Tailored Synthetic sRNAs Dynamically Tune Multilayer Genetic Circuits.

Predictable and controllable tuning of genetic circuits to regulate gene expression, including modulation of existing circuits or constructs without the need for redesign or rebuilding, is a persistent challenge in synthetic biology. Here, we propose rationally designed new small RNAs (sRNAs) that dynamically modulate gene expression of genetic circuits with a broad range (high, medium, and low) of repression. We designed multiple multilayer genetic circuits in which the variable effector element is a transcription factor (TF) controlling downstream the production of a reporter protein. The sRNAs target TFs instead of a reporter gene, and harnessing the intrinsic RNA-interference pathway in E. coli allowed for a wide range of expression modulation of the reporter protein, including the most difficult to achieve dynamic switch to an OFF state. The synthetic sRNAs are expressed independently of the circuit(s), thus allowing for repression without modifying the circuit itself. Our work provides a frame for achieving independent modulation of gene expression and dynamic and modular control of the multilayer genetic circuits by only including an independent control circuit expressing synthetic sRNAs, without altering the structure of existing genetic circuits.

Computerised cognitive assessment in patients with traumatic brain injury: an observational study of feasibility and sensitivity relative to established clinical scales.

Background: Online technology could potentially revolutionise how patients are cognitively assessed and monitored. However, it remains unclear whether assessments conducted remotely can match established pen-and-paper neuropsychological tests in terms of sensitivity and specificity. Methods: This observational study aimed to optimise an online cognitive assessment for use in traumatic brain injury (TBI) clinics. The tertiary referral clinic in which this tool has been clinically implemented typically sees patients a minimum of 6 months post-injury in the chronic phase. Between March and August 2019, we conducted a cross-group, cross-device and factor analyses at the St. Mary's Hospital TBI clinic and major trauma wards at Imperial College NHS trust and St. George's Hospital in London (UK), to identify a battery of tasks that assess aspects of cognition affected by TBI. Between September 2019 and February 2020, we evaluated the online battery against standard face-to-face neuropsychological tests at the Imperial College London research centre. Canonical Correlation Analysis (CCA) determined the shared variance between the online battery and standard neuropsychological tests. Finally, between October 2020 and December 2021, the tests were integrated into a framework that automatically generates a results report where patients' performance is compared to a large normative dataset. We piloted this as a practical tool to be used under supervised and unsupervised conditions at the St. Mary's Hospital TBI clinic in London (UK). Findings: The online assessment discriminated processing-speed, visual-attention, working-memory, and executive-function deficits in TBI. CCA identified two significant modes indicating shared variance with standard neuropsychological tests (r = 0.86, p < 0.001 and r = 0.81, p = 0.02). Sensitivity to cognitive deficits after TBI was evident in the TBI clinic setting under supervised and unsupervised conditions (F (15,555) = 3.99; p < 0.001). Interpretation: Online cognitive assessment of TBI patients is feasible, sensitive, and efficient. When combined with normative sociodemographic models and autogenerated reports, it has the potential to transform cognitive assessment in the healthcare setting. Funding: This work was funded by a National Institute for Health Research (NIHR) Invention for Innovation (i4i) grant awarded to DJS and AH (II-LB-0715-20006).

Algal mediated intervention for the retrieval of emerging pollutants from aqueous media.

Water is a crucial elemental contributor for all sectors; however, the agricultural sector alone accounts for 70% of the world's total water withdrawal. The anthropogenic activity from various industries including agriculture, textiles, plastics, leather, and defence has resulted in the release of contaminants into water systems, resulting harm to the ecosystem and biotic community. Algae-based organic pollutant removal uses several methods, such as biosorption, bioaccumulation, biotransformation, and biodegradation. The adsorption of methylene blue by algal species Chlamydomonas sp. showed a maximum adsorption capacity of 2744.5 mg/g with 96.13% removal efficiency; on the other hand, Isochrysis galbana demonstrated a maximum of 707 µg/g nonylphenol accumulation in the cell with 77% removal efficiency indicating the potential of algal systems as efficient retrieval system for organic contaminants. This paper is a compilation of detailed information about biosorption, bioaccumulation, biotransformation, biodegradation, and their mechanism, along with the genetic alteration of algal biomass. Where the genetic engineering and mutations on algae can be advantageously utilized for the enhancement of removal efficiency without any secondary toxicity.

Article Application of neural network in metal adsorption using biomaterials (BMs): a review.

With growing environmental consciousness, biomaterials (BMs) have garnered attention as sustainable materials for the adsorption of hazardous water contaminants. These BMs are engineered using surface treatments or physical alterations to enhance their adsorptive properties. The lab-scale methods generally employ a One Variable at a Time (OVAT) approach to analyze the impact of biomaterial modifications, their characteristics and other process variables such as pH, temperature, dosage, etc., on the removal of metals via adsorption. Although implementing the adsorption procedure using BMs seems simple, the conjugate effects of adsorbent properties and process attributes implicate complex nonlinear interactions. As a result, artificial neural networks (ANN) have gained traction in the quest to understand the complex metal adsorption processes on biomaterials, with applications in environmental remediation and water reuse. This review discusses recent progress using ANN frameworks for metal adsorption using modified biomaterials. Subsequently, the paper comprehensively evaluates the development of a hybrid-ANN system to estimate isothermal, kinetic and thermodynamic parameters in multicomponent adsorption systems.

Application of MXenes for water treatment and energy-efficient desalination: A review.

MXenes are a new type of two-dimensional (2D) material which are rapidly gaining traction for a range of environmental, chemical and medical applications. MXenes and MXene-composites exhibit high surface area, superlative chemical stability, thermal conductivity, hydrophilicity and are environmentally compatible. Consequently, MXenes have been successfully employed for hydrogen storage, semiconductor manufacture and lithium ion batteries. In recent years, MXenes have been utilized in numerous environmental applications for treating contaminated surface waters, ground and industrial/ municipal wastewaters and for desalination, often outperforming conventional materials in each field. MXene-composites can adsorb multiple organic and inorganic contaminants, and undergo Faradaic capacitive deionization (CDI) when utilized for electrochemical applications. This approach allows for a significant decrease in the energy demand by overcoming the concentration polarization limitation of conventional CDI electrodes, offering a solution for low-energy desalination of brackish waters. This article presents a state-of-the-art review on water treatment and desalination applications of MXenes and MXene-composites. An investigation into the kinetics and isotherms is presented, as well as the impact of water constituents and operating conditions are also discussed. The applications of MXenes for CDI, pervaporation desalination and solar thermal desalination are also examined based on the reviewed literature. The effects of the water composition and operational protocols on the regeneration efficacy and long-term usage are also highlighted.

COVID-19 pandemic lesson learned- critical parameters and research needs for UVC inactivation of viral aerosols.

The COVID-19 pandemic highlighted public awareness of airborne disease transmission in indoor settings and emphasized the need for reliable air disinfection technologies. This increased awareness will carry in the post-pandemic era along with the ever-emerging SARS-CoV variants, necessitating effective and well-defined protocols, methods, and devices for air disinfection. Ultraviolet (UV)-based air disinfection demonstrated promising results in inactivating viral bioaerosols. However, the reported data diversity on the required UVC doses has hindered determining the best UVC practices and led to confusion among the public and regulators. This article reviews available information on critical parameters influencing the efficacy of a UVC air disinfection system and, consequently, the required dose including the system's components as well as operational and environmental factors. There is a consensus in the literature that the interrelation of humidity and air temperature has a significant impact on the UVC susceptibility, which translate to changing the UVC efficacy of commercialized devices in indoor settings under varying conditions. Sampling and aerosolization techniques reported to have major influence on the result interpretation and it is recommended to use several sampling methods simultaneously to generate comparable and conclusive data. We also considered the safety concerns and the potential safe alternative of UVC, far-UVC. Finally, the gaps in each critical parameter and the future research needs of the field are represented. This paper is the first step to consolidating literature towards developing a standard validation protocol for UVC air disinfection devices which is determined as the one of the research needs.

Biological ion exchange capable of sulphate-based secondary ion exchange during long-term DOC removal.

Biological ion exchange (BIEX) offers removal of dissolved organic carbon (DOC) with greatly reduced regeneration frequency. In the present work, a strong base anionic exchange resin was operated without regeneration and using inlet water with either Low (12 mg L-1) or High (60 mg L-1) sulphate and DOC of 2.75 or 5.0 mg L-1. Filters operated continuously for 226 days (16,500 bed volumes) and achieved DOC removal varying from 32% to 50%. Initially, sulphate and DOC were retained by the resin with chloride being released. During this period, DOC removal occurred due to traditional mechanisms, referred to as primary ion exchange. Following this initial period, DOC removal continued even though the conventionally defined resin capacity was exhausted (based on chloride loading). During the later period, no chloride release was observed, but instead sulphate was released. Although suggested by others, the present study is the first to confirm the direct exchange in charge equivalence of anions removed (DOC and nitrate) to released (sulphate) during the secondary ion exchange mechanism. Further, increasing inlet sulphate from 12 to 60 mg L-1 resulted in a 19% decrease in DOC removal. Finally, percent DOC removal was affected only by an increase of inlet DOC but not changes to the counter ion or after DOC loading on the resin increased to 1/3 of total capacity. This work promotes BIEX as a viable alternative to biological activated carbon and a leading solution for low-maintenance DOC removal.

Permutation parity machines for neural cryptography.

Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied.

Algorithm for thermodynamically based prediction of DNA/DNA cross-hybridisation.

A careful design of DNA strands is crucial for several biological applications such as microarray techniques, Polymerase Chain Reaction (PCR), and DNA computing. For this, the important criterion under laboratory conditions is the hybridisation energy of two DNA strands. During the last decade, a thermodynamic model was developed that allows for the calculation of the DNA/DNA hybridisation energy and recently also the cross-hybridisation energy of structural motifs. Employing this model a new algorithm for the secondary structure prediction of DNA/DNA cross-hybridisation complexes called HYBGRAPH is introduced. The method is based on Gibbs free energy minimisation and the paradigm of dynamic programming.

Exploiting the features of the finite state automata for biomolecular computing.

Here, we review patents that have emerged in the field of DNA-based computing focusing thereby on the discoveries using the concept of molecular finite state automata. A finite state automaton, operating on a finite sequence of symbols and converting information from one to another, provides a basis for developing molecular-scale autonomous programmable models of biomolecular computation at cellular level. We also provide a brief overview on inventions which methodologically support the DNA-based computational approach.

An autonomous DNA model for finite state automata.

In this paper we introduce an autonomous DNA model for finite state automata. This model called sticker automaton model is based on the hybridisation of single stranded DNA molecules (stickers) encoding transition rules and input data. The computation is carried out in an autonomous manner by one enzyme which allows us to determine whether a resulting double-stranded DNA molecule belongs to the automaton's language or not.

Computational genes: a tool for molecular diagnosis and therapy of aberrant mutational phenotype.

BACKGROUND: A finite state machine manipulating information-carrying DNA strands can be used to perform autonomous molecular-scale computations at the cellular level. RESULTS: We propose a new finite state machine able to detect and correct aberrant molecular phenotype given by mutated genetic transcripts. The aberrant mutations trigger a cascade reaction: specific molecular markers as input are released and induce a spontaneous self-assembly of a wild type protein or peptide, while the mutational disease phenotype is silenced. We experimentally demostrated in in vitro translation system that a viable protein can be autonomously assembled. CONCLUSION: Our work demostrates the basic principles of computational genes and particularly, their potential to detect mutations, and as a response thereafter administer an output that suppresses the aberrant disease phenotype and/or restores the lost physiological function.

Biomolecular autonomous solution of the Hamiltonian path problem via hairpin formation.

The Hamiltonian path problem is one of the famous hard combinatorial problems. We provide the first molecular-scale autonomous solution of the decision Hamiltonian path problem. It is based on the formation of secondary structures of DNA molecules.