The pAblo·pCasso self-curing vector toolset for unconstrained cytidine and adenine base-editing in Gram-negative bacteria.

A synthetic biology toolkit, exploiting clustered regularly interspaced short palindromic repeats (CRISPR) and modified CRISPR-associated protein (Cas) base-editors, was developed for genome engineering in Gram-negative bacteria. Both a cytidine base-editor (CBE) and an adenine base-editor (ABE) have been optimized for precise single-nucleotide modification of plasmid and genome targets. CBE comprises a cytidine deaminase conjugated to a Cas9 nickase from Streptococcus pyogenes (SpnCas9), resulting in C→T (or G→A) substitutions. Conversely, ABE consists of an adenine deaminase fused to SpnCas9 for A→G (or T→C) editing. Several nucleotide substitutions were achieved using these plasmid-borne base-editing systems and a novel protospacer adjacent motif (PAM)-relaxed SpnCas9 (SpRY) variant. Base-editing was validated in Pseudomonas putida and other Gram-negative bacteria by inserting premature STOP codons into target genes, thereby inactivating both fluorescent proteins and metabolic (antibiotic-resistance) functions. The functional knockouts obtained by engineering STOP codons via CBE were reverted to the wild-type genotype using ABE. Additionally, a series of induction-responsive vectors was developed to facilitate the curing of the base-editing platform in a single cultivation step, simplifying complex strain engineering programs without relying on homologous recombination and yielding plasmid-free, modified bacterial cells.

Complex regulation in a Comamonas platform for diverse aromatic carbon metabolism.

Critical to a sustainable energy future are microbial platforms that can process aromatic carbons from the largely untapped reservoir of lignin and plastic feedstocks. Comamonas species present promising bacterial candidates for such platforms because they can use a range of natural and xenobiotic aromatic compounds and often possess innate genetic constraints that avoid competition with sugars. However, the metabolic reactions of these species are underexplored, and the regulatory mechanisms are unknown. Here we identify multilevel regulation in the conversion of lignin-related natural aromatic compounds, 4-hydroxybenzoate and vanillate, and the plastics-related xenobiotic aromatic compound, terephthalate, in Comamonas testosteroni KF-1. Transcription-level regulation controls initial catabolism and cleavage, but metabolite-level thermodynamic regulation governs fluxes in central carbon metabolism. Quantitative 13C mapping of tricarboxylic acid cycle and cataplerotic reactions elucidates key carbon routing not evident from enzyme abundance changes. This scheme of transcriptional activation coupled with metabolic fine-tuning challenges outcome predictions during metabolic manipulations.

An Approach to Develop Digital Twins in Industry.

The industry is currently undergoing a digital revolution driven by the integration of several enabling technologies. These include automation, robotics, cloud computing, industrial cybersecurity, systems integration, digital twins, etc. Of particular note is the increasing use of digital twins, which offer significant added value by providing realistic and fully functional process simulations. This paper proposes an approach for developing digital twins in industrial environments. The novelty lies in not only focusing on obtaining the model of the industrial system and integrating virtual reality and/or augmented reality but also in emphasizing the importance of incorporating other enabled technologies of Industry 4.0, such as system integration, connectivity with standard and specific industrial protocols, cloud services, or new industrial automation systems, to enhance the capabilities of the digital twin. Furthermore, a proposal of the software tools that can be used to achieve this incorporation is made. Unity is chosen as the real-time 3D development tool for its cross-platform capability and streamlined industrial system modeling. The integration of augmented reality is facilitated by the Vuforia SDK. Node-RED is selected as the system integration option, and communications are carried out with MQTT protocol. Finally, cloud-based services are recommended for effective data storage and processing. Furthermore, this approach has been used to develop a digital twin of a robotic electro-pneumatic cell.

A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components.

This paper presents an innovative technique, Advanced Predictor of Electrical Parameters, based on machine learning methods to predict the degradation of electronic components under the effects of radiation. The term degradation refers to the way in which electrical parameters of the electronic components vary with the irradiation dose. This method consists of two sequential steps defined as 'recognition of degradation patterns in the database' and 'degradation prediction of new samples without any kind of irradiation'. The technique can be used under two different approaches called 'pure data driven' and 'model based'. In this paper, the use of Advanced Predictor of Electrical Parameters is shown for bipolar transistors, but the methodology is sufficiently general to be applied to any other component.

Plasmonic Response of Complex Nanoparticle Assemblies.

Optical properties of nanoparticle assemblies reflect distinctive characteristics of their building blocks and spatial organization, giving rise to emergent phenomena. Integrated experimental and computational studies have established design principles connecting the structure to properties for assembled clusters and superlattices. However, conventional electromagnetic simulations are too computationally expensive to treat more complex assemblies. Here we establish a fast, materials agnostic method to simulate the optical response of large nanoparticle assemblies incorporating both structural and compositional complexity. This many-bodied, mutual polarization method resolves limitations of established approaches, achieving rapid, accurate convergence for configurations including thousands of nanoparticles, with some overlapping. We demonstrate these capabilities by reproducing experimental trends and uncovering far- and near-field mechanisms governing the optical response of plasmonic semiconductor nanocrystal assemblies including structurally complex gel networks and compositionally complex mixed binary superlattices. This broadly applicable framework will facilitate the design of complex, hierarchically structured, and dynamic assemblies for desired optical characteristics.

Expression and Characterization of Two α-l-Arabinofuranosidases from Talaromyces amestolkiae: Role of These Enzymes in Biomass Valorization.

α-l-arabinofuranosidases are glycosyl hydrolases that catalyze the break between α-l-arabinofuranosyl substituents or between α-l-arabinofuranosides and xylose from xylan or xylooligosaccharide backbones. While they belong to several glycosyl hydrolase (GH) families, there are only 24 characterized GH62 arabinofuranosidases, making them a small and underrepresented group, with many of their features remaining unknown. Aside from their applications in the food industry, arabinofuranosidases can also aid in the processing of complex lignocellulosic materials, where cellulose, hemicelluloses, and lignin are closely linked. These materials can be fully converted into sugar monomers to produce secondary products like second-generation bioethanol. Alternatively, they can be partially hydrolyzed to release xylooligosaccharides, which have prebiotic properties. While endoxylanases and ß-xylosidases are also necessary to fully break down the xylose backbone from xylan, these enzymes are limited when it comes to branched polysaccharides. In this article, two new GH62 α-l-arabinofuranosidases from Talaromyces amestolkiae (named ARA1 and ARA-2) have been heterologously expressed and characterized. ARA-1 is more sensitive to changes in pH and temperature, whereas ARA-2 is a robust enzyme with wide pH and temperature tolerance. Both enzymes preferentially act on arabinoxylan over arabinan, although ARA-1 has twice the catalytic efficiency of ARA-2 on this substrate. The production of xylooligosaccharides from arabinoxylan catalyzed by a T. amestolkiae endoxylanase was significantly increased upon pretreatment of the polysaccharide with ARA-1 or ARA-2, with the highest synergism values reported to date. Finally, both enzymes (ARA-1 or ARA-2 and endoxylanase) were successfully applied to enhance saccharification by combining them with a ß-xylosidase already characterized from the same fungus.

A Fungal Versatile GH10 Endoxylanase and Its Glycosynthase Variant: Synthesis of Xylooligosaccharides and Glycosides of Bioactive Phenolic Compounds.

The study of endoxylanases as catalysts to valorize hemicellulosic residues and to obtain glycosides with improved properties is a topic of great industrial interest. In this work, a GH10 ß-1,4-endoxylanase (XynSOS), from the ascomycetous fungus Talaromyces amestolkiae, has been heterologously produced in Pichia pastoris, purified, and characterized. rXynSOS is a highly glycosylated monomeric enzyme of 53 kDa that contains a functional CBM1 domain and shows its optimal activity on azurine cross-linked (AZCL)-beechwood xylan at 70 °C and pH 5. Substrate specificity and kinetic studies confirmed its versatility and high affinity for beechwood xylan and wheat arabinoxylan. Moreover, rXynSOS was capable of transglycosylating phenolic compounds, although with low efficiencies. For expanding its synthetic capacity, a glycosynthase variant of rXynSOS was developed by directed mutagenesis, replacing its nucleophile catalytic residue E236 by a glycine (rXynSOS-E236G). This novel glycosynthase was able to synthesize ß-1,4-xylooligosaccharides (XOS) of different lengths (four, six, eight, and ten xylose units), which are known to be emerging prebiotics. rXynSOS-E236G was also much more active than the native enzyme in the glycosylation of a broad range of phenolic compounds with antioxidant properties. The interesting capabilities of rXynSOS and its glycosynthase variant make them promising tools for biotechnological applications.

Fungal glycosyl hydrolases for sustainable plant biomass valorization: Talaromyces amestolkiae as a model fungus.

As the main decomposers and recyclers in nature, fungi secrete complex mixtures of extracellular enzymes for degradation of plant biomass, which is essential for mobilization of the organic carbon fixed by the photosynthesis in vegetal cells. Biotechnology can emulate the closed natural biological cycles, using lignocellulosic biomass as a renewable resource and lignocellulolytic fungal enzymes as catalysts to sustainably produce consumer goods. Cellulose and hemicellulose are the major polysaccharides on Earth, and the main enzymes involved in their hydrolytic depolymerization are cellulases (endoglucanases, cellobiohydrolases, and ß-glucosidases) and hemicellulases (mainly endoxylanases and ß-xylosidases). This work will focus on the enzymes secreted by the filamentous ascomycete Talaromyces amestolkiae and on some of their biotechnological applications. Their excellent hydrolytic activity was demonstrated by the partial degradation of xylans to prebiotic oligosaccharides by the endoxylanase XynN, or by the saccharification of lignocellulosic wastes to monosaccharides (fermentable to ethanol) either by the whole secretomes or by isolated enzymes used as supplements of commercial cocktails. However, apart from their expected hydrolytic activity, some of the ß-glycosidases produced by this strain catalyze the transfer of a sugar molecule to specific aglycons by transglycosylation. As the synthesis of customized glycoconjugates is a major goal for biocatalysis, mutant variants of the ß-xyloxidase BxTW1 and the ß-glucosidases BGL-1 and BGL-2 were obtained by directed mutagenesis, substantially improving the regioselective production yields of bioactive glycosides since they showed reduced or null hydrolytic activity.

Is There a Relationship Between Age, Personal Factors or Surgical Findings, and Outcome After Temporomandibular Joint Arthroscopy?

PURPOSE: The variables involved in prognosis after treatment of internal derangement (ID) of the temporomandibular joint (TMJ) are unclear. The purpose of this study was to estimate the frequency and identify which factors are associated with treatment success (or failure) among patients with ID managed with arthroscopy. MATERIAL AND METHODS: A retrospective cohort study was carried out of patients undergoing TMJ arthroscopy over a 9-year-period. The predictor variable was composed of a set of demographic, clinical, and operative findings, including, as primary variable, the patient's age. The primary outcome variable was based on the American Association of Oral and Maxillofacial Surgery (AAOMS) criteria of pain (measured by visual analogue scale (VAS)) and maximal interincisal opening (MIO) defined as VAS ≤ 3 and MIO greater 35 mm and grouped as success or failure. The improvement in pain and functional values were compared with the age by using the Pearson correlation coefficient, whereas categorical variables were tested using chi-squared analysis, and mean values were compared with Student t-test or ANOVA. Subsequently, a logistic regression model was used, and the odds ratios (OR) of the evaluated comparisons were calculated. RESULTS: A total of 212 patients were included in this study. In terms of arthroscopic findings, the presence of severe chondromalacia, adhesions or disc perforation (P < .001), was related with older patients. However, there was no statistically significant correlation between age and the postoperative improvement referred to pain or MIO. According to the AAOMS criteria, the procedure was successful in 54.24% of the cases. Two factors were related with a favorable outcome in the adjusted regression analysis: a higher presurgical MIO (OR 0.91, P < .001) and the presence of adhesions (OR 0.41, P = .003). CONCLUSION: Age has no influence on the outcome after arthroscopy. A higher presurgical MIO and the presence of adhesions provide, in the long-term, a favorable prognosis.

Universal Gelation of Metal Oxide Nanocrystals via Depletion Attractions.

Nanocrystal gelation provides a powerful framework to translate nanoscale properties into bulk materials and to engineer emergent properties through the assembled microstructure. However, many established gelation strategies rely on chemical reactions and specific interactions, e.g., stabilizing ligands or ions on the nanocrystals' surfaces, and are therefore not easily transferable. Here, we report a general gelation strategy via nonspecific and purely entropic depletion attractions applied to three types of metal oxide nanocrystals. The gelation thresholds of two compositionally distinct spherical nanocrystals agree quantitatively, demonstrating the adaptability of the approach for different chemistries. Consistent with theoretical phase behavior predictions, nanocrystal cubes form gels at a lower polymer concentration than nanocrystal spheres, allowing shape to serve as a handle to control gelation. These results suggest that the fundamental underpinnings of depletion-driven assembly, traditionally associated with larger colloidal particles, are also applicable at the nanoscale.

Intersecting Xenobiology and Neometabolism To Bring Novel Chemistries to Life.

The diversity of life relies on a handful of chemical elements (carbon, oxygen, hydrogen, nitrogen, sulfur and phosphorus) as part of essential building blocks; some other atoms are needed to a lesser extent, but most of the remaining elements are excluded from biology. This circumstance limits the scope of biochemical reactions in extant metabolism - yet it offers a phenomenal playground for synthetic biology. Xenobiology aims to bring novel bricks to life that could be exploited for (xeno)metabolite synthesis. In particular, the assembly of novel pathways engineered to handle nonbiological elements (neometabolism) will broaden chemical space beyond the reach of natural evolution. In this review, xeno-elements that could be blended into nature's biosynthetic portfolio are discussed together with their physicochemical properties and tools and strategies to incorporate them into biochemistry. We argue that current bioproduction methods can be revolutionized by bridging xenobiology and neometabolism for the synthesis of new-to-nature molecules, such as organohalides.

A glucotolerant ß-glucosidase from the fungus Talaromyces amestolkiae and its conversion into a glycosynthase for glycosylation of phenolic compounds.

BACKGROUND: The interest for finding novel ß-glucosidases that can improve the yields to produce second-generation (2G) biofuels is still very high. One of the most desired features for these enzymes is glucose tolerance, which enables their optimal activity under high-glucose concentrations. Besides, there is an additional focus of attention on finding novel enzymatic alternatives for glycoside synthesis, for which a mutated version of glycosidases, named glycosynthases, has gained much interest in recent years. RESULTS: In this work, a glucotolerant ß-glucosidase (BGL-1) from the ascomycete fungus Talaromyces amestolkiae has been heterologously expressed in Pichia pastoris, purified, and characterized. The enzyme showed good efficiency on p-nitrophenyl glucopyranoside (pNPG) (Km= 3.36 ± 0.7 mM, kcat= 898.31 s-1), but its activity on cellooligosaccharides, the natural substrates of these enzymes, was much lower, which could limit its exploitation in lignocellulose degradation applications. Interestingly, when examining the substrate specificity of BGL-1, it showed to be more active on sophorose, the ß-1,2 disaccharide of glucose, than on cellobiose. Besides, the transglycosylation profile of BGL-1 was examined, and, for expanding its synthetic capacities, it was converted into a glycosynthase. The mutant enzyme, named BGL-1-E521G, was able to use α-D-glucosyl-fluoride as donor in glycosylation reactions, and synthesized glucosylated derivatives of different pNP-sugars in a regioselective manner, as well as of some phenolic compounds of industrial interest, such as epigallocatechin gallate (EGCG). CONCLUSIONS: In this work, we report the characterization of a novel glucotolerant 1,2-ß-glucosidase, which also has a considerable activity on 1,4-ß-glucosyl bonds, that has been cloned in P. pastoris, produced, purified and characterized. In addition, the enzyme was converted into an efficient glycosynthase, able to transfer glucose molecules to a diversity of acceptors for obtaining compounds of interest. The remarkable capacities of BGL-1 and its glycosynthase mutant, both in hydrolysis and synthesis, suggest that it could be an interesting tool for biotechnological applications.

Feature Extraction from Building Submetering Networks Using Deep Learning.

The understanding of the nature and structure of energy use in large buildings is vital for defining novel energy and climate change strategies. The advances on metering technology and low-cost devices make it possible to form a submetering network, which measures the main supply and other intermediate points providing information of the behavior of different areas. However, an analysis by means of classical techniques can lead to wrong conclusions if the load is not balanced. This paper proposes the use of a deep convolutional autoencoder to reconstruct the whole consumption measured by the submeters using the learnt features in order to analyze the behavior of different building areas. The display of weights and information of the latent space provided by the autoencoder allows us to obtain precise details of the influence of each area in the whole building consumption and its dependence on external factors such as temperature. A submetering network is deployed in the León University Hospital building in order to test the proposed methodology. The results show different correlations between environmental variables and building areas and indicate that areas can be grouped depending on their function in the building performance. Furthermore, this approach is able to provide discernible results in the presence of large differences with respect to the consumption ranges of the different areas, unlike conventional approaches where the influence of smaller areas is usually hidden.

Another (Internal) Epineurium: Beyond the Anatomical Barriers of Nerves.

The epineurium has been accepted as the outer anatomical barrier of the peripheral nerves. Our objective was to characterize the microanatomy of the layers surrounding nerves using different tissue-specific staining methods. Two hundred forty-two cross sections of human sciatic and median nerves, and brachial plexuses of eight fresh unembalmed cadavers, were examined. The samples were fixed in formaldehyde solution and stained with hematoxylin-eosin, Masson's trichrome, or epithelial membrane antigen under standard conditions. Because epithelial membrane antigen only stains the perineurium, we demonstrated using hematoxylin-eosin and Masson's trichrome that there were different collagen layers inside and outside the nerves. All fascicles had a collagen layer that surrounded the perineurium and were in close contact with it, with no adipose tissue between them. Unlike the perineurium, this layer, an "internal epineurium," contained no cells, and it surrounded one or a small group of fascicles. Bundling these fascicles or small groups of fascicles together was the true epineurium, and between the true and internal epineurium, we consistently found an adipose-containing compartment. More proximal to this, the tibial and common peroneal nerves were bundled together by another collagen layer, the circumneurium, which also had a fat-cell-containing compartment deep to it. There were scattered collagen fibers among the adipocytes. Using tissue-specific staining, we were able to demonstrate a collagen layer, the "internal epineurium." Outside the nerves, we identified several fat-containing concentric compartments. Those compartments were limited by collagen fiber layers that were also similar to the epineurium. Clin. Anat. 33:199-206, 2020. © 2019 Wiley Periodicals, Inc.

CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks.

BACKGROUND: Deep learning techniques have been successfully applied to bioimaging problems; however, these methods are highly data demanding. An approach to deal with the lack of data and avoid overfitting is the application of data augmentation, a technique that generates new training samples from the original dataset by applying different kinds of transformations. Several tools exist to apply data augmentation in the context of image classification, but it does not exist a similar tool for the problems of localization, detection, semantic segmentation or instance segmentation that works not only with 2 dimensional images but also with multi-dimensional images (such as stacks or videos). RESULTS: In this paper, we present a generic strategy that can be applied to automatically augment a dataset of images, or multi-dimensional images, devoted to classification, localization, detection, semantic segmentation or instance segmentation. The augmentation method presented in this paper has been implemented in the open-source package CLoDSA. To prove the benefits of using CLoDSA, we have employed this library to improve the accuracy of models for Malaria parasite classification, stomata detection, and automatic segmentation of neural structures. CONCLUSIONS: CLoDSA is the first, at least up to the best of our knowledge, image augmentation library for object classification, localization, detection, semantic segmentation, and instance segmentation that works not only with 2 dimensional images but also with multi-dimensional images.

Exploiting xylan as sugar donor for the synthesis of an antiproliferative xyloside using an enzyme cascade.

BACKGROUND: Currently, industrial societies are seeking for green alternatives to conventional chemical synthesis. This demand has merged with the efforts to convert lignocellulosic biomass into value-added products. In this context, xylan, as one of main components of lignocellulose, has emerged as a raw material with high potential for advancing towards a sustainable economy. RESULTS: In this study, the recombinant endoxylanase rXynM from the ascomycete Talaromyces amestolkiae has been heterologously expressed in Pichia pastoris and used as one of the catalysts of an enzyme cascade developed to synthesize the antiproliferative 2-(6-hydroxynaphthyl) ß-D-xylopyranoside, by transglycosylation of 2,6-dihydroxynaphthalene. The approach combines the use of two fungal xylanolytic enzymes, rXynM and the ß-xylosidase rBxTW1 from the same fungus, with the cost-effective substrate xylan. The reaction conditions for the cascade were optimized by a Central Composite Design. Maximal productions of 0.59 and 0.38 g/L were reached using beechwood xylan and birchwood xylan, respectively. For comparison, xylans from other sources were tested in the same reaction, suggesting that a specific optimization is required for each xylan variety. The results obtained using this enzyme cascade and xylan were similar or better to those previously reported for a single catalyst and xylobiose, an expensive sugar donor. CONCLUSIONS: Beechwood and birchwood xylan, two polysaccharides easily available from biomass, were used in a novel enzyme cascade to synthetize an antiproliferative agent. The approach represents a green alternative to the conventional chemical synthesis of 2-(6-hydroxynaphthyl) ß-D-xylopyranoside using a cost-effective substrate. The work highlights the role of xylan as a raw material for producing value-added products and the potential of fungal xylanolytic enzymes in the biomass conversion.

Transglycosylation products generated by Talaromyces amestolkiae GH3 ß-glucosidases: effect of hydroxytyrosol, vanillin and its glucosides on breast cancer cells.

BACKGROUND: Transglycosylation represents one of the most promising approaches for obtaining novel glycosides, and plant phenols and polyphenols are emerging as one of the best targets for creating new molecules with enhanced capacities. These compounds can be found in diet and exhibit a wide range of bioactivities, such as antioxidant, antihypertensive, antitumor, neuroprotective and anti-inflammatory, and the eco-friendly synthesis of glycosides from these molecules can be a suitable alternative for increasing their health benefits. RESULTS: Transglycosylation experiments were carried out using different GH3 ß-glucosidases from the fungus Talaromyces amestolkiae. After a first screening with a wide variety of potential transglycosylation acceptors, mono-glucosylated derivatives of hydroxytyrosol, vanillin alcohol, 4-hydroxybenzyl alcohol, and hydroquinone were detected. The reaction products were analyzed by thin-layer chromatography, high-pressure liquid chromatography, and mass spectrometry. Hydroxytyrosol and vanillyl alcohol were selected as the best options for transglycosylation optimization, with a final conversion yield of 13.8 and 19% of hydroxytyrosol and vanillin glucosides, respectively. NMR analysis confirmed the structures of these compounds. The evaluation of the biological effect of these glucosides using models of breast cancer cells, showed an enhancement in the anti-proliferative capacity of the vanillin derivative, and an improved safety profile of both glucosides. CONCLUSIONS: GH3 ß-glucosidases from T. amestolkiae expressed in P. pastoris were able to transglycosylate a wide variety of acceptors. Between them, phenolic molecules like hydroxytyrosol, vanillin alcohol, 4-hydroxybenzyl alcohol, and hydroquinone were the most suitable for its interesting biological properties. The glycosides of hydroxytyrosol and vanillin were tested, and they improved the biological activities of the original aglycons on breast cancer cells.

A Deep Learning Approach for Fusing Sensor Data from Screw Compressors.

Chillers are commonly used for thermal regulation to maintain indoor comfort in medium and large buildings. However, inefficiencies in this process produce significant losses, and optimization tasks are limited because of accessibility to the system. Data analysis techniques transform measurements coming from several sensors into useful information. Recent deep learning approaches have achieved excellent results in many applications. These techniques can be used for computing new data representations that provide comprehensive information from the device. This allows real-time monitoring, where information can be checked with current working operation to detect any type of anomaly in the process. In this work, a model based on a 1D convolutional neural network is proposed for fusing data in order to predict four different control stages of a screw compressor in a chiller. The evaluation of the method was performed using real data from a chiller in a hospital building. Results show a satisfactory performance and acceptable training time in comparison with other recent methods. In addition, the model is capable of predicting control states of other screw compressors different than the one used in the training. Furthermore, two failure cases are simulated, providing an early alarm detection when a continuous wrong classification is performed by the model.