Bacteria-Responsive Self-Assembly of Antimicrobial Peptide Nanonets for Trap-and-Kill of Antibiotic-Resistant Strains

Bacterial trapping using nanonets is a ubiquitous immune defense mechanism against infectious microbes. These nanonets can entrap microbial cells, effectively arresting their dissemination and rendering them more vulnerable to locally secreted microbicides. Inspired by this evolutionarily conserved anti-infective strategy, a series of 15 to 16 residue-long synthetic β-hairpin peptides is herein constructed with the ability to self-assemble into nanonets in response to the presence of bacteria, enabling spatiotemporal control over microbial killing. Using amyloid-specific K114 assay and confocal microscopy, the membrane components lipoteichoic acid and lipopolysaccharide are shown to play a major role in determining the amyloid-nucleating capacity as triggered by Gram-positive and Gram-negative bacteria respectively. These nanonets displayed both trapping and killing functionalities, hence offering a direct improvement from the trap-only biomimetics in literature. By substituting a single turn residue of the non-amyloidogenic BTT1 peptide, the nanonet-forming BTT1-3A analog is produced with comparable antimicrobial potency. With the same sequence manipulation approach, BTT2-4A analog modified from BTT2 peptide showed improved antimicrobial potency against colistin-resistant clinical isolates. The peptide nanonets also demonstrated robust stability against proteolytic degradation, and promising in vivo efficacy and biosafety profile. Overall, these bacteria-responsive peptide nanonets are promising clinical anti-infective alternatives for circumventing antibiotic resistance.     

Design and implementation of efficient QCA full-adders using fault-tolerant majority gates

The Journal of Supercomputing - CMOS technology is facing physical limitations in scaling the manufacturing process. Therefore, to deepen the development of better designs in a smaller area, it is...     

Ocular recognition databases and competitions: a survey

The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufficient to extract iris information. In addition to providing information about an individual’s identity, features extracted from these traits can also be explored to obtain other information such as the individual’s gender, the influence of drug use, the use of contact lenses, spoofing, among others. This work presents a survey of the databases created for ocular recognition, detailing their protocols and how their images were acquired. We also describe and discuss the most popular ocular recognition competitions (contests), highlighting the submitted algorithms that achieved the best results using only iris trait and also fusing iris and periocular region information. Finally, we describe some relevant works applying deep learning techniques to ocular recognition and point out new challenges and future directions. Considering that there are a large number of ocular databases, and each one is usually designed for a specific problem, we believe this survey can provide a broad overview of the challenges in ocular biometrics.

     

Hierarchical classification of data streams: a systematic literature review

The classification task usually works with flat and batch learners, assuming problems as stationary and without relations between class labels. Nevertheless, several real-world problems do not assume these premises, i.e., data have labels organized hierarchically and are made available in streaming fashion, meaning that their behavior can drift over time. Existing studies on hierarchical classification do not consider data streams as input of their process, and thus, data is assumed as stationary and handled through batch learners. The same can be said about works on streaming data, as the hierarchical classification is overlooked. Studies concerning each area individually are promising, yet, do not tackle their intersection. This study analyzes the main characteristics of the state-of-the-art works on hierarchical classification for streaming data concerning five aspects: (i) problems tackled, (ii) datasets, (iii) algorithms, (iv) evaluation metrics, and (v) research gaps in the area. We performed a systematic literature review of primary studies and retrieved 3,722 papers, of which 42 were identified as relevant and used to answer the aforementioned research questions. We found that the problems handled by hierarchical classification of data streams include mainly classification of images, human activities, texts, and audio; the datasets are mostly created or synthetic data; the algorithms and evaluation metrics are well-known techniques or based on those; and research gaps are related to dynamic context, data complexity, and computational resources constraints. We also provide implications for future research and experiments to consider common characteristics shared amongst hierarchical classification and data stream classification.

     

Photocatalytic activity of a new composite material of Fe (III) oxide nanoparticles wrapped by a matrix of polymeric carbon nitride and amorphous carbon

Polymeric carbon nitride was synthesized from urea and doped with Cu and Fe to act as co-catalysts. The material doped with Fe was a new composite material composed of Fe(III) oxides (acting as a co-catalyst) wrapped by the polymer layers and amorphous carbon. Furthermore, the copper doped material was described in a previous report. The photocatalytic degradation of the azo dye direct blue 1 (DB) was studied using as photocatalysts: pure carbon nitride (CN), carbon nitride doped with Cu (CN-Cu) and carbon nitride doped with Fe (CN-Fe). The catalysts were characterized by Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), by X-ray photoelectron spectroscopy (XPS) and Brunauer–Emmett–Teller method (BET), etc. The adsorption phenomenon was studied using the Langmuir and Freundlich models. For the kinetic study, a solution of 500 mg L^?1 of DB1 was treated with each catalyst, visible light and H₂O₂. The dye concentration was measured by spectrophotometry at the wavelength of 565 nm, and the removal of the total organic content (TOC) was quantified. BET analysis yielded surface areas of 60.029, 20.116 and 70.662 m²g^?1 for CN, CN-Cu and CN-Fe, respectively. The kinetics of degradation were pseudo-first order, whose constants were 0.093, 0.039 and 0.110 min^?1 for CN, CN-Cu and CN-Fe, respectively. The total organic carbon (TOC) removal reached the highest value of 14.46% with CN-Fe.     

Use of 3D printing for biofuel production: efficient catalyst for sustainable biodiesel production from wastes

This work deals with the sustainable biodiesel production from low-cost renewable feedstock (waste and non-edible oils) using a heterogeneous catalyst constituted by potassium loaded on an amorphous aluminum silicate naturally occurring as volcanic material (pumice). The main challenge to biodiesel production from low-quality oils (used oils and greases) is the high percentage of free fatty acids (FFAs) and water in the feedstock that causes undesirable side reactions. The catalytic materials studied were tested in the transesterification reaction when using low-quality oils containing a high proportion of free fatty acids (FFAs) and water. Results indicated that the amount of acid and basic sites on the catalytic surface increases upon increasing potassium loading in the catalyst, displaying better performance for biodiesel production. Indeed, the modification of the aluminum silicate substrate upon potassium incorporation results in a catalytic material containing both acidic and basic sites, which are responsible for both triglycerides transesterification and FFA esterification reactions. The studied catalyst not only showed good performance in the biodiesel production reaction but also good tolerance to FFA and water contained in the feedstock for biodiesel production. The catalytic material was microstructured by 3D printing in order to design a catalytic stirring system with high mechanical strength, efficient and reusable. The use of 3D printing in biofuel production is a novelty that brings good solutions for catalyst production.     

CeO<Subscript>2</Subscript> and Co<Subscript>3</Subscript>O<Subscript>4</Subscript>–CeO<Subscript>2</Subscript> nanoparticles: effect of the synthesis method on the structure and catalytic properties in COPrOx and methanation reactions

CeO₂ and Co₃O₄–CeO₂ nanoparticles were synthesized, thoroughly characterized, and evaluated in the COPrOx reaction. The CeO₂ nanoparticles were synthesized by the diffusion-controlled precipitation method with ethylene glycol. A notably higher yield was obtained when H₂O₂ was used in the synthesis procedure. For comparison, two commercial samples of CeO₂ nanoparticles (Nyacol^®)—one calcined and the other sintered—were also studied. Catalytic results of bare CeO₂ calcined at 500 °C showed a strong influence of the method of synthesis. Despite having similar BET area values, the CeO₂ synthesized without H₂O₂ was the most active sample. Co₃O₄–CeO₂ catalysts with three different Co/(Co + Ce) atomic ratios, 0.1, 0.3, and 0.5, were prepared by the wet impregnation of the CeO₂ nanoparticles. TEM and STEM observations showed that impregnation produced mixed oxides composed of small CeO₂ nanoparticles located both over the surface and inside the Co₃O₄ crystals. The mixed oxide catalysts prepared with a cobalt atomic ratio of 0.5 showed methane formation, which started at 200 °C due to the reaction between CO₂ and H₂. However, above 250 °C, the reaction between CO and H₂ became important, thus contributing to CO elimination with a small H₂ loss. As a result, CO could be totally eliminated in a wide temperature range, from 200 to 400 °C. The methanation reaction was favored by the reduction of the cobalt oxide, as suggested by the TPR experiments. This result is probably originated in Ce–Co interactions, related to the method of synthesis and the surface area of the mixed oxides obtained.     

Coupled temperature and moisture effects on the tensile behavior of strain hardening cementitious composites (SHCC) reinforced with PVA fibers

This paper reports the experimental findings on the tensile behavior of strain-hardening cement-based composites (SHCC). The composites were subjected to the combined effects of elevated temperatures and internal moisture condition. Uniaxial tensile tests on dumbbell-shaped SHCC specimens with in situ temperature control were performed at 22, 60 and 100 °C. In addition, the effect of the internal humidity of SHCC (95, 50, 20 and 0%) coupled to the elevated temperatures was investigated. It was shown that the tensile strength decreases and the strain capacity increases with an increase in temperature. The influence of the internal moisture conditions was more significant in high temperatures. The strain capacity reduced significantly with a decrease in the humidity level. The crack pattern of the SHCC specimens was determined. Furthermore, single fiber pullout tests were performed under the considered high temperatures condition. Finally, the results are discussed based on the thermogravimetry analysis of the PVA fiber, alterations on its microstructure and surface coating.