Hasil untuk "Maps"

Katja Luck, Dae-Kyum Kim, L. Lambourne et al.

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype–phenotype relationships1,2. Here we present a human ‘all-by-all’ reference interactome map of human binary protein interactions, or ‘HuRI’. With approximately 53,000 protein–protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein–protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes. A human binary protein interactome map that includes around 53,000 protein–protein interactions involving more than 8,000 proteins provides a reference for the study of human cellular function in health and disease.

He Zhang, Vishal M. Patel

We propose a new end-to-end single image dehazing method, called Densely Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map, atmospheric light and dehazing all together. The end-to-end learning is achieved by directly embedding the atmospheric scattering model into the network, thereby ensuring that the proposed method strictly follows the physics-driven scattering model for dehazing. Inspired by the dense network that can maximize the information flow along features from different levels, we propose a new edge-preserving densely connected encoder-decoder structure with multi-level pyramid pooling module for estimating the transmission map. This network is optimized using a newly introduced edge-preserving loss function. To further incorporate the mutual structural information between the estimated transmission map and the dehazed result, we propose a joint-discriminator based on generative adversarial network framework to decide whether the corresponding dehazed image and the estimated transmission map are real or fake. An ablation study is conducted to demonstrate the effectiveness of each module evaluated at both estimated transmission map and dehazed result. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods. Code and dataset is made available at: https://github.com/hezhangsprinter/DCPDN

Peter J. Thul, C. Lindskog

The correct spatial distribution of proteins is vital for their function and often mis‐localization or ectopic expression leads to diseases. For more than a decade, the Human Protein Atlas (HPA) has constituted a valuable tool for researchers studying protein localization and expression in human tissues and cells. The centerpiece of the HPA is its unique antibody collection for mapping the entire human proteome by immunohistochemistry and immunocytochemistry. By these approaches, more than 10 million images showing protein expression patterns at a single‐cell level were generated and are publicly available at www.proteinatlas.org. The antibody‐based approach is combined with transcriptomics data for an overview of global expression profiles. The present article comprehensively describes the HPA database functions and how users can utilize it for their own research as well as discusses the future path of spatial proteomics.

A. Subramanian, Rajiv Narayan, S. M. Corsello et al.

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io. HIGHLIGHTS A new gene expression profiling method, L1000, dramatically lowers cost The Connectivity Map database now includes 1.3 million publicly accessible L1000 perturbational profiles This expanded Connectivity Map facilitates discovery of small molecule mechanism of action and functional annotation of genetic variants The work establishes feasibility and utility of a truly comprehensive Connectivity Map

Heng Li

Motivation Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms. Results Minimap2 is a general-purpose alignment program to map DNA or long mRNA sequences against a large reference database. It works with accurate short reads of ≥100 bp in length, ≥1 kb genomic reads at error rate ∼15%, full-length noisy Direct RNA or cDNA reads and assembly contigs or closely related full chromosomes of hundreds of megabases in length. Minimap2 does split-read alignment, employs concave gap cost for long insertions and deletions and introduces new heuristics to reduce spurious alignments. It is 3-4 times as fast as mainstream short-read mappers at comparable accuracy, and is ≥30 times faster than long-read genomic or cDNA mappers at higher accuracy, surpassing most aligners specialized in one type of alignment. Availability and implementation https://github.com/lh3/minimap2. Supplementary information Supplementary data are available at Bioinformatics online.

Peter J. Thul, Lovisa Åkesson, Mikaela Wiking et al.

Aviad Tsherniak, F. Vazquez, Phillip G Montgomery et al.

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Dai Yamazaki, Daiki Ikeshima, R. Tawatari et al.

Spaceborne digital elevation models (DEMs) are a fundamental input for many geoscience studies, but they still include nonnegligible height errors. Here we introduce a high‐accuracy global DEM at 3″ resolution (~90 m at the equator) by eliminating major error components from existing DEMs. We separated absolute bias, stripe noise, speckle noise, and tree height bias using multiple satellite data sets and filtering techniques. After the error removal, land areas mapped with ±2 m or better vertical accuracy were increased from 39% to 58%. Significant improvements were found in flat regions where height errors larger than topography variability, and landscapes such as river networks and hill‐valley structures, became clearly represented. We found the topography slope of previous DEMs was largely distorted in most of world major floodplains (e.g., Ganges, Nile, Niger, and Mekong) and swamp forests (e.g., Amazon, Congo, and Vasyugan). The newly developed DEM will enhance many geoscience applications which are terrain dependent.

M. Nilsson, D. Griggs, M. Visbeck

Rita Santos, O. Ursu, A. Gaulton et al.

M. Uhlén, Linn Fagerberg, B. Hallström et al.

Peter Sudmant, T. Rausch, E. Gardner et al.

Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.

Ross B. Girshick

This paper proposes Fast R-CNN, a clean and fast framework for object detection. Compared to traditional R-CNN, and its accelerated version SPPnet, Fast R-CNN trains networks using a multi-task loss in a single training stage. The multi-task loss simplifies learning and improves detection accuracy. Unlike SPPnet, all network layers can be updated during fine-tuning. We show that this difference has practical ramifications for very deep networks, such as VGG16, where mAP suffers when only the fully-connected layers are updated. Compared to"slow"R-CNN, Fast R-CNN is 9x faster at training VGG16 for detection, 213x faster at test-time, and achieves a significantly higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn

T. Behrens, Timothy H. Muller, James C. R. Whittington et al.

It is proposed that a cognitive map encoding the relationships between entities in the world supports flexible behavior, but the majority of the neural evidence for such a system comes from studies of spatial navigation. Recent work describing neuronal parallels between spatial and non-spatial behaviors has rekindled the notion of a systematic organization of knowledge across multiple domains. We review experimental evidence and theoretical frameworks that point to principles unifying these apparently disparate functions. These principles describe how to learn and use abstract, generalizable knowledge and suggest that map-like representations observed in a spatial context may be an instance of general coding mechanisms capable of organizing knowledge of all kinds. We highlight how artificial agents endowed with such principles exhibit flexible behavior and learn map-like representations observed in the brain. Finally, we speculate on how these principles may offer insight into the extreme generalizations, abstractions, and inferences that characterize human cognition.

Xiao Sun, Bin Xiao, Shuang Liang et al.

State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as not differentiable and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time.

Marlen Niederberger, Julia Spranger

Objectives: In health sciences, the Delphi technique is primarily used by researchers when the available knowledge is incomplete or subject to uncertainty and other methods that provide higher levels of evidence cannot be used. The aim is to collect expert-based judgments and often to use them to identify consensus. In this map, we provide an overview of the fields of application for Delphi techniques in health sciences in this map and discuss the processes used and the quality of the findings. We use systematic reviews of Delphi techniques for the map, summarize their findings and examine them from a methodological perspective. Methods: Twelve systematic reviews of Delphi techniques from different sectors of the health sciences were identified and systematically analyzed. Results: The 12 systematic reviews show, that Delphi studies are typically carried out in two to three rounds with a deliberately selected panel of experts. A large number of modifications to the Delphi technique have now been developed. Significant weaknesses exist in the quality of the reporting. Conclusion: Based on the results, there is a need for clarification with regard to the methodological approaches of Delphi techniques, also with respect to any modification. Criteria for evaluating the quality of their execution and reporting also appear to be necessary. However, it should be noted that we cannot make any statements about the quality of execution of the Delphi studies but rather our results are exclusively based on the reported findings of the systematic reviews.

Kimberly L. Stachenfeld, M. Botvinick, S. Gershman

A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity and policy dependence in place cells suggests that the representation is not purely spatial. We approach this puzzle from a reinforcement learning perspective: what kind of spatial representation is most useful for maximizing future reward? We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. Furthermore, we argue that entorhinal grid cells encode a low-dimensionality basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.

G. Green, E. Schlafly, C. Zucker et al.

We present a new three-dimensional map of dust reddening, based on Gaia parallaxes and stellar photometry from Pan-STARRS 1 and 2MASS. This map covers the sky north of a decl. of −30°, out to a distance of a few kiloparsecs. This new map contains three major improvements over our previous work. First, the inclusion of Gaia parallaxes dramatically improves distance estimates to nearby stars. Second, we incorporate a spatial prior that correlates the dust density across nearby sightlines. This produces a smoother map, with more isotropic clouds and smaller distance uncertainties, particularly to clouds within the nearest kiloparsec. Third, we infer the dust density with a distance resolution that is four times finer than in our previous work, to accommodate the improvements in signal-to-noise enabled by the other improvements. As part of this work, we infer the distances, reddenings, and types of 799 million stars. (Our 3D dust map can be accessed at doi:10.7910/DVN/2EJ9TX or through the Python package dustmaps, while our catalog of stellar parameters can be accessed at doi:10.7910/DVN/AV9GXO. More information about the map, as well as an interactive viewer, can be found at argonaut.skymaps.info.) We obtain typical reddening uncertainties that are ∼30% smaller than those reported in the Gaia DR2 catalog, reflecting the greater number of photometric passbands that enter into our analysis.

K. Appel, W. Haken

As has become standard, the four color map problem will be considered in the dual sense as the problem of whether the vertices of every planar graph (without loops) can be colored with at most four colors in such a way that no pair of vertices which lie on a common edge have the same color. The restriction to triangulations with all vertices of degree at least five is a consequence of the work of A. B. Kempe. Over the past 100 years, a number of authors including A. B. Kempe, G. D. Birkhoff, and H. Heesch have developed a theory of reducibility to attack the problem. Simultaneously, a theory of unavoidable sets has been developed and the fusion of these has led to the proof. A configuration is a subgraph of a planar triangulation consisting of a circuit (called the ring) and its interior. A configuration is called reducible if it can be shown by certain standard methods that it cannot be immersed in a minimal counterexample to the four color conjecture. (For details, see [3] or [4].) A set of configurations is called unavoidable if every planar triangulation contains some member of the set. From the definitions, it is immediate that the four color theorem is proved if an unavoidable set of reducible configurations is provided. The most efficient known method of producing unavoidable sets of configurations is called the method of discharging. This method treats the planar triangulation as an electrical network with charge assigned to the vertices. Euler's formula is used to show that the initial charge distribution, giving positive charge to vertices of degree five and negative charge to vertices of degree greater than six, has positive total charge. Next, the initial charge is redistributed in a manner which obeys the principle of conservation of charge. This means that some vertices must end up with positive charge. Such an algorithm can be made sufficiently sophisticated that a finite list of neighborhoods of all possible vertices of ultimately positive charge can be described in detail.

Raul Mur-Artal, J. D. Tardós

In recent years there have been excellent results in visual-inertial odometry techniques, which aim to compute the incremental motion of the sensor with high accuracy and robustness. However, these approaches lack the capability to close loops and trajectory estimation accumulates drift even if the sensor is continually revisiting the same place. In this letter, we present a novel tightly coupled visual-inertial simultaneous localization and mapping system that is able to close loops and reuse its map to achieve zero-drift localization in already mapped areas. While our approach can be applied to any camera configuration, we address here the most general problem of a monocular camera, with its well-known scale ambiguity. We also propose a novel IMU initialization method, which computes the scale, the gravity direction, the velocity, and gyroscope and accelerometer biases, in a few seconds with high accuracy. We test our system in the 11 sequences of a recent micro-aerial vehicle public dataset achieving a typical scale factor error of $1\%$ and centimeter precision. We compare to the state-of-the-art in visual-inertial odometry in sequences with revisiting, proving the better accuracy of our method due to map reuse and no drift accumulation.