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Results 1,892

Periodicals

Interaction of surfactant and protein at the o/w interface and its effect on colloidal and biological properties of polymeric nanocarriers

The use of polymer-based surfactants in the double-emulsion (water/oil/water, W/O/W) solvent-evaporation technique is becoming a widespread strategy for preparing biocompatible and biodegradable polymeric nanoparticles (NPs) loaded with biomolecules of interest in biomedicine, or biotechnology. This approach enhances the stability of the NPs, reduces their size and recognition by the mononuclear phagocytic system, and protects the encapsulated biomolecule against losing biological activity. Different protocols to add the surfactant during the synthesis lead to different NP colloidal properties and biological activity.

Biotechnology• 2024• arXiv
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Periodicals

TUNet: Incorporating segmentation maps to improve classification

Determining the localization of specific protein in human cells is important for understanding cellular functions and biological processes of underlying diseases. Among imaging techniques, high-throughput fluorescence microscopy imaging is an efficient biotechnology to stain the protein of interest in a cell. In this work, we present a novel classification model Twin U-Net (TUNet) for processing and classifying the belonging of protein in the Atlas images. Several notable Deep Learning models including GoogleNet and Resnet have been employed for comparison. Results have shown that our system obtaining competitive performance.

Biotechnology• 2019• arXiv
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Periodicals

Well-posedness of a Pulsed Electric Field Model in Biological Media and its Finite Element Approximation

This work aims at providing a mathematical and numerical framework for the analysis on the effects of pulsed electric fields on biological media. Biological tissues and cell suspensions are described as having a heteregeneous permittivity and a heteregeneous conductivity. Well-posedness of the model problem and the regularity of its solution are established. A fully discrete finite element scheme is proposed for the numerical approximation of the potential distribution as a function of time and space simultaneously for an arbitrary shaped pulse, and it is demonstrated to enjoy the optimal convergence order in both space and time. The proposed numerical scheme has potential applications in the fields of medicine, food sciences, and biotechnology.

Biotechnology• 2015• arXiv
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Periodicals

Laser Induced Forward Transfer of conducting polymers

We report on laser printing of conducting polymers directly from the solid phase. Laser Induced Forward Transfer is employed to deposit P3HT:PCBM films on glass/ITO/PEDOT:PSS substrates. P3HT:PCBM is widely used as the active material in organic solar cells. Polyaniline films, which are also printed by Laser Induced Forward Transfer, find many applications in the field of biotechnology. Laser printing parameters are optimized and results are presented. To apply solid-phase laser printing, P3HT:PCBM films are spun cast on quartz substrates, while aniline is in-situ polymerized on quartz substrates.

Biotechnology• 2012• arXiv
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Periodicals

A Structural Analysis of Population Graphs

The format of graphing algorithms for genomic data has been a debate in recent biotechnology. In this paper, we discuss the construction of population graphs using said genomic data. We first examine the GENPOFAD distance measurement, developed by Joly et. al., and prove that this constitutes a metric function. We develop an algorithm to construct graphs to visualize the relationships between individuals in a population. We then provide a statistical analysis of these simulated population graphs, and show that they are distinct from randomly generated graphs, and also show differences from small-world graphs.

Biotechnology• 2025• arXiv
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Periodicals

Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalization

With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images. Among different histopathological image analysis tasks, nuclei instance segmentation plays a fundamental role in a wide range of clinical and research applications. While many semi- and fully-automatic computerized methods have been proposed for nuclei instance segmentation, deep learning (DL)-based approaches have been shown to deliver the best performances. However, the performance of such approaches usually degrades when tested on unseen datasets. In this work, we propose a novel method to improve the generalization capability of a DL-based automatic segmentation approach. Besides utilizing one of the state-of-the-art DL-based models as a baseline, our method incorporates non-deterministic train time and deterministic test time stain normalization, and ensembling to boost the segmentation performance. We trained the model with one single training set and evaluated its segmentation performance on seven test datasets. Our results show that the proposed method provides up to 4.9%, 5.4%, and 5.9% better average performance in segmenting nuclei based on Dice score, aggregated Jaccard index, and panoptic quality score, respectively, compared to the baseline segmentation model.

Biotechnology• 2023• arXiv
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Periodicals

Towards Risk Analysis of the Impact of AI on the Deliberate Biological Threat Landscape

The perception that the convergence of biological engineering and artificial intelligence (AI) could enable increased biorisk has recently drawn attention to the governance of biotechnology and artificial intelligence. The 2023 Executive Order, Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, requires an assessment of how artificial intelligence can increase biorisk. Within this perspective, quantitative and qualitative frameworks for evaluating biorisk are presented. Both frameworks are exercised using notional scenarios and their benefits and limitations are then discussed. Finally, the perspective concludes by noting that assessment and evaluation methodologies must keep pace with advances of AI in the life sciences.

Biotechnology• 2024• arXiv
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Periodicals

High Througput pKa Prediction Using Semi Empirical Methods

A large proportion of organic molecules relevant to medicine and biotechnology contain one or more ionizable groups, which means that fundamental physical and chemical properties (e.g. the charge of the molecule) depend on the pH of the surroundings via the corresponding pKa values of the molecules. As drug- and material design increasingly is being done through high throughput screens, fast - yet accurate - computational pKa prediction methods are becoming crucial to the design process. Current empirical pKa predictors are increasingly found to fail because they are being applied to parts of chemical space for which experimental parameterization data is lacking. We propose to develop a pKa predictor that, due its quantum mechanical foundation, is more generally applicable but still fast enough to be used in high throughput screening. The method has the potential to impact virtually any biotechnological design process involving organic molecules as we will demonstrate for metabolic engineering and organic battery design.

Biotechnology• 2015• arXiv
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Periodicals

On the Unimportance of Superintelligence

Humankind faces many existential threats, but has limited resources to mitigate them. Choosing how and when to deploy those resources is, therefore, a fateful decision. Here, I analyze the priority for allocating resources to mitigate the risk of superintelligences. Part I observes that a superintelligence unconnected to the outside world (de-efferented) carries no threat, and that any threat from a harmful superintelligence derives from the peripheral systems to which it is connected, e.g., nuclear weapons, biotechnology, etc. Because existentially-threatening peripheral systems already exist and are controlled by humans, the initial effects of a superintelligence would merely add to the existing human-derived risk. This additive risk can be quantified and, with specific assumptions, is shown to decrease with the square of the number of humans having the capability to collapse civilization. Part II proposes that biotechnology ranks high in risk among peripheral systems because, according to all indications, many humans already have the technological capability to engineer harmful microbes having pandemic spread. Progress in biomedicine and computing will proliferate this threat. ``Savant'' software that is not generally superintelligent will underpin much of this progress, thereby becoming the software responsible for the highest and most imminent existential risk -- ahead of hypothetical risk from superintelligences. The analysis concludes that resources should be preferentially applied to mitigating the risk of peripheral systems and savant software. Concerns about superintelligence are at most secondary, and possibly superfluous.

Biotechnology• 2021• arXiv
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Periodicals

Enzyme-Based Logic Systems for Information Processing

We review enzymatic systems which involve biocatalytic reactions utilized for information processing (biocomputing). Extensive ongoing research in biocomputing, mimicking Boolean logic gates has been motivated by potential applications in biotechnology and medicine. Furthermore, novel sensor concepts have been contemplated with multiple inputs processed biochemically before the final output is coupled to transducing "smart-material" electrodes and other systems. These applications have warranted recent emphasis on networking of biocomputing gates. First few-gate networks have been experimentally realized, including coupling, for instance, to signal-responsive electrodes for signal readout. In order to achieve scalable, stable network design and functioning, considerations of noise propagation and control have been initiated as a new research direction. Optimization of single enzyme-based gates for avoiding analog noise amplification has been explored, as were certain network-optimization concepts. We review and exemplify these developments, as well as offer an outlook for possible future research foci. The latter include design and uses of non-Boolean network elements, e.g., filters, as well as other developments motivated by potential novel sensor and biotechnology applications.

Biotechnology• 2009• arXiv
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Periodicals

Patent Value Characterization -- An Empirical Analysis of Elevator Industry Patents

The global patent application count has steadily increased, achieving eight consecutive years of growth.The global patent industry has shown a general trend of expansion. This is attributed to the increasing innovation activities, particularly in the fields of technology, healthcare, and biotechnology. Some emerging market countries, such as China and India, have experienced significant growth in the patent domain, becoming important participants in global patent activities.

Biotechnology• 2024• arXiv
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Periodicals

Admittivity imaging from multi-frequency micro-electrical impedance tomography

The aim of this paper is to propose an optimal control optimization algorithm for reconstructing admittivity distributions (i.e., both conductivity and permittivity) from multi-frequency micro-electrical impedance tomography. A convergent and stable optimization scheme is shown to be obtainable from multi-frequency data. The results of this paper have potential applicability in cancer imaging, cell culturing and differentiation, food sciences, and biotechnology.

Biotechnology• 2014• arXiv
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Periodicals

Open Access, Intellectual Property, and How Biotechnology Becomes a New Software Science

Innovation is slowing greatly in the pharmaceutical sector. It is considered here how part of the problem is due to overly limiting intellectual property relations in the sector. On the other hand, computing and software in particular are characterized by great richness of intellectual property frameworks. Could the intellectual property ecosystem of computing come to the aid of the biosciences and life sciences? We look at how the answer might well be yes, by looking at (i) the extent to which a drug mirrors a software program, and (ii) what is to be gleaned from trends in research publishing in the life and biosciences.

Biotechnology• 2009• arXiv
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Periodicals

Suppression of the Spectral Cross Talk of Optogenetic Switching by Stimulated Depletion Quenching. Theoretical Analysis

Optogenetics is a rapidly growing field of biotechnology, potentially allowing a deeper understanding and control of complex biological networks. The major challenge is the multiplexed control of several optogenetic components in the presence of significant spectral cross talk. We propose and demonstrate through simulations a new control approach of Stimulated Depletion Quenching. This approach is applied to the phytochrome Cph8 bidirectional optogenetic switch, and the results show significant improvement of its dynamic range.

Biotechnology• 2018• arXiv
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Periodicals

Uncovering Bottlenecks and Optimizing Scientific Lab Workflows with Cycle Time Reduction Agents

Scientific laboratories, particularly those in pharmaceutical and biotechnology companies, encounter significant challenges in optimizing workflows due to the complexity and volume of tasks such as compound screening and assay execution. We introduce Cycle Time Reduction Agents (CTRA), a LangGraph-based agentic workflow designed to automate the analysis of lab operational metrics. CTRA comprises three main components: the Question Creation Agent for initiating analysis, Operational Metrics Agents for data extraction and validation, and Insights Agents for reporting and visualization, identifying bottlenecks in lab processes. This paper details CTRA's architecture, evaluates its performance on a lab dataset, and discusses its potential to accelerate pharmaceutical and biotechnological development. CTRA offers a scalable framework for reducing cycle times in scientific labs.

Biotechnology• 2025• arXiv
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Periodicals

Protein Microarrays with Carbon Nanotubes as Multi-Color Raman Labels

Detection of biomolecules is important in proteomics and clinical diagnosis and treatment of diseases. Here, we apply functionalized, macromolecular, single walled carbon nanotubes SWNTs as multi-color Raman labels to protein arrays for highly sensitive, multiplexed protein detection. Raman detection utilizes the sharp peaks of SWNTs with minimal background interference, affording a high signal to noise ratio needed for ultra-sensitive detection. Surface-enhanced Raman scattering SERS combined with the strong resonance Raman intensity of SWNTs, affords detection sensitivity down to 1 fM, a three order of magnitude improvement over most of reported fluorescence-based protein detections. We show that human autoantibodies to Proteinase 3 aPR3, a biomarker for the autoimmune disease Wegeners granulomatosis, is detected by Raman in human serum up to a 107 dilution. Moreover, SWNT Raman tags are stable against photobleaching and quenching, and by conjugating different antibodies to pure 12C and 13C SWNT isotopes, we demonstrate two-color SWNT Raman-based protein detection in a multiplexed fashion.

Biotechnology• 2008• arXiv
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Periodicals

Molecular and Serologic Diagnostic Technologies for SARS-CoV-2

The COVID-19 pandemic has presented many challenges that have spurred biotechnological research to address specific problems. Diagnostics is one area where biotechnology has been critical. Diagnostic tests play a vital role in managing a viral threat by facilitating the detection of infected and/or recovered individuals. From the perspective of what information is provided, these tests fall into two major categories, molecular and serological. Molecular diagnostic techniques assay whether a virus is present in a biological sample, thus making it possible to identify individuals who are currently infected. Additionally, when the immune system is exposed to a virus, it responds by producing antibodies specific to the virus. Serological tests make it possible to identify individuals who have mounted an immune response to a virus of interest and therefore facilitate the identification of individuals who have previously encountered the virus. These two categories of tests provide different perspectives valuable to understanding the spread of SARS-CoV-2. Within these categories, different biotechnological approaches offer specific advantages and disadvantages. Here we review the categories of tests developed for the detection of the SARS-CoV-2 virus or antibodies against SARS-CoV-2 and discuss the role of diagnostics in the COVID-19 pandemic.

Biotechnology• 2022• arXiv
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Periodicals

Emerging Search Regimes: Measuring Co-evolutions among Research, Science, and Society

Scientometric data is used to investigate empirically the emergence of search regimes in Biotechnology, Genomics, and Nanotechnology. Complex regimes can emerge when three independent sources of variance interact. In our model, researchers can be considered as the nodes that carry the science system. Research is geographically situated with site-specific skills, tacit knowledge and infrastructures. Second, the emergent science level refers to the formal communication of codified knowledge published in journals. Third, the socio-economic dynamics indicate the ways in which knowledge production relates to society. Although Biotechnology, Genomics, and Nanotechnology can all be characterised by rapid growth and divergent dynamics, the regimes differ in terms of self-organization among these three sources of variance. The scope of opportunities for researchers to contribute within the constraints of the existing body of knowledge are different in each field. Furthermore, the relevance of the context of application contributes to the knowledge dynamics to various degrees.

Biotechnology• 2011• arXiv
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Periodicals

The Impact of Acquisitions in the Biotechnology Sector on R&D Productivity

This study examines the effects of acquisitions on the retention and R&D productivity of inventors in the biotech sector, using data from 15,318 inventors involved in 1,375 acquisitions between 1990 and 2010. We employ a staggered difference-in-differences approach and find that acquisitions lead to a 13.5% decrease in inventor retention and a 35% drop in citation-weighted patent productivity post-acquisition. The productivity decline is more severe for inventors who remain with the acquiring firm, particularly for those whose expertise is closely tied to the target company. However, older inventors and those whose expertise aligns with the acquiring company's existing R&D portfolio tend to retain higher productivity levels after the acquisition.

Biotechnology• 2022• arXiv
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Periodicals

Formation and Thermal Stability of Gold-Silica Nanohybrids: Insight into the Mechanism and Morphology by Electron Tomography

Gold-silica hybrids are appealing in different fields of applications like catalysis, sensors, drug delivery, and biotechnology. In most cases, the morphology and distribution of the hetero-units play significant roles in their functional behavior. Methods of synthesizing these hybrids, with variable ordering of the hetero-units, are replete; however, a complete characterization in three dimensions could not be achieved yet. A simple route to the synthesis of Au-decorated SiO2 spheres is demonstrated and a study on the 3D ordering of the hetero-units by scanning transmission electron microscopy (STEM) tomography is presented at the final stage, intermediate stages of formation, and after heating the hybrid. The final hybrid evolves from a soft self-assembled structure of Au nano-particles. The hybrid shows good thermal stability up to 400 C, beyond which the Au particles start migrating inside the SiO2 matrix. This study provides an insight in the formation mechanism and thermal stability of the structures which are crucial factors for designing and applying such hybrids in fields of catalysis and biotechnology. As the method is general, it can be applied to make similar hybrids based on SiO2 by tuning the reaction chemistry as needed.

Biotechnology• 2015• arXiv
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Periodicals

Mechanisms of DNA separation in entropic trap arrays: A Brownian dynamics simulation

Using Brownian dynamics simulations, we study the migration of long charged chains in an electrophoretic microchannel device consisting of an array of microscopic entropic traps with alternating deep regions and narrow constrictions. Such a device has been designed and fabricated recently by Han et al. for the separation of DNA molecules (Science, 2000). Our simulation reproduces the experimental observation that the mobility increases with the length of the DNA. A detailed data analysis allows to identify the reasons for this behavior. Two distinct mechanisms contribute to slowing down shorter chains. One has been described earlier by Han et al.: the chains are delayed at the entrance of the constriction and escape with a rate that increases with chain length. The other, actually dominating mechanism is here reported for the first time: Some chains diffuse out of their main path into the corners of the box, where they remain trapped for a long time. The probability that this happens increases with the diffusion constant, i.e., the inverse chain length.

Biotechnology• 2004• arXiv
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Periodicals

Is Patent Law Technology Specific?

Although patent law purports to cover all manner of technologies, we have noticed recent divergence in the standards applied to biotechnology and to software patents: the Federal Circuit has applied a very permissive standard of obviousness in biotechnology, but a highly restrictive disclosure requirement. The opposite holds true for software patents, which seems to us exactly contrary to sound policy for either industry. These patent standards are grounded in the legal fiction of the "person having ordinary skill in the art" or PHOSITA. We discuss the appropriateness of the PHOSITA standard, concluding that it properly lends flexibility to the patent system. We then discuss the difficulty of applying this standard in different industries, offering suggestions as to how it might be modified to avoid the problems seen in biotechnology and software patents.

Biotechnology• 2001• arXiv
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Periodicals

Holistic Bioprocess Development Across Scales Using Multi-Fidelity Batch Bayesian Optimization

Bioprocesses are central to modern biotechnology, enabling sustainable production in pharmaceuticals, specialty chemicals, cosmetics, and food. However, developing high-performing processes is costly and complex, requiring iterative, multi-scale experimentation from microtiter plates to pilot reactors. Conventional Design of Experiments (DoE) approaches often struggle to address process scale-up and the joint optimization of reaction conditions and biocatalyst selection. We propose a multi-fidelity batch Bayesian optimization framework to accelerate bioprocess development and reduce experimental costs. The method integrates Gaussian Processes tailored for multi-fidelity modeling and mixed-variable optimization, guiding experiment selection across scales and biocatalysts. A custom simulation of a Chinese Hamster Ovary bioprocess, capturing non-linear and coupled scale-up dynamics, is used for benchmarking against multiple simulated industrial DoE baselines. Multiple case studies show how the proposed workflow can achieve a reduction in experimental costs and increased yield. This work provides a data-efficient strategy for bioprocess optimization and highlights future opportunities in transfer learning and uncertainty-aware design for sustainable biotechnology.

Biotechnology• 2025• arXiv
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Periodicals

ExpressInHost: A codon tuning tool for the expression of recombinant proteins in host microorganisms

ExpressInHost (https://gitlab.com/a.raguin/expressinhost) is a GTK/C++ based user friendly graphical interface that allows tuning the codon sequence of an mRNA for recombinant protein expression in a host microorganism. Heterologous gene expression is widely implemented in biotechnology companies and academic research laboratories. However, expression of recombinant proteins can be challenging. On the one hand, maximising translation speed is important, especially in scalable production processes relevant to biotechnology companies, but on the other hand, solubility problems often arise as a consequence, since translation "pauses" might be key to allow the nascent polypeptide chain to fold appropriately. To address this challenge, we have developed a software that offers three distinct modes to tune codon sequences using the genetic code redundancy. The tuning strategies implemented take into account the specific tRNA resources of the host and that of the native organism. They balance rapid translation and native speed mimicking, which might be important to allow proper protein folding, thereby avoiding protein solubility problems.

Biotechnology• 2021• arXiv
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