scholarly journals Strong eventual consistency of the collaborative editing framework WOOT

2022 ◽  
Author(s):  
Emin Karayel ◽  
Edgar Gonzàlez
Keyword(s):  
New York ◽  
Large Scale ◽  
Computer Supported Cooperative Work ◽  
Data Types ◽  
New Class ◽  
Collaborative Editing ◽  
Replicated Data ◽  
Novel Approach ◽  
Consistency Property ◽  
Eventual Consistency

AbstractCommutative Replicated Data Types (CRDTs) are a promising new class of data structures for large-scale shared mutable content in applications that only require eventual consistency. The WithOut Operational Transforms (WOOT) framework is the first CRDT for collaborative text editing introduced by Oster et al. (In: Conference on Computer Supported Cooperative Work (CSCW). ACM, New York, pp 259–268, 2006a). Its eventual consistency property was verified only for a bounded model to date. While the consistency of many other previously published CRDTs had been shown immediately with their publication, the property for WOOT remained open for 14 years. We use a novel approach identifying a previously unknown sort-key based protocol that simulates the WOOT framework to show its consistency. We formalize the proof using the Isabelle/HOL proof assistant to machine-check its correctness.

A Plug-and-Play Approach for the De Novo Generation of Dually Functionalised Bispecifics

2019 ◽  
Author(s):  
Antoine Maruani ◽  
Peter A. Szijj ◽  
Calise Bahou ◽  
João C. F. Nogueira ◽  
Stephen Caddick ◽  
...  
Keyword(s):  
De Novo ◽  
Therapeutic Index ◽  
Antibody Fragments ◽  
Bispecific Antibodies ◽  
Full Potential ◽  
Mechanisms Of Resistance ◽  
Large Excess ◽  
Chemical Methods ◽  
New Class ◽  
Novel Approach

<p>Diseases are multifactorial, with redundancies and synergies between various pathways. However, most of the antibody-based therapeutics in clinical trials and on the market interact with only one target thus limiting their efficacy. The targeting of multiple epitopes could improve the therapeutic index of treatment and counteract mechanisms of resistance. To this effect, a new class of therapeutics emerged: bispecific antibodies.</p><p>Bispecific formation using chemical methods is rare and low yielding and/or requires a large excess of one of the two proteins to avoid homodimerisation. In order for chemically prepared bispecifics to deliver their full potential, high-yielding, modular and reliable cross-linking technologies are required. Herein, we describe a novel approach not only for the rapid and high-yielding chemical generation of bispecific antibodies from native antibody fragments, but also for the site-specific dual functionalisation of the resulting bioconjugates. Based on orthogonal clickable functional groups, this strategy enables the assembly of functionalised bispecifics with controlled loading in a modular and convergent manner.</p>

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

Large-scale High-throughput Screening Revealed 5'-(carbonylamino)-2,3'- bithiophene-4'-carboxylate as Novel Template for Antibacterial Agents

2020 ◽  
Vol 17 (5) ◽  
pp. 716-724
Author(s):  
Yan A. Ivanenkov ◽  
Renat S. Yamidanov ◽  
Ilya A. Osterman ◽  
Petr V. Sergiev ◽  
Vladimir A. Aladinskiy ◽  
...  
Keyword(s):  
Antibacterial Activity ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Antibacterial Agents ◽  
Sos Response ◽  
Luciferase Assay ◽  
Translational Machinery ◽  
E Coli ◽  
New Class

Background: The key issue in the development of novel antimicrobials is a rapid expansion of new bacterial strains resistant to current antibiotics. Indeed, World Health Organization has reported that bacteria commonly causing infections in hospitals and in the community, e.g. E. Coli, K. pneumoniae and S. aureus, have high resistance vs the last generations of cephalosporins, carbapenems and fluoroquinolones. During the past decades, only few successful efforts to develop and launch new antibacterial medications have been performed. This study aims to identify new class of antibacterial agents using novel high-throughput screening technique. Methods: We have designed library containing 125K compounds not similar in structure (Tanimoto coeff.< 0.7) to that published previously as antibiotics. The HTS platform based on double reporter system pDualrep2 was used to distinguish between molecules able to block translational machinery or induce SOS-response in a model E. coli system. MICs for most active chemicals in LB and M9 medium were determined using broth microdilution assay. Results: In an attempt to discover novel classes of antibacterials, we performed HTS of a large-scale small molecule library using our unique screening platform. This approach permitted us to quickly and robustly evaluate a lot of compounds as well as to determine the mechanism of action in the case of compounds being either translational machinery inhibitors or DNA-damaging agents/replication blockers. HTS has resulted in several new structural classes of molecules exhibiting an attractive antibacterial activity. Herein, we report as promising antibacterials. Two most active compounds from this series showed MIC value of 1.2 (5) and 1.8 μg/mL (6) and good selectivity index. Compound 6 caused RFP induction and low SOS response. In vitro luciferase assay has revealed that it is able to slightly inhibit protein biosynthesis. Compound 5 was tested on several archival strains and exhibited slight activity against gram-negative bacteria and outstanding activity against S. aureus. The key structural requirements for antibacterial potency were also explored. We found, that the unsubstituted carboxylic group is crucial for antibacterial activity as well as the presence of bulky hydrophobic substituents at phenyl fragment. Conclusion: The obtained results provide a solid background for further characterization of the 5'- (carbonylamino)-2,3'-bithiophene-4'-carboxylate derivatives discussed herein as new class of antibacterials and their optimization campaign.

A Bridge Too Far?

2018 ◽  
Author(s):  
Charlotte P. Lee ◽  
Kjeld Schmidt
Keyword(s):  
Information Systems ◽  
Science And Technology Studies ◽  
Large Scale ◽  
Cooperative Work ◽  
Computer Supported Cooperative Work ◽  
Practical Action ◽  
Systems Research ◽  
Information Infrastructures ◽  
Information Systems Research ◽  
Critical Investigation

The study of computing infrastructures has grown significantly due to the rapid proliferation and ubiquity of large-scale IT-based installations. At the same time, recognition has also grown of the usefulness of such studies as a means for understanding computing infrastructures as material complements of practical action. Subsequently the concept of “infrastructure” (or “information infrastructures,” “cyberinfrastructures,” and “infrastructuring”) has gained increasing importance in the area of Computer-Supported Cooperative Work (CSCW) as well as in neighboring areas such as Information Systems research (IS) and Science and Technology Studies (STS). However, as such studies have unfolded, the very concept of “infrastructure” is being applied in different discourses, for different purposes, in myriad different senses. Consequently, the concept of “infrastructure” has become increasingly muddled and needs clarification. The chapter presents a critical investigation of the vicissitudes of the concept of “infrastructure” over the last 35 years.

Do elite soccer players cover longer distance when losing? Differences between attackers and defenders

2020 ◽  
pp. 174795412098227
Author(s):  
Carlos Lago-Peñas ◽  
Anton Kalén ◽  
Miguel Lorenzo-Martinez ◽  
Roberto López-Del Campo ◽  
Ricardo Resta ◽  
...  
Keyword(s):  
New York ◽  
High Speed ◽  
Large Scale ◽  
Tracking System ◽  
Soccer Players ◽  
Running Performance ◽  
Situational Variables ◽  
Total Distance ◽  
Professional Soccer ◽  
Score Line

This study aimed to evaluate the effects playing position, match location (home or away), quality of opposition (strong or weak), effective playing time (total time minus stoppages), and score-line on physical match performance in professional soccer players using a large-scale analysis. A total of 10,739 individual match observations of outfield players competing in the Spanish La Liga during the 2018–2019 season were recorded using a computerized tracking system (TRACAB, Chyronhego, New York, USA). The players were classified into five positions (central defenders, players = 94; external defenders, players = 82; central midfielders, players = 101; external midfielders, players = 72; and forwards, players = 67) and the following match running performance categories were considered: total distance covered, low-speed running (LSR) distance (0–14 km · h−1), medium-speed running (MSR) distance (14–21 km · h−1), high-speed running (HSR) distance (>21 km · h−1), very HSR (VHSR) distance (21–24 km · h−1), sprint distance (>24 km · h−1) Overall, match running performance was highly dependent on situational variables, especially the score-line condition (winning, drawing, losing). Moreover, the score-line affected players running performance differently depending on their playing position. Losing status increased the total distance and the distance covered at MSR, HSR, VHSR and Sprint by defenders, while attacking players showed the opposite trend. These findings may help coaches and managers to better understand the effects of situational variables on physical performance in La Liga and could be used to develop a model for predicting the physical activity profile in competition.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals Novel approach for large‐scale monitoring of submerged aquatic vegetation – a nationwide example from Sweden

2021 ◽  
Author(s):  
Silvia Huber ◽  
Lars B. Hansen ◽  
Lisbeth T. Nielsen ◽  
Mikkel L. Rasmussen ◽  
Jonas Sølvsteen ◽  
...  
Keyword(s):  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Novel Approach

scholarly journals Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

2021 ◽  
Author(s):  
Jin Zhou ◽  
Qing Zhang ◽  
Jian-Hao Fan ◽  
Wei Sun ◽  
Wei-Shi Zheng
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Generic Model ◽  
Small Subset ◽  
Deep Convolutional Neural Networks ◽  
Personal Taste ◽  
Hinge Loss ◽  
Novel Approach ◽  
Large Scale Dataset ◽  
Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

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