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Periodicals

Silanization Strategies for Tailoring Peptide Functionalization on Silicon Surfaces: Implications for Enhancing Stem Cell Adhesion

Biomaterial surface engineering and integrating cell-adhesive ligands are crucial in biological research and biotechnological applications. The interplay between cells and their microenvironment, influenced by chemical and physical cues, impacts cellular behavior. Surface modification of biomaterials profoundly affects cellular responses, especially at the cell-surface interface. This work focuses on enhancing cellular activities through material manipulation, emphasizing silanization for further functionalization with bioactive molecules like RGD peptides to improve cell adhesion. The grafting of three distinct silanes onto silicon wafers using both spin coating and immersion methods was investigated. This study sheds light on the effects of different alkyl chain lengths and protecting groups on cellular behavior, providing valuable insights into optimizing silane-based self-assembled monolayers (SAMs) before peptide or protein grafting for the first time. Specifically, it challenges the common use of APTES molecules in this context. These findings advance our understanding of surface modification strategies, paving the way for tailoring biomaterial surfaces to modulate cellular behavior for diverse biotechnological applications.

Biotechnology• 2024• arXiv
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Periodicals

Deep metric learning improves lab of origin prediction of genetically engineered plasmids

Genome engineering is undergoing unprecedented development and is now becoming widely available. To ensure responsible biotechnology innovation and to reduce misuse of engineered DNA sequences, it is vital to develop tools to identify the lab-of-origin of engineered plasmids. Genetic engineering attribution (GEA), the ability to make sequence-lab associations, would support forensic experts in this process. Here, we propose a method, based on metric learning, that ranks the most likely labs-of-origin whilst simultaneously generating embeddings for plasmid sequences and labs. These embeddings can be used to perform various downstream tasks, such as clustering DNA sequences and labs, as well as using them as features in machine learning models. Our approach employs a circular shift augmentation approach and is able to correctly rank the lab-of-origin $90\%$ of the time within its top 10 predictions - outperforming all current state-of-the-art approaches. We also demonstrate that we can perform few-shot-learning and obtain $76\%$ top-10 accuracy using only $10\%$ of the sequences. This means, we outperform the previous CNN approach using only one-tenth of the data. We also demonstrate that we are able to extract key signatures in plasmid sequences for particular labs, allowing for an interpretable examination of the model's outputs.

Biotechnology• 2021• arXiv
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Periodicals

Leveraging Deep Generative Model For Computational Protein Design And Optimization

Proteins are the fundamental macromolecules that play diverse and crucial roles in all living matter and have tremendous implications in healthcare, manufacturing, and biotechnology. Their functions are largely determined by the sequences of amino acids that compose them and their unique three-dimensional structures when folded. The recent surge in highly accurate computational protein structure prediction tools has equipped scientists with the means to derive preliminary structural insights without the onerous costs of experimental structure determination. These breakthroughs hold profound promise for building robust and efficient in silico protein design systems. While the prospect of designing de novo proteins with precise computational accuracy remains a grand challenge in biochemical engineering, conventional assembly-based and rational design methods often grapple with the expansive design space, resulting in suboptimal design success rates. Despite recently emerged deep learning-based models have shown promise in improving the efficiency of the computational protein design process, a significant gap persists between current design paradigms and their experimental realization. This thesis will investigate the potential of deep generative models in refining protein structure and sequence design methods, aiming to develop frameworks capable of crafting novel protein sequences with predetermined structures or specific functionalities. By harnessing extensive protein databases and cutting-edge neural architectures, this research aims to enhance precision and robustness in current protein design paradigms, potentially paving the way for advancements across various scientific fields.

Biotechnology• 2024• arXiv
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Periodicals

Static magnetic field stimulates growth of maize seeds

The physical pre-treatment of seeds is a growing field of research in agricultural biotechnology. Among its possibilities, magnetopriming of seeds has received increasing attention in the last two decades. Inspired by remarkable reports of the literature on the effects of static magnetic fields (SMF) on maize seeds, we performed similar experiments, though expanding the range of SMF up to 350 mT, higher than most of the reported studies. With exposure durations of 1 h, we tested 7 different SMF intensities, from 50 to 350 mT at increments of 50 mT. We challenged our findings with an exhaustive analysis of the background static and alternated magnetic fields inside the stove where the germinations took place, in order to rule out background fields as a confounding variable. We found a maximum effect of 108.9 % increase (more than double) in the average total length of plantules at 150 mT, at the 10th day of germination. All other intensities, except 350 mT, also induced a significant growth stimulation. While not seeming to represent a determinant factor, our analysis calls the attention to the possible relevance of different conditions in different levels and zones of standard stoves. Our findings are in line with numerous studies pointing to magnetopriming of maize seeds as a novel, viable and reproducible physical treatment for the enhancement of germination.

Biotechnology• 2023• arXiv
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Periodicals

Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation

Motivation: Innovative microfluidic systems carry the promise to greatly facilitate spatio-temporal analysis of single cells under well-defined environmental conditions, allowing novel insights into population heterogeneity and opening new opportunities for fundamental and applied biotechnology. Microfluidics experiments, however, are accompanied by vast amounts of data, such as time series of microscopic images, for which manual evaluation is infeasible due to the sheer number of samples. While classical image processing technologies do not lead to satisfactory results in this domain, modern deep learning technologies such as convolutional networks can be sufficiently versatile for diverse tasks, including automatic cell tracking and counting as well as the extraction of critical parameters, such as growth rate. However, for successful training, current supervised deep learning requires label information, such as the number or positions of cells for each image in a series; obtaining these annotations is very costly in this setting. Results: We propose a novel Machine Learning architecture together with a specialized training procedure, which allows us to infuse a deep neural network with human-powered abstraction on the level of data, leading to a high-performing regression model that requires only a very small amount of labeled data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.

Biotechnology• 2020• arXiv
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Periodicals

Complex Dynamics of Real Quantum, Classical and Hybrid Micro-Machines

Any real interaction process produces many incompatible system versions, or realisations, giving rise to omnipresent dynamic randomness and universally defined complexity (arXiv:physics/9806002). Since quantum behaviour dynamically emerges as the lowest complexity level (arXiv:quant-ph/9902016), quantum interaction randomness can only be relatively strong, which reveals the causal origin of quantum indeterminacy (arXiv:quant-ph/9511037) and true quantum chaos (arXiv:quant-ph/9511035), but rigorously excludes the possibility of unitary quantum computation, even in an "ideal", noiseless system. Any real computation is an internally chaotic (multivalued) process of system complexity development occurring in different regimes. Unitary quantum machines, including their postulated "magic", cannot be realised as such because their dynamically single-valued scheme is incompatible with the irreducibly high dynamic randomness at quantum complexity levels and should be replaced by explicitly chaotic, intrinsically creative machines already realised in living organisms and providing their quite different, realistic kind of magic. The related concepts of reality-based, complex-dynamical nanotechnology, biotechnology and intelligence are outlined, together with the ensuing change in research strategy. The unreduced, dynamically multivalued solution to the many-body problem reveals the true, complex-dynamical basis of solid-state dynamics, including the origin and internal dynamics of macroscopic quantum states. The critical, "end-of-science" state of unitary knowledge and the way to positive change are causally specified within the same, universal concept of complexity.

Biotechnology• 2002• arXiv
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Periodicals

Design of Outdoor Autonomous Moble Robot

This study presents the design of a six-wheeled outdoor autonomous mobile robot. The main design goal of our robot is to increase its adaptability and flexibility when moving outdoors. This six-wheeled robot platform was equipped with some sensors, such as a global positioning system (GPS), high definition (HD) webcam, light detection and ranging (LiDAR), and rotary encoders. A personal mobile computer and 86Duino ONE microcontroller were used as the algorithm computing platform. In terms of control, the lateral offset and head angle offset of the robot were calculated using a differential GPS or a camera to detect structured and unstructured road boundaries. The lateral offset and head angle offset were fed to a fuzzy controller. The control input was designed by Q-learning of the differential speed between the left and right wheels. This made the robot track a reference route so that it could stay in its own lane. 2D LiDAR was also used to measure the relative distance from the front obstacle. The robot would immediately stop to avoid a collision when the distance between the robot and obstacle was less than a specific safety distance. A custom-designed rocker arm gave the robot the ability to climb a low step. Body balance could be maintained by controlling the angle of the rocker arm when the robot changed its pose. The autonomous mobile robot has been used for delivery service on our campus road by integrating the above system functionality.

Biotechnology• 2022• arXiv
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Periodicals

Learning to Design RNA

Designing RNA molecules has garnered recent interest in medicine, synthetic biology, biotechnology and bioinformatics since many functional RNA molecules were shown to be involved in regulatory processes for transcription, epigenetics and translation. Since an RNA's function depends on its structural properties, the RNA Design problem is to find an RNA sequence which satisfies given structural constraints. Here, we propose a new algorithm for the RNA Design problem, dubbed LEARNA. LEARNA uses deep reinforcement learning to train a policy network to sequentially design an entire RNA sequence given a specified target structure. By meta-learning across 65000 different RNA Design tasks for one hour on 20 CPU cores, our extension Meta-LEARNA constructs an RNA Design policy that can be applied out of the box to solve novel RNA Design tasks. Methodologically, for what we believe to be the first time, we jointly optimize over a rich space of architectures for the policy network, the hyperparameters of the training procedure and the formulation of the decision process. Comprehensive empirical results on two widely-used RNA Design benchmarks, as well as a third one that we introduce, show that our approach achieves new state-of-the-art performance on the former while also being orders of magnitudes faster in reaching the previous state-of-the-art performance. In an ablation study, we analyze the importance of our method's different components.

Biotechnology• 2018• arXiv
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Periodicals

Phenomenology of disruptive breakup mechanism of a levitated evaporating emulsion droplet

Atomization of emulsion droplets is ubiquitous across a variety of application domains ranging from NextGen combustors to fabrication of biomedical implants. An understanding of the atomization mechanism in emulsions can result in a paradigm shift in customized designs of efficient systems, be it in energy or biotechnology sectors. In this paper, we specifically study the breakup mechanism of an evaporating contact-free (acoustic levitation) emulsion droplet (water-oil) under external heating. Three distinct regimes are observed during the lifespan of the evaporating droplet. Initially, the droplet diameter regresses linearly with time, followed by vapor bubble nucleation due to a significant difference in the boiling temperature among the components of the emulsion. The collapse of this bubble results in a high-intensity breakup of the droplet leading to the propulsion of residual liquid in the form of a crown-like sheet. The area of the expanding crown varies linearly with the square of the time. It is hypothesized that the expansion of the liquid sheet centrifuges the larger water sub-droplets towards the edge, resulting in unique spatial segregation. Subsequently, we report the first observation of complex patches (representing water sub-droplets) and the rupture of the thin sheet adjacent to patches into holes (with hole growth rate ranging from 1.2 to 1.4 m/s) in the context of an evaporating isolated emulsion droplet. The hole formation results in the creation of ligaments which undergo breakup into secondary droplets with Sauter mean diameter (SMD) ~ 50 μm.

Biotechnology• 2019• arXiv
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Periodicals

Magnetic Nanoparticle Based Molecular Communication in Microfluidic Environments

The possibility to guide and control magnetic nanoparticles in a non-invasive manner has spawned various applications in biotechnology such as targeted drug delivery and sensing of biological substances. These applications are facilitated by the engineering of the size, selective chemical reactivity, and general chemical composition of the employed particles. Motivated by their widespread use and favorable properties, in this paper, we provide a theoretical study of the potential benefits of magnetic nanoparticles for the design of molecular communication systems. In particular, we consider magnetic nanoparticle based communication in a microfluidic channel where an external magnetic field is employed to attract the information-carrying particles to the receiver. We show that the particle transport affected by Brownian motion, fluid flow, and an external magnetic field can be mathematically modeled as diffusion with drift. Thereby, we reveal that the key parameters determining the magnetic force are the particle size and the magnetic field gradient. Moreover, we derive an analytical expression for the channel impulse response, which is used to evaluate the potential gain in the expected number of observed nanoparticles due to the magnetic field. Furthermore, adopting the symbol error rate as performance metric, we show that using magnetic nanoparticles can enable reliable communication in the presence of disruptive fluid flow. Numerical results obtained by particle-based simulation validate the accuracy of the derived analytical expressions.

Biotechnology• 2018• arXiv
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Periodicals

Prot42: a Novel Family of Protein Language Models for Target-aware Protein Binder Generation

Unlocking the next generation of biotechnology and therapeutic innovation demands overcoming the inherent complexity and resource-intensity of conventional protein engineering methods. Recent GenAI-powered computational techniques often rely on the availability of the target protein's 3D structures and specific binding sites to generate high-affinity binders, constraints exhibited by models such as AlphaProteo and RFdiffusion. In this work, we explore the use of Protein Language Models (pLMs) for high-affinity binder generation. We introduce Prot42, a novel family of Protein Language Models (pLMs) pretrained on vast amounts of unlabeled protein sequences. By capturing deep evolutionary, structural, and functional insights through an advanced auto-regressive, decoder-only architecture inspired by breakthroughs in natural language processing, Prot42 dramatically expands the capabilities of computational protein design based on language only. Remarkably, our models handle sequences up to 8,192 amino acids, significantly surpassing standard limitations and enabling precise modeling of large proteins and complex multi-domain sequences. Demonstrating powerful practical applications, Prot42 excels in generating high-affinity protein binders and sequence-specific DNA-binding proteins. Our innovative models are publicly available, offering the scientific community an efficient and precise computational toolkit for rapid protein engineering.

Biotechnology• 2025• arXiv
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Periodicals

Understanding and Diagnosing Deep Reinforcement Learning

Deep neural policies have recently been installed in a diverse range of settings, from biotechnology to automated financial systems. However, the utilization of deep neural networks to approximate the value function leads to concerns on the decision boundary stability, in particular, with regard to the sensitivity of policy decision making to indiscernible, non-robust features due to highly non-convex and complex deep neural manifolds. These concerns constitute an obstruction to understanding the reasoning made by deep neural policies, and their foundational limitations. Hence, it is crucial to develop techniques that aim to understand the sensitivities in the learnt representations of neural network policies. To achieve this we introduce a theoretically founded method that provides a systematic analysis of the unstable directions in the deep neural policy decision boundary across both time and space. Through experiments in the Arcade Learning Environment (ALE), we demonstrate the effectiveness of our technique for identifying correlated directions of instability, and for measuring how sample shifts remold the set of sensitive directions in the neural policy landscape. Most importantly, we demonstrate that state-of-the-art robust training techniques yield learning of disjoint unstable directions, with dramatically larger oscillations over time, when compared to standard training. We believe our results reveal the fundamental properties of the decision process made by reinforcement learning policies, and can help in constructing reliable and robust deep neural policies.

Biotechnology• 2024• arXiv
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Periodicals

Structural transitions and energy landscape for Cowpea Chlorotic Mottle Virus capsid mechanics from nanomanipulation in vitro and in silico

Physical properties of capsids of plant and animal viruses are important factors in capsid self-assembly, survival of viruses in the extracellular environment, and their cell infectivity. Virus shells can have applications as nanocontainers and delivery vehicles in biotechnology and medicine. Combined AFM experiments and computational modeling on sub-second timescales of the indentation nanomechanics of Cowpea Chlorotic Mottle Virus (CCMV) capsid show that the capsid's physical properties are dynamic and local characteristics of the structure, which depend on the magnitude and geometry of mechanical input. Surprisingly, under large deformations the CCMV capsid transitions to the collapsed state without substantial local structural alterations. The enthalpy change in this deformation state dH = 11.5 - 12.8 MJ/mol is mostly due to large-amplitude out-of-plane excitations, which contribute to the capsid bending, and the entropy change TdS = 5.1 - 5.8 MJ/mol is mostly due to coherent in-plane rearrangements of protein chains, which result in the capsid stiffening. Dynamic coupling of these modes defines the extent of elasticity and reversibility of capsid mechanical deformation. This emerging picture illuminates how unique physico-chemical properties of protein nanoshells help define their structure and morphology, and determine their viruses' biological function.

Biotechnology• 2015• arXiv
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Periodicals

Can large language models democratize access to dual-use biotechnology?

Large language models (LLMs) such as those embedded in 'chatbots' are accelerating and democratizing research by providing comprehensible information and expertise from many different fields. However, these models may also confer easy access to dual-use technologies capable of inflicting great harm. To evaluate this risk, the 'Safeguarding the Future' course at MIT tasked non-scientist students with investigating whether LLM chatbots could be prompted to assist non-experts in causing a pandemic. In one hour, the chatbots suggested four potential pandemic pathogens, explained how they can be generated from synthetic DNA using reverse genetics, supplied the names of DNA synthesis companies unlikely to screen orders, identified detailed protocols and how to troubleshoot them, and recommended that anyone lacking the skills to perform reverse genetics engage a core facility or contract research organization. Collectively, these results suggest that LLMs will make pandemic-class agents widely accessible as soon as they are credibly identified, even to people with little or no laboratory training. Promising nonproliferation measures include pre-release evaluations of LLMs by third parties, curating training datasets to remove harmful concepts, and verifiably screening all DNA generated by synthesis providers or used by contract research organizations and robotic cloud laboratories to engineer organisms or viruses.

Biotechnology• 2023• arXiv
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Periodicals

A Selection Criterion for Patterns in Reaction-Diffusion Systems

Alan Turing's work in Morphogenesis has received wide attention during the past 60 years. The central idea behind his theory is that two chemically interacting diffusible substances are able to generate stable spatial patterns, provided certain conditions are met. Turing's proposal has already been confirmed as a pattern formation mechanism in several chemical and biological systems and, due to their wide applicability, there is a great deal of interest in deciphering how to generate specific patterns under controlled conditions. However, techniques allowing one to predict what kind of spatial structure will emerge from Turing systems, as well as generalized reaction-diffusion systems, remain unknown. Here, we consider a generalized reaction diffusion system on a planar domain and provide an analytic criterion to determine whether spots or stripes will be formed. It is motivated by the existence of an associated energy function that allows bringing in the intuition provided by phase transitions phenomena. This criterion is proved rigorously in some situations, generalizing well known results for the scalar equation where the pattern selection process can be understood in terms of a potential. In more complex settings it is investigated numerically. Our criterion can be applied to efficiently design Biotechnology and Developmental Biology experiments, or simplify the analysis of hypothesized morphogenetic models.

Biotechnology• 2013• arXiv
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Periodicals

Writing on Nanocrystals: Patterning Colloidal Inorganic Nanocrystal Films through Irradiation-Induced Chemical Transformations of Surface Ligands

In the past couple of decades, colloidal inorganic nanocrystals and, more specifically, semiconductor quantum dots have emerged as crucial materials for the development of nanoscience and nanotechnology, with applications in very diverse areas such as optoelectronics and biotechnology. Films made of inorganic NCs deposited on a substrate can be patterned by e-beam lithography, altering the structure of their capping ligands and thus allowing exposed areas to remain on the substrate while non-exposed areas are redispersed in a solvent, as in a standard lift-off process. This methodology can be described as a direct lithography process, since the exposure is performed directly on the material of interest, in contrast with conventional lithography which uses a polymeric resist as a mask for subsequent material deposition or etching. A few reports from the late 1990 and early 2000 used such direct lithography to fabricate electrical wires from metallic NCs. However, the poor conductivity obtained through this process hindered the widespread use of the technique. In the early 2010, the same method was used to define fluorescent patterns on QD films, allowing for further applications in biosensing. For the past 2 ,3 years, direct lithography on NC films with e-beams and X rays has gone through an important development as it has been demonstrated that it can tune further transformations on the NCs, leading to more complex patternings and opening a whole new set of possible applications.

Biotechnology• 2018• arXiv
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Periodicals

The overlooked need for Ethics in Complexity Science: Why it matters

Complexity science, despite its broad scope and potential impact, has not kept pace with fields like artificial intelligence, biotechnology and social sciences in addressing ethical concerns. The field lacks a comprehensive ethical framework, leaving us, as a community, vulnerable to ethical challenges and dilemmas. Other areas have gone through similar experiences and created, with discussions and working groups, their guides, policies and recommendations. Therefore, here we highlight the critical absence of formal guidelines, dedicated ethical committees, and widespread discussions on ethics within the complexity science community. Drawing on insights from the disciplines mentioned earlier, we propose a roadmap to enhance ethical awareness and action. Our recommendations include (i) initiating supportive mechanisms to develop ethical guidelines specific to complex systems research, (ii) creating open-access resources, and (iii) fostering inclusive dialogues to ensure that complexity science can responsibly tackle societal challenges and achieve a more inclusive environment. By initiating this dialogue, we aim to encourage a necessary shift in how ethics is integrated into complexity research, positioning the field to address contemporary challenges more effectively.

Biotechnology• 2024• arXiv
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Periodicals

Advanced Deep Learning Methods for Protein Structure Prediction and Design

After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students.

Biotechnology• 2025• arXiv
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Periodicals

Computational Assignment of Chemical Shifts for Protein Residues

Fast and accurate protein structure prediction is one of the major challenges in structural biology, biotechnology and molecular biomedicine. These fields require 3D protein structures for rational design of proteins with improved or novel properties. X-ray crystallography is the most common approach even with its low success rate, but lately NMR based approaches have gained popularity. The general approach involves a set of distance restraints used to guide a structure prediction, but simple NMR triple-resonance experiments often provide enough structural information to predict the structure of small proteins. Previous protein folding simulations that have utilised experimental data have weighted the experimental data and physical force field terms more or less arbitrarily, and the method is thus not generally applicable to new proteins. Furthermore a complete and near error-free assignment of chemical shifts obtained by the NMR experiments is needed, due to the static, or deterministic, assignment. In this thesis I present Chemshift, a module for handling chemical shift assignments, implemented in the protein structure determination program Phaistos. This module treats both the assignment of experimental data, as well as the weighing compared to physical terms, in a probabilistic framework where no data is discarded. Provided a partial assignment of NMR peaks, the module is able to improve the assignment with the intension to utilise this in the protein folding with little bias.

Biotechnology• 2013• arXiv
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Periodicals

PPDiff: Diffusing in Hybrid Sequence-Structure Space for Protein-Protein Complex Design

Designing protein-binding proteins with high affinity is critical in biomedical research and biotechnology. Despite recent advancements targeting specific proteins, the ability to create high-affinity binders for arbitrary protein targets on demand, without extensive rounds of wet-lab testing, remains a significant challenge. Here, we introduce PPDiff, a diffusion model to jointly design the sequence and structure of binders for arbitrary protein targets in a non-autoregressive manner. PPDiffbuilds upon our developed Sequence Structure Interleaving Network with Causal attention layers (SSINC), which integrates interleaved self-attention layers to capture global amino acid correlations, k-nearest neighbor (kNN) equivariant graph layers to model local interactions in three-dimensional (3D) space, and causal attention layers to simplify the intricate interdependencies within the protein sequence. To assess PPDiff, we curate PPBench, a general protein-protein complex dataset comprising 706,360 complexes from the Protein Data Bank (PDB). The model is pretrained on PPBenchand finetuned on two real-world applications: target-protein mini-binder complex design and antigen-antibody complex design. PPDiffconsistently surpasses baseline methods, achieving success rates of 50.00%, 23.16%, and 16.89% for the pretraining task and the two downstream applications, respectively. The code, data and models are available at https://github.com/JocelynSong/PPDiff.

Biotechnology• 2025• arXiv
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Periodicals

Protein Design with Dynamic Protein Vocabulary

Protein design is a fundamental challenge in biotechnology, aiming to design novel sequences with specific functions within the vast space of possible proteins. Recent advances in deep generative models have enabled function-based protein design from textual descriptions, yet struggle with structural plausibility. Inspired by classical protein design methods that leverage natural protein structures, we explore whether incorporating fragments from natural proteins can enhance foldability in generative models. Our empirical results show that even random incorporation of fragments improves foldability. Building on this insight, we introduce ProDVa, a novel protein design approach that integrates a text encoder for functional descriptions, a protein language model for designing proteins, and a fragment encoder to dynamically retrieve protein fragments based on textual functional descriptions. Experimental results demonstrate that our approach effectively designs protein sequences that are both functionally aligned and structurally plausible. Compared to state-of-the-art models, ProDVa achieves comparable function alignment using less than 0.04% of the training data, while designing significantly more well-folded proteins, with the proportion of proteins having pLDDT above 70 increasing by 7.38% and those with PAE below 10 increasing by 9.6%.

Biotechnology• 2025• arXiv
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Patents and intellectual property assets as non-fungible tokens: key technologies and challenges

With the explosive development of decentralized finance, we witness a phenomenal growth in tokenization of all kinds of assets, including equity, funds, debt, and real estate. By taking advantage of blockchain technology, digital assets are broadly grouped into fungible and non-fungible tokens (NFT). Here non-fungible tokens refer to those with unique and non-substitutable properties. NFT has widely attracted attention, and its protocols, standards, and applications are developing exponentially. It has been successfully applied to digital fantasy artwork, games, collectibles, etc. However, there is a lack of research in utilizing NFT in issues such as Intellectual Property. Applying for a patent and trademark is not only a time-consuming and lengthy process but also costly. NFT has considerable potential in the intellectual property domain. It can promote transparency and liquidity and open the market to innovators who aim to commercialize their inventions efficiently. The main objective of this paper is to examine the requirements of presenting intellectual property assets, specifically patents, as NFTs. Hence, we offer a layered conceptual NFT-based patent framework. Furthermore, a series of open challenges about NFT-based patents and the possible future directions are highlighted. The proposed framework provides fundamental elements and guidance for businesses in taking advantage of NFTs in real-world problems such as grant patents, funding, biotechnology, and so forth.

Biotechnology• 2023• arXiv
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Trends in Cuban research output: publications and patents

Cuban science and technology are known for important achievements, particularly in human healthcare and biotechnology. During the second half of XX century, the country developed a system of scientific institutions to address and solve major economical, cultural, social and health problems. However, the economic crisis faced by the island during the last three decades has had a major impact in Cuban scientific research. In addition to decreased investment, the emigration of thousands of young as well as senior scientists to other countries have had a major impact in Cuban research output. To date, no systematic analysis regarding scientific publications, citations, or patents granted to Cuban authors during this period, are available. Here, an analysis of Cuban scientific production since 1970, with an especial focus on the last three decades (1990 - 2019), is provided. All national metrics are compared with other countries, emphasizing those from Latin America. Preliminary results show that Cuban scientific publications are increased at a lower rate (two-fold) compare with several Latin American countries (five-fold average). In addition, since 2014 the annual number of Cuban scientific publications is decreasing. Finally, the analysis shows that most young Cuban authors with the higher index of citations (1990-2019) are working abroad. All the data and the code related to this study are open and can be found in GitHub (https://github.com/ypriverol/cubascience).

Biotechnology• 2020• arXiv
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Natural calcium carbonate for biomedical applications

Sea shells are found to be a very rich natural resource for calcium carbonate. Sea shells are made up of CaCO3 mainly in the aragonite form, which are columnar or fibrous or microsphere structured crystals. The bioactivity of nanoparticles of sea shell has been studied in this work. The sea shells collected were thoroughly washed, dried and pulverized. The powder was sieved and particles in the range of 45 to 63 microns were collected. The powdered sea shells were characterized using X-Ray Diffraction and Field Emission Scanning Electron Microscopy. The XRD data showed that the particles were mainly microspheres. Traces of calcite and vaterite were also present. Experiments were conducted to study the aspirin and strontium ranelate drug loading into the sea shell powder using soak and dry method. Different concentrations of drug solution was made in ethanol and water. The shell powder was soaked in drug solutions and was kept soaking for 48 hrs with intermittent ultrasonication. The mixture was gently dried in a vacuum oven. The in vitro drug release studies were done using Phosphate Buffered Saline. The FESEM images displayed a distribution of differently sized and shaped particles. The sea shells due to its natural porosity and crystallinity are expected to be useful for drug delivery. About 50% drug entrapment efficiency for aspirin and 39% for strontium ranelate was seen. A burst release of the drug (80 percent) was observed within two hours for both the drugs studied. Rest of the drug was released slowly in 19 hrs. Further modification of the sea shell with non toxic polymers is also planned as a part of this work. Sea shell powder has become a potential candidate for drug delivery due to all the aforementioned advantages.

Biotechnology• 2016• arXiv
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