Molecular physical layer for 6G in wave-denied environments

The sixth generation (6G) of wireless systems are likely to operate in environments and scales that wireless services have not penetrated effectively. Many of these environments are not suitable for efficient data bearing wave propagation. Molecular signals have the potential to deliver information by exploiting both new modulation mechanisms via chemical encoding and new multi-scale propagation physics. While the fusion of biophysical models and communication theory has rapidly advanced the molecular communication field, there is a lack of real-world macro-scale applications. Here, we introduce application areas in defense and security, ranging from underwater search and rescue to covert communications; and cyber-physical systems, such as using molecular signals for health monitoring in underground networked systems. These engineering applications not only demand new wireless communication technologies ranging from DNA encoding to molecular graph signal processing, but also demonstrate the potential for molecular communication to contribute in traditional but challenging engineering areas. Together, it is increasingly believed that molecular communication can be a new physical layer for 6G, accessing and extracting data from extreme wave-denied environments

Label-Free Protein Analysis Using Liquid Chromatography with Gravimetric Detection.

The detection and analysis of proteins in a label-free manner under native solution conditions is an increasingly important objective in analytical bioscience platform development. Common approaches to detect native proteins in solution often require specific labels to enhance sensitivity. Dry mass sensing approaches, by contrast, using mechanical resonators, can operate in a label-free manner and offer attractive sensitivity. However, such approaches typically suffer from a lack of analyte selectivity as the interface between standard protein separation techniques and micro-resonator platforms is often constrained by qualitative mechanical sensor performance in the liquid phase. Here, we describe a strategy that overcomes this limitation by coupling liquid chromatography with a quartz crystal microbalance (QCM) platform by using a microfluidic spray dryer. We explore a strategy which allows first to separate a protein mixture in a physiological buffer solution using size exclusion chromatography, permitting specific protein fractions to be selected, desalted, and subsequently spray-dried onto the QCM for absolute mass analysis. By establishing a continuous flow interface between the chromatography column and the spray device via a flow splitter, simultaneous protein mass detection and sample fractionation is achieved, with sensitivity down to a 100 μg/mL limit of detection. This approach for quantitative label-free protein mixture analysis offers the potential for detection of protein species under physiological conditions.ERC EPSRC Frances and Augustus Newman Foundation Oppenheimer Early Career Fellowship Nanotechnologies Doctoral Training Centre Fluidic Analytics Lt

Label-Free Protein Analysis Using Liquid Chromatography with Gravimetric Detection.

The detection and analysis of proteins in a label-free manner under native solution conditions is an increasingly important objective in analytical bioscience platform development. Common approaches to detect native proteins in solution often require specific labels to enhance sensitivity. Dry mass sensing approaches, by contrast, using mechanical resonators, can operate in a label-free manner and offer attractive sensitivity. However, such approaches typically suffer from a lack of analyte selectivity as the interface between standard protein separation techniques and micro-resonator platforms is often constrained by qualitative mechanical sensor performance in the liquid phase. Here, we describe a strategy that overcomes this limitation by coupling liquid chromatography with a quartz crystal microbalance (QCM) platform by using a microfluidic spray dryer. We explore a strategy which allows first to separate a protein mixture in a physiological buffer solution using size exclusion chromatography, permitting specific protein fractions to be selected, desalted, and subsequently spray-dried onto the QCM for absolute mass analysis. By establishing a continuous flow interface between the chromatography column and the spray device via a flow splitter, simultaneous protein mass detection and sample fractionation is achieved, with sensitivity down to a 100 μg/mL limit of detection. This approach for quantitative label-free protein mixture analysis offers the potential for detection of protein species under physiological conditions.ERC EPSRC Frances and Augustus Newman Foundation Oppenheimer Early Career Fellowship Nanotechnologies Doctoral Training Centre Fluidic Analytics Lt

Enhanced Quality Factor Label-free Biosensing with Micro-Cantilevers Integrated into Microfluidic Systems.

Microelectromechanical systems (MEMS) have enabled the development of a new generation of sensor platforms. Acoustic sensor operation in liquid, the native environment of biomolecules, causes, however, significant degradation of sensing performance due to viscous drag and relies on the availability of capture molecules to bind analytes of interest to the sensor surface. Here, we describe a strategy to interface MEMS sensors with microfluidic platforms through an aerosol spray. Our sensing platform comprises a microfluidic spray nozzle and a microcantilever array operated in dynamic mode within a closed loop oscillator. A solution containing the analyte is sprayed uniformly through picoliter droplets onto the microcantilever surface; the micrometer-scale drops evaporate rapidly and leave the solutes behind, adding to the mass of the cantilever. This sensing scheme results in a 50-fold increase in the quality factor compared to operation in liquid, yet allows the analytes to be introduced into the sensing system from a solution phase. It achieves a 370 femtogram limit of detection, and we demonstrate quantitative label-free analysis of inorganic salts and model proteins. These results demonstrate that the standard resolution limits of cantilever sensing in dynamic mode can be overcome with the integration of spray microfluidics with MEMS

Review of physical layer security in molecular internet of nano-things

Molecular networking has been identified as a key enabling technology for Internet-of-Nano-Things (IoNT): microscopic devices that can monitor, process information, and take action in a wide range of medical applications. As the research matures into prototypes, the cybersecurity challenges of molecular networking are now being researched on at both the cryptographic and physical layer level. Due to the limited computation capabilities of IoNT devices, physical layer security (PLS) is of particular interest. As PLS leverages on channel physics and physical signal attributes, the fact that molecular signals differ significantly from radio frequency signals and propagation means new signal processing methods and hardware is needed. Here, we review new vectors of attack and new methods of PLS, focusing on 3 areas: (1) information theoretical secrecy bounds for molecular communications, (2) key-less steering and decentralized key-based PLS methods, and (3) new methods of achieving encoding and encryption through bio-molecular compounds. The review will also include prototype demonstrations from our own lab that will inform future research and related standardization efforts

High operational and environmental stability of high-mobility conjugated polymer field-effect transistors through the use of molecular additives.

Due to their low-temperature processing properties and inherent mechanical flexibility, conjugated polymer field-effect transistors (FETs) are promising candidates for enabling flexible electronic circuits and displays. Much progress has been made on materials performance; however, there remain significant concerns about operational and environmental stability, particularly in the context of applications that require a very high level of threshold voltage stability, such as active-matrix addressing of organic light-emitting diode displays. Here, we investigate the physical mechanisms behind operational and environmental degradation of high-mobility, p-type polymer FETs and demonstrate an effective route to improve device stability. We show that water incorporated in nanometre-sized voids within the polymer microstructure is the key factor in charge trapping and device degradation. By inserting molecular additives that displace water from these voids, it is possible to increase the stability as well as uniformity to a high level sufficient for demanding industrial applications.We gratefully acknowledge financial support from Innovate UK (PORSCHED project) and the Engineering and Physical Sciences Research Council though a Programme Grant (EP/M005141/1). I.N. acknowledges studentship support from FlexEnable Ltd. K.B. gratefully acknowledges financial support from the Deutsche Forschungsgemeinschaft (BR 4869/1-1). B.R., M.K.R., and J.L.B. thank the financial support from King Abdullah University of Science and Technology (KAUST), the KAUST Competitive Research Grant program, and the Office of Naval Research Global (Award N62909-15-1-2003 );This is the author accepted manuscript. The final version is available from Nature Publishing Group via https://doi.org/10.1038/nmat478

Wheat genomic selection in a practical breeding programme: theory and application

National audienc

A R-based integrated pipeline for genomic and application to a wheat breeding programme

International audienc

BWGS: A R package for genomic selection and its application to a wheat breeding programme

We developed an integrated R library called BWGS to enable easy computation of Genomic Estimates of Breeding values (GEBV) for genomic selection. BWGS, for BreedWheat Genomic selection, was developed in the framework of a cooperative private-public partnership project called Breedwheat (https://breedwheat.fr) and relies on existing R-libraries, all freely available from CRAN servers. The two main functions enable to run 1) replicated random cross validations within a training set of genotyped and phenotyped lines and 2) GEBV prediction, for a set of genotyped-only lines. Options are available for 1) missing data imputation, 2) markers and training set selection and 3) genomic prediction with 15 different methods, either parametric or semi-parametric. The usefulness and efficiency of BWGS are illustrated using a population of wheat lines from a real breeding programme. Adjusted yield data from historical trials (highly unbalanced design) were used for testing the options of BWGS. On the whole, 760 candidate lines with adjusted phenotypes and genotypes for 47 839 robust SNP were used. With a simple desktop computer, we obtained results which compared with previously published results on wheat genomic selection. As predicted by the theory, factors that are most influencing predictive ability, for a given trait of moderate heritability, are the size of the training population and a minimum number of markers for capturing every QTL information. Missing data up to 40%, if randomly distributed, do not degrade predictive ability once imputed, and up to 80% randomly distributed missing data are still acceptable once imputed with Expectation-Maximization method of package rrBLUP. It is worth noticing that selecting markers that are most associated to the trait do improve predictive ability, compared with the whole set of markers, but only when marker selection is made on the whole population. When marker selection is made only on the sampled training set, this advantage nearly disappeared, since it was clearly due to overfitting. Few differences are observed between the 15 prediction models with this dataset. Although non-parametric methods that are supposed to capture non-additive effects have slightly better predictive accuracy, differences remain small. Finally, the GEBV from the 15 prediction models are all highly correlated to each other. These results are encouraging for an efficient use of genomic selection in applied breeding programmes and BWGS is a simple and powerful toolbox to apply in breeding programmes or training activities

Virtual selection for comparing wheat breeding schemes at constant total cost

Proceedings: Book of abstracts, ISBN: 978-3-900932-48-0Poster 223 p.435Virtual selection for comparing wheat breeding schemes at constant total cost. 13. International Wheat Genetics Symposiu