Field Of Biology Research Articles

Enhancing the detection efficiency of the 3D positive ion detector

Enhancing the 3D positive ion detector's efficiency is the objective of this study. The detector finds numerous applications in the fields of radiation biology, radiation dosimetry, radiation protection, radiation measurement etc. Generally, in 3D positive ion detector the cathode used is either high resistive glass or gold coated over ceramic glass. The detector efficiency has been reported as 2%max with high resistive glass cathode material and 9.2%max with gold coated ceramic glass. In the present study, we have significantly improved the detection efficiency by replacing these materials with an oxygen-free high conductivity (OFHC) copper cathode. This modification resulted in a substantial increase in the detector efficiency, reaching a maximum of 31.3%max. The study also presents a comparison between this enhanced performance and the previously reported efficiencies demonstrating the effectiveness of using high conductivity materials to improve ion detection capabilities.

Rapid and Sensitive Detection of Mutations in SARS-CoV-2 by Surface-Enhanced Raman Spectroscopy.

Since the outbreak of the novel coronavirus (SARS-CoV-2), the world has suffered significant losses. At present, the pneumonia disease caused by SARS-CoV-2 virus has not been eliminated, and SARS-CoV-2 has a high mutation rate, and its variant strains also have a high prevalence rate, which has always threatened the health of all mankind. This study aims to develop a rapid and sensitive method to complement existing SARS-CoV-2 diagnostic tools by utilizing surface-enhanced Raman spectroscopy (SERS) for the direct detection of the intrinsic SERS signal from the S proteins of SARS-CoV-2 and its variants (Omicron and Delta) within 5 min using a portable Raman spectrometer. The linear range of S protein detection of Wild-type, Omicron and Delta variants ranged from 1.0 × 10-7 to 1.0 × 10-3 g·mL-1, the limits of detection (LOD) were down to ∼10-8 g·mL-1 level. Our proposed method uses portable Raman spectrometer for direct detection, which is characterized by its simplicity, rapidity, portability, and wide applicability. It enables simultaneous detection of diverse mutated targets, thereby playing a pivotal role in the early diagnosis of viral diseases. Moreover, it holds promising prospects in the fields of chemistry, biology, and medicine.

Endothelial protease-activated receptor 4: impotent or important?

The protease thrombin, which increases its levels with various pathologies, can signal through the G protein-coupled receptors protease-activated receptors 1 and 4 (PAR1/PAR4). PAR1 is a high-affinity receptor for thrombin, whereas PAR4 is a low-affinity receptor. Finding functions for PAR4 in endothelial cells (ECs) has been an elusive goal over the last two decades. Several studies have demonstrated a lack of functionality for PAR4 in ECs, with many claiming that PAR4 function is confined mostly to platelets. A recent study from our lab identified low expressing but functional PAR4 in hepatic ECs in vivo. We also found that PAR4 likely has a higher signaling potency than PAR1. Given this potency, ECs seem to limit PAR4 signaling except for extreme cases. As a result, we claim PAR4 is not an impotent receptor because it is low expressing, but rather PAR4 is low expressing because it is a very potent receptor. Since we have finally shown PAR4 to be present and functional on ECs in vivo, it is important to outline why such controversy arose over the last two decades and, more importantly, why the receptor was undervalued on ECs. This timely review aims to inspire investigators in the field of vascular biology to study the regulatory aspect of endothelial PAR4 and its relationship with the more highly expressed PAR1.

A Pedagogical Proposal for Enhancing Undergraduate Biology Education through Complex Systems Thinking (CST)

This article is centered around the key question: how do we address teaching about biology, based on complex systems? In the dynamic landscape of biological sciences, the exploration of life's intricacies demands a holistic and interconnected approach. Complexity science and systems thinking offer a transformative framework for understanding the dynamic and interconnected nature of biological systems. As educators in the field of biology, it is imperative to equip undergraduate students with the tools to navigate and comprehend the ever-growing complexity of living systems. Traditional reductionist views, while valuable in understanding specific components of biological phenomena, often fall short in capturing the intricate interconnections and emergent properties that characterize living systems. In the pursuit of understanding these complexities, undergraduate biology education must transcend traditional reductionistic silos and embrace holistic interdisciplinary approaches such as complexity science and systems thinking. This article delves into the integration of Complex Systems Thinking (CST) in undergraduate biology courses, highlighting its significance, pedagogical implication, and practical strategies for implementation. Furthermore, it describes the key characteristics, and proposes a few pedagogical concepts associated with complexity, emergence, and systems thinking. The primary goal is to provide biology educators with the tools and enough basic information about the ‘features of complexity’, utilized in Big History, so that they can consider why and how such an approach might be applied in the education of both biologists and a scientifically literate citizenry.

2024: A Year of Quantum Progress

2024 has proven to be a significant year for a number of scientific and technological advancements. In this News and Views article we review briefly some of the relevant developments in the field of information, quantum computation, AI, biology and evolutionary sciences.

Versatile Stimuli-Responsive Controlled Release of Pinanediol-Caged Boronic Esters for Spatiotemporal and Nitroreductase-Selective Glucose Bioimaging.

Boronic acids have been widely applied in various biological fields, particularly achieving significant practical progress in boronic acid-based glucose sensing. However, boronic acids exhibit nonspecific binding to other nucleophiles, and the inherent lability of boronic esters in biological systems limits their further applications. Herein, we developed a stimuli-responsive controllable caging strategy to achieve photoresponsive spatiotemporally and nitroreductase-responsive cancer cell-selective glucose sensing. We introduced o-/p-nitroaryl-containing self-immolative linkers onto δ-pinanediol derivatives, effectively caging boronic acids and blocking glucose recognition. Upon triggering by specific stimuli, the caged boronic esters decompose, releasing boronic acids and thereby restoring glucose recognition of the diboronic acid-based sensor. The proof of concept was confirmed through intracellular glucose bioimaging in living cells. Upon regional UV irradiation, we could monitor intracellular glucose with excellent spatiotemporal selectivity. Furthermore, we used the cancer biomarker nitroreductases as the internal stimuli and utilized the caged glucose sensor to selectively label hypoxic cancer cells in a cocultured living cell sample. We believe that our stimuli-responsive caging strategies will hold promising potential for the controlled release of other boronic acids in various biological contexts.

SEPO-FI: Deep-learning based software to calculate fusion index of muscle cells.

The fusion index is a critical metric for quantitatively assessing the transformation of in vitro muscle cells into myotubes in the biological and medical fields. Traditional methods for calculating this index manually involve the labor-intensive counting of numerous muscle cell nuclei in images, which necessitates determining whether each nucleus is located inside or outside the myotubes, leading to significant inter-observer variation. To address these challenges, this study proposes a three-stage process that integrates the strengths of pattern recognition and deep-learning to automatically calculate the fusion index. The experimental results demonstrate that the proposed process achieves significantly higher performance in cell nuclei detection and classification, with an F1-score of 0.953, whereas traditional object detection methods achieve less than 0.5. In addition, the fusion index obtained using the proposed method is closely aligned with the human-assessed values, showing minimal discrepancy and strong agreement with human evaluations. This process is incorporated into the development of "SEPO-FI" as public software, automating cell detection and classification to enable effective fusion index calculation and broaden access to this methodology within the scientific community.

Singularities in Computational Optics

Phase singularities in optical fields are associated with a non-vanishing curl component of phase gradients. Huygen’s diverging spherical wavefronts that primary/secondary point sources emit, during propagation, a have zero curl component. Therefore, the propagation of waves that contain phase singularities exhibits new exciting features. Their effect is also felt in computational optics. These singularities provide orbital angular momentum and robustness to beams and remove degeneracies in interferometry and diffractive optics. Recently, the improvisations in a variety of computation algorithms have resulted in the vortices leaving their footprint in fast-expanding realms such as diffractive optics design, multiplexing, signal processing, communication, imaging and microscopy, holography, biological fields, deep learning, and ptychography. This review aims at giving a gist of the advancements that have been reported in multiple fields to enable readers to understand the significance of the singularities in computation optics.

Lysosome-Mitochondrial Crosstalk in Cellular Stress and Disease

The perception of lysosomes and mitochondria as entirely separate and independent entities that degrade material and produce ATP, respectively, has been challenged in recent years as not only more complex roles for both organelles, but also an unanticipated level of interdependence are being uncovered. Coupled lysosome and mitochondrial function and dysfunction involve complex crosstalk between the two organelles which goes beyond mitochondrial quality control and lysosome-mediated clearance of damaged mitochondria through mitophagy. Our understanding of crosstalk between these two essential metabolic organelles has been transformed by major advances in the field of membrane contact sites biology. We now know that membrane contact sites between lysosomes and mitochondria play central roles in inter-organelle communication. This importance of mitochondria–lysosome contacts (MLCs) in cellular homeostasis, evinced by the growing number of diseases that have been associated with their dysregulation, is starting to be appreciated. How MLCs are regulated and how their coordination with other pathways of lysosome–mitochondria crosstalk is achieved are the subjects of ongoing scrutiny, but this review explores the current understanding of the complex crosstalk governing the function of the two organelles and its impact on cellular stress and disease.

Optimization of international talent training program in biological and biomechanical field of Shaanxi universities by integrating Transformer-GRU model under the “Belt and Road” initiative

In the field of biology of Shaanxi universities, there are problems such as insufficient internationalization of course content, limited internationalization level of teachers, and difficulty in meeting the personalized needs of students’ foreign language ability. To this end, under the Belt and Road Initiative, this paper proposes an intelligent solution based on Transformer and GRU (Gated Recurrent Unit) models, aiming to improve the quality of international education in the field of biology of universities by optimizing course content and teaching methods. This study first uses the Transformer model to integrate and analyze a large number of international education resources, identify global cutting-edge knowledge and cross-cultural education elements in biology courses, including biomechanical principles that underpin biological functions and interactions. This provides scientific support for the optimization of course content. At the same time, the GRU model is used to dynamically analyze the progress of teachers’ international teaching and students’ learning feedback. Just as organisms can adjust their metabolic rate according to the changes in the environment, the model automatically adjusts the pace and difficulty of the subsequent teaching content according to the students’ speed of mastery and difficulties in understanding biomechanics knowledge, ensuring that each student can keep up with the pace of teaching. Additionally, the integration of biomechanical concepts into the curriculum helps students understand the mechanical properties and behaviors of biological systems, fostering interdisciplinary thinking and enhancing their global vision. Experimental results show that the students in Class A who adopt this research program are significantly better than the control Class B in terms of knowledge mastery, interdisciplinary thinking, and global vision (P < 0.05); after the experiment, the average foreign language ability score of the students in Class A is 7.04 points higher than that of Class B; the overall satisfaction of the students in Class A with the new teaching program is as high as 82.5%. This paper, based on the combination of Transformer and GRU models, can effectively promote the international talent training process of biology majors in Shaanxi universities, particularly by incorporating biomechanical insights, thereby enhancing the competitiveness of this major in global academic and scientific research cooperation.

Functional Post-Synthetic Chemistry of Metal–Organic Cages According to Molecular Architecture and Specific Geometry of Origin

Metal–organic cages (MOCs) are discrete supramolecular entities consisting of metal nodes and organic connectors or linkers; MOCs are noted for their high porosity and processability. Chemically, they can be post-synthetically modified (PSM) and new functional groups can be introduced, presenting attractive qualities, and it is expected that their new properties will differ from those of the original compound. This is why they are highly regarded in the fields of biology and chemistry. The present review deals with the current PSM strategies used for MOCs, including covalent, coordination, and noncovalent methods and their structural benefits. The main emphasis of this review is to show to what extent and under what circumstances a MOC can be designed to obtain a tailored geometric architecture. Although sometimes unclear when examining supramolecular systems, particularizing the design of and systematic approaches to the development and characterization of families of MOCs provides new insights into structure–function relationships, which will guide future developments.

Information Entropy in Chimera States of Human Dynamics

In human dynamics, functioning relies on intricate coordination patterns. Networks of synchronized oscillators in various biological and semiotic fields shape these dynamics. We have observed stability, instability, and transitions at multiple levels, indicating that coordination happens on all scales. We have examined coordination models for simplified and complex dynamics. In empirical research, we can frequently observe chimera states as the coexistence of coherence and incoherence, even in homogeneous networks. They are more evident in the heterogenous networks’ standard in human dynamics, where oscillators and nodes are mixed as different types. This paper proposes a simplified and overarching model for mixed chimeras. We discuss the information dynamics in these types of networks and their pattern transitions.

Machine learning reveals novel compound for the improved production of chitooligosaccharides in Escherichia coli.

In order to improve predictability of outcome and reduce costly rounds of trial-and-error, machine learning models have been of increasing importance in the field of synthetic biology. Besides applications in predicting genome annotation, process parameters and transcription initiation frequency, such models have also been of help for pathway optimization. The latter is a common strategy in metabolic engineering and improves the production of a desirable compound by optimizing enzyme expression levels of the production pathway. However, engineering steps might not lead to sufficient improvement, and bottlenecks may remain hidden among the hundreds of metabolic reactions occurring in a living cell, especially if the production pathway is highly interconnected with other parts of the cell's metabolism. Here, we use the synthesis of chitooligosaccharides (COS) to show that the production from such complex pathways can be improved by using machine learning models and feature importance analysis to find new compounds with an impact on COS production. We screened Escherichia coli libraries of engineered transcription regulators with an expected broad range of metabolic diversity and trained several machine learning models to predict COS production titers. Subsequent feature analysis led to the finding of iron, whose addition we could show improved COS production in vivo up to 2-fold. Additionally, the analysis revealed important clues for future engineering steps.

Single-plate kinome screening in live-cells to enable highly cost-efficient kinase inhibitor profiling.

Cancer research, cancer treatment, and the field of chemical biology are examples which heavily rely on the discovery of selective kinase inhibitors. While determining on-target potency is often feasible for most laboratories, the equally critical but frequently neglected selectivity screening remains less accessible to the broader scientific community. This limitation can stem from various factors, such as the unavailability of a large number of purified kinases or the costs associated with commercial screening systems. To address these challenges and enable a wider range of scientists, this protocol leverages a commercial kinome selectivity screen to facilitate a low-cost, two-day, single-plate selectivity screen against 192 kinases.

Systems Biology of Streptophyte Cell Evolution.

More than 500 million years ago, a streptophyte algal population established a foothold on land and started terraforming Earth through an unprecedented radiation. This event is called plant terrestrialization and yielded the Embryophyta. Recent advancements in the field of plant evolutionary developmental biology (evo-devo) have propelled our knowledge of the closest algal relatives of land plants, the zygnematophytes, highlighting that several aspects of plant cell biology are shared between embryophytes and their sister lineage. High-throughput exploration determined that routes of signaling cascades, biosynthetic pathways, and molecular physiology predate plant terrestrialization. But how do they assemble into biological programs, and what do these programs tell us about the principal functions of the streptophyte cell? Here, we make the case that streptophyte algae are unique organisms for understanding the systems biology of the streptophyte cell, informing on not only the origin of embryophytes but also their fundamental biology.

The application value assessment of CRISPR in screening and optimizing bacteria for industrial production

CRISPR is a gene editing technology derived from bacteria and has been widely used in the current biological field. However, the use range of it is still mainly in laboratories. The industrial production field can rarely see its role, such as the screening and optimization of production strains. This article compares the advantages and disadvantages of CRISPR with traditional natural sampling or mutagenesis breeding combined with selective culture medium screening technology in actual production in technical and non-technical fields, emphasizing that CRISPR technology has great advantages in integrating excellent traits and improving breeding efficiency. However, it is still slightly inferior to traditional technology in terms of breeding costs, experimental conditions, and social acceptance. The significance of this study lies in a relatively comprehensive analysis of the various indicators worth comparing when CRISPR technology is actually applied to industrial breeding, which can provide a certain reference value for whether the technology should be officially put into industrial use. However, there are still many defects of CRISPR technology which is difficult to correct; although the direction of its improvement is fairly clear, its never easy to work, and the development will still need some time.

QBiCo: a method to assess global DNA conversion performance in epigenetics via single-copy genes and repetitive elements

BackgroundHuman DNA methylation profiling offers great promises in various biomedical applications, including ageing, cancer and even forensics. So far, most DNA methylation techniques are based on a chemical process called sodium bisulfite conversion, which specifically converts non-methylated cytosines into uracils. However, despite the popularity of this approach, it is known to cause DNA fragmentation and loss affecting standardization, while incomplete conversion may result in potential misinterpretation of methylation-based outcomes.ResultsTo offer the community a solution, we developed qBiCo - a novel quality-control method to address the quantity and quality of bisulfite-converted DNA. qBiCo is a 5-plex, TaqMan® probe-based, quantitative (q)PCR assay that amplifies single- and multi-copy DNA fragments of converted and non-converted nature. It estimates four parameters: converted DNA concentration, fragmentation, global conversion efficiency, and potential PCR inhibition. We optimized qBiCo using synthetic DNA standards and assessed it using standard developmental validation criteria, showcasing that qBiCo is reliable, robust and sensitive down to picogram level. We also evaluated its performance by testing decreasing DNA amounts using several commercial bisulfite conversion kits. Depending on the starting DNA quantity, bisulfite-converted DNA recoveries ranged from 8.5 to 100%, conversion efficiencies from 78 to 99.9%, while certain kits highly fragment DNA, demonstrating large variability in their performance. Towards building a prototype tool, we further optimized key functionalities, for example, by replacing the poorest performing single-plex assay and creating a more representative DNA standard. Aiming to scale-up and move towards implementation, we successfully transferred and validated our novel method in six different qPCR platforms from different major manufacturers.ConclusionsOverall, with the present study, we offer researchers in the epigenetic field a novel long-awaited QC tool that for the first time allows them to measure key quality and quantity parameters of the most popular DNA conversion process. The tool also enables standardization to prevent inconsistent data and false outcomes in the future, regardless of the downstream experimental analysis of DNA methylation-based research and applications across different fields of biology and biomedicine.

The Relationship Between Wolf-to-dog Evolution and Geographic Variation and its Implications for Biodiversity

The evolutionary relationship between wolves and dogs is a fascinating subject in the field of biology, revealing how natural selection and human activity work together to shape the evolutionary trajectory of species. This paper takes the East Asian grey wolf as a research subject and analyse the evolutionary process from wolf to dog and the impacts caused by geographic distribution and human activities. At the same time, geographical isolation from Eurasian wolves also led to the differential evolution of wolves. Human activities, especially domestication and selection by humans, accelerated the evolution and diversity of dogs. The East Asian gray Wolf may be a key contributor to the East Asian dog breed, with humans selectively breeding wolves, forming a variety of dog breeds to meet different needs. The global distribution of dogs reflects the significant influence of geographical factors, with dog breeds in different regions forming unique breed differences due to differences in adaptation to the environment, as well as the influence of selective breeding by humans. This paper provides new insights into the formation of species diversity and reveal the important role of human activities in biological evolution.

Evaluation of safe exposure time for two-photon microscopy imaging of acrylic-painted mock-ups

Abstract Nonlinear optical microscopies are widely used in the biological and biomedical fields, as they are non-invasive techniques that permit the safe structural and morphological characterisation of cells and tissues. They are increasingly being used in the Cultural Heritage field because of their ability to overcome some limits of the well-established optical techniques. However, since nonlinear optical microscopies use pulsed laser sources with high peak power, their application in Cultural Heritage raises concerns due to artworks' unique, priceless, and delicate nature. In this paper, we present a new method for evaluating the photo-induced damage when using a near-infrared femtosecond pulsed laser to perform two-photon excited fluorescence imaging of acrylic-painted mock-ups. In particular, we explore herein several irradiation conditions, varying the exposure time and excitation power, in order to provide useful experimental indications for safely imaging acrylic paints with two-photon microscopy.

HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data.

High-dimensional mixed-effects models are an increasingly important form of regression in which the number of covariates rivals or exceeds the number of samples, which are collected in groups or clusters. The penalized likelihood approach to fitting these models relies on a coordinate descent algorithm that lacks guarantees of convergence to a global optimum. Here, we empirically study the behavior of this algorithm on simulated and real examples of three types of data that are common in modern biology: transcriptome, genome-wide association, and microbiome data. Our simulations provide new insights into the algorithm's behavior in these settings, and, comparing the performance of two popular penalties, we demonstrate that the smoothly clipped absolute deviation (SCAD) penalty consistently outperforms the least absolute shrinkage and selection operator (LASSO) penalty in terms of both variable selection and estimation accuracy across omics data. To empower researchers in biology and other fields to fit models with the SCAD penalty, we implement the algorithm in a Julia package, HighDimMixedModels.jl.