Hasil untuk "q-bio"

Travis E. Gibson

There are a number of errors in "mbtransfer: Microbiome intervention analysis using transfer functions and mirror statistics" PLOS Comp Bio (2024) spanning multiple aspects of the paper. The wrong inputs were provided to comparator methods for model training, when forecasting one method was provided initial conditions in the wrong units, and performance metrics were calculated without proper unit conversion. The false discovery rate and power analysis conclusions provided in the text are not supported by theory or the empirical testing that was performed within the paper. The paper also has data leakage issues, equations are written down incorrectly, and incorrect definitions/terminology are used.

Urvashi Verma, Margaret Lewis, Jordan Lehman et al.

The soybean aphid (\emph{Aphis glycines}) is an invasive insect pest that continues to cause large-scale damage to soybean crops in the North Central United States. The current manuscript proposes several mathematical models for the top-down bio-control of the aphid, as well as control via pesticides and neonicotinoids. The models are motivated empirically, and constructed based on laboratory experiments conducted to test control of aphids by Lacewing larvae, as well as by a parasitic wasp (\emph{Aphidius colemani}). The effectiveness of these models is compared by taking into account factors such as economic injury levels for soybeans, life history traits such as cannibalism amongst the predator, and intraguild predation between competing bio-control agents such as predators and parasitoids. The models predict multiple population peaks and transient chaotic dynamics when a predator and/or insecticides are used. It is observed that parasitoids, in conjunction with predators, are more efficient at stabilizing the population dynamics than insecticide use. They also suggest a combination of predators, parasitoids, and insecticides would be more efficient at suppressing aphid populations than using only predators or parasitoids. The models also qualitatively capture the features seen in long-time field data from 2000-2013. We discuss applications of our results to pest management strategies for soybean aphids in the context of a changing climate, as well as regime shifts.

Huiying Wang, Shunni Zhu, Mostafa E. Elshobary et al.

The process of preprocessing techniques such as acid and alkali pretreatment in lignocellulosic industry generates substantial solid residues and lignocellulosic pretreatment wastewater (LPW) containing glucose, xylose and toxic byproducts. In this study, furfural and vanillin were selected as model toxic byproducts. Kurthia huakuii as potential strain could tolerate to high concentrations of inhibitors. The results indicated that vanillin exhibited a higher inhibitory effect on K. huakuii (3.95 % inhibition rate at 1 g/L than furfural (0.45 %). However, 0.5 g/L vanillin promoted the bacterial growth (-2.35 % inhibition rate). Interestingly, the combination of furfural and vanillin exhibited antagonistic effects on bacterial growth (Q<0.85). Furfural and vanillin could be bio-transformed into less toxic molecules (furfuryl alcohol, furoic acid, vanillyl alcohol, and vanillic acid) by K. huakuii, and inhibitor degradation rate could be promoted by expression of antioxidant enzymes. This study provides important insights into how bacteria detoxify inhibitors in LPW, potentially enhancing resource utilization.

Qingzhong Hua, Lu Ren

At present, the treatment options available for idiopathic pulmonary fibrosis are both limited and often come with severe side effects, emphasizing the pressing requirement for innovative therapeutic alternatives. Myofibroblasts, which hold a central role in pulmonary fibrosis, have a close association with the Smad signaling pathway induced by transforming growth factor-β1 (TGF-β1) and the transformation of myofibroblasts driven by oxidative stress. Liquiritigenin, an active compound extracted from the traditional Chinese herb licorice, boasts a wide array of biomedical properties, such as anti-fibrosis and anti-oxidation. The primary objective of this study was to examine the impact of liquiritigenin on bleomycin-induced pulmonary fibrosis in mice and the underlying mechanisms. The anti-pulmonary fibrosis and anti-oxidant effects of liquiritigenin in vivo were tested by HE staining, Masson staining, DHE staining and bio-chemical methods. In vitro, primary mouse lung fibroblasts were treated with TGF-β1 with or without liquiritigenin, the effects of liquiritigenin in inhibiting differentiation of myofibroblasts and facilitating the translocation of Nrf2 were valued using Quantitative real-time polymerase chain reaction (Q-PCR), western blotting and immunofluorescence. Nrf2 siRNA and SIRT1 siRNA were used to investigate the mechanism underlies liquiritigenin’s effect in inhibiting myofibroblast differentiation. Liquiritigenin displayed a dose-dependent reduction effect in bleomycin-induced fibrosis. In laboratory experiments, it was evident that liquiritigenin possessed the ability to enhance and activate sirtuin1 (SIRT1), thereby facilitating the nuclear translocation of Nrf2 and mitigating the oxidative stress-induced differentiation of primary mouse myofibroblasts. Moreover, our investigation unveiled that SIRT1 not only regulated myofibroblast differentiation via Nrf2-mediated antioxidant responses against oxidative stress but also revealed liquiritigenin's activation of SIRT1, enabling direct binding to Smad. This led to decreased phosphorylation of the Smad complex, constrained nuclear translocation, and suppressed acetylation of the Smad complex, ultimately curtailing the transcription of fibrotic factors. Validation in live subjects provided substantial evidence for the anti-fibrotic efficacy of liquiritigenin through the SIRT1/Nrf2 signaling pathway. Our findings imply that targeting myofibroblast differentiation via the SIRT1/Nrf2 signaling pathway may constitute a pivotal strategy for liquiritigenin-based therapy against pulmonary fibrosis.

Ziwei Yang, Rikuto Kotoge, Xihao Piao et al.

Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer research. Empowering these successful machine learning models are the high-quality training datasets with sufficient data volume and adequate preprocessing. However, while there exist several public data portals, including The Cancer Genome Atlas (TCGA) multi-omics initiative or open-bases such as the LinkedOmics, these databases are not off-the-shelf for existing machine learning models. In this paper, we introduce MLOmics, an open cancer multi-omics database aiming at serving better the development and evaluation of bioinformatics and machine learning models. MLOmics contains 8,314 patient samples covering all 32 cancer types with four omics types, stratified features, and extensive baselines. Complementary support for downstream analysis and bio-knowledge linking are also included to support interdisciplinary analysis.

Hanna M. Tolle, Andrea I Luppi, Anil K. Seth et al.

Biological neural networks can perform complex computations to predict their environment, far above the limited predictive capabilities of individual neurons. While conventional approaches to understanding these computations often focus on isolating the contributions of single neurons, here we argue that a deeper understanding requires considering emergent dynamics - dynamics that make the whole system "more than the sum of its parts". Specifically, we examine the relationship between prediction performance and emergence by leveraging recent quantitative metrics of emergence, derived from Partial Information Decomposition, and by modelling the prediction of environmental dynamics in a bio-inspired computational framework known as reservoir computing. Notably, we reveal a bidirectional coupling between prediction performance and emergence, which generalises across task environments and reservoir network topologies, and is recapitulated by three key results: 1) Optimising hyperparameters for performance enhances emergent dynamics, and vice versa; 2) Emergent dynamics represent a near sufficient criterion for prediction success in all task environments, and an almost necessary criterion in most environments; 3) Training reservoir computers on larger datasets results in stronger emergent dynamics, which contain task-relevant information crucial for performance. Overall, our study points to a pivotal role of emergence in facilitating environmental predictions in a bio-inspired computational architecture.

Babak Nouri-Moghaddam, Mehdi Ghazanfari, Mohammad Fathian

Microarray technology is known as one of the most important tools for collecting DNA expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with challenges such as small sample size, a significant number of genes, imbalanced data, etc. that make classification models inefficient. Thus, a new hybrid solution based on multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, to reduce the dataset's dimensions, a multi-filter model uses a combination of five filter methods to remove redundant and irrelevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, and hypervolume indicator criterion with five hybrid multi-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior performance compared to other multi-objective methods. Furthermore, the proposed Ensemble Classifier model could provide better classification accuracy and generalizability in microarray data compared to conventional ensemble methods.

Ugo Boscain, Dario Prandi, Ludovic Sacchelli et al.

The reconstruction mechanisms built by the human auditory system during sound reconstruction are still a matter of debate. The purpose of this study is to propose a mathematical model of sound reconstruction based on the functional architecture of the auditory cortex (A1). The model is inspired by the geometrical modelling of vision, which has undergone a great development in the last ten years. There are however fundamental dissimilarities, due to the different role played by the time and the different group of symmetries. The algorithm transforms the degraded sound in an 'image' in the time-frequency domain via a short-time Fourier transform. Such an image is then lifted in the Heisenberg group and it is reconstructed via a Wilson-Cowan differo-integral equation. Preliminary numerical experiments are provided, showing the good reconstruction properties of the algorithm on synthetic sounds concentrated around two frequencies.

Chaitanya K. Ryali, John J. Hopfield, Leopold Grinberg et al.

The fruit fly Drosophila's olfactory circuit has inspired a new locality sensitive hashing (LSH) algorithm, FlyHash. In contrast with classical LSH algorithms that produce low dimensional hash codes, FlyHash produces sparse high-dimensional hash codes and has also been shown to have superior empirical performance compared to classical LSH algorithms in similarity search. However, FlyHash uses random projections and cannot learn from data. Building on inspiration from FlyHash and the ubiquity of sparse expansive representations in neurobiology, our work proposes a novel hashing algorithm BioHash that produces sparse high dimensional hash codes in a data-driven manner. We show that BioHash outperforms previously published benchmarks for various hashing methods. Since our learning algorithm is based on a local and biologically plausible synaptic plasticity rule, our work provides evidence for the proposal that LSH might be a computational reason for the abundance of sparse expansive motifs in a variety of biological systems. We also propose a convolutional variant BioConvHash that further improves performance. From the perspective of computer science, BioHash and BioConvHash are fast, scalable and yield compressed binary representations that are useful for similarity search.

Marta M. Stepniewska-Dziubinska, Piotr Zielenkiewicz, Pawel Siedlecki

In recent years machine learning (ML) took bio- and cheminformatics fields by storm, providing new solutions for a vast repertoire of problems related to protein sequence, structure, and interactions analysis. ML techniques, deep neural networks especially, were proven more effective than classical models for tasks like predicting binding affinity for molecular complex. In this work we investigated the earlier stage of drug discovery process - finding druggable pockets on protein surface, that can be later used to design active molecules. For this purpose we developed a 3D fully convolutional neural network capable of binding site segmentation. Our solution has high prediction accuracy and provides intuitive representations of the results, which makes it easy to incorporate into drug discovery projects. The model's source code, together with scripts for most common use-cases is freely available at http://gitlab.com/cheminfIBB/kalasanty

Jianfeng Wang, Ling Lin, Qunfeng Cheng et al.

Tomislav Plesa

Biochemical reactions involving three or more reactants, called higher-molecular reactions, play an important role in theoretical systems and synthetic biology. In particular, such reactions underpin a variety of important bio-dynamical phenomena, such as multi-stability/multi-modality, oscillations, bifurcations, and noise-induced effects. However, only reactions with at most two reactants, called bi-molecular reactions, are experimentally feasible. To bridge the gap, in this paper we put forward an algorithm for systematically approximating arbitrary higher-molecular reactions with bi-molecular ones, while preserving the underlying stochastic dynamics. Properties of the algorithm and convergence are established via singular perturbation theory. The algorithm is applied to a variety of higher-molecular biochemical networks, and is shown to play an important role in nucleic-acid-based synthetic biology.

A. Sciarrino, P. Sorba

The importance of the notion of symmetry in physics is well established: could it also be the case for the genetic code? In this spirit, a model for the Genetic Code based on continuous symmetries and entitled the "Crystal Basis Model" has been proposed a few years ago. The present paper is a review of the model, of some of its first applications as well as of its recent developments. Indeed, after a motivated presentation of our mathematical model, we illustrate its pertinence by applying it for the elaboration and verification of sum rules for codon usage probabilities, as well as for establishing relations and some predictions between physical-chemical properties of amino-acids. Then, defining in this context a "bio-spin" structure for the nucleotides and codons, the interaction between a couple of codon-anticodon can simply be represented by a (bio) spin-spin potential. This approach will constitute the second part of the paper where, imposing the minimum energy principle, an analysis of the evolution of the genetic code can be performed with good agreement with the generally accepted scheme. A more precise study of this interaction model provides informations on codon bias, consistent with data.

Alexey V. Melkikh, Alexey V. Melkikh, Andrei Khrennikov

A review of the mechanisms of speciation is performed. The mechanisms of the evolution of species, taking into account the feedback of the state of the environment and mechanisms of the emergence of complexity, are considered. It is shown that these mechanisms, at the molecular level, cannot work steadily in terms of classical mechanics. Quantum mechanisms of changes in the genome, based on the long-range interaction potential between biologically important molecules, are proposed as one of possible explanation. Different variants of interactions of the organism and environment based on molecular recognition and leading to new species origins are considered. Experiments to verify the model are proposed. This bio-physical study is completed by the general operational model of based on quantum information theory. The latter is applied to model epigenetic evolution.

Kuo-Chi Liu, Yao Wang, Yuen-Shin Chen

W. Lai, S. Chakravarty, Yi Zou et al.

Y. Chiang, Yung Chang, Ching Chuang et al.

J. Halder, M. H. Kodandaram, A. Rai et al.

Vikram Kumar, R. Swaminathan, Harjindra Singh

Zhe Chen, Yanling Zhao, Shuxian Liu et al.