Hasil untuk "Biology (General)"

Adriana Rendon, K. Schäkel

Research on psoriasis pathogenesis has largely increased knowledge on skin biology in general. In the past 15 years, breakthroughs in the understanding of the pathogenesis of psoriasis have been translated into targeted and highly effective therapies providing fundamental insights into the pathogenesis of chronic inflammatory diseases with a dominant IL-23/Th17 axis. This review discusses the mechanisms involved in the initiation and development of the disease, as well as the therapeutic options that have arisen from the dissection of the inflammatory psoriatic pathways. Our discussion begins by addressing the inflammatory pathways and key cell types initiating and perpetuating psoriatic inflammation. Next, we describe the role of genetics, associated epigenetic mechanisms, and the interaction of the skin flora in the pathophysiology of psoriasis. Finally, we include a comprehensive review of well-established widely available therapies and novel targeted drugs.

Natalie Meyer, L. Penn

Sean B. Carroll

W. Bond, J. Keeley

J. M. Jay

R. Jefferson

H. Godfray

B. Fox, J. Hendry, Disease J J Selikoff

Organized into two sections. Part 1 is sufficient for students of Radiology and Nuclear Medicine and follows the syllabus published by RSNA. Students in Radiation Oncology need the general information contained in Part 1, but also need the more specialized information contained in Part 2. New chapters introduce new therapies on medical countermeasures to radiation exposure and new molecular techniques in radiology. Mirrors the format of the Syllabus in Radiation Biology prepared by the Radiological Society of North America (RSNA). Written for residents, researchers, and graduate students in radiology, nuclear medicine, radiation oncology, and medical physics. Generally considered the most comprehensive textbook on cellular and molecular radiobiology.

N. Becker, D. Petrić, C. Boase et al.

A. Palmer, C. Strobeck

H. Regan, M. Colyvan, M. Burgman

L. Lehmann, L. Lehmann, L. Keller

Isabella Frighetto Bomfiglio, Isabelli Seiler de Medeiros Mendes, Diego Bonatto

DNA cloning methods are fundamental tools in molecular biology, synthetic biology, and genetic engineering that enable precise DNA manipulation for various scientific and biotechnological applications. This review systematically summarizes the major restriction-free overlapping sequence cloning (RFOSC) techniques currently used in synthetic biology and examines their development, efficiency, practicality, and specific applications. In vitro methods, including Gibson Assembly, Circular Polymerase Extension Cloning (CPEC), Polymerase Incomplete Primer Extension (PIPE), Overlap Extension Cloning (OEC), Flap Endonuclease Cloning (FEN-Cloning), and commercially available techniques such as TOPO and In-Fusion, have been discussed alongside hybrid approaches such as Ligation-Independent Cloning (LIC), Sequence-Independent Cloning (SLIC), and T5 Exonuclease-Dependent Assembly (TEDA). Additionally, in vivo methods leveraging host recombination machinery, including Yeast Homologous Recombination (YHR), In Vivo Assembly (IVA), Transformation-Associated Recombination (TAR), and innovative approaches such as Multiple-Round In Vivo Site-Specific Assembly (MISSA) and Phage Enzyme-Assisted Direct Assembly (PEDA), are critically evaluated. The review highlights that method selection should consider the scale, complexity, cost, and specific needs of individual research projects, noting that no single technique is universally optimal. Future trends suggest the increased integration of enzymatic efficiency, host versatility, and automation, broadening the accessibility and capabilities of DNA assembly technologies.

Amr S. Mohamed, Emily Nguyen, Deepa Kundur

Amidst the growing demand for implementing advanced control and decision-making algorithms|to enhance the reliability, resilience, and stability of power systems|arises a crucial concern regarding the safety of employing machine learning techniques. While these methods can be applied to derive more optimal control decisions, they often lack safety assurances. This paper proposes a framework based on control barrier functions to facilitate safe learning and deployment of reinforcement learning agents for power system control applications, specifically in the context of automatic generation control. We develop the safety barriers and reinforcement learning framework necessary to establish trust in reinforcement learning as a safe option for automatic generation control - as foundation for future detailed verification and application studies.

Benedikt Hartl, Michael Levin, Léo Pio-Lopez

Neural Cellular Automata (NCA) represent a powerful framework for modeling biological self-organization, extending classical rule-based systems with trainable, differentiable (or evolvable) update rules that capture the adaptive self-regulatory dynamics of living matter. By embedding Artificial Neural Networks (ANNs) as local decision-making centers and interaction rules between localized agents, NCA can simulate processes across molecular, cellular, tissue, and system-level scales, offering a multiscale competency architecture perspective on evolution, development, regeneration, aging, morphogenesis, and robotic control. These models not only reproduce biologically inspired target patterns but also generalize to novel conditions, demonstrating robustness to perturbations and the capacity for open-ended adaptation and reasoning. Given their immense success in recent developments, we here review current literature of NCAs that are relevant primarily for biological or bioengineering applications. Moreover, we emphasize that beyond biology, NCAs display robust and generalizing goal-directed dynamics without centralized control, e.g., in controlling or regenerating composite robotic morphologies or even on cutting-edge reasoning tasks such as ARC-AGI-1. In addition, the same principles of iterative state-refinement is reminiscent to modern generative Artificial Intelligence (AI), such as probabilistic diffusion models. Their governing self-regulatory behavior is constraint to fully localized interactions, yet their collective behavior scales into coordinated system-level outcomes. We thus argue that NCAs constitute a unifying computationally lean paradigm that not only bridges fundamental insights from multiscale biology with modern generative AI, but have the potential to design truly bio-inspired collective intelligence capable of hierarchical reasoning and control.

Pengqiang Yu, Kejia Wu, Dongsheng Li et al.

This paper introduces an analytical method for passive earth pressure calculation based on a rigorous stress field analysis within the sliding wedge. Unlike traditional horizontal layer methods, this approach directly solves for the stress state at any point within the wedge by analyzing the equilibrium of 2D differential soil elements under appropriate boundary conditions, eliminating the need for a priori assumptions about the soil arch shape. The method yields the passive earth pressure distribution on the retaining structure and derives the soil arch shape analytically from major principal stress trajectories. This derived arch shape differs notably from conventional circular or parabolic assumptions, especially at higher soil–wall friction angles. Parametric studies show that the passive earth pressure coefficient increases with internal friction angle and surcharge. However, a key finding is the non-monotonic dependence of the passive earth pressure coefficient on the soil–wall friction angle, contrasting with many existing theories. Comparisons show predictions by the proposed method align well with experimental data, particularly offering a better representation of pressure distributions in the lower regions of retaining walls compared to Coulomb theory and other existing methods.

S. David Stupski, Laura Casas Ferrer, Jacob S. Harrison et al.

Fieldwork is an essential component not just for organismal biology but also for the expanding umbrella of disciplines that have turned their attention toward the physics of living systems. Observing organisms in nature is a critical component of discovery; however, conducting field research can be a barrier for scientists who do not have experience working with organisms under challenging field conditions. Here, we propose 7 critical steps for organizing and executing interdisciplinary, curiosity-driven field research. Our steps are drawn from insights gained from the in Situ Jungle Biomechanics Lab (JBL), a field research course that helps early-career scientists from both physical and life sciences gain experience in both organizing and conducting interdisciplinary field research in the Amazon Rainforest. We emphasize a curiosity-driven approach towards the scientific inquiry of living systems, one we believe is crucial for discovery while working with wild organisms under highly unpredictable field conditions. We further provide guidance on teamwork when conducting fieldwork, including creating an inclusive environment and advocating for codes of conduct and team structures that aid in conflict resolution. Finally, we outline what we call the in situ approach to fieldwork, one that requires engagements with the environment, scientific community, and local peoples where field sites exist.

Nahid Moradi, Mina Soufi-Zomorrod, Simzar Hosseinzadeh et al.

Introduction: This study aimed to assess the potential of poly (acrylic acid)/tricalcium phosphate nanoparticles (PAA/triCaPNPs) scaffold in terms of biocompatibility and osteoconductivity properties the in-vivo evaluation as well as to investigate the performance of PAA/triCaPNPs scaffold (with or without exosomes derived from UC-MSCs) for bone regeneration of rat critical-sized defect. Methods: PAA/triCaPNPs scaffold was made from acrylic acid (AA) monomer, N,N’-methylenebisacrylamide (MBAA), sodium bicarbonate (SBC), and ammonium persulfate (APS) through freeze-drying method. For in vivo evaluation, we randomly divided 24 rats into three groups. The rat calvarial bone defects were treated as follows: (1) Control group: defects without any treatment, (2) scaffold group: defects treated with scaffold only, (3) scaffold+exo group: defects treated with scaffold enriched with exosomes (1 μg/μL, 150 μg per rat). Eight- and 12-weeks post-surgery, half of the animals were sacrificed and bone regeneration was examined through micro-computerized tomography (µ-CT), histological staining, and immunohistochemistry (IHC). Results: Quantitative analysis based on µ-CT scan images at 8 and 12 weeks post-implantation clearly indicated that healing rate for defects that were filled with scaffold enriched with exosome was significantly higher than defects filled with scaffold without exosome. The H&E and Masson staining results revealed that more new bone-like form developed in the scaffold+exo group than that in control and scaffold groups. Further, IHC staining for osteocalcin and CD31 confirmed that more bone healing in the scaffold+exo group at 12 weeks could be associated with osteogenesis and angiogenesis concurrently. Conclusion: In the present study, we aimed to investigate the therapeutic potential of PAA/triCaPNPs scaffold as a carrier of human UC-MSC-derived exosome to achieve the exosome-controlled release on calvarial bone defect. The in vivo results indicated that the exosome-enriched scaffold could effectively minify the defect area and improve the bone healing in rat model, and as such it could be an option for exosome-based therapy.

Yuxi Wei, Lijuan Chen, Qi Feng et al.

Soil salinization has become the most expansive form of soil degradation in arid and semiarid regions, and the management of soil salinization is imperative for achieving sustainable development. Soil microorganisms are supposed to play an integral role in controlling soil salinization, and the effects of high-salt environments on microbial community have been widely investigated, but there is currently limited comprehensive study on taxon co-occurrence patterns and assembly processes under different salt intensities. Here, based on high-throughput sequencing technologies, we analysed bacterial community structure and assembly mechanism under salt intensity in arid and semiarid regions. The results demonstrated that bacterial diversity was negatively correlated with soil salinity, and community structure also varied with changes in salt intensity. Solonchaks (soils with high soluble salt accumulation) had the lowest average degree of bacterial co-occurrence network, and there was a lower level of connectivity and correlation among bacteria in solonchaks compared to other salt-affected soils. The highest competitive connections among soil bacteria were detected in light-intensity saline soils, whereas overall collaborative connections increased with soil salinity. For co-occurrence network stability, the rare taxa (with each taxon’s relative abundance < 0.1%) were more essential than the abundant taxa (> 1%). As soil salinity increased, stochastic processes gradually dominated the community assembly, and the dispersal limitation contributed from 45.18% to 58.73%. These findings offered valuable information about how soil salt intensity affected soil bacterial community and would be useful in controlling soil salinization.

Wenqiang Liu, Li Yan, Ningning Ma et al.

With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eliminating the need for intricate mathematical derivations and offering greater flexibility. Deep learning technologies have demonstrated outstanding performance in anomaly detection problems and gained widespread recognition. However, when dealing with multivariate data anomaly detection problems, deep learning faces challenges such as large-scale data annotation and handling relationships between complex data variables. To address these challenges, this study proposes an innovative and lightweight deep learning model—the Attention-Based Deep Convolutional Autoencoding Prediction Network (AT-DCAEP). The model consists of a characterization network based on convolutional autoencoders and a prediction network based on attention mechanisms. The AT-DCAEP exhibits excellent performance in multivariate time series data anomaly detection without the need for pre-labeling large-scale datasets, making it an efficient unsupervised anomaly detection method. We extensively tested the performance of AT-DCAEP on six publicly available datasets, and the results show that compared to current state-of-the-art methods, AT-DCAEP demonstrates superior performance, achieving the optimal balance between anomaly detection performance and computational cost.