Bio-coloration of bacterial cellulose assisted by immobilized laccase

In this work a process for the bio-coloration of bacterial cellulose (BC) membranes was developed. Laccase from Myceliophthora thermophila was immobilized onto BC membranes and retained up to 88% of residual activity after immobilization. Four compounds belonging to the flavonoids family were chosen to test the in situ polymerase activity of immobilized laccase. All the flavonoids were successfully polymerized by laccase giving rise to yellow, orange and dark brown oligomers which conferred color to the BC support. The optimal bio-coloration conditions were studied for two of the tested flavonoids, catechol and catechin, by varying the concentration and time of incubation. High color depth and resistance to washing were obtained for both compounds. The highly porous bacterial cellulose material demonstrated great performance as a bio-coloration support, in contrast to other materials cited in literature, like cotton or wool. The process developed is presented as an environmentally friendly alternative for bacterial cellulose bio-coloration and will contribute deeply for the development of new fashionable products within this material.The authors would like to acknowledge Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI‑01‑0145‑FEDER‑006684) and BioTecNorte operation (NORTE‑01‑0145‑FEDER‑000004) funded by Euro‑ pean Regional Development Fund under the scope of Norte2020‑Programa Operacional Regional do Norte. The authors would like also to acknowl‑ edge the Basic Science Research Program through the National Research Foundation of Korea (NRF), which was funded by the Ministry of Education (2017R1D1A1B03031959).info:eu-repo/semantics/publishedVersio

Adaptive control of recurrent neural networks using conceptors

Recurrent neural networks excel at predicting and generating complex high-dimensional temporal patterns. Due to their inherent nonlinear dynamics and memory, they can learn unbounded temporal dependencies from data. In a machine learning setting, the network’s parameters are adapted during a training phase to match the requirements of a given task/problem increasing its computational capabilities. After the training, the network parameters are kept fixed to exploit the learned computations. The static parameters, therefore, render the network unadaptive to changing conditions, such as an external or internal perturbation. In this paper, we demonstrate how keeping parts of the network adaptive even after the training enhances its functionality and robustness. Here, we utilize the conceptor framework and conceptualize an adaptive control loop analyzing the network’s behavior continuously and adjusting its time-varying internal representation to follow a desired target. We demonstrate how the added adaptivity of the network supports the computational functionality in three distinct tasks: interpolation of temporal patterns, stabilization against partial network degradation, and robustness against input distortion. Our results highlight the potential of adaptive networks in machine learning beyond training, enabling them to not only learn complex patterns but also dynamically adjust to changing environments, ultimately broadening their applicability.</p

How the other half lives: CRISPR-Cas's influence on bacteriophages

CRISPR-Cas is a genetic adaptive immune system unique to prokaryotic cells used to combat phage and plasmid threats. The host cell adapts by incorporating DNA sequences from invading phages or plasmids into its CRISPR locus as spacers. These spacers are expressed as mobile surveillance RNAs that direct CRISPR-associated (Cas) proteins to protect against subsequent attack by the same phages or plasmids. The threat from mobile genetic elements inevitably shapes the CRISPR loci of archaea and bacteria, and simultaneously the CRISPR-Cas immune system drives evolution of these invaders. Here we highlight our recent work, as well as that of others, that seeks to understand phage mechanisms of CRISPR-Cas evasion and conditions for population coexistence of phages with CRISPR-protected prokaryotes.Comment: 24 pages, 8 figure

Adaptive control of recurrent neural networks using conceptors

Recurrent Neural Networks excel at predicting and generating complex high-dimensional temporal patterns. Due to their inherent nonlinear dynamics and memory, they can learn unbounded temporal dependencies from data. In a Machine Learning setting, the network's parameters are adapted during a training phase to match the requirements of a given task/problem increasing its computational capabilities. After the training, the network parameters are kept fixed to exploit the learned computations. The static parameters thereby render the network unadaptive to changing conditions, such as external or internal perturbation. In this manuscript, we demonstrate how keeping parts of the network adaptive even after the training enhances its functionality and robustness. Here, we utilize the conceptor framework and conceptualize an adaptive control loop analyzing the network's behavior continuously and adjusting its time-varying internal representation to follow a desired target. We demonstrate how the added adaptivity of the network supports the computational functionality in three distinct tasks: interpolation of temporal patterns, stabilization against partial network degradation, and robustness against input distortion. Our results highlight the potential of adaptive networks in machine learning beyond training, enabling them to not only learn complex patterns but also dynamically adjust to changing environments, ultimately broadening their applicability

CRISPRcompar: a website to compare clustered regularly interspaced short palindromic repeats

Clustered regularly interspaced short palindromic repeat (CRISPR) elements are a particular family of tandem repeats present in prokaryotic genomes, in almost all archaea and in about half of bacteria, and which participate in a mechanism of acquired resistance against phages. They consist in a succession of direct repeats (DR) of 24–47 bp separated by similar sized unique sequences (spacers). In the large majority of cases, the direct repeats are highly conserved, while the number and nature of the spacers are often quite diverse, even among strains of a same species. Furthermore, the acquisition of new units (DR + spacer) was shown to happen almost exclusively on one side of the locus. Therefore, the CRISPR presents an interesting genetic marker for comparative and evolutionary analysis of closely related bacterial strains. CRISPRcompar is a web service created to assist biologists in the CRISPR typing process. Two tools facilitates the in silico investigation: CRISPRcomparison and CRISPRtionary. This website is freely accessible at http://crispr.u-psud.fr/CRISPRcompar/

Structural basis for CRISPR RNA-guided DNA recognition by Cascade

The CRISPR (clustered regularly interspaced short palindromic repeats) immune system in prokaryotes uses small guide RNAs to neutralize invading viruses and plasmids. In Escherichia coli, immunity depends on a ribonucleoprotein complex called Cascade. Here we present the composition and low-resolution structure of Cascade and show how it recognizes double-stranded DNA (dsDNA) targets in a sequence-specific manner. Cascade is a 405-kDa complex comprising five functionally essential CRISPR-associated (Cas) proteins (CasA1B2C6D1E1) and a 61-nucleotide CRISPR RNA (crRNA) with 5′-hydroxyl and 2′,3′-cyclic phosphate termini. The crRNA guides Cascade to dsDNA target sequences by forming base pairs with the complementary DNA strand while displacing the noncomplementary strand to form an R-loop. Cascade recognizes target DNA without consuming ATP, which suggests that continuous invader DNA surveillance takes place without energy investment. The structure of Cascade shows an unusual seahorse shape that undergoes conformational changes when it binds target DNA.

The role of statistical learning in the acquisition of motion event construal in a second language

Learning to talk about motion in a second language is very difficult because it involves restructuring deeply entrenched patterns from the first language (Slobin 1996). In this paper we argue that statistical learning (Saffran et al. 1997) can explain why L2 learners are only partially successful in restructuring their second language grammars. We explore to what extent L2 learners make use of two mechanisms of statistical learning, entrenchment and pre-emption (Boyd and Goldberg 2011) to acquire target-like expressions of motion and retreat from overgeneralisation in this domain. Paying attention to the frequency of existing patterns in the input can help learners to adjust the frequency with which they use path and manner verbs in French but is insufficient to acquire the boundary crossing constraint (Slobin and Hoiting 1994) and learn what not to say. We also look at the role of language proficiency and exposure to French in explaining the findings

PILER-CR: Fast and accurate identification of CRISPR repeats

BACKGROUND: Sequencing of prokaryotic genomes has recently revealed the presence of CRISPR elements: short, highly conserved repeats separated by unique sequences of similar length. The distinctive sequence signature of CRISPR repeats can be found using general-purpose repeat- or pattern-finding software tools. However, the output of such tools is not always ideal for studying these repeats, and significant effort is sometimes needed to build additional tools and perform manual analysis of the output. RESULTS: We present PILER-CR, a program specifically designed for the identification and analysis of CRISPR repeats. The program executes rapidly, completing a 5 Mb genome in around 5 seconds on a current desktop computer. We validate the algorithm by manual curation and by comparison with published surveys of these repeats, finding that PILER-CR has both high sensitivity and high specificity. We also present a catalogue of putative CRISPR repeats identified in a comprehensive analysis of 346 prokaryotic genomes. CONCLUSION: PILER-CR is a useful tool for rapid identification and classification of CRISPR repeats. The software is donated to the public domain. Source code and a Linux binary are freely available at

Yersinia pestis Lineages in Mongolia

BACKGROUND: Whole genome sequencing allowed the development of a number of high resolution sequence based typing tools for Yersinia (Y.) pestis. The application of these methods on isolates from most known foci worldwide and in particular from China and the Former Soviet Union has dramatically improved our understanding of the population structure of this species. In the current view, Y. pestis including the non or moderate human pathogen Y. pestis subspecies microtus emerged from Yersinia pseudotuberculosis about 2,600 to 28,600 years ago in central Asia. The majority of central Asia natural foci have been investigated. However these investigations included only few strains from Mongolia. METHODOLOGY/PRINCIPAL FINDINGS: Clustered Regularly Interspaced Short Prokaryotic Repeats (CRISPR) analysis and Multiple-locus variable number of tandem repeats (VNTR) analysis (MLVA) with 25 loci was performed on 100 Y. pestis strains, isolated from 37 sampling areas in Mongolia. The resulting data were compared with previously published data from more than 500 plague strains, 130 of which had also been previously genotyped by single nucleotide polymorphism (SNP) analysis. The comparison revealed six main clusters including the three microtus biovars Ulegeica, Altaica, and Xilingolensis. The largest cluster comprises 78 isolates, with unique and new genotypes seen so far in Mongolia only. Typing of selected isolates by key SNPs was used to robustly assign the corresponding clusters to previously defined SNP branches. CONCLUSIONS/SIGNIFICANCE: We show that Mongolia hosts the most recent microtus clade (Ulegeica). Interestingly no representatives of the ancestral Y. pestis subspecies pestis nodes previously identified in North-western China were identified in this study. This observation suggests that the subsequent evolution steps within Y. pestis pestis did not occur in Mongolia. Rather, Mongolia was most likely re-colonized by more recent clades coming back from China contemporary of the black death pandemic, or more recently in the past 600 years

Insight into Microevolution of Yersinia pestis by Clustered Regularly Interspaced Short Palindromic Repeats

BACKGROUND: Yersinia pestis, the pathogen of plague, has greatly influenced human history on a global scale. Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR), an element participating in immunity against phages' invasion, is composed of short repeated sequences separated by unique spacers and provides the basis of the spoligotyping technology. In the present research, three CRISPR loci were analyzed in 125 strains of Y. pestis from 26 natural plague foci of China, the former Soviet Union and Mongolia were analyzed, for validating CRISPR-based genotyping method and better understanding adaptive microevolution of Y. pestis. METHODOLOGY/PRINCIPAL FINDINGS: Using PCR amplification, sequencing and online data processing, a high degree of genetic diversity was revealed in all three CRISPR elements. The distribution of spacers and their arrays in Y. pestis strains is strongly region and focus-specific, allowing the construction of a hypothetic evolutionary model of Y. pestis. This model suggests transmission route of microtus strains that encircled Takla Makan Desert and ZhunGer Basin. Starting from Tadjikistan, one branch passed through the Kunlun Mountains, and moved to the Qinghai-Tibet Plateau. Another branch went north via the Pamirs Plateau, the Tianshan Mountains, the Altai Mountains and the Inner Mongolian Plateau. Other Y. pestis lineages might be originated from certain areas along those routes. CONCLUSIONS/SIGNIFICANCE: CRISPR can provide important information for genotyping and evolutionary research of bacteria, which will help to trace the source of outbreaks. The resulting data will make possible the development of very low cost and high-resolution assays for the systematic typing of any new isolate