Hasil untuk "Biology (General)"

Jie Li, Yuhao Li, Xiaoyang Zhou et al.

Abstract Chronic inflammation in adipose tissue is widely recognized as a pivotal link connecting obesity to a spectrum of related chronic diseases, including type 2 diabetes, non-alcoholic fatty liver disease, and cardiovascular disorders. In this pathogenic process, the dysregulated interaction between adipocytes and adipose-resident immune cells plays a critical regulatory role; however, the underlying mechanisms governing this abnormal interaction remain largely unknown. In this study, we showed that upregulated β2-microglobulin expression in hypertrophic adipocytes during obesity not only mediated the activation of adipose-resident CD8+ T cells in a cell contact-dependent manner but also facilitated iron overload and the ferroptosis of adipocytes, thereby promoting the M1 polarization of adipose tissue macrophages. Conversely, specific ablation of β2-microglobulin in adipocytes effectively suppressed the activation and accumulation of adipose-resident CD8+ T cells, as well as adipocyte ferroptosis and M1 polarization, ultimately preventing high-fat diet-induced obesity and its related inflammation and metabolic disorders. Additionally, adeno-associated virus-mediated adipose-targeted knockdown of β2-microglobulin has been demonstrated to therapeutically alleviate high-fat diet-induced obesity, as well as its related chronic inflammation and metabolic disorders. Furthermore, our bioinformatic analysis of human adipose transcriptome data revealed a strong correlation between adipose β2-microglobulin and obesity. More importantly, β2-microglobulin is significantly upregulated in adipocytes isolated from patients with obesity. Thus, our findings highlight the pivotal role of adipocytes in obesity-associated chronic inflammation and metabolic disorders via β2-microglobulin-dependent mechanisms.

Hyuk Lee

As edge computing environments become increasingly dynamic, the need for efficient job scheduling and proactive fault prevention is becoming paramount. In such environments, minimizing machine downtime and maintaining productivity are critical challenges. In this paper, we propose an integrated approach to scheduling optimization that combines deep learning-based fault prediction with Satisfiability Modulo Theories (SMT)-based scheduling techniques. The proposed system predicts fault probabilities for machines in real time by leveraging operational state features such as temperature, vibration, tool wear, and operating hours. These fault predictions are then used as inputs to the SMT solver, which dynamically optimizes job scheduling. The system ensures task completion within deadlines while minimizing fault risks and optimizing resource utilization. To achieve this, the deep learning model continuously updates fault probabilities through a rolling prediction mechanism, allowing the scheduling system to proactively adapt to changing machine conditions. The SMT solver incorporates these predictions into its optimization process, ensuring that the schedule dynamically reflects the latest system state. The proposed method has been evaluated in simulated production line scenarios, demonstrating significant reductions in machine faults, improved scheduling efficiency, and enhanced overall system reliability. By integrating predictive maintenance with optimization techniques, this research contributes to the development of robust and adaptive scheduling systems for dynamic production environments.

Lei Feng

Some astrobiological models suggest that molecular clouds may serve as habitats for extraterrestrial life. This study reviews recent theoretical work addressing the physical and biochemical prerequisites for life in such environments, with particular focus on three subjects: (1) bioenergetic pathways under extreme low-temperature conditions; (2) the emergence and preservation of biomolecular chirality; and (3) detection methodologies for potential biosignatures. In this paper, we formally introduce the molecular cloud biology concept, which integrates all physicochemical and metabolic processes hypothesized to sustain life within molecular clouds. As a potential branch of astrobiology, molecular cloud biology warrants interdisciplinary collaborative research to validate its foundational assumptions and explore its scientific implications.

Jens‐Peter Barnekow Lillesø, Davide Barsotti, James Kalema et al.

Abstract Forest and landscape restoration are increasingly popular nature‐based solutions to mitigate climate change and safeguard biodiversity. Restoration planning and monitoring implies that a reference ecosystem has been defined to which the restored site can be compared, but how to best select such reference? We tested three different potential natural vegetation (PNV) maps of the same areas in Kenya and Uganda for their utility as ecological references with independent data that were not used when those maps were made. These independent datasets included presence observations of woody species from 76 sites in forest reserves in Kenya and Uganda, and classification of surveyed species into a system that included “forest‐only” and “nonforest‐only” ecological types. Our tests show that (1) the three vegetation maps largely agree on the environmental envelopes/ranges within which forests occur. (2) There are large differences in how well the maps predict the presence of forest‐only species. (3) Two maps, based on empirical observations (V4A and White), predict forest types well, whereas the third, based on climate envelopes only (NS), performs poorly. (4) A large area in Uganda is potentially in one of two alternative stable states. We conclude that it is possible to evaluate the utility of PNV maps at a more detailed scale than the level of biome and ecoregion. This indicates that it is possible to map PNV at scales required for reference for restoration and management of forest vegetation. We recommend that empirically based maps of potential natural vegetation are used in restoration planning (biome and PNV maps based on climate envelopes alone may be unreliable tools) as a baseline model for predicting the distribution of reference ecosystems under current and future conditions. It could conveniently be done by deconstructing the existing biome maps, supported by rapid botanical surveys.

Chenfei Dong, Yue Zhang, Jiayue Zeng et al.

Abstract Colorectal cancer (CRC) ranks as the second most lethal cancer worldwide because of its high rate of metastasis, and approximately 20% of CRC patients have metastases at initial diagnosis. Metabolic reprogramming, a hallmark of cancer cells, has been implicated in the process of metastasis. We previously demonstrated that fucosyltransferase 2 (FUT2) promotes the malignancy of CRC cells, however, the underlying mechanisms remain unclear. Here, bioinformatic analysis revealed that FUT2 is associated with the malignant phenotype and fatty acid metabolism in CRC. FUT2 knockdown decreased glucose uptake and de novo fatty acid synthesis, which in turn inhibited the proliferation and metastasis of CRC cells. Mechanistically, FUT2 promotes YAP1 nuclear translocation and stabilizes mSREBP-1 by fucosylation, thus promoting de novo fatty acid synthesis in CRC cells. In summary, this study demonstrates that FUT2 promotes the proliferation and metastasis of CRC cells by reprogramming fatty acid metabolism via YAP/TAZ signaling and SREBP-1, indicating that FUT2 might be a potential target for developing therapeutic strategies against CRC.

Luca Ciotti

It has been proposed that the flat rotation curves observed at large radii in disk galaxies can be interpreted as an effect of General Relativity (GR) instead of the presence of dark matter (DM) halos in Newtonian gravity. In Ciotti (2022) the problem is rigorously explored in the special setting of the weak-field, low-velocity gravitomagnetic limit of GR. The rotation curves are obtained for purely baryonic disk models with realistic density profiles, and compared with the predictions of Newtonian gravity for the same disks, in absence of DM. The rotation curves are indistinguishable, with percentual GR corrections at all radii of the order of $\approx 10^{-6}$ or less, so that DM halos are required in gravitomagnetism as in Newtonian gravity. From a more general point of view, a list of the most urgent problems that must be addressed by any proposed GR-based alternative to the existence of DM, is given.

Mario Fargnoli, Luca Murgianu

Nowadays, services related to IT technologies have assumed paramount importance in most sectors, creating complex systems involving different stakeholders. Such systems are subject to unpredictable risks that differ from what is usually expected and cannot be properly managed using traditional risk assessment approaches. Consequently, ensuring their reliability represents a critical task for companies, which need to adopt resilience engineering tools to reduce the occurrence of failures and malfunctions. With this goal in mind, the current study proposes a risk assessment procedure for cloud migration processes that integrates the application of the Functional Resonance Analysis Method (FRAM) with tools aimed at defining specific performance requirements for the suppliers of this service. In particular, the Critical-To-Quality (CTQ) method was used to define the quality drivers of the IT platform customers, while technical standards were applied to define requirements for a security management system, including aspects relevant to the supply chain. Such an approach was verified by means of its application to a real-life case study, which concerns the analysis of the risks inherent to the supply chain related to cloud migration. The results achieved can contribute to augmenting knowledge in the field of IT systems’ risk assessment, providing a base for further research.

Lu Guo, Yun-Hao Wang, Rui Cui et al.

Motile bacteria navigate toward favorable conditions and away from unfavorable environments using chemotaxis. Mechanisms of sensing attractants are well understood; however, molecular aspects of how bacteria sense repellents have not been established. Here, we identified malate as a repellent recognized by the MCP2201 chemoreceptor in a bacterium Comamonas testosteroni and showed that it binds to the same site as an attractant citrate. Binding determinants for a repellent and an attractant had only minor differences, and a single amino acid substitution in the binding site inverted the response to malate from a repellent to an attractant. We found that malate and citrate affect the oligomerization state of the ligand-binding domain in opposing way. We also observed opposing effects of repellent and attractant binding on the orientation of an alpha helix connecting the sensory domain to the transmembrane helix. We propose a model to illustrate how positive and negative signals might be generated.

Xinyu Gong, Jason Holmes, Yiwei Li et al.

Recent advances in Large Language Models (LLMs) have presented new opportunities for integrating Artificial General Intelligence (AGI) into biological research and education. This study evaluated the capabilities of leading LLMs, including GPT-4, GPT-3.5, PaLM2, Claude2, and SenseNova, in answering conceptual biology questions. The models were tested on a 108-question multiple-choice exam covering biology topics in molecular biology, biological techniques, metabolic engineering, and synthetic biology. Among the models, GPT-4 achieved the highest average score of 90 and demonstrated the greatest consistency across trials with different prompts. The results indicated GPT-4's proficiency in logical reasoning and its potential to aid biology research through capabilities like data analysis, hypothesis generation, and knowledge integration. However, further development and validation are still required before the promise of LLMs in accelerating biological discovery can be realized.

Stuart Semple, Ramon Ferrer-i-Cancho, Morgan L. Gustison

Linguistic laws, the common statistical patterns of human language, have been investigated by quantitative linguists for nearly a century. Recently, biologists from a range of disciplines have started to explore the prevalence of these laws beyond language, finding patterns consistent with linguistic laws across multiple levels of biological organisation, from molecular (genomes, genes, and proteins) to organismal (animal behaviour) to ecological (populations and ecosystems). We propose a new conceptual framework for the study of linguistic laws in biology, comprising and integrating distinct levels of analysis, from description to prediction to theory building. Adopting this framework will provide critical new insights into the fundamental rules of organisation underpinning natural systems, unifying linguistic laws and core theory in biology.

Mathieu Chevalier, Pablo Tedesco, Gael Grenouillet

Background Population dynamics are driven by a number of biotic (e.g., density-dependence) and abiotic (e.g., climate) factors whose contribution can greatly vary across study systems (i.e., populations). Yet, the extent to which the contribution of these factors varies across populations and between species and whether spatial patterns can be identified has received little attention. Methods Here, we used a long-term (1982–2011), broad scale (182 sites distributed across metropolitan France) dataset to study spatial patterns in the population’s dynamics of three freshwater fish species presenting contrasted life-histories and patterns of elevation range shifts in recent decades. We used a hierarchical Bayesian approach together with an elasticity analysis to estimate the relative contribution of a set of biotic (e.g., strength of density dependence, recruitment rate) and abiotic (mean and variability of water temperature) factors affecting the site-specific dynamic of two different size classes (0+ and >0+ individuals) for the three species. We then tested whether the local contribution of each factor presented evidence for biogeographical patterns by confronting two non-mutually exclusive hypotheses: the “range-shift” hypothesis that predicts a gradient along elevation or latitude and the “abundant-center” hypothesis that predicts a gradient from the center to the edge of the species’ distributional range. Results Despite contrasted life-histories, the three species displayed similar large-scale patterns in population dynamics with a much stronger contribution of biotic factors over abiotic ones. Yet, the contribution of the different factors strongly varied within distributional ranges and followed distinct spatial patterns. Indeed, while abiotic factors mostly varied along elevation, biotic factors—which disproportionately contributed to population dynamics—varied along both elevation and latitude. Conclusions Overall while our results provide stronger support for the range-shift hypothesis, they also highlight the dual effect of distinct factors on spatial patterns in population dynamics and can explain the overall difficulty to find general evidence for geographic gradients in natural populations. We propose that considering the separate contribution of the factors affecting population dynamics could help better understand the drivers of abundance-distribution patterns.

N. V. Krishnendu, Frank Ohme

The detections of gravitational-wave (GW) signals from compact binary coalescence by ground-based detectors have opened up the era of GW astronomy. These observations provide opportunities to test Einstein's general theory of relativity at the strong-field regime. Here we give a brief overview of the various GW-based tests of General Relativity (GR) performed by the LIGO-Virgo collaboration on the detected GW events to date. After providing details for the tests performed in four categories, we discuss the prospects for each test in the context of future GW detectors. The four categories of tests include the consistency tests, parametrized tests for GW generation and propagation, tests for the merger remnant properties, and GW polarization tests.

Johannes Borgqvist, Fredrik Ohlsson, Ruth E. Baker

We discuss the role and merits of symmetry methods for the analysis of biological systems. In particular, we consider systems of first order ordinary differential equations and provide a comprehensive review of the geometrical foundations pertinent to symmetries of such systems. Subsequently, we present an algorithm for finding infinitesimal generators of symmetries for systems with rational reaction terms, and an open-source implementation of the algorithm using symbolic computations. We discuss two complementary perspectives on symmetries in mechanistic modelling; as tools for the analysis of a given model or as a geometrical principle for incorporating biological properties in the construction of new models. Through numerous examples of relevance to modelling in biology we demonstrate the different uses of symmetry methods, and also discuss how to infer symmetries from experimental data.

Velia Penza, Stephen J. Russell, Autumn J. Schulze

Long polycytidine (polyC) tracts varying in length from 50 to 400 nucleotides were first described in the 5′-noncoding region (NCR) of genomes of picornaviruses belonging to the Cardio- and Aphthovirus genera over 50 years ago, but the molecular basis of their function is still unknown. Truncation or complete deletion of the polyC tracts in picornaviruses compromises virulence and pathogenicity but do not affect replicative fitness in vitro, suggesting a role as “viral security” RNA element. The evidence available suggests that the presence of a long polyC tract is required for replication in immune cells, which impacts viral distribution and targeting, and, consequently, pathogenic progression. Viral attenuation achieved by reduction of the polyC tract length has been successfully used for vaccine strategies. Further elucidation of the role of the polyC tract in viral replication cycle and its connection with replication in immune cells has the potential to expand the arsenal of tools in the fight against cancer in oncolytic virotherapy (OV). Here, we review the published data on the biological significance and mechanisms of action of the polyC tract in viral pathogenesis in Cardio- and Aphthoviruses.

Yung-Yue Chen, Ming-Zhen Ellis-Tiew, Wei-Chun Chen et al.

In this investigation, a smart collision avoidance control design, which integrates a collision avoidance navigation and a nonlinear optimal control method, is developed for unmanned surface vessels (USVs) under randomly incoming ships and fixed obstacle encounter situations. For achieving collision avoidance navigation, a fuzzy collision risk indicator and a fuzzy collision avoidance acting timing indicator are developed. These two risk indicators can offer effective pre-alarms for making the controlled USVs to perform dodge actions in time when obstacles appear. As to nonlinear optimal control law, it provides a precise trajectory tracking ability for the controlled USVs to follow a collision avoidance trajectory, which is generated via a smart collision avoidance trajectory generator. Finally, a power allocation method is used to transform the desired control law into available actuator outputs to guide the USVs to follow a desired collision avoidance trajectory. From simulation results, the proposed collision avoidance strategy reveals a promising collision avoidance performance and an accurate trajectory tracking ability with respect to fixed objects and randomly moving ships under the effect of environmental ocean disturbances.

Piotr T. Chrusciel

A review of selected topics in mathematical general relativity

Joris Deelen

A study of over 40,000 individuals suggests that carrying a small number of ultra-rare genetic variants is associated with a longer lifespan.

Warisa Thangjai, Sa-Aat Niwitpong, Suparat Niwitpong

The log-normal distribution is often used to analyze environmental data like daily rainfall amounts. The rainfall is of interest in Thailand because high variable climates can lead to periodic water stress and scarcity. The mean, standard deviation or coefficient of variation of the rainfall in the area is usually estimated. The climate moisture index is the ratio of plant water demand to precipitation. The climate moisture index should use the coefficient of variation instead of the standard deviation for comparison between areas with widely different means. The larger coefficient of variation indicates greater dispersion, whereas the lower coefficient of variation indicates the lower risk. The common coefficient of variation, is the weighted coefficients of variation based on k areas, presents the average daily rainfall. Therefore, the common coefficient of variation is used to describe overall water problems of k areas. In this paper, we propose four novel approaches for the confidence interval estimation of the common coefficient of variation of log-normal distributions based on the fiducial generalized confidence interval (FGCI), method of variance estimates recovery (MOVER), computational, and Bayesian approaches. A Monte Carlo simulation was used to evaluate the coverage probabilities and average lengths of the confidence intervals. In terms of coverage probability, the results show that the FGCI approach provided the best confidence interval estimates for most cases except for when the sample case was equal to six populations (k = 6) and the sample sizes were small (nI < 50), for which the MOVER confidence interval estimates were the best. The efficacies of the proposed approaches are illustrated with example using real-life daily rainfall datasets from regions of Thailand.

D. L. Wright

This document explores the production of peanuts (Arachis hypogaea) in Florida and their potential as a biofuel crop. Peanuts thrive in the sandy, infertile soils of the southeastern United States, particularly in Florida, where they cover approximately 170,000 acres annually. With a high oil content of 45-52%, peanuts can produce over 150 gallons of biodiesel per acre, making them a promising biofuel source. The crop's biology, production practices, potential yields, challenges, and environmental considerations are discussed. Peanuts' adaptability, established production practices, and the potential for breeding disease-resistant, low-input varieties position them as a competitive candidate in the biofuels market. This document was first time published in January 2008. 

Poonam Malhotra, Graham H. Read, Joanne B. Weidhaas

Recent studies in cancer diagnostics have identified microRNAs (miRNAs) as promising cancer biomarkers. Single nucleotide polymorphisms (SNPs) in miRNA binding sites, seed regions, and coding sequences can help predict breast cancer risk, aggressiveness, response to stimuli, and prognosis. This review also documents significant known miR-SNPs in miRNA biogenesis genes and their effects on gene regulation in breast cancer, taking into account the genetic background and ethnicity of the sampled populations. When applicable, miR-SNPs are evaluated in the context of other patient factors, including mutations, hormonal status, and demographics. Given the power of miR-SNPs to predict patient cancer risk, prognosis, and outcomes, further study of miR-SNPs is warranted to improve efforts towards personalized medicine.