P. Spolaore, Claire Joannis‐Cassan, E. Duran
et al.
The first use of microalgae by humans dates back 2000 years to the Chinese, who used Nostoc to survive during famine. However, microalgal biotechnology only really began to develop in the middle of the last century. Nowadays, there are numerous commercial applications of microalgae. For example, (i) microalgae can be used to enhance the nutritional value of food and animal feed owing to their chemical composition, (ii) they play a crucial role in aquaculture and (iii) they can be incorporated into cosmetics. Moreover, they are cultivated as a source of highly valuable molecules. For example, polyunsaturated fatty acid oils are added to infant formulas and nutritional supplements and pigments are important as natural dyes. Stable isotope biochemicals help in structural determination and metabolic studies. Future research should focus on the improvement of production systems and the genetic modification of strains. Microalgal products would in that way become even more diversified and economically competitive.
Imaging transparent samples remains an ongoing challenge in the study of unstained biological cells and material samples. Widely used methods trade off system complexity, cost and bulk, computational efficiency and information content. Here we demonstrate the use of a nonlocal metasurface located in the object plane for obtaining single-shot, low-noise differential phase contrast images visualising phase gradients along orthogonal directions in a sample obtained at wavelengths of 613 nm and 656 nm. Furthermore, we show that these images are sufficient to calculate the quantitative phase introduced into the transmitted optical field by the sample. We find that the recovered phase of an optical field generated by a spatial light modulator is in good agreement with specified values. We also present information-rich differential phase contrast images of unstained HeLa cells with the recovered phase excursion values consistent with the literature. Our results demonstrate the potential for metasurfaces as a platform for extracting information from an optical field for use in next-generation compact imaging systems with applications in medical diagnostics, biotechnology, and materials science.
The Ribonucleic Acid (RNA) inverse folding problem, designing nucleotide sequences that fold into specific tertiary structures, is a fundamental computational biology problem with important applications in synthetic biology and bioengineering. The design of complex three-dimensional RNA architectures remains computationally demanding and mostly unresolved, as most existing approaches focus on secondary structures. In order to address tertiary RNA inverse folding, we present BeeRNA, a bio-inspired method that employs the Artificial Bee Colony (ABC) optimization algorithm. Our approach combines base-pair distance filtering with RMSD-based structural assessment using RhoFold for structure prediction, resulting in a two-stage fitness evaluation strategy. To guarantee biologically plausible sequences with balanced GC content, the algorithm takes thermodynamic constraints and adaptive mutation rates into consideration. In this work, we focus primarily on short and medium-length RNAs ($<$ 100 nucleotides), a biologically significant regime that includes microRNAs (miRNAs), aptamers, and ribozymes, where BeeRNA achieves high structural fidelity with practical CPU runtimes. The lightweight, training-free implementation will be publicly released for reproducibility, offering a promising bio-inspired approach for RNA design in therapeutics and biotechnology.
Control of magnetism through voltage-driven ionic processes (i.e., magneto-ionics) holds potential for next-generation memories and computing. This stems from its non-volatility, flexibility in adjusting the magnitude and speed of magnetic modulation, and energy efficiency. Since magneto-ionics depends on factors like ionic radius and electronegativity, identifying alternative mobile ions is crucial to embrace new phenomena and applications. Here, the feasibility of C as a prospective magneto-ionic ion is investigated in a Fe-C system by electrolyte gating. In contrast to most magneto-ionic systems, Fe-C presents a dual-ion mechanism: Fe and C act as cation and anion, respectively, moving uniformly in opposite directions under an applied electric field. This leads to a 7-fold increase in saturation magnetization with magneto-ionic rates larger than 1 emu cm-3 s-1, and a 25-fold increase in coercivity. Since carbides exhibit minimal cytotoxicity, this introduces a biocompatible dimension to magneto-ionics, paving the way for the convergence of spintronics and biotechnology.
DNA is an attractive candidate for data storage. Its millennial durability and nanometer scale offer exceptional data density and longevity. Its relevance to medical applications also drives advances in DNA-related biotechnology. To protect our data against errors, a straightforward approach uses one error-correcting code per DNA strand, with a Reed--Solomon code protecting the collection of strands. A downside is that current technology can only synthesize strands 200--300 nucleotides long. At this block length, the inner code rate suffers a significant finite-length penalty, making its effective capacity hard to characterize. Last year, we proposed $\textit{Geno-Weaving}$ in a JSAIT publication. The idea is to protect the same position across multiple strands using one code; this provably achieves capacity against substitution errors. In this paper, we extend the idea to combat deletion errors and show two more advantages of Geno-Weaving: (1) Because the number of strands is 3--4 orders of magnitude larger than the strand length, the finite-length penalty vanishes. (2) At realistic deletion rates $0.1\%$--$10\%$, Geno-Weaving designed for BSCs works well empirically, bypassing the need to tailor the design for deletion channels.
Shuang Zhang, Carleton Coffin, Karyn L. Rogers
et al.
Studying the growth and metabolism of microbes provides critical insights into their evolutionary adaptations to harsh environments, which are essential for microbial research and biotechnology applications. In this study, we developed an AI-driven image analysis system to efficiently segment individual cells and quantitatively analyze key cellular features. This system is comprised of four main modules. First, a denoising algorithm enhances contrast and suppresses noise while preserving fine cellular details. Second, the Segment Anything Model (SAM) enables accurate, zero-shot segmentation of cells without additional training. Third, post-processing is applied to refine segmentation results by removing over-segmented masks. Finally, quantitative analysis algorithms extract essential cellular features, including average intensity, length, width, and volume. The results show that denoising and post-processing significantly improved the segmentation accuracy of SAM in this new domain. Without human annotations, the AI-driven pipeline automatically and efficiently outlines cellular boundaries, indexes them, and calculates key cellular parameters with high accuracy. This framework will enable efficient and automated quantitative analysis of high-resolution fluorescence microscopy images to advance research into microbial adaptations to grow and metabolism that allow extremophiles to thrive in their harsh habitats.
Abstract The physicochemical properties of oral drug delivery carriers significantly influence their interactions with the intestinal barrier, which, in turn, affect their in vivo fate and therapeutic efficacy. As a delivery system with a hierarchical structure and high stability, Pickering emulsions have demonstrated significant advantages in enhancing the bioavailability of encapsulated bioactive compounds. However, the specific role of their inherent flexibility in modulating interactions with the intestinal barrier and influencing delivery efficiency remains poorly understood. In this study, a flexible Pickering emulsion (FPPE) stabilized by chitosan nanoparticles was developed for encapsulating epigallocatechin gallate, aiming to investigate the role and underlying mechanism of its flexible structure in mucus penetration, cellular uptake, and biodistribution in the intestinal tract. Upon reaching the intestinal tissue, FPPE exhibited deformable adaptation, facilitating efficient mucus permeation, prolonged gastrointestinal retention, and enhanced drug absorption. Moreover, FPPE enhanced macrophage uptake and mitochondrial membrane potential, reduced reactive oxygen species scavenging, and triggered macrophage reprogramming. Compared to rigid chitosan microspheres, FPPE more effectively alleviated intestinal inflammation and promoted intestinal barrier repair in a dextran sulfate sodium-induced acute colitis mouse model. This study reveals how Pickering emulsion flexibility facilitates intestinal mucus delivery, guiding their future design and application. Graphical Abstract
Stefanus Renaldi Wijaya, Augusto Martins, Katie Morris
et al.
The detection of low-molecular-weight biomarkers is essential for diagnosing and managing various diseases, including neurodegenerative conditions such as Alzheimer’s disease. A biomarker’s low molecular weight is a challenge for label-free optical modalities, as the phase change they detect is directly proportional to the mass bound on the sensor’s surface. To address this challenge, we used a resonant Young’s slit interferometer geometry and implemented several innovations, such as phase noise matching and optimisation of the fringe spacing, to maximise the signal-to-noise ratio. As a result, we achieved a limit of detection of 2.9 × 10<sup>−6</sup> refractive index units (RIU). We validated our sensor’s low molecular weight capability by demonstrating the detection of Aβ-42, a 4.5 kDa peptide indicative of Alzheimer’s disease, and reached the clinically relevant pg/mL regime. This system builds on the guided mode resonance modality we previously showed to be compatible with handheld operation using low-cost components. We expect this development will have far-reaching applications beyond Aβ-42 and become a workhorse tool for the label-free detection of low-molecular-weight biomarkers across a range of disease types.
Carola M. Buness, Avi Rana, Corinna C. Maass
et al.
Ciliated microswimmers and flagellated bacteria alter their swimming trajectories to follow the direction of an applied electric field exhibiting electrotaxis. Both for matters of application and physical modelling, it is instructive to study such behaviour in synthetic swimmers. We show here that under an external electric field, self-propelling active droplets autonomously modify their swimming trajectories in microchannels, even undergoing `U-turns', to exhibit robust electrotaxis. Depending on the relative initial orientations of the microswimmer and the external electric field, the active droplet can also navigate upstream of an external flow following a centre-line motion, instead of the oscillatory upstream trajectory observed in absence of electric field. Using a hydrodynamic theory model, we show that the electrically induced angular velocity and electrophoretic effects, along with the microswimmer motility and its hydrodynamic interactions with the microchannel walls, play crucial roles in dictating the electrotactic trajectories and dynamics. Specifically, the transformation in the trajectories during upstream swimming against an external flow under an electric field can be understood as a reverse Hopf bifurcation for a dynamical system. Our study provides a simple methodology and a systematic understanding of manoeuvring active droplets in microconfinements for micro-robotic applications especially in biotechnology.