Linear-Chain Nanostructured Carbon with a Silver Film Plated on Metal Components Has a Promising Effect for the Treatment of Periprosthetic Joint Infection

Background: Due to the aging of the world population, the number of joint diseases, along with the number of arthroplasties, has increased, simultaneously increasing the amount of complications, including periprosthetic joint infection (PPI). In this study, to combat a PPI, we investigated the antimicrobial properties of the new composite cover for titanium implants, silver-doped carbyne-like carbon (S-CLC) film. Methods: The first assay investigated the antimicrobial activity against Pseudomonas aeruginosa and releasing of silver ions from S-CLC films into growth media covered with S-CLC with a thickness of 1, 2, and 4 mm. The second assay determined the direct antibacterial properties of the S-CLC film’s surface against Staphylococcus aureus, Enterococcus faecalis, or P. aeruginosa. The third assay studied the formation of microbial biofilms of S. aureus or P. aeruginosa on the S-CLC coating. Silver-doped carbyne-like carbon (S-CLC)-covered or titanium plates alone were used as controls. Results: S-CLC films, compared to controls, prevented P. aeruginosa growth on 1 mm thickness agar; had direct antimicrobial properties against S. aureus, E. faecalis, and P. aeruginosa; and could prevent P. aeruginosa biofilm formation. Conclusions: S-CLC films on the Ti surface could successfully fight the most common infectious agent in PPI, and prevented biofilm formation.

Circuit Topology for Bottom-Up Engineering of Molecular Knots

The art of tying knots is exploited in nature and occurs in multiple applications ranging from being an essential part of scouting programs to engineering molecular knots. Biomolecular knots, such as knotted proteins, bear various cellular functions, and their entanglement is believed to provide them with thermal and kinetic stability. Yet, little is known about the design principles of naturally evolved molecular knots. Intra-chain contacts and chain entanglement contribute to the folding of knotted proteins. Circuit topology, a theory that describes intra-chain contacts, was recently generalized to account for chain entanglement. This generalization is unique to circuit topology and not motivated by other theories. In this conceptual paper, we systematically analyze the circuit topology approach to a description of linear chain entanglement. We utilize a bottom-up approach, i.e., we express entanglement by a set of four fundamental structural units subjected to three (or five) binary topological operations. All knots found in proteins form a well-defined, distinct group which naturally appears if expressed in terms of these basic structural units. We believe that such a detailed, bottom-up understanding of the structure of molecular knots should be beneficial for molecular engineering.

Quantum computing for classical problems: Variational Quantum Eigensolver for activated processes

The theory of stochastic processes impacts both physical and social sciences. At the molecular scale, stochastic dynamics is ubiquitous because of thermal fluctuations. The Fokker-Plank-Smoluchowski equation models the time evolution of the probability density of selected degrees of freedom in the diffusive regime and it is a workhorse of physical chemistry. In this paper, we report the development and implementation of a Variational Quantum Eigensolver procedure to solve the Fokker-Planck-Smoluchowski eigenvalue problem. We show that such an algorithm, typically adopted to address quantum chemistry problems, can be applied effectively to classical systems paving the way to new applications of quantum computers. We compute the conformational transition rate in a linear chain of rotors experiencing nearest-neighbor interaction. We provide a method to encode on the quantum computer the probability distribution for a given conformation of the chain and assess its scalability in terms of operations. Performance analysis on noisy quantum emulators and quantum devices (IBMQ Santiago) is provided for a small chain showing results in good agreement with the classical benchmark without further addition of any error mitigation technique.

Self-controlling photonic-on-chip networks with deep reinforcement learning

We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks.

Quantum Steering in Two- and Three-Mode ??-Symmetric Systems

We consider two PT-symmetric models, consisting of two or three single-mode cavities. In both models, the cavities are coupled to each other by linear interactions, forming a linear chain. Additionally, the first and last of such cavities interact with an environment. Since the models are PT-symmetric, they are described by non-Hermitian Hamiltonians that, for a specific range of system parameters, possess real eigenvalues. We show that in the models considered in the article, the steering generation process strongly depends on the coupling strengths and rates of the gains/losses in energy. Moreover, we find the values of parameters describing the system for which the steering appears.

Microbial Cold Shock Proteins: Overview of their Function and Mechanism of Action

: The organism responds to a decrease in temperature by producing a series of cold shock proteins (CSPs). These proteins play a critical role in growing and functioning characteristic at low temperatures. CSPs have been discovered in a wide range of organisms and show enormous diversity; their mechanisms of action are also complicated. Transcription and translation in microorganisms typically occur via a single linear chain, but upon exposure to low temperatures, RNA forms a complex secondary structure that prevents ribosomes from binding to it, slowing down translation. CSPs bind to mRNA as RNA molecular chaperones to keep the mRNA secondary structure in a single-stranded linear conformation, allowing successful translation at low temperatures.

Decoding the conductance of disordered nanostructures: a quantum inverse problem

Obtaining conductance spectra for a concentration of disordered impurities distributed over a nanoscale device with sensing capabilities is a well-defined problem. However, to do this inversely, i.e., extracting information about the scatters from the conductance spectrum alone, is not an easy task. In the presence of impurities, even advanced techniques of inversion can become particularly challenging. This article extends the applicability of a methodology we proposed capable of extracting composition information about a nanoscale sensing device using the conductance spectrum. The inversion tool decodes the conductance spectrum to yield the concentration and nature of the disorders responsible for conductance fluctuations in the spectra. We present the method for simple one-dimensional systems like an electron gas with randomly distributed delta functions and a linear chain of atoms. We prove the generality and robustness of the method using materials with complex electronic structures like hexagonal boron nitride, graphene nanoribbons, and carbon nanotubes. We also go on to probe distribution of disorders on the sublattice structure of the materials using the proposed inversion tool.