Towards dense object tracking in a 2D honeybee hive

From human crowds to cells in tissue, the detection and efficient tracking of multiple objects in dense configurations is an important and unsolved problem. In the past, limitations of image analysis have restricted studies of dense groups to tracking a single or subset of marked individuals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here, we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive to automatically recognize all individuals in a dense group from raw image data. We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. We additionally exploit temporal regularities of the video recording in a recurrent manner and achieve near human-level performance while reducing the network size by 94% compared to the original U-Net architecture. Given our novel application of CNNs, we generate extensive problem-specific image data in which labeled examples are produced through a custom interface with Amazon Mechanical Turk. This dataset contains over 375,000 labeled bee instances across 720 video frames at 2 FPS, representing an extensive resource for the development and testing of tracking methods. We correctly detect 96% of individuals with a location error of ~7% of a typical body dimension, and orientation error of 12 degrees, approximating the variability of human raters. Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently within one network architecture.Comment: 15 pages, including supplementary figures. 1 supplemental movie available as an ancillary fil

Analysis of HIV-host interaction on different scales

The human immunodeficiency virus depends on molecular pathways of the host for efficient replication and spread. The intricate network of host-virus interactions shapes the virus\u27; evolution by driving the pathogen to evade immune recognition and constraining it to maintain its capacity to replicate. Study of the HIV-host interactions provides important insights into viral evolution, pathogenicity and potential treatment strategies. This thesis presents an analysis of HIV-host interactions on several scales, ranging from individual protein interactions to whole genomes. On the scale of individual interaction we analyze structural and physical determinants of the interaction between host TRIM5alpha and virus capsid — an interaction of potential therapeutic interest due to the capacity of TRIM5alpha to block retroviral infections. On the scale of viral population we present two studies of a highly variable region of the virus genome involved in the interaction with host cell coreceptors upon virus cell entry. The studies provide insights into the virus evolution and the physicochemical and structural properties related to its interaction with cellular coreceptors. On the scale of the single cell we develop models of HIV cell entry involving virus, host and environmental factors. The models represent a comprehensive picture of the virus phenotype and allow one to view the variability of virus phenotypes on 2D phenotype maps. On the genomic scale we perform a large-scale analysis of all HIV-host interactions. This study reveals insights into general patterns of the host-pathogen evolution and suggests candidate host proteins involved in interactions potentially important for the infection and interesting for further study on other scales. Interactions and processes crucial for the HIV infection reemerge across the scales pointing to the importance of integrative, multi-scale studies of host-pathogen biology.Das Humane Immundefizienz-Virus hängt von molekularen Mechanismen des Wirts für seine effiziente Replikation und Ausbreitung ab. Das komplizierte Netzwerk von Wirt-Virus Interaktionen formt die Evolution des Virus, indem es den Erreger dazu bringt, sich der Erkennung durch das Immunsystem zu entziehen und seine Replikationskapazität aufrecht zu erhalten. Das Studium der HIV-Wirt Interaktionen erlaubt wichtige Einblicke in die viralen Evolution, die Pathogenität des Virus, sowie mögliche Behandlungsstrategien. Diese Arbeit stellt eine Analyse der HIV-Wirt-Interaktionen in mehreren Größenordnungen vor, von einzelnen Protein-Interaktionen bis hin zur Analyse ganzer Genome. In Hinblick auf einzelne Interaktionen untersuchen wir strukturelle und physikalische Determinanten der Interaktion zwischen dem Wirtfaktor TRIM5alpha; und dem viralen Kapsid - eine Interaktion, die von therapeutischem Interesse ist wegen der Fähigkeit von TRIM5alpha, retrovirale Infektionen zu blockieren. In Hinblick auf virale Populationen präsentieren wir zwei Studien einer hochvariablen Region des viralen Genoms, die in der Interaktion des Virus mit zellulären Rezeptoren des Wirts beim viralen Zelleintritt involviert sind. Diese Studien geben Einblick in die virale Evolution und die physikalisch-chemischen und strukturellen Eigenschaften des Virus bezüglich dessen Interaktion mit zellulären Ko-Rezeptoren. Auf der Skala der einzelnen Zelle entwickeln wir Modelle des HIV Zelleintritts welche das Virus, den Wirt und Umgebungsfaktoren berücksichtigen. Diese Modelle bieten ein umfassendes Bild des viralen Phänotyps und erlauben es, die Variabilität des Virus auf 2D-Phänotyp-Karten zu visualisieren. Im genomweiten Maßstab führen wir eine groß angelegte Analyse aller HIV-Wirt-Interaktionen durch. Diese Studie erlaubt Einblicke in allgemeine Muster der Wirt-Pathogen-Evolution und identifiziert Kandidaten für Wirtsproteine, deren Interaktionen potenziell wichtig für die virale Infektion sind und deren weitere Untersuchung in anderen Größenordnungen von Interesse ist. Interaktionen und Prozesse, die von entscheidender Bedeutung für die HIV-Infektion sind zeigen sich wiederholt in allen untersuchten Maßstäben und unterstreichen die Bedeutung einer integrativen und multi-skalaren Untersuchung der Wirt-Pathogen-Biologie

Electrostatic Potential of Human Immunodeficiency Virus Type 2 and Rhesus Macaque Simian Immunodeficiency Virus Capsid Proteins

Human immunodeficiency virus type 2 (HIV-2) and simian immunodeficiency virus isolated from a macaque monkey (SIVmac) are assumed to have originated from simian immunodeficiency virus isolated from sooty mangabey (SIVsm). Despite their close similarity in genome structure, HIV-2 and SIVmac show different sensitivities to TRIM5α, a host restriction factor against retroviruses. The replication of HIV-2 strains is potently restricted by rhesus (Rh) monkey TRIM5α, while that of SIVmac strain 239 (SIVmac239) is not. Viral capsid protein is the determinant of this differential sensitivity to TRIM5α, as the HIV-2 mutant carrying SIVmac239 capsid protein evaded Rh TRIM5α-mediated restriction. However, the molecular determinants of this restriction mechanism are unknown. Electrostatic potential on the protein-binding site is one of the properties regulating protein–protein interactions. In this study, we investigated the electrostatic potential on the interaction surface of capsid protein of HIV-2 strain GH123 and SIVmac239. Although HIV-2 GH123 and SIVmac239 capsid proteins share more than 87% amino acid identity, we observed a large difference between the two molecules with the HIV-2 GH123 molecule having predominantly positive and SIVmac239 predominantly negative electrostatic potential on the surface of the loop between α-helices 4 and 5 (L4/5). As L4/5 is one of the major determinants of Rh TRIM5α sensitivity of these viruses, the present results suggest that the binding site of the Rh TRIM5α may show complementarity to the HIV-2 GH123 capsid surface charge distribution

A single amino acid substitution of the human immunodeficiency virus type 1 capsid protein affects viral sensitivity to TRIM5α

<p>Abstract</p> <p>Background</p> <p>Human immunodeficiency virus type 1 (HIV-1) productively infects only humans and chimpanzees but not Old World monkeys, such as rhesus and cynomolgus (CM) monkeys. To establish a monkey model of HIV-1/AIDS, several HIV-1 derivatives have been constructed. We previously reported that efficient replication of HIV-1 in CM cells was achieved after we replaced the loop between α-helices 6 and 7 (L6/7) of the capsid protein (CA) with that of SIVmac239 in addition to the loop between α-helices 4 and 5 (L4/5) and <it>vif</it>. This virus (NL-4/5S6/7SvifS) was supposed to escape from host restriction factors cyclophilin A, CM TRIM5α, and APOBEC3G. However, the replicative capability of NL-4/5S6/7SvifS in human cells was severely impaired.</p> <p>Results</p> <p>By long-term cultivation of human CEMss cells infected with NL-4/5S6/7SvifS, we succeeded in rescuing the impaired replicative capability of the virus in human cells. Sequence analysis of the CA region of the adapted virus revealed a G-to-E substitution at the 116th position of the CA (G116E). Introduction of this substitution into the molecular DNA clone of NL-4/5S6/7SvifS indeed improved the virus' replicative capability in human cells. Although the G116E substitution occurred during long-term cultivation of human cells infected with NL-4/5S6/7SvifS, the viruses with G116E unexpectedly became resistant to CM, but not human TRIM5α-mediated restriction. The 3-D model showed that position 116 is located in the 6^thhelix near L4/5 and L6/7 and is apparently exposed to the protein surface. The amino acid substitution at the 116^thposition caused a change in the structure of the protein surface because of the replacement of G (which has no side chain) with E (which has a long negatively charged side chain).</p> <p>Conclusions</p> <p>We succeeded in rescuing the impaired replicative capability of NL-4/5S6/7SvifS and report a mutation that improved the replicative capability of the virus. Unexpectedly, HIV-1 with this mutation became resistant to CM TRIM5α-mediated restriction.</p

A C. elegans model for neurodegeneration in Cockayne syndrome.

Cockayne syndrome (CS) is a congenital syndrome characterized by growth and mental retardation, and premature ageing. The complexity of CS and mammalian models warrants simpler metazoan models that display CS-like phenotypes that could be studied in the context of a live organism. Here, we provide a characterization of neuronal and mitochondrial aberrations caused by a mutation in the csb-1 gene in Caenorhabditis elegans. We report a progressive neurodegeneration in adult animals that is enhanced upon UV-induced DNA damage. The csb-1 mutants show dysfunctional hyperfused mitochondria that degrade upon DNA damage, resulting in diminished respiratory activity. Our data support the role of endogenous DNA damage as a driving factor of CS-related neuropathology and underline the role of mitochondrial dysfunction in the disease

Towards dense object tracking in a 2D honeybee hive

From human crowds to cells in tissue, the detection and efficient tracking of multiple objects in dense configurations is an important and unsolved problem. In the past, limitations of image analysis have restricted studies of dense groups to tracking a single or subset of marked individuals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here, we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive to automatically recognize all individuals in a dense group from raw image data. We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. We additionally exploit temporal regularities of the video recording in a recurrent manner and achieve near human-level performance while reducing the network size by 94% compared to the original U-Net architecture. Given our novel application of CNNs, we generate extensive problem-specific image data in which labeled examples are produced through a custom interface with Amazon Mechanical Turk. This dataset contains over 375,000 labeled bee instances across 720 video frames at 2FPS, representing an extensive resource for the development and testing of tracking methods. We correctly detect 96% of individuals with a location error of ~ 7% of a typical body dimension, and orientation error of 12°, approximating the variability of human raters. Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently within one network architecture.Funding for this work was provided by the OIST Graduate University to ASM and GS. Additional funding was provided by KAKENHI grants 16H06209 and 16KK0175 from the Japan Society for the Promotion of Science to AS

Exceptional evolutionary divergence of human muscle and brain metabolomes parallels human cognitive and physical uniqueness

Metabolite concentrations reflect the physiological states of tissues and cells. However, the role of metabolic changes in species evolution is currently unknown. Here, we present a study of metabolome evolution conducted in three brain regions and two non-neural tissues from humans, chimpanzees, macaque monkeys, and mice based on over 10,000 hydrophilic compounds. While chimpanzee, macaque, and mouse metabolomes diverge following the genetic distances among species, we detect remarkable acceleration of metabolome evolution in human prefrontal cortex and skeletal muscle affecting neural and energy metabolism pathways. These metabolic changes could not be attributed to environmental conditions and were confirmed against the expression of their corresponding enzymes. We further conducted muscle strength tests in humans, chimpanzees, and macaques. The results suggest that, while humans are characterized by superior cognition, their muscular performance might be markedly inferior to that of chimpanzees and macaque monkeys.Publisher PDFPeer reviewe

V3 Loop Sequence Space Analysis Suggests Different Evolutionary Patterns of CCR5- and CXCR4-Tropic HIV

The V3 loop of human immunodeficiency virus type 1 (HIV-1) is critical for coreceptor binding and is the main determinant of which of the cellular coreceptors, CCR5 or CXCR4, the virus uses for cell entry. The aim of this study is to provide a large-scale data driven analysis of HIV-1 coreceptor usage with respect to the V3 loop evolution and to characterize CCR5- and CXCR4-tropic viral phenotypes previously studied in small- and medium-scale settings. We use different sequence similarity measures, phylogenetic and clustering methods in order to analyze the distribution in sequence space of roughly 1000 V3 loop sequences and their tropism phenotypes. This analysis affords a means of characterizing those sequences that are misclassified by several sequence-based coreceptor prediction methods, as well as predicting the coreceptor using the location of the sequence in sequence space and of relating this location to the CD4+ T-cell count of the patient. We support previous findings that the usage of CCR5 is correlated with relatively high sequence conservation whereas CXCR4-tropic viruses spread over larger regions in sequence space. The incorrectly predicted sequences are mostly located in regions in which their phenotype represents the minority or in close vicinity of regions dominated by the opposite phenotype. Nevertheless, the location of the sequence in sequence space can be used to improve the accuracy of the prediction of the coreceptor usage. Sequences from patients with high CD4+ T-cell counts are relatively highly conserved as compared to those of immunosuppressed patients. Our study thus supports hypotheses of an association of immune system depletion with an increase in V3 loop sequence variability and with the escape of the viral sequence to distant parts of the sequence space

Regulation of Clock-Controlled Genes in Mammals

The complexity of tissue- and day time-specific regulation of thousands of clock-controlled genes (CCGs) suggests that many regulatory mechanisms contribute to the transcriptional output of the circadian clock. We aim to predict these mechanisms using a large scale promoter analysis of CCGs