scholarly journals Artificial Intelligence-Powered Automated Holotomographic Microscopy Enables Label-Free Quantitative Biology

2021 ◽  
Vol 29 (5) ◽  
pp. 24-32
Author(s):  
Hugo Moreno ◽  
Lorenzo Archetti ◽  
Emma Gibbin ◽  
Alexandre E. Grandchamp ◽  
Mathieu Fréchin
Keyword(s):  
State Of The Art ◽  
Three Dimensions ◽  
Cell Segmentation ◽  
Quantitative Biology ◽  
Biological Processes ◽  
Label Free ◽  
Environment Control ◽  
New Type ◽  
New Applications ◽  
Multiple Conditions

Abstract:Holotomographic microscopy (HTM) measures the refractive index (RI) tomograms of living cells and tissues in three dimensions. The ability to observe biological processes at high spatial and temporal resolution opens uncharted territories for cell biologists, however, current HTM devices have a limited throughput. We show here the first automated multi-well plate-compatible HTM device, the CX-A. Thanks to state-of-the-art environment control and a new type of autofocus, the CX-A can record multiple conditions in parallel over large fields of view, while its software EVE supports automated single-cell segmentation and quantification. This opens the door to new applications for HTM, from drug screening to systems biology.

scholarly journals G-Tric: generating three-way synthetic datasets with triclustering solutions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
João Lobo ◽  
Rui Henriques ◽  
Sara C. Madeira
Keyword(s):  
State Of The Art ◽  
Synthetic Data ◽  
Ground Truth ◽  
Real Data ◽  
Three Dimensions ◽  
Additional Advantage ◽  
Urban Dynamics ◽  
Data Generator ◽  
Real World Datasets ◽  
Synthetic Datasets

Abstract Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

scholarly journals In vivo interactome profiling by enzyme‐catalyzed proximity labeling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yangfan Xu ◽  
Xianqun Fan ◽  
Yang Hu
Keyword(s):  
State Of The Art ◽  
Catalytic Efficiency ◽  
Biological Processes ◽  
Protein Protein Interaction ◽  
Current State ◽  
Protein Interactome ◽  
Potential Applications ◽  
Protein Protein Interaction Networks ◽  
Temporal And Spatial

AbstractEnzyme-catalyzed proximity labeling (PL) combined with mass spectrometry (MS) has emerged as a revolutionary approach to reveal the protein-protein interaction networks, dissect complex biological processes, and characterize the subcellular proteome in a more physiological setting than before. The enzymatic tags are being upgraded to improve temporal and spatial resolution and obtain faster catalytic dynamics and higher catalytic efficiency. In vivo application of PL integrated with other state of the art techniques has recently been adapted in live animals and plants, allowing questions to be addressed that were previously inaccessible. It is timely to summarize the current state of PL-dependent interactome studies and their potential applications. We will focus on in vivo uses of newer versions of PL and highlight critical considerations for successful in vivo PL experiments that will provide novel insights into the protein interactome in the context of human diseases.

scholarly journals Robust CNN Compression Framework for Security-Sensitive Embedded Systems

2021 ◽  
Vol 11 (3) ◽  
pp. 1093
Author(s):  
Jeonghyun Lee ◽  
Sangkyun Lee
Keyword(s):  
Embedded Systems ◽  
Optimization Problem ◽  
State Of The Art ◽  
Classification Problems ◽  
Proximal Gradient Method ◽  
Knowledge Distillation ◽  
New Type ◽  
Adversarial Examples ◽  
Adversarial Training ◽  
Memory Efficient

Convolutional neural networks (CNNs) have achieved tremendous success in solving complex classification problems. Motivated by this success, there have been proposed various compression methods for downsizing the CNNs to deploy them on resource-constrained embedded systems. However, a new type of vulnerability of compressed CNNs known as the adversarial examples has been discovered recently, which is critical for security-sensitive systems because the adversarial examples can cause malfunction of CNNs and can be crafted easily in many cases. In this paper, we proposed a compression framework to produce compressed CNNs robust against such adversarial examples. To achieve the goal, our framework uses both pruning and knowledge distillation with adversarial training. We formulate our framework as an optimization problem and provide a solution algorithm based on the proximal gradient method, which is more memory-efficient than the popular ADMM-based compression approaches. In experiments, we show that our framework can improve the trade-off between adversarial robustness and compression rate compared to the existing state-of-the-art adversarial pruning approach.

scholarly journals Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ying Li ◽  
Hang Sun ◽  
Shiyao Feng ◽  
Qi Zhang ◽  
Siyu Han ◽  
...  
Keyword(s):  
Protein Interactions ◽  
State Of The Art ◽  
Recognition Performance ◽  
Feature Learning ◽  
Biological Processes ◽  
Multimodal Features ◽  
Learning Architectures ◽  
Motif Information ◽  
Experimental Comparisons ◽  
Better Than

Abstract Background Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. Results We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. Conclusions This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver (http://csbg-jlu.site/lpc/predict) is developed to be convenient for users.

Introductory remarks

1981 ◽  
Vol 302 (1468) ◽  
pp. 219-219
Keyword(s):  
State Of The Art ◽  
Biological Processes ◽  
Industrial Workers ◽  
Practical Application ◽  
Electroanalytical Methods ◽  
Secondary Battery ◽  
On Line ◽  
The Subject ◽  
General Aspects ◽  
Cross Fertilization

As its title suggests, the purpose of this Discussion Meeting is to review the present state of the art in industrial electrochemistry. We have sought to bring together academic and industrial workers in this field as well as other interested participants. I hope that as the meeting proceeds, a cross-fertilization of ideas will occur both in the formal sessions and during the breaks. The organizers of this Meeting have given considerable thought to the order in which the different aspects of electrochemistry should be presented. Evidently we had to begin with the fundamentals, after which we decided to deal with the general aspects of electrosynthesis including the developing possibilities of supplying energy to biological processes by electrochem ical means. This led naturally to consideration of electrochemical engineering and electroanalytical methods for on-line control. In one session we shall move to a very practical application of electrochemistry, namely batteries. Beginning with Volta’s simple cell, this application is one of the oldest in electrochemistry. In spite of all the advances in the subject, the possibilities of new primary and secondary battery systems remain as wide as ever. I, for one, shall be most interested to hear the progress reports of our three speakers.

scholarly journals Design and implementation of E-discovery as a service based on cloud computing

2013 ◽  
Vol 10 (2) ◽  
pp. 703-724 ◽  
Author(s):  
Taerim Lee ◽  
Hun Kim ◽  
Kyung-Hyune Rhee ◽  
Uk Shin
Keyword(s):  
Cloud Computing ◽  
Direct Contact ◽  
State Of The Art ◽  
Service Systems ◽  
Target System ◽  
Design And Implementation ◽  
Discovery Service ◽  
New Type ◽  
It Compliance ◽  
Network Device

Recently, as IT Compliance becomes more diverse, companies have to take a great amount of effort to comply with it and prepare countermeasures. Especially, E-Discovery is also one of the most notable compliances for IT and law. In order to minimize the time and cost for E-Discovery, many service systems and solutions using the state-of-the-art technology have been competitively developed. Among them, Cloud Computing is one of the most exclusive skills as a computing infrastructure for E-Discovery Service. Unfortunately, these products actually do not cover all kinds of E-Discovery works and have many drawbacks as well as considerable limitations. This paper, therefore, proposes a new type of E-Discovery Service Structure based on Cloud Computing called EDaaS(E-Discovery as a Service) to make the best usage of its advantages and overcome the limitations of the existing E-Discovery solutions. EDaaS enables E-Discovery participants to smoothly collaborate by removing constraints on working places and minimizing the number of direct contact with target systems. What those who want to use the EDaaS need is only a network device for using the Internet. Moreover, EDaaS can help to reduce the waste of time and human resources because no specific software to install on every target system is needed and the relatively exact time of completion can be obtained from it according to the amount of data for the manpower control. As a result of it, EDaaS can solve the litigant?s cost problem.

scholarly journals Digital entrepreneurship in Morocco: measurement model validation

2021 ◽  
Vol 119 ◽  
pp. 03004
Author(s):  
Zakia Saoura ◽  
Ahmed Abriane ◽  
Aniss Moumen
Keyword(s):  
Quantitative Research ◽  
State Of The Art ◽  
Measurement Model ◽  
Arab Countries ◽  
Three Dimensions ◽  
Measurement Scale ◽  
Global Entrepreneurship Monitor ◽  
Entrepreneurial Motivations ◽  
Digital Entrepreneurship ◽  
Do So

According to the 2017 Global Entrepreneurship Monitor report, there are 6.5 million adults aged 18-64 planning to start an entrepreneurial career by 2020. However, the gap between attempt and effective creations remains one of the largest within Arab countries (40% versus 9%). Given these statistics, we ask the question about the profile of the Moroccan entrepreneur. In this paper, we opted for a quantitative research methodology on an exploratory sample. We distributed a questionnaire to a sample of eighty Moroccan entrepreneurs representing different regions of Morocco. The objective of our study is to validate a measurement scale of three dimensions: 1/ entrepreneurial motivations, 2/ skills, and 3/ behaviour in the Moroccan context. To do so, we present, in the first part, a literature review on digital entrepreneurship. Then, we establish a state of the art of entrepreneurship in Morocco. Then, we show our methodology. Finally, we reveal and discuss the results of our study.

scholarly journals Consistent global structures of complex RNA states through multidimensional chemical mapping

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Clarence Yu Cheng ◽  
Fang-Chieh Chou ◽  
Wipapat Kladwang ◽  
Siqi Tian ◽  
Pablo Cordero ◽  
...  
Keyword(s):  
Secondary Structure ◽  
3D Structure ◽  
Three Dimensions ◽  
Biological Processes ◽  
Chemical Mapping ◽  
Entry Site ◽  
Tertiary Structures ◽  
Internal Ribosome Entry ◽  
Non Coding Rna ◽  
Paired End Sequencing

Accelerating discoveries of non-coding RNA (ncRNA) in myriad biological processes pose major challenges to structural and functional analysis. Despite progress in secondary structure modeling, high-throughput methods have generally failed to determine ncRNA tertiary structures, even at the 1-nm resolution that enables visualization of how helices and functional motifs are positioned in three dimensions. We report that integrating a new method called MOHCA-seq (Multiplexed •OH Cleavage Analysis with paired-end sequencing) with mutate-and-map secondary structure inference guides Rosetta 3D modeling to consistent 1-nm accuracy for intricately folded ncRNAs with lengths up to 188 nucleotides, including a blind RNA-puzzle challenge, the lariat-capping ribozyme. This multidimensional chemical mapping (MCM) pipeline resolves unexpected tertiary proximities for cyclic-di-GMP, glycine, and adenosylcobalamin riboswitch aptamers without their ligands and a loose structure for the recently discovered human HoxA9D internal ribosome entry site regulon. MCM offers a sequencing-based route to uncovering ncRNA 3D structure, applicable to functionally important but potentially heterogeneous states.

scholarly journals Machine Learning Enables Live Label-Free Phenotypic Screening in Three Dimensions

2018 ◽  
Vol 16 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Eoghan O'Duibhir ◽  
Jasmin Paris ◽  
Hannah Lawson ◽  
Catarina Sepulveda ◽  
Dahlia Doughty Shenton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Three Dimensions ◽  
Phenotypic Screening ◽  
Label Free

Printable and flexible electronics: from TFTs to bioelectronic devices

2015 ◽  
Vol 3 (48) ◽  
pp. 12347-12363 ◽  
Author(s):  
M. Magliulo ◽  
M. Y. Mulla ◽  
M. Singh ◽  
E. Macchia ◽  
A. Tiwari ◽  
...  
Keyword(s):  
Flexible Electronics ◽  
State Of The Art ◽  
The State ◽  
Label Free ◽  
Electronic Biosensors

This review discusses the state-of-the-art strategies for realizing TFTs by printing compatible techniques, focusing the attention on label-free electronic biosensors.

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