scholarly journals Robust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Theodore G. Smith ◽  
Anuli C. Uzozie ◽  
Siyuan Chen ◽  
Philipp F. Lange
Keyword(s):  
Large Scale ◽  
Kinase Inhibitors ◽  
State Of The Art ◽  
Technological Development ◽  
Post Translational Modification ◽  
Sequence Context ◽  
Linear Motif ◽  
Local Sequence ◽  
Linear Motifs ◽  
Technological Improvements

AbstractThe local sequence context is the most fundamental feature determining the post-translational modification (PTM) of proteins. Recent technological improvements allow for the detection of new and less prevalent modifications. We found that established state-of-the-art algorithms for the detection of PTM motifs in complex datasets failed to keep up with this technological development and are no longer robust. To overcome this limitation, we developed RoLiM, a new linear motif deconvolution algorithm and webserver, that enables robust and unbiased identification of local amino acid sequence determinants in complex biological systems demonstrated here by the analysis of 68 modifications found across 30 tissues in the human draft proteome map. Furthermore, RoLiM analysis of a large-scale phosphorylation dataset comprising 30 kinase inhibitors of 10 protein kinases in the EGF signalling pathway identified prospective substrate motifs for PI3K and EGFR.

scholarly journals Post-translational modification of cardiac proteasomes: functional delineation enabled by proteomics

2012 ◽  
Vol 303 (1) ◽  
pp. H9-H18 ◽  
Author(s):  
Sarah B. Scruggs ◽  
Nobel C. Zong ◽  
Ding Wang ◽  
Enrico Stefani ◽  
Peipei Ping
Keyword(s):  
Subcellular Localization ◽  
Large Scale ◽  
State Of The Art ◽  
Site Specificity ◽  
Protein Homeostasis ◽  
Cardiac Physiology ◽  
Post Translational Modification ◽  
Post Translational Modifications ◽  
Catalytic Activities ◽  
Lines Of Evidence

Proteasomes are ubiquitously expressed multicatalytic complexes that serve as key regulators of protein homeostasis. There are several lines of evidence indicating that proteasomes exist in heterogeneous subpopulations in cardiac muscle, differentiated, in part, by post-translational modifications (PTMs). PTMs regulate numerous facets of proteasome function, including catalytic activities, complex assembly, interactions with associating partners, subcellular localization, substrate preference, and complex turnover. Classical technologies used to identify PTMs on proteasomes have lacked the ability to determine site specificity, quantify stoichiometry, and perform large-scale, multi-PTM analysis. Recent advancements in proteomic technologies have largely overcome these limitations. We present here a discussion on the importance of PTMs in modulating proteasome function in cardiac physiology and pathophysiology, followed by the presentation of a state-of-the-art proteomic workflow for identifying and quantifying PTMs of cardiac proteasomes.

scholarly journals Documentary data and the study of past droughts: a global state of the art

2018 ◽  
Vol 14 (12) ◽  
pp. 1915-1960 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Climatic Trends ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

scholarly journals VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

2021 ◽  
Vol 7 (3) ◽  
pp. 50
Author(s):  
Anselmo Ferreira ◽  
Ehsan Nowroozi ◽  
Mauro Barni
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Child Pornography ◽  
Forensic Analysis ◽  
Synthetic Image ◽  
Image Detection ◽  
Face Images ◽  
Large Scale Dataset ◽  
Scanned Images ◽  
Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

scholarly journals SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

2021 ◽  
Vol 40 (3) ◽  
pp. 1-13
Author(s):  
Lumin Yang ◽  
Jiajie Zhuang ◽  
Hongbo Fu ◽  
Xiangzhi Wei ◽  
Kun Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Large Scale ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Structure Information ◽  
Graph Neural Networks ◽  
Node Labels ◽  
Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

scholarly journals Context-aware recommender systems and cultural heritage: a survey

2021 ◽  
Author(s):  
Mario Casillo ◽  
Francesco Colace ◽  
Dajana Conte ◽  
Marco Lombardi ◽  
Domenico Santaniello ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Recommender Systems ◽  
Context Awareness ◽  
Technological Development ◽  
Context Aware ◽  
Human Beings ◽  
Context Analysis ◽  
Analysis Techniques ◽  
The Right ◽  
Technological Improvements

AbstractIn the Big Data era, every sector has adapted to technological development to service the vast amount of information available. In this way, each field has benefited from technological improvements over the years. The cultural and artistic field was no exception, and several studies contributed to the aim of the interaction between human beings and artistic-cultural heritage. In this scenario, systems able to analyze the current situation and recommend the right services play a crucial role. In particular, in the Recommender Systems field, Context-Awareness helps to improve the recommendations provided. This article aims to present a general overview of the introduction of Context analysis techniques in Recommender Systems and discuss some challenging applications to the Cultural Heritage field.

scholarly journals On the relation between mini-halos and AGN feedback in clusters of galaxies

2020 ◽  
Vol 499 (2) ◽  
pp. 2934-2958
Author(s):  
A Richard-Laferrière ◽  
J Hlavacek-Larrondo ◽  
R S Nemmen ◽  
C L Rhea ◽  
G B Taylor ◽  
...  
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Vital Role ◽  
Clusters Of Galaxies ◽  
Relativistic Jets ◽  
X Ray ◽  
Core Cluster ◽  
Central Galaxy ◽  
Central Regions ◽  
Radio Power

ABSTRACT A variety of large-scale diffuse radio structures have been identified in many clusters with the advent of new state-of-the-art facilities in radio astronomy. Among these diffuse radio structures, radio mini-halos are found in the central regions of cool core clusters. Their origin is still unknown and they are challenging to discover; less than 30 have been published to date. Based on new VLA observations, we confirmed the mini-halo in the massive strong cool core cluster PKS 0745−191 (z = 0.1028) and discovered one in the massive cool core cluster MACS J1447.4+0827 (z = 0.3755). Furthermore, using a detailed analysis of all known mini-halos, we explore the relation between mini-halos and active galactic nucleus (AGN) feedback processes from the central galaxy. We find evidence of strong, previously unknown correlations between mini-halo radio power and X-ray cavity power, and between mini-halo and the central galaxy radio power related to the relativistic jets when spectrally decomposing the AGN radio emission into a component for past outbursts and one for ongoing accretion. Overall, our study indicates that mini-halos are directly connected to the central AGN in clusters, following previous suppositions. We hypothesize that AGN feedback may be one of the dominant mechanisms giving rise to mini-halos by injecting energy into the intra-cluster medium and reaccelerating an old population of particles, while sloshing motion may drive the overall shape of mini-halos inside cold fronts. AGN feedback may therefore not only play a vital role in offsetting cooling in cool core clusters, but may also play a fundamental role in re-energizing non-thermal particles in clusters.

scholarly journals Extrinsic Camera Calibration with Line-Laser Projection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1091
Author(s):  
Izaak Van Crombrugge ◽  
Rudi Penne ◽  
Steve Vanlanduit
Keyword(s):  
Camera Calibration ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Bundle Adjustment ◽  
Field Of View ◽  
Extrinsic Calibration ◽  
Practical Procedure ◽  
Partial Overlap

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

Sign in / Sign up

Export Citation Format

Share Document