scholarly journals A proximity biotinylation map of a human cell

2019 ◽  
Author(s):  
Christopher D. Go ◽  
James D.R. Knight ◽  
Archita Rajasekharan ◽  
Bhavisha Rathod ◽  
Geoffrey G. Hesketh ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Human Cell ◽  
Large Scale ◽  
Mitochondrial Dynamics ◽  
Hek293 Cells ◽  
Fine Grained ◽  
Cellular Processes ◽  
Biochemical Fractionation ◽  
Cellular Compartments ◽  
Better Than

Compartmentalization is an essential characteristic of eukaryotic cells, ensuring that cellular processes are partitioned to defined subcellular locations. High throughput microscopy1 and biochemical fractionation coupled with mass spectrometry2-6 have helped to define the proteomes of multiple organelles and macromolecular structures. However, many compartments have remained refractory to such methods, partly due to lysis and purification artefacts and poor subcompartment resolution. Recently developed proximity-dependent biotinylation approaches such as BioID and APEX provide an alternative avenue for defining the composition of cellular compartments in living cells (e.g. 7-10). Here we report an extensive BioID-based proximity map of a human cell, comprising 192 markers from 32 different compartments that identifies 35,902 unique high confidence proximity interactions and localizes 4,145 proteins expressed in HEK293 cells. The recall of our localization predictions is on par with or better than previous large-scale mass spectrometry and microscopy approaches, but with higher localization specificity. In addition to assigning compartment and subcompartment localization for many previously unlocalized proteins, our data contain fine-grained localization information that, for example, allowed us to identify proteins with novel roles in mitochondrial dynamics. As a community resource, we have created humancellmap.org, a website that allows exploration of our data in detail, and aids with the analysis of BioID experiments.

A Direct-Dated Ceramic AMS Sequence from the Gaspereau Lake Reservoir Site Complex, Maine–Maritimes Region, Northeastern North America

Radiocarbon ◽  
2020 ◽  
Vol 62 (2) ◽  
pp. 419-437
Author(s):  
Cora A Woolsey
Keyword(s):  
Mass Spectrometry ◽  
Large Scale ◽  
Late Woodland ◽  
Ceramic Assemblage ◽  
Maritime Provinces ◽  
Northeastern North America ◽  
Fine Grained ◽  
Food Residue ◽  
Ams Dates ◽  
Made In

ABSTRACTThe Gaspereau Lake Reservoir Site Complex in Nova Scotia, Canada, yielded a large ceramic assemblage that permitted the first fine-grained analysis of ceramic change in the region at the Middle–Late Woodland Transition from ca. 1550 BP to ca. 1150 BP. The aim of this study was to refine the standard regional chronology first proposed by researchers J B Petersen and D Sanger. To do this, ceramics were directly dated using accelerator mass spectrometry (AMS), and the assemblage was categorized and analyzed to identify clusters of attributes. Ten AMS dates were acquired on carbonized food residue on the interiors of pottery and yielded the largest continuous ceramic sequence in the Maritime Provinces of Canada. This sequence was used to infer a change in manufacturing practices between the Middle (2150–1300 BP) and Late (1300–500 BP) Woodland periods and to propose five new subperiods between 1650 BP and 950 BP. Increasing incidence of coil breaks and temper percentage from the Middle to the Late Woodland were found to be chronologically sensitive. The analysis showed that, at Gaspereau Lake, a gradual shift from finely decorated and manufactured pottery to expediently made pottery suggests that pottery was made in larger numbers to support large-scale gatherings.

scholarly journals Intracellular proteome compartmentalization: a biotin ligase-based proximity labeling approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Olanrewaju Ayodeji Durojaye
Keyword(s):  
Mass Spectrometry ◽  
Underlying Mechanism ◽  
Cellular Structures ◽  
Biological Processes ◽  
Interacting Proteins ◽  
Cellular Processes ◽  
Biotin Ligase ◽  
Labeling Approach ◽  
Cellular Compartments

AbstractSpecialized biological processes occur in different regions and organelles of the cell. Additionally, the function of proteins correlate greatly with their interactions and subcellular localization. Understanding the mechanism underlying the specialized functions of cellular structures therefore requires a detailed identification of proteins within spatially defined domains of the cell. Furthermore, the identification of interacting proteins is also crucial for the elucidation of the underlying mechanism of complex cellular processes. Mass spectrometry methods have been utilized systematically for the characterization of the proteome of isolated organelles and protein interactors purified through affinity pull-down or following crosslinking. However, the available methods of purification have limited these approaches, as it is difficult to derive intact organelles of high purity in many circumstances. Furthermore, contamination that leads to the identification of false positive is widespread even when purification is possible. Here, we present a highlight of the BioID proximity labeling approach which has been used to effectively characterize the proteomic composition of several cellular compartments. In addition, an observed limitation of this method based on proteomic spatiotemporal dynamics, was also discussed.

scholarly journals FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks

2021 ◽  
Vol 17 (9) ◽  
pp. e1009410
Author(s):  
Andrea Tangherloni ◽  
Marco S. Nobile ◽  
Paolo Cazzaniga ◽  
Giulia Capitoli ◽  
Simone Spolaor ◽  
...  
Keyword(s):  
Graphics Processing Units ◽  
Large Scale ◽  
Biochemical Networks ◽  
Coarse Grained ◽  
Stiff Systems ◽  
Fine Grained ◽  
Central Processing ◽  
Cellular Processes ◽  
The One ◽  
Graphics Processing

Mathematical models of biochemical networks can largely facilitate the comprehension of the mechanisms at the basis of cellular processes, as well as the formulation of hypotheses that can be tested by means of targeted laboratory experiments. However, two issues might hamper the achievement of fruitful outcomes. On the one hand, detailed mechanistic models can involve hundreds or thousands of molecular species and their intermediate complexes, as well as hundreds or thousands of chemical reactions, a situation generally occurring in rule-based modeling. On the other hand, the computational analysis of a model typically requires the execution of a large number of simulations for its calibration or to test the effect of perturbations. As a consequence, the computational capabilities of modern Central Processing Units can be easily overtaken, possibly making the modeling of biochemical networks a worthless or ineffective effort. To the aim of overcoming the limitations of the current state-of-the-art simulation approaches, we present in this paper FiCoS, a novel “black-box” deterministic simulator that effectively realizes both a fine-grained and a coarse-grained parallelization on Graphics Processing Units. In particular, FiCoS exploits two different integration methods, namely, the Dormand–Prince and the Radau IIA, to efficiently solve both non-stiff and stiff systems of coupled Ordinary Differential Equations. We tested the performance of FiCoS against different deterministic simulators, by considering models of increasing size and by running analyses with increasing computational demands. FiCoS was able to dramatically speedup the computations up to 855×, showing to be a promising solution for the simulation and analysis of large-scale models of complex biological processes.

Algorithm of combining chromatography mass spectrometry-untargeted profiling and multivariate analysis for identification of marker-substances in samples of complex composition

2020 ◽  
Vol 86 (7) ◽  
pp. 12-19
Author(s):  
I. V. Plyushchenko ◽  
D. G. Shakhmatov ◽  
I. A. Rodin
Keyword(s):  
Mass Spectrometry ◽  
Multivariate Analysis ◽  
Large Scale ◽  
Complex Composition ◽  
Unified Protocol ◽  
Chromatography Mass Spectrometry ◽  
Marker Substances ◽  
Selection Testing ◽  
Untargeted Profiling

A viral development of statistical data processing, computing capabilities, chromatography-mass spectrometry, and omics technologies (technologies based on the achievements of genomics, transcriptomics, proteomics, metabolomics) in recent decades has not led to formation of a unified protocol for untargeted profiling. Systematic errors reduce the reproducibility and reliability of the obtained results, and at the same time hinder consolidation and analysis of data gained in large-scale multi-day experiments. We propose an algorithm for conducting omics profiling to identify potential markers in the samples of complex composition and present the case study of urine samples obtained from different clinical groups of patients. Profiling was carried out by the method of liquid chromatography mass spectrometry. The markers were selected using methods of multivariate analysis including machine learning and feature selection. Testing of the approach was performed using an independent dataset by clustering and projection on principal components.

PENGEMBANGAN MEDIA BIG BOOK UNTUK MENINGKATKAN HASIL BELAJAR PECAHAN SENILAI SISWA SD

2018 ◽  
Vol 16 (1) ◽  
pp. 67-76
Author(s):  
Disyacitta Neolia Firdana ◽  
Trimurtini Trimurtini
Keyword(s):  
Learning Outcomes ◽  
Large Scale ◽  
Fourth Grade ◽  
Learning Activities ◽  
Equivalent Fractions ◽  
Fourth Grade Students ◽  
Teacher Needs ◽  
The Media ◽  
Test Result ◽  
Better Than

This research aimed to determine the properness and effectiveness of the big book media on learning equivalent fractions of fourth grade students. The method of research is Research and Development  (R&D). This study was conducted in fourth grade of SDN Karanganyar 02 Kota Semarang. Data sources from media validation, material validation, learning outcomes, and teacher and students responses on developed media. Pre-experimental research design with one group pretest-posttest design. Big book developed consist of equivalent fractions material, students learning activities sheets with rectangle and circle shape pictures, and questions about equivalent fractions. Big book was developed based on students and teacher needs. This big book fulfill the media validity of 3,75 with very good criteria and scored 3 by material experts with good criteria. In large-scale trial, the result of students posttest have learning outcomes completness 82,14%. The result of N-gain calculation with result 0,55 indicates the criterion “medium”. The t-test result 9,6320 > 2,0484 which means the average of posttest outcomes is better than the average of pretest outcomes. Based on that data, this study has produced big book media which proper and effective as a media of learning equivalent fractions of fourth grade elementary school.

scholarly journals Strategic resources and smallholder performance at the bottom of the pyramid

2019 ◽  
Vol 22 (3) ◽  
pp. 365-380 ◽  
Author(s):  
Matthias Olthaar ◽  
Wilfred Dolfsma ◽  
Clemens Lutz ◽  
Florian Noseleit
Keyword(s):  
Large Scale ◽  
Business Environment ◽  
Agricultural Economics ◽  
Local Market ◽  
Bottom Of The Pyramid ◽  
Fine Grained ◽  
Strategic Resources ◽  
Relative Contribution ◽  
The Relationship ◽  
Resource Based Theory

In a competitive business environment at the Bottom of the Pyramid smallholders supplying global value chains may be thought to be at the whims of downstream large-scale players and local market forces, leaving no room for strategic entrepreneurial behavior. In such a context we test the relationship between the use of strategic resources and firm performance. We adopt the Resource Based Theory and show that seemingly homogenous smallholders deploy resources differently and, consequently, some do outperform others. We argue that the ‘resource-based theory’ results in a more fine-grained understanding of smallholder performance than approaches generally applied in agricultural economics. We develop a mixed-method approach that allows one to pinpoint relevant, industry-specific resources, and allows for empirical identification of the relative contribution of each resource to competitive advantage. The results show that proper use of quality labor, storage facilities, time of selling, and availability of animals are key capabilities.

scholarly journals Inverse Infiltration Modeling of Dike Covers Made of Dredged Material Using PEST and AMALGAM

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 41
Author(s):  
Tim Jurisch ◽  
Stefan Cantré ◽  
Fokke Saathoff
Keyword(s):  
Large Scale ◽  
Measurement Data ◽  
Fine Grained ◽  
Dredged Materials ◽  
Measurement Results ◽  
Large Scale Field ◽  
Eigenvalue Ratio ◽  
Ill Posed ◽  
Materials Used ◽  
Installation Technology

A variety of studies recently proved the applicability of different dried, fine-grained dredged materials as replacement material for erosion-resistant sea dike covers. In Rostock, Germany, a large-scale field experiment was conducted, in which different dredged materials were tested with regard to installation technology, stability, turf development, infiltration, and erosion resistance. The infiltration experiments to study the development of a seepage line in the dike body showed unexpected measurement results. Due to the high complexity of the problem, standard geo-hydraulic models proved to be unable to analyze these results. Therefore, different methods of inverse infiltration modeling were applied, such as the parameter estimation tool (PEST) and the AMALGAM algorithm. In the paper, the two approaches are compared and discussed. A sensitivity analysis proved the presumption of a non-linear model behavior for the infiltration problem and the Eigenvalue ratio indicates that the dike infiltration is an ill-posed problem. Although this complicates the inverse modeling (e.g., termination in local minima), parameter sets close to an optimum were found with both the PEST and the AMALGAM algorithms. Together with the field measurement data, this information supports the rating of the effective material properties of the applied dredged materials used as dike cover material.

scholarly journals Assessment of Numerical Methods for Plunging Breaking Wave Predictions

2021 ◽  
Vol 9 (3) ◽  
pp. 264
Author(s):  
Shanti Bhushan ◽  
Oumnia El Fajri ◽  
Graham Hubbard ◽  
Bradley Chambers ◽  
Christopher Kees
Keyword(s):  
Solitary Wave ◽  
Wave Breaking ◽  
Large Scale ◽  
Turbulence Models ◽  
Navier Stokes ◽  
Wave Crest ◽  
Breaking Wave ◽  
Rans Turbulence Models ◽  
Run Up ◽  
Better Than

This study evaluates the capability of Navier–Stokes solvers in predicting forward and backward plunging breaking, including assessment of the effect of grid resolution, turbulence model, and VoF, CLSVoF interface models on predictions. For this purpose, 2D simulations are performed for four test cases: dam break, solitary wave run up on a slope, flow over a submerged bump, and solitary wave over a submerged rectangular obstacle. Plunging wave breaking involves high wave crest, plunger formation, and splash up, followed by second plunger, and chaotic water motions. Coarser grids reasonably predict the wave breaking features, but finer grids are required for accurate prediction of the splash up events. However, instabilities are triggered at the air–water interface (primarily for the air flow) on very fine grids, which induces surface peel-off or kinks and roll-up of the plunger tips. Reynolds averaged Navier–Stokes (RANS) turbulence models result in high eddy-viscosity in the air–water region which decays the fluid momentum and adversely affects the predictions. Both VoF and CLSVoF methods predict the large-scale plunging breaking characteristics well; however, they vary in the prediction of the finer details. The CLSVoF solver predicts the splash-up event and secondary plunger better than the VoF solver; however, the latter predicts the plunger shape better than the former for the solitary wave run-up on a slope case.

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 Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3065
Author(s):  
Libo Wang ◽  
Rui Li ◽  
Dongzhi Wang ◽  
Chenxi Duan ◽  
Teng Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Texture Features ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Research Field ◽  
Learning Approaches ◽  
Fine Grained ◽  
Urban Scene ◽  
Fine Resolution ◽  
With Memory

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

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