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Periodicals

AI Model Passport: Data and System Traceability Framework for Transparent AI in Health

The increasing integration of Artificial Intelligence (AI) into health and biomedical systems necessitates robust frameworks for transparency, accountability, and ethical compliance. Existing frameworks often rely on human-readable, manual documentation which limits scalability, comparability, and machine interpretability across projects and platforms. They also fail to provide a unique, verifiable identity for AI models to ensure their provenance and authenticity across systems and use cases, limiting reproducibility and stakeholder trust. This paper introduces the concept of the AI Model Passport, a structured and standardized documentation framework that acts as a digital identity and verification tool for AI models. It captures essential metadata to uniquely identify, verify, trace and monitor AI models across their lifecycle - from data acquisition and preprocessing to model design, development and deployment. In addition, an implementation of this framework is presented through AIPassport, an MLOps tool developed within the ProCAncer-I EU project for medical imaging applications. AIPassport automates metadata collection, ensures proper versioning, decouples results from source scripts, and integrates with various development environments. Its effectiveness is showcased through a lesion segmentation use case using data from the ProCAncer-I dataset, illustrating how the AI Model Passport enhances transparency, reproducibility, and regulatory readiness while reducing manual effort. This approach aims to set a new standard for fostering trust and accountability in AI-driven healthcare solutions, aspiring to serve as the basis for developing transparent and regulation compliant AI systems across domains.

Biotechnology• 2025• arXiv
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Periodicals

ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding

Understanding biological processes, drug development, and biotechnological advancements requires a detailed analysis of protein structures and functions, a task that is inherently complex and time-consuming in traditional protein research. To streamline this process, we introduce ProteinGPT, a state-of-the-art multimodal large language model for proteins that enables users to upload protein sequences and/or structures for comprehensive analysis and responsive inquiries. ProteinGPT integrates protein sequence and structure encoders with linear projection layers to ensure precise representation adaptation and leverages a large language model (LLM) to generate accurate, contextually relevant responses. To train ProteinGPT, we constructed a large-scale dataset of 132,092 proteins, each annotated with 20-30 property tags and 5-10 QA pairs per protein, and optimized the instruction-tuning process using GPT-4o. Experiments demonstrate that ProteinGPT effectively generates informative responses to protein-related questions, achieving high performance on both semantic and lexical metrics and significantly outperforming baseline models and general-purpose LLMs in understanding and responding to protein-related queries. Our code and data are available at https://github.com/ProteinGPT/ProteinGPT.

Biotechnology• 2024• arXiv
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Periodicals

Architecture for Health Initiative (Arch4Health): Computational Challenges in Health-Related Applications and the Role of Computer Architecture in Addressing Them

Recent biotechnological advances enable high-throughput, low-cost, and accurate biological data generation. This wealth of data enables unique opportunities for advancing healthcare. Despite these opportunities, efficiently analyzing large-scale biological data poses significant challenges for conventional computing systems. These systems often cannot keep up with the high-throughput rate at which data is generated, and they face additional constraints related to energy efficiency, scalability, privacy, and security. Therefore, to facilitate the wide adoption of recent advances in healthcare, there is a need to optimize the computing systems to enable high-performance, energy-efficient, low-cost, private, and secure analysis of biological data. We introduce the Architecture for Health (Arch4Health) initiative, which aims to (i) identify and analyze key computational challenges in current and future health- and life science-related applications and (ii) explore how computer architects and computing system designers can advance healthcare by addressing these challenges. In this short paper, we first present the motivations behind the Arch4Health initiative and, second, elaborate on its vision and goals, related topics, Arch4Health workshops, and future outlooks.

Biotechnology• 2026• arXiv
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Periodicals

Feature selection revisited in the single-cell era

Feature selection techniques are essential for high-dimensional data analysis. In the last two decades, their popularity has been fuelled by the increasing availability of high-throughput biomolecular data where high-dimensionality is a common data property. Recent advances in biotechnologies enable global profiling of various molecular and cellular features at single-cell resolution, resulting in large-scale datasets with increased complexity. These technological developments have led to a resurgence in feature selection research and application in the single-cell field. Here, we revisit feature selection techniques and summarise recent developments. We review their versatile application to a range of single-cell data types including those generated from traditional cytometry and imaging technologies and the latest array of single-cell omics technologies. We highlight some of the challenges and future directions on which feature selection could have a significant impact. Finally, we consider the scalability and make general recommendations on the utility of each type of feature selection method. We hope this review serves as a reference point to stimulate future research and application of feature selection in the single-cell era.

Biotechnology• 2021• arXiv
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Periodicals

An $\textit{in vivo}$ comparison study between strontium nanoparticles and rhBMP2

The osteoinductive property of strontium was repeatedly proven in the last decades. Compelling $\textit{in vitro}$ data demonstrated that strontium hydroxyapatite nanoparticles exert a dual action, by promoting osteoblasts-driven matrix secretion and inhibiting osteoclasts-driven matrix resorption. Recombinant human bone morphogenetic protein 2 (rhBMP2) is a powerful osteoinductive biologic, used for the treatment of vertebral fractures and critically-sized bone defects. Although effective, the use of rhBMP2 has limitations due its recombinant morphogen nature. In this study, we examined the comparison between two osteoinductive agents: rhBMP2 and the innovative strontium-substituted hydroxyapatite nanoparticles. To test their effectiveness, we independently loaded Gelfoam sponges with the two osteoinductive agents and used the sponges as agent-carriers. Gelfoam are FDA-approved biodegradable medical devices used as delivery system for musculoskeletal defects. Their porous structure and spongy morphology make them attractive in orthopedic field. The abiotic characterization of the loaded sponges, involving ion release pattern and structure investigation, was followed by $\textit{in vivo}$ implantation onto the periosteum of healthy mice and comparison of the effects induced by each implant was performed. The results demonstrated the use of sponges loaded with strontium nanoparticles as potential bone grafts might provide better outcomes for complex fractures. Strontium nanoparticles are a novel and effective non-biologic treatment for bone injuries and can be used as novel powerful therapeutics for bone regeneration.

Biotechnology• 2020• arXiv
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Periodicals

Osmolyte-Induced Protein Stability Changes Explained by Graph Theory

Enhanced stabilisation of protein structures via the presence of inert excipients is a key mechanism adopted both by physiological systems and in biotechnological applications. While the intrinsic stability of proteins is ultimately fixed by their amino acid composition and organisation, the interactions between excipients and proteins together with their concentrations introduce an additional layer of complexity and in turn, method of modulating protein stability. Here, we combined experimental measurements with molecular dynamics simulations and graph-theory based analyses to assess the stabilising/destabilising effects of different kinds of osmolytes on proteins during heat-mediated denaturation. We found that (i) proteins in solution with stability-enhancing osmolytes tend to have more compact interaction networks than those assumed in presence of destabilising excipients; (ii) a strong negative correlation (R = -0.85) characterises the relationship between the melting temperature Tm and the preferential interaction coefficient defined by the radial distribution functions of osmolytes and water around the protein and (iii) a positive correlation exists between osmolyte-osmolyte clustering and the extent of preferential exclusion from the local domain of the protein, suggesting that exclusion may be driven by enhanced steric hindrance of aggregated osmolytes.

Biotechnology• 2023• arXiv
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Periodicals

A planar lipid bilayer in an electric field: membrane instability, flow field and electrical impedance

For many biotechnological applications it would be useful to better understand the effects produced by electric fields on lipid membranes. This review discusses several aspects of the electrostatic properties of a planar lipid membrane with its surrounding electrolyte in a normal DC or AC electric field. In the planar geometry, the analysis of electrokinetic equations can be carried out quite far, allowing to characterize analytically the steady state and the dynamics of the charge accumulation in the Debye layers, which results from the application of the electric field. For a conductive membrane in an applied DC electric field, we characterize the corrections to the elastic moduli, the appearance of a membrane undulation instability and the associated flows which are built up near the membrane. For a membrane in an applied AC electric field, we analytically derive the impedance from the underlying electrokinetic equations. We discuss different relevant effects due to the membrane conductivity or due to the bulk diffusion coefficients of the ions. Of particular interest is the case where the membrane has selective conductivity for only one type of ion. These results, and future extensions thereof, should be useful for the interpretation of impedance spectroscopy data used to characterize e.g. ion channels embedded in planar bilayers.

Biotechnology• 2011• arXiv
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Periodicals

Resonant Gold Nanoparticles Achieve Plasmon-Enhanced Pan-Microbial Pathogen Inactivation in the Shockwave Regime

Pan-microbial inactivation technologies that do not require high temperatures, reactive chemical compounds, or UV radiation could address gaps in current infection control strategies and provide efficient sterilization of biologics in the biotechnological industry. Here, we demonstrate that femtosecond (fs) laser irradiation of resonant gold nanoparticles (NPs) under conditions that allow for E-field mediated cavitation and shockwave generation achieve an efficient plasmon-enhanced photonic microbial pathogen inactivation. We demonstrate that this NP-enhanced, physical inactivation approach is effective against a diverse group of pathogens, including both enveloped and non-enveloped viruses, and a variety of bacteria and mycoplasma. Photonic inactivation is wavelength-dependent and in the absence of plasmonic enhancement from NPs, negligible levels of microbial inactivation are observed in the near-infrared (NIR) at 800 nm. This changes upon addition of resonant plasmonic NPs, which provide a strong enhancement of inactivation of viral and bacterial contaminants. Importantly, the plasmon-enhanced 800 nm femtosecond (fs)-pulse induced inactivation was selective to pathogens. No measurable damage was observed for antibodies included as representative biologics under identical conditions.

Biotechnology• 2018• arXiv
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Periodicals

Alveolar mimics with periodic strain and its effect on the cell layer formation

We report on the development of a new model of alveolar air-tissue interface on a chip. The model consists of an array of suspended hexagonal monolayers of gelatin nanofibers supported by microframes and a microfluidic device for the patch integration. The suspended monolayers are deformed to a central displacement of 40-80 um at the air-liquid interface by application of air pressure in the range of 200-1000 Pa. With respect to the diameter of the monolayers that is 500 um, this displacement corresponds to a linear strain of 2-10% in agreement with the physiological strain range in the lung alveoli. The culture of A549 cells on the monolayers for an incubation time 1-3 days showed viability in the model. We exerted a periodic strain of 5% at a frequency of 0.2 Hz during 1 hour to the cells. We found that the cells were strongly coupled to the nanofibers, but the strain reduced the coupling and induced remodeling of the actin cytoskeleton, which led to a better tissue formation. Our model can serve as a versatile tool in lung investigations such as in inhalation toxicology and therapy.

Biotechnology• 2020• arXiv
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Periodicals

Global analysis of VHHs framework regions with a structural alphabet

The VHHs are antigen-binding region/domain of camelid heavy chain antibodies (HCAb). They have many interesting biotechnological and biomedical properties due to their small size, high solubility and stability, and high affinity and specificity for their antigens. HCAb and classical IgGs are evolutionary related and share a common fold. VHHs are composed of regions considered as constant, called the frameworks (FRs) connected by Complementarity Determining Regions (CDRs), a highly variable region that provide interaction with the epitope. Actually, no systematic structural analyses had been performed on VHH structures despite a significant number of structures. This work is the first study to analyse the structural diversity of FRs of VHHs. Using a structural alphabet that allows approximating the local conformation, we show that each of the four FRs do not have a unique structure but exhibit many structural variant patterns. Moreover, no direct simple link between the local conformational change and amino acid composition can be detected. These results indicate that long-range interactions affect the local conformation of FRs and impact the building of structural models.

Biotechnology• 2016• arXiv
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Periodicals

Yield Trajectory Tracking for Hyperbolic Age-Structured Population Systems

For population systems modeled by age-structured hyperbolic partial differential equations (PDEs) that are bilinear in the input and evolve with a positive-valued infinite-dimensional state, global stabilization of constant yield set points was achieved in prior work. Seasonal demands in biotechnological production processes give rise to time-varying yield references. For the proposed control objective aiming at a global attractivity of desired yield trajectories, multiple non-standard features have to be considered: a non-local boundary condition, a PDE state restricted to the positive orthant of the function space and arbitrary restrictive but physically meaningful input constraints. Moreover, we provide Control Lyapunov Functionals ensuring an exponentially fast attraction of adequate reference trajectories. To achieve this goal, we make use of the relation between first-order hyperbolic PDEs and integral delay equations leading to a decoupling of the input-dependent dynamics and the infinite-dimensional internal one. Furthermore, the dynamic control structure does not necessitate exact knowledge of the model parameters or online measurements of the age-profile. With a Galerkin-based numerical simulation scheme using the key ideas of the Karhunen-Loève-decomposition, we demonstrate the controller's performance.

Biotechnology• 2017• arXiv
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Periodicals

Spectral Kinetic Modeling and Long-term Behavior Assessment of Arthrospira platensis Growth in Photobioreactor under Red (620 nm) Light Illumination

The ability to cultivate the cyanobacterium Arhtrospira platensis in artificially lightened photobioreactors using high energetic efficiency (quasi-monochromatic) red LED was investigated. In order to reach the same maximal productivities as with the polychromatic lightening control conditions (red + blue, P/2e- = 1.275), the need to work with an optimal range of wavelength around 620 nm was first established on batch and continuous cultures. The long-term physiological and kinetic behavior was then verified in a continuous photobioreactor illuminated only with red (620 nm) LED, showing that the maximum productivities can be maintained over 30 residence times with only minor changes in the pigment content of the cells corresponding to a well-known adaptation mechanism of the photosystems, but without any effect on growth and stoichiometry. For both poly and monochromatic incident light inputs, a predictive spectral knowledge model was proposed and validated for the first time, allowing the calculation of the kinetics and stoichiometry observed in any photobioreactor cultivating A. platensis, or other cyanobacteria if the parameters were updated. It is shown that the photon flux (with a specified wavelength) must be used instead of light energy flux as a relevant control variable for the growth. The experimental and theoretical results obtained in this study demonstrate that it is possible to save the energy consumed by the lightening device of photobioreactors using red LED, the spectral range of which is defined according to the action spectrum of photosynthesis. This appears to be crucial information for applications in which the energy must be rationalized, as it is the case for life support systems in closed environments like a permanent spatial base or a submarine.

Biotechnology• 2020• arXiv
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Periodicals

Molecular techniques employed in CTG(Ser1) and CTG(Ala) D-xylose metabolizing yeast clades for strain design and industrial applications

D-xylose is the second most abundant monosaccharide found in lignocellulose and is of biotechnological importance for producing second-generation ethanol and other high-value chemical compounds. D-xylose conversion to ethanol is promoted by microbial fermentation, mainly by bacteria, yeasts, or filamentous fungi. Considering yeasts, species belonging to the CTG(Ser1) or CTG(Ala) clade display a remarkable ability to ferment D-xylose to ethanol and other compounds; however, these yeasts are not employed on an industrial scale due to the poor fermentative performance compared to conventional yeasts, like Saccharomyces cerevisiae, and also due to the lack of a molecular toolbox for development of new strains tailored to fermentation stress tolerance and performance. Thus, the purpose of this review is to evaluate the major molecular tools (e.g., transformation markers and techniques, vectors, regulatory sequences, and gene editing techniques) available for the most studied yeasts of CTG(Ser1) clade, like Scheffersomyces, Spathaspora, Candida and Yamadazyma species, and the CTG(Ala) clade representative Pachysolen tannophilus. Furthermore, we synthesized the current state-of-the-art molecular developments and perspectives for D-xylose fermenting yeast strain design.

Biotechnology• 2024• arXiv
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Periodicals

Fermentation kinetics including product and substrate inhibitions plus biomass death: a mathematical analysis

Fermentation is generally modelled by kinetic equations giving the time evolutions for biomass, substrate, and product concentrations. Although these equations can be solved analytically in simple cases if substrate/product inhibition and biomass death are included, they are typically solved numerically. We propose an analytical treatment of the kinetic equations --including cell death and an arbitrary number of inhibitions-- in which constant yield needs not be assumed. Equations are solved in phase space, i.e. the biomass concentration is written explicitly as a function of the substrate concentration.

Biotechnology• 2005• arXiv
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Periodicals

SBSM-Pro: Support Bio-sequence Machine for Proteins

Proteins play a pivotal role in biological systems. The use of machine learning algorithms for protein classification can assist and even guide biological experiments, offering crucial insights for biotechnological applications. We introduce the Support Bio-Sequence Machine for Proteins (SBSM-Pro), a model purpose-built for the classification of biological sequences. This model starts with raw sequences and groups amino acids based on their physicochemical properties. It incorporates sequence alignment to measure the similarities between proteins and uses a novel multiple kernel learning (MKL) approach to integrate various types of information, utilizing support vector machines for classification prediction. The results indicate that our model demonstrates commendable performance across ten datasets in terms of the identification of protein function and posttranslational modification. This research not only exemplifies state-of-the-art work in protein classification but also paves avenues for new directions in this domain, representing a beneficial endeavor in the development of platforms tailored for the classification of biological sequences. SBSM-Pro is available for access at http://lab.malab.cn/soft/SBSM-Pro/.

Biotechnology• 2023• arXiv
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Periodicals

A probabilistic analysis of the Fermi paradox in terms of the Drake formula: the role of the L factor

In evaluating the number of technological civilizations N in the Galaxy through the Drake formula, emphasis is mostly put on the astrophysical and biotechnological factors describing the emergence of a civilization and much less on its the lifetime, which is intimately related to its demise. It is argued here that this factor is in fact the most important regarding the practical implications of the Drake formula, because it determines the maximal extent of the "sphere of influence" of any technological civilization. The Fermi paradox is studied in the terms of a simplified version of the Drake formula, through Monte Carlo simulations of N civilizations expanding in the Galaxy during their space faring lifetime L. In the framework of that scheme, the probability of "direct contact" is determined as the fraction of the Galactic volume occupied collectively by the "spheres of influence" of N civilizations. The results of the analysis are used to determine regions in the parameter space where the Fermi paradox holds. It is argued that in a large region of the diagram the corresponding parameters suggest rather a "weak" Fermi paradox. Future research may reveal whether a "strong" paradox holds in some part of the parameter space. Finally, it is argued that the value of N is not bound by N=1 from below, contrary to what is usually assumed, but it may have a statistical interpretation.

Biotechnology• 2020• arXiv
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Periodicals

A bioreactor-based architecture for in vivo model-based and sim-to-real learning control of microbial consortium composition

Microbial consortia offer significant biotechnological advantages over monocultures for bioproduction. However, industrial deployment is hampered by the lack of scalable architectures to ensure stable coexistence between populations. Existing strategies rely on genetic modifications, which impose metabolic load, or environmental changes, which can reduce production. We present a versatile control architecture to regulate density and composition of a two-strain consortium without genetic engineering or drastic environmental changes. Our bioreactor-based control architecture comprises a mixing chamber where both strains are co-cultured and a reservoir sustaining the slower-growing strain. For both chambers we develop model-based and sim-to-real learning controllers. The control architecture is then validated in vivo on a two-strain Escherichia coli consortium, achieving precise and robust regulation of consortium density and composition, including tracking of time-varying references and recovery from perturbations.

Biotechnology• 2025• arXiv
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Periodicals

Exploring the world of rhamnolipids: A critical review of their production, interfacial properties, and potential application

Rhamnolipids are very promising sugar-based biosurfactants, generally produced by bacteria, with a wide range of properties that can be exploited at an industrial and technological level, e.g. in cosmetics, food science, or oil recovery, to provide benefits for human health and the environment. This has led to intensive research into optimizing their production to increase yields and minimize costs, which is challenging because biotechnological methods for rhamnolipid production result in complex product mixtures and require the introduction of complex separation strategies to ensure the purity of the rhamnolipid obtained. This is an important issue for the introduction of rhamnolipids to the market due to the differences that exist between the properties of the different congeners. This review attempts to provide an overview of the interfacial properties, potential applications, and recent advances in understanding the molecular mechanisms that govern the adsorption to interfaces and assembly in solution of rhamnolipids. In addition, the review also discusses some general aspects related to the production and purification methods of rhamnolipids, highlighting the need for further research to fully exploit their potential. It is hoped that this review will contribute to the growing body of knowledge about rhamnolipids and stimulate further research in this field.

Biotechnology• 2025• arXiv
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Periodicals

Nanopore Fabrication by Controlled Dielectric Breakdown

Nanofabrication techniques for achieving dimensional control at the nanometer scale are generally equipment-intensive and time-consuming. The use of energetic beams of electrons or ions has placed the fabrication of nanopores in thin solid-state membranes within reach of some academic laboratories, yet these tools are not accessible to many researchers and are poorly suited for mass-production. Here we describe a fast and simple approach for fabricating a single nanopore down to 2-nm in size with sub-nm precision, directly in solution, by controlling dielectric breakdown at the nanoscale. The method relies on applying a voltage across an insulating membrane to generate a high electric field, while monitoring the induced leakage current. We show that nanopores fabricated by this method produce clear electrical signals from translocating DNA molecules. Considering the tremendous reduction in complexity and cost, we envision this fabrication strategy would not only benefit researchers from the physical and life sciences interested in gaining reliable access to solid-state nanopores, but may provide a path towards manufacturing of nanopore-based biotechnologies.

Biotechnology• 2013• arXiv
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Periodicals

A Simple and Reliable Formula for Assessment of Maximum Volumetric Productivities in Photobioreactors

This paper establishes and discusses the consistency and the range of applicability of a simple, but general and predictive analytical formula, enabling to easily assess the maximum volumetric biomass growth rates (the productivities) in several kinds of photobioreactors with more or less 15 percents of deviation. Experimental validations are performed on photobioreactors of very different conceptions and designs, cultivating the cyanobacterium Arthrospira platensis, on a wide range of volumes and hemispherical incident light fluxes. The practical usefulness of the proposed formula is demonstrated by the fact that it appears completely independent of the characteristics of the material phase (as the type of reactor, the kind of mixing, the biomass concentration), according to the first principle of thermodynamics and to the Gauss-Ostrogradsky theorem. Its ability to give the maximum (only) kinetic performance of photobioreactors cultivating many different photoautotrophic strains (cyanobacteria, green algae, eukaryotic microalgae) is theoretically discussed but experimental results are reported to a future work of the authors or to any other contribution arising from the scientific community working in the field of photobioreactor engineering and potentially interested by this approach.

Biotechnology• 2020• arXiv
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Closing the gap between research and projects in climate change innovation in Europe

Innovation is a key component to equip our society with tools to adapt to new climatic conditions. The development of research-action interfaces shifts useful ideas into operationalized knowledge allowing innovation to flourish. In this paper we quantify the existing gap between climate research and innovation action in Europe using a novel framework that combines artificial intelligence (AI) methods and network science. We compute the distance between key topics of research interest from peer review publications and core issues tackled by innovation projects funded by the most recent European framework programmes. Our findings reveal significant differences exist between and within the two layers. Economic incentives, agricultural and industrial processes are differently connected to adaptation and mitigation priorities. We also find a loose research-action connection in bioproducts, biotechnologies and risk assessment practices, where applications are still too few compared to the research insights. Our analysis supports policy-makers to measure and track how research funding result in innovation action, and to adjust decisions if stated priorities are not achieved.

Biotechnology• 2023• arXiv
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Periodicals

The Light Quanta Modulated Physiological Response of Brassica Juncea Seedlings Subjected to Ni(II) Stress

This work is a study of the inter-relationship between parameters that principally affect metal up-take in the plant. The relationships between the concentration of metal in the growth medium, Cs, the concentration of metal absorbed by the plant, Cp, and the total biomass achieved, M, all of which are factors relevant to the efficiency of phytoremediation of the plant, have been investigated via the macro-physiological response of Brassica juncea seedlings to Ni(II) stress. The factorial growth experiments treated the Ni(II) concentration in the agar gel and the diurnal light quanta (DLQ) as independently variable parameters. Observations included the evidence of light enhancement of Ni toxicity at the root as well as at the whole plant level, the shoot mass index as a possible indicator of shoot metal sequestration in B. juncea, the logarithmic variation of Cp with Cs and the power-law dependence of M on Cp. The sum total of these observations indicate that for the metal accumulator B. juncea with regard to its capacity to accumulate Ni, the overall metabolic nature of the plant is important; neither rapid biomass increase nor a high metal concentration capability favor the removal of high metal mass from the medium, but rather the plant with the moderate photosynthetically driven biomass growth and moderate metal concentrations demonstrated the ability to remove the maximum mass of metal from the medium. The implications of these observations in the context of the perceived need in phytoremediation engineering to maximize Cp and M simultaneously in the same plant, are discussed.

Biotechnology• 2007• arXiv
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Periodicals

Analysis of the first Genetic Engineering Attribution Challenge

The ability to identify the designer of engineered biological sequences -- termed genetic engineering attribution (GEA) -- would help ensure due credit for biotechnological innovation, while holding designers accountable to the communities they affect. Here, we present the results of the first Genetic Engineering Attribution Challenge, a public data-science competition to advance GEA. Top-scoring teams dramatically outperformed previous models at identifying the true lab-of-origin of engineered sequences, including an increase in top-1 and top-10 accuracy of 10 percentage points. A simple ensemble of prizewinning models further increased performance. New metrics, designed to assess a model's ability to confidently exclude candidate labs, also showed major improvements, especially for the ensemble. Most winning teams adopted CNN-based machine-learning approaches; however, one team achieved very high accuracy with an extremely fast neural-network-free approach. Future work, including future competitions, should further explore a wide diversity of approaches for bringing GEA technology into practical use.

Biotechnology• 2021• arXiv
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Green reconstruction of MIL-100 (Fe) in water for high crystallinity and enhanced guest encapsulation

MIL-100 (Fe) is a highly porous metal-organic framework (MOF), considered as a promising carrier for drug delivery, and for gas separation and capture applications. However, this functional material suffers from elaborated and toxic synthesis that may hinder its biomedical use and large-scale production to afford commercial applications. Herein, we report a green mechanochemical water immersion approach to yield highly crystalline MIL100 (Fe) material. Subsequently, we have harnessed this strategy for facile fabrication of drug@MOF composite systems, comprising (guests) 5-fluorouracil, caffeine, or aspirin encapsulated in the pores of (host) MIL-100 (Fe). Inelastic neutron scattering was uniquely used to probe the guest host interactions arising from pore confinement of the drug molecules, giving additional insights into the reconstruction mechanism. Our results pave the way to the green production of MIL type materials and bespoke guest-encapsulated composites by minimizing the use of toxic chemicals, whilst enhancing energy efficiency and material's life cycle central to biotechnological applications.

Biotechnology• 2021• arXiv
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