搜索结果：Applied biochemistry and biotechnology

Most research questions in agricultural and applied economics are of a causal nature, i.e., how one or more variables (e.g., policies, prices, the weather) affect one or more other variables (e.g., income, crop yields, pollution). Only some of these research questions can be studied experimentally. Most empirical studies in agricultural and applied economics thus rely on observational data. However, estimating causal effects with observational data requires appropriate research designs and a transparent discussion of all identifying assumptions, together with empirical evidence to assess the probability that they hold. This paper provides an overview of various approaches that are frequently used in agricultural and applied economics to estimate causal effects with observational data. It then provides advice and guidelines for agricultural and applied economists who are intending to estimate causal effects with observational data, e.g., how to assess and discuss the chosen identification strategies in their publications.

Selected applications of novel techniques in Agricultural Biotechnology, Health Food formulations and Medical Biotechnology are being reviewed with the aim of unraveling future developments and policy changes that are likely to open new niches for Biotechnology and prevent the shrinking or closing the existing ones. Amongst the selected novel techniques with applications to both Agricultural and Medical Biotechnology are: immobilized bacterial cells and enzymes, microencapsulation and liposome production, genetic manipulation of microorganisms, development of novel vaccines from plants, epigenomics of mammalian cells and organisms, as well as biocomputational tools for molecular modeling related to disease and Bioinformatics. Both fundamental and applied aspects of the emerging new techniques are being discussed in relation to their anticipated impact on future biotechnology applications together with policy changes that are needed for continued success in both Agricultural and Medical Biotechnology. Several novel techniques are illustrated in an attempt to convey the most representative and powerful tools that are currently being developed for both immediate and long term applicat

This study investigates the impact of educational comics as an active learning strategy in physics workshops for undergraduate students in Chemistry and Pharmacy and Biochemistry during the second semester of 2025. Conceptual understanding was assessed using the Force Concept Inventory (FCI), and student motivation and attitudes toward physics were evaluated through a Likert-type survey administered in pre- and post-test formats. The results show an average normalized gain of g = 0.21 on the FCI, corresponding to a low-to-medium range according to physics education research. A higher gain is observed in items directly related to the intervened content (g = 0.23) compared to non-intervened items (g = 0.19), suggesting that instructional design influences domain-specific conceptual development. At the motivational level, improvements are observed in student interest, self-efficacy, and perceived usefulness of physics, along with a reduction in negative emotional responses toward the subject. These findings indicate that educational comics can serve as an effective pedagogical scaffold, promoting positive learning dispositions and supporting targeted conceptual development in non-phys

A parametric study of an underwater pulsed plasma discharge in pin-to-pin electrode configuration has been performed. The influence of two parameters has been reported, the water conductivity (from 50 to 500 $μ$S/cm) and the applied voltage (from 6 to 16 kV). Two complementary diagnostics, time resolved refractive index-based techniques and electrical measurements have been performed in order to study the discharge propagation and breakdown phenomena in water according to the two parameters. A single high voltage of duration between 100 $μ$S and 1 ms is applied between two 100 $μ$m diameter platinum tips separated by 2 mm and immersed in the aqueous solution. This work, which provides valuable complementary results of paper [1], is of great interest to better understand the mechanisms of initiation and propagation of pin-to-pin discharge in water. For low conductivity (from 50 to 100 $μ$S/cm) results have confirmed two regimes of discharge (cathode and anode) and the increase of the applied voltage first makes the breakdown more achievable and then favors the apparition of the anode regime. For 500 $μ$S/cm results have highlighted cathode regime for low applied voltage but a mixed

Digital twins (DTs) are revolutionizing the biotechnology industry by enabling sophisticated digital representations of biological assets, microorganisms, drug development processes, and digital health applications. However, digital twinning at micro and nano scales, particularly in modeling complex entities like bacteria, presents significant challenges in terms of requiring advanced Internet of Things (IoT) infrastructure and computing approaches to achieve enhanced accuracy and scalability. In this work, we propose a novel framework that integrates the Internet of Bio-Nano Things (IoBNT) with advanced machine learning techniques, specifically convolutional neural networks (CNN) and federated learning (FL), to effectively tackle the identified challenges. Within our framework, IoBNT devices are deployed to gather image-based biological data across various physical environments, leveraging the strong capabilities of CNNs for robust machine vision and pattern recognition. Subsequently, FL is utilized to aggregate insights from these disparate data sources, creating a refined global model that continually enhances accuracy and predictive reliability, which is crucial for the effecti

The escalating cost, extended timelines, and low success rates in pharmaceutical research demand a fundamental rethinking of biotechnology R&D infrastructure. This chapter introduces the concept of the AI-Integrated Biotechnology Hub, a purpose-built research ecosystem uniting residential, commercial, clinical, and research facilities under a central, AI-driven operating system. Designed as a multi-sided platform, the hub leverages continuous, multi-modal health data collection, advanced smart living environments, and federated learning models to enable secure, privacy-preserving biomedical research. By integrating real estate, biotechnology facilities, research hospitals, and community services, the model maximizes data utility, accelerates drug discovery, and enhances resident well-being. Transparency, accountability, and ethical stewardship are critical pillars of governance, enacted through dynamic consent, data trusts, and multi-stakeholder oversight. Scalable across urban and vertical architectures, this paradigm offers a viable, sustainable pathway toward improving healthspan, fostering innovation, and reshaping the economics of global drug development.

Viruses are the most abundant biological entities on Earth and play central roles in shaping microbiomes and influencing ecosystem functions. Yet, most viral genes remain uncharacterized, comprising what is commonly referred to as "viral dark matter." Metagenomic studies across diverse environments consistently show that 40-90% of viral genes lack known homologs or annotated functions. This persistent knowledge gap limits our ability to interpret viral sequence data, understand virus-host interactions, and assess the ecological or applied significance of viral genes. Among the most intriguing components of viral dark matter are auxiliary viral genes (AVGs), including auxiliary metabolic genes (AMGs), regulatory genes (AReGs), and host physiology-modifying genes (APGs), which may alter host function during infection and contribute to microbial metabolism, stress tolerance, or resistance. In this review, we explore recent advances in the discovery and functional characterization of viral dark matter. We highlight representative examples of novel viral proteins across diverse ecosystems including human microbiomes, soil, oceans, and extreme environments, and discuss what is known, and

The biotechnology industry poses challenges and possibilities for startups and small businesses. It is characterized by high charges and complex policies, making it difficult for such agencies to set up themselves. However, it additionally offers avenues for innovation and increase. This paper delves into powerful techniques that can be a resource in managing biotechnology innovation, which includes partnerships, highbrow assets improvement, virtual technologies, customer engagement, and government investment. These strategies are important for fulfillment in an industry that is constantly evolving. By embracing agility and area of interest focus, startups and small companies can successfully compete in this dynamic discipline.

In a recent article [Phys. Rev. Applied 6, 014017 (2016)], Chyba and Hand propose a new scheme to generate electric power continuously at the expense of Earth's kinetic energy of rotation, by using an appropriately shaped cylindrical shell of a well chosen conducting ferrite, rigidly attached to the Earth. No experimental confirmation is reported for the new prediction. In the present Refutation, I first use today's standard electromagnetism and essentially the same model as Chyba and Hand to show in a very simple way that no device of the proposed type can produce continuous electric power, whatever its configuration or size, in agreement with widespread expectation. Next, I show that the prediction of non-zero continuous power by Chyba and Hand results from a confusion of frames of reference at a critical step of their derivation. When the confusion is clarified, the prediction becomes exactly zero and the article under discussion appears as pointless. At the end, I comment about the persistent invocation by Chyba and Hand of the misleading legacy notion that quasi-static magnetic fields have an intrinsic velocity, and other questionable concepts.

Biotechnology Industry 5.0 is advancing with the integration of cutting-edge technologies like Machine Learning (ML), the Internet Of Things (IoT), and cloud computing. It is no surprise that an industry that utilizes data from customers and can alter their lives is a target of a variety of attacks. This chapter provides a perspective of how Machine Learning Security Operations (MLSecOps) can help secure the biotechnology Industry 5.0. The chapter provides an analysis of the threats in the biotechnology Industry 5.0 and how ML algorithms can help secure with industry best practices. This chapter explores the scope of MLSecOps in the biotechnology Industry 5.0, highlighting how crucial it is to comply with current regulatory frameworks. With biotechnology Industry 5.0 developing innovative solutions in healthcare, supply chain management, biomanufacturing, pharmaceuticals sectors, and more, the chapter also discusses the MLSecOps best practices that industry and enterprises should follow while also considering ethical responsibilities. Overall, the chapter provides a discussion of how to integrate MLSecOps into the design, deployment, and regulation of the processes in biotechnology

In the past few decades, the life sciences have experienced an unprecedented accumulation of data, ranging from genomic sequences and proteomic profiles to heavy-content imaging, clinical assays, and commercial biological products for research. Traditional static databases have been invaluable in providing standardized and structured information. However, they fall short when it comes to facilitating exploratory data interrogation, real-time query, multidimensional comparison and dynamic visualization. Integrated data-driven research environments aiming at supporting user-driven data queries and visualization offer promising new avenues for making the best use of the vast and heterogeneous data streams collected in biological research. This perspective article discusses the potential of interactive and integrated frameworks, highlighting the importance of implementing this model in biotechnology research, while going through the state-of-the-art in database design, technical choices behind modern data management systems, and emerging needs in multidisciplinary research. Special attention is given to data interrogation strategies, user interface design, and comparative analysis capa

We demonstrate the continuous broadband microwave receivers based on AC Stark shifts and Floquet States of Rydberg levels in a cesium atomic vapor cell. The resonant transition frequency of two adjacent Rydberg states 78$S_{1/2}$ and 78$P_{1/2}$ is tuned based on AC Stark effect of 70~MHz Radio frequency (RF) field that is applied outside the vapor cell. Meanwhile, the Rydberg states also exhibit Floquet even-order sidebands that are used to extend the bandwidths further. We achieve microwave electric field measurements over 1.172~GHz of continuous frequency range. The sensitivity of the Rydberg receiver with heterodyne technique in the absence of RF field is 280.2~nVcm$^{-1}$Hz$^{-1/2}$, while it is dramatically decreased with tuning the resonant transition frequency in the presence of RF field. Surprisingly, the sensitivity can be greatly improved if the microwave field couples the Floquet sideband transition. The achieving of continuous frequency and high sensitivity microwave detection will promote the application of Rydberg receiver in the radar technique and wireless communication.

This manuscript is a preprint version of Part 1 (General Introduction and Synopsis) of the book Applied Evaluative Informetrics, to be published by Springer in the summer of 2017. This book presents an introduction to the field of applied evaluative informetrics, and is written for interested scholars and students from all domains of science and scholarship. It sketches the field's history, recent achievements, and its potential and limits. It explains the notion of multi-dimensional research performance, and discusses the pros and cons of 28 citation-, patent-, reputation- and altmetrics-based indicators. In addition, it presents quantitative research assessment as an evaluation science, and focuses on the role of extra-informetric factors in the development of indicators, and on the policy context of their application. It also discusses the way forward, both for users and for developers of informetric tools.

In recent years considerable portion of the computer science community has focused its attention on understanding living cell biochemistry and efforts to understand such complication reaction environment have spread over wide front, ranging from systems biology approaches, through network analysis (motif identification) towards developing language and simulators for low level biochemical processes. Apart from simulation work, much of the efforts are directed to using mean field equations (equivalent to the equations of classical chemical kinetics) to address various problems (stability, robustness, sensitivity analysis, etc.). Rarely is the use of mean field equations questioned. This review will provide a brief overview of the situations when mean field equations fail and should not be used. These equations can be derived from the theory of diffusion controlled reactions, and emerge when assumption of perfect mixing is used.

Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a transformative tool in biotechnology.

The number of people able to end Earth's technical civilization has heretofore been small. Emerging dual-use technologies, such as biotechnology, may give similar power to thousands or millions of individuals. To quantitatively investigate the ramifications of such a marked shift on the survival of both terrestrial and extraterrestrial technical civilizations, this paper presents a two-parameter model for civilizational lifespans, i.e. the quantity $L$ in Drake's equation for the number of communicating extraterrestrial civilizations. One parameter characterizes the population lethality of a civilization's biotechnology and the other characterizes the civilization's psychosociology. $L$ is demonstrated to be less than the inverse of the product of these two parameters. Using empiric data from Pubmed to inform the biotechnology parameter, the model predicts human civilization's median survival time as decades to centuries, even with optimistic psychosociological parameter values, thereby positioning biotechnology as a proximate threat to human civilization. For an ensemble of civilizations having some median calculated survival time, the model predicts that, after 80 times that dura

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) constitute a multi-functional, constantly evolving immune system in bacteria and archaea cells. A heritable, molecular memory is generated of phage, plasmids, or other mobile genetic elements that attempt to attack the cell. This memory is used to recognize and interfere with subsequent invasions from the same genetic elements. This versatile prokaryotic tool has also been used to advance applications in biotechnology. Here we review a large body of CRISPR-Cas research to explore themes of evolution and selection, population dynamics, horizontal gene transfer, specific and cross-reactive interactions, cost and regulation, non-immunological CRISPR functions that boost host cell robustness, as well as applicable mechanisms for efficient and specific genetic engineering. We offer future directions that can be addressed by the physics community. Physical understanding of the CRISPR-Cas system will advance uses in biotechnology, such as developing cell lines and animal models, cell labeling and information storage, combatting antibiotic resistance, and human therapeutics.

The present paper extends the literature investigating key drivers leading certain patents to exert a stronger influence on the subsequent technological developments (inventions) than other ones. We investigated six key determinants, as (i) the use of scientific knowledge, (ii) the breadth of the technological base, (iii) the existence of collaboration in patent development, (iv) the number of claims, (v) the scope, and (vi) the novelty, and how the effect of these determinants varies when patent influence - as measured by the number of forward citations the patent received - is distinguished as within and across the industrial and organizational boundaries. We conducted an empirical analysis on a sample of 5671 patents granted to 293 US biotechnology firms from 1976 to 2003. Results reveal that the contribution of the determinants to patent influence differs across the domains that are identified by the industrial and organizational boundaries. Findings, for example, show that the use of scientific knowledge negatively affects patent influence outside the biotechnology industry, while it positively contributes to make a patent more relevant for the assignee's subsequent technologi

We study a minimal model of a crawling eukaryotic cell with a chemical polarity controlled by a reaction-diffusion mechanism describing Rho GTPase dynamics. The size, shape, and speed of the cell emerge from the combination of the chemical polarity, which controls the locations where actin polymerization occurs, and the physical properties of the cell, including its membrane tension. We find in our model both highly persistent trajectories, in which the cell crawls in a straight line, and turning trajectories, where the cell transitions from crawling in a line to crawling in a circle. We discuss the controlling variables for this turning instability, and argue that turning arises from a coupling between the reaction-diffusion mechanism and the shape of the cell. This emphasizes the surprising features that can arise from simple links between cell mechanics and biochemistry. Our results suggest that similar instabilities may be present in a broad class of biochemical descriptions of cell polarity.