Hasil untuk "Biology (General)"

M. Karplus, J. Kuriyan

M. Longair, D. Baker, J. Armstrong

Jeong Wook Lee, D. Na, J. Park et al.

L. Galluzzi, O. Kepp, M. V. Heiden et al.

W. J. Lucas, A. Groover, Raffael Lichtenberger et al.

B. Halliwell

P. Tompa

S. Gavrilets, J. Losos

Nicolae Sapoval, Amirali Aghazadeh, Michael G. Nute et al.

Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences. Deep learning has enabled advances in understanding biology. In this review, the authors outline advances, and limitations of deep learning in five broad areas and the future challenges for the biosciences.

S. Burley, R. Roeder

E. Aron, Arthur Aron, Jadzia Jagiellowicz

Nobuto Takeuchi, Kunihiko Kaneko

The Central Dogma of molecular biology, as originally proposed by Crick, asserts that information passed into protein cannot flow back out. This principle has been interpreted as underpinning modern understandings of heredity and evolution, implying the unidirectionality of information flow from nucleic acids to proteins. Here, we propose a generalisation of the Central Dogma as a division of labour between the transmission and expression of information: the transmitter (nucleic acids) perpetuates information across generations, whereas the expressor (protein) enacts this information to facilitate the transmitter's function without itself perpetuating information. We argue that this generalisation offers two benefits. First, it provides a unifying perspective for comparing the Central Dogma to analogous divisions of labour observed at vastly different biological scales, including multicellular organisms, eukaryotic cells, organelles, and bacteria. Second, it offers a theoretical framework to explain the Central Dogma as an outcome of evolution. Specifically, we review a mathematical model suggesting that the Central Dogma originates through spontaneous symmetry breaking driven by evolutionary conflicts between different levels of selection. By reframing the Central Dogma as an informational relationship between components of a system, this generalisation underscores its broader relevance across the biological hierarchy and sheds light on its evolutionary origin.

Kevin Hudnall

We extend a formal framework that previously derived time from the multifractal structure of biological lineages (Hudnall \& D'Souza, 2025). That work showed that time itself is multifractal -- not a universal background dimension, but an observer-dependent geometry. Here we develop the corresponding theory of measurement: showing that a multifractal conception of time not only permits measurement, but grounds it more rigorously in the structure of biology. The tree of life is modeled as the outcome of stochastic, convex branching, and we show how information-theoretic and fractal measures render its multifractal geometry into measurable, observer-relative time intervals. At the core is a dilation equation that expresses relative time elapse between entities as dimensionless ratios. Operational standards such as the SI second remain valid, but our framework makes explicit their lineage-dependence. This framework unifies measurement theory with biological form, preserves full compatibility with established science, and provides a biologically grounded theory of observation. It enables comparative analyses of duration and kinematics across lineages, with predictions that are directly open to experimental validation.

Chaobo Zhang, Chengshuai Xu, Zhenxia Zhu et al.

Eutrophication facilitates the proliferation of cyanobacteria, ultimately leading to the formation of harmful cyanobacterial blooms. Prodigiosin, known for its algicidal properties, presents significant potential for application in water pollution remediation. This study aims to identify and characterize a novel strain with superior prodigiosin production capabilities and to elucidate the algicidal mechanism of prodigiosin against <i>Microcystis aeruginosa</i> and <i>Anabaena</i> sp. by assessing the photosynthetic responses of algal cells in the presence of prodigiosin. The findings revealed the isolation and identification of a new strain, ZC52, classified as <i>Serratia marcescens</i>. The optimal medium composition was determined to be 20.0 mL·L⁻¹ glycerol, 15.0 g·L⁻¹ beef bone peptone, 15.0 g·L⁻¹ magnesium sulfate heptahydrate, 0.15 g·L⁻¹ corn dry powder, and 0.250% tyrosine, resulting in a 47.40% increase in prodigiosin yield, thereby achieving a production level of 7.644 g·L⁻¹. Moreover, the algicidal activity exhibited a concentration-dependent relationship, with 10.0 mg·L⁻¹ of prodigiosin leading to approximately 53.25% and 30.44% inhibition of chlorophyll a content within 24 h, demonstrating the potential of prodigiosin as an effective algicidal compound. Meanwhile, exposure to 10.0 mg·L⁻¹ of prodigiosin resulted in reductions of 46.88% and 21.02% in the Fv/Fm values of <i>M. aeruginosa</i> and <i>Anabaena</i> sp., respectively. Our results indicated that prodigiosin can inhibit the accumulation of photosynthetic pigments and significantly diminish algal photosynthetic efficiency. This study not only identifies valuable microbial resources for prodigiosin production but also provides a theoretical framework and empirical evidence to support the scientific management of cyanobacterial blooms.

Anelda van der Walt, Kim Martin, Sumir Panji et al.

Xindong Wei, Anfeng Si, Shuai Zhao et al.

Abstract Background Circular (circ)RNAs have emerged as crucial contributors to cancer progression. Nonetheless, the expression regulation, biological functions, and underlying mechanisms of circRNAs in mediating hepatocellular carcinoma (HCC) progression remain insufficiently elucidated. Methods We identified circUCK2(2,3) through circRNA sequencing, RT–PCR, and Sanger sequencing. CircUCK2(2,3) levels were measured in two independent HCC cohorts using quantitative real-time PCR (qRT–PCR). We explored the functions of circUCK2(2,3) using gain- and loss-of-function assays. Techniques such as RNA-sequencing, RNA immunoprecipitation (RIP), polysome fractionation, RNA pulldown, dual luciferase reporter assay, inhibitors of EGFR downstream signaling, CRISPR–Cas9, and medium transfer assays were employed to investigate the regulatory mechanisms and the protumoral activities of circUCK2(2,3). Additionally, in vitro cytotoxic assays and patient-derived xenograft (PDX) models assessed the effects of circUCK2(2,3) on the cytotoxic synergy of lenvatinib and EGFR inhibitors. Results CircUCK2(2,3) is upregulated in HCC tissues and serves as an independent risk factor for poor recurrence-free survival. The expression of circUCK2(2,3) is independent on its host gene, UCK2, but is regulated by its upstream promoter and flanking inverted complementary sequences. Functionally, circUCK2(2,3) enhances HCC proliferation, migration, and invasion, both in vitro and in vivo. Mechanistically, by sponging miR-149-5p, circUCK2(2,3) increases CNIH4 levels, which in turn amplifies TGFα secretion, resulting in the activation of EGFR and downstream pAKT and pERK signaling pathways. Moreover, circUCK2(2,3) overexpression sensitizes HCC cells to EGFR inhibitors, and increases the synergistic cytotoxicity of combined lenvatinib and EGFR inhibitor treatment. Conclusions CircUCK2(2,3) regulates a novel oncogenic pathway, miR-149-5p–CNIH4–TGFα–EGFR, in HCC, presenting a viable therapeutic target and biomarker for the precision treatment of HCC. Graphical Abstract

Xiu Feng, Xiaoling Wang, Ren Zhu et al.

ABSTRACT The endemic fishes in the upper Yellow River and its adjacent waters on the Qinghai‐Tibet Plateau are very sensitive and vulnerable to human activities and climate changes. However, the status of populations, such as genetic diversity and population genetic structure, remains unclear, limiting further conservation and utilization of their natural resources. Here, genetic diversity and population genetic structure of the highly specialized Schizothoracinae fishes were analyzed by using concatenated mitochondrial COI and D‐loop sequences of 517 samples from 21 geographic populations in Qinghai Lake (QHL), the canyon section and the river source section of the upper Yellow River (CUYR and RUYR), Qaidam River (DQDR), and Golmud River (GMR). The results showed that populations from CUYR, RUYR, and QHL showed higher genetic diversity than the DQDR populations, and the GMR population exhibited the lowest haplotype diversity but high nucleotide diversity. In the upper Yellow River, dam‐affected populations with low resource supplementation showed reduced genetic diversity, while those with frequent stocking events exhibited comparable genetic diversity relative to undisturbed populations. All samples were divided into three distinct phylogenetic lineages, with QHL and DQDR samples belonging to only one single and distinct lineage each, and GMR samples included in both lineages, with CUYR and RUYR samples assigned to all lineages. Low genetic differentiation was detected between GMR and RUYR, QHL and CUYR, and CUYR and RUYR. These findings indicated that the population genetic structure of endemic Schizothoracinae fishes in the upper Yellow River and its adjacent waters was primarily shaped by hydrological connectivity and isolation caused by historical geological events, and the genetic diversity was influenced by dam construction and stocking activities.

Longfei Xu, Xuefeng Xu

This paper investigates the coupled relationship between solid-phase temperature fields and droplet evaporation, focusing on the effects of substrate thermal conduction properties on droplet evaporation behavior. A mathematical model is developed to analyze the impacts of substrate thermal conductivity, thickness, and lower-surface temperature on evaporation rate, surface temperature, and evaporation flux. A dimensionless relative evaporation rate (HCs) is introduced to characterize the influence of substrate thermal conduction. Results show that increasing substrate thermal conductivity enhances droplet surface temperature and evaporation flux, thereby monotonically increasing evaporation rate until it approaches the rate of the evaporative cooling model. Conversely, increasing substrate thickness lengthens the heat transfer path, reducing heat conducted to the solid–liquid interface and decreasing evaporation rate. Changes in substrate lower-surface temperature significantly affect evaporation rate, but HCs remains nearly unaffected. The concept of equivalent substrates is proposed and verified through dimensionless analysis and simulations. It is found that different combinations of substrate thickness and thermal conductivity exhibit consistent effects on droplet evaporation, with minimal relative errors in evaporation rate and total heat transfer at the solid–liquid interface. This confirms the existence of the equivalent substrate phenomenon. Additionally, the effects of droplet properties, such as contact angle and evaporative cooling coefficient (<i>Ec</i>), on the equivalent substrate phenomenon are explored, revealing negligible impacts. These findings provide theoretical guidance for optimizing droplet evaporation processes in practical applications, such as micro/nanoscale thermal management systems.

R. Blakytny, E. Jude

Omar Abdelghani Attafi, Damiano Clementel, Konstantinos Kyritsis et al.

Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The DOME recommendations aim to enhance the validation and reproducibility of ML research by establishing standards for key aspects such as data handling and processing, optimization, evaluation, and model interpretability. The recommendations help to ensure that key details are reported transparently by providing a structured set of questions. Here, we introduce the DOME Registry (URL: registry.dome-ml.org), a database that allows scientists to manage and access comprehensive DOME-related information on published ML studies. The registry uses external resources like ORCID, APICURON and the Data Stewardship Wizard to streamline the annotation process and ensure comprehensive documentation. By assigning unique identifiers and DOME scores to publications, the registry fosters a standardized evaluation of ML methods. Future plans include continuing to grow the registry through community curation, improving the DOME score definition and encouraging publishers to adopt DOME standards, promoting transparency and reproducibility of ML in the life sciences.