral DNA in a step termed interfere nce. Multiple subtypes of type I exist that show differences in the Cascade composition. This work focuses on a minimal Cascade variant found in Shewanella putrefaciens CN-32. In comparison to the well-studied type I-E Cascade from Escherichia coli, this complex is missing two proteins usually required for target recognition, yet it is still able to provide immunity. Recombinant I-Fv Cascade was previously purified from E. coli and it was possible to modulate the complex by extending or shortening the backbone, resulting in synthetic variants with altered protein stoichiometry. In the present study, I-Fv Cascade was further analyzed by in vitro methods. Target binding was observed and the 3D structure revealed structural variations that replace the missing subunits, potentially to evade viral anti-CRISPR proteins. The nuclease/helicase of this system, Cas2/3fv, is a fusion of the Cas3 protein with the interference-unrelated protein Cas2. A standalone Cas3fv was purified without the Cas2 domain and in vitro cleavage assays showed that Cas3fv degrades both free ssDNA as well as Cascade-bound substrates. The complete Cas2/3fv protein forms a complex with the protein Cas1 and was shown to reduce cleave of free ssDNA, potentially as a regulatory mechanism against unspecific cleavage. Furthermore, we established a process termed “RNA wrapping”. Synthetic Cascade assemblies can be created by directing the general RNA-binding ability of the characteristic Cas7fv backbone protein on an RNA of choice such as reporter gene transcripts. Specific complex formation can be initiated in vivo by including a repeat sequence from the crRNA upstream a given target sequence and binding of the Cas5fv protein. The created complexes contain the initial 100 nt of the tagged RNA which can be isolated afterwards. While incorporated in complexes, RNA is stabilized and protected from degradation by RNases. Complex formation can be used to silence reporter gene transcripts. Furthermore, we provided initial indications that the backbone of synthetic complexes can be modified by addition of reporter proteins