Hasil untuk "q-bio"

D. Mohan, S. Rajput, vinod k. singh et al.

M. Menossi, F. Salcedo, A. Mansilla et al.

A novel bio‐based and biodegradable plastic based on poly(lactic acid) (PLA) and corn starch (CS) was developed as a single‐use rigid packaging material. Chitosan (Q) and eucalyptus essential oil (EEO) were incorporated as antimicrobial and antioxidant agents. The effects of these additives were evaluated using a three‐factor, two‐level factorial design, assessing the melt flow index (MFI) and tensile properties. The optimized formulation, containing 5 wt% of EEO and 3 wt% of Q, increased the thermal stability of CS due to the PLA matrix with two degradation steps: 145°C and 318°C. It also demonstrated low water interaction, with a solubility of approximately 0.6% and a moisture content of 5%, attributed to the absence of plasticizers. Additionally, the material achieved nearly 70% antioxidant activity through the synergistic effect of EEO and Q. Successful thermoforming trials confirmed the processability of the optimized formulation. Comparative analysis with polypropylene revealed that the bio‐based material exhibited higher tensile strength while offering the critical advantage of biodegradability. These findings highlight the potential of this active, thermoformable bio‐based material as a sustainable alternative to conventional non‐biodegradable plastics.

Marco Cafiso, Paolo Paradisi

Neural network models capable of storing memory have been extensively studied in computer science and computational neuroscience. The Hopfield network is a prototypical example of a model designed for associative, or content-addressable, memory and has been analyzed in many forms. Further, ideas and methods from complex network theory have been incorporated into artificial neural networks and learning, emphasizing their structural properties. Nevertheless, the temporal dynamics also play a vital role in biological neural networks, whose temporal structure is a crucial feature to examine. Biological neural networks display complex intermittency and, thus, can be studied through the lens of the temporal complexity (TC) theory. The TC approach look at the metastability of self-organized states, characterized by a power-law decay in the inter-event time distribution and in the total activity distribution or a scaling behavior in the corresponding event-driven diffusion processes. In this study, we present a temporal complexity (TC) analysis of a biologically-inspired Hopfield-type neural network model. We conducted a comparative assessment between scale-free and random network topologies, with particular emphasis on their global activation patterns. Our parametric analysis revealed comparable dynamical behaviors across both neural network architectures. Furthermore, our investigation into temporal complexity characteristics uncovered that seemingly distinct dynamical patterns exhibit similar temporal complexity behaviors. In particular, similar power-law decay in the activity distribution and similar complexity levels are observed in both topologies, but with a much reduced noise in the scale-free topology. Notably, most of the complex dynamical profiles were consistently observed in scale-free network configurations, thus confirming the crucial role of hubs in neural network dynamics.

Mehyar Mlaweh, Tristan Cazenave, Ines Alaya

The Ribonucleic Acid (RNA) inverse folding problem, designing nucleotide sequences that fold into specific tertiary structures, is a fundamental computational biology problem with important applications in synthetic biology and bioengineering. The design of complex three-dimensional RNA architectures remains computationally demanding and mostly unresolved, as most existing approaches focus on secondary structures. In order to address tertiary RNA inverse folding, we present BeeRNA, a bio-inspired method that employs the Artificial Bee Colony (ABC) optimization algorithm. Our approach combines base-pair distance filtering with RMSD-based structural assessment using RhoFold for structure prediction, resulting in a two-stage fitness evaluation strategy. To guarantee biologically plausible sequences with balanced GC content, the algorithm takes thermodynamic constraints and adaptive mutation rates into consideration. In this work, we focus primarily on short and medium-length RNAs ($<$ 100 nucleotides), a biologically significant regime that includes microRNAs (miRNAs), aptamers, and ribozymes, where BeeRNA achieves high structural fidelity with practical CPU runtimes. The lightweight, training-free implementation will be publicly released for reproducibility, offering a promising bio-inspired approach for RNA design in therapeutics and biotechnology.

Xiaoyu Zhang, Zhou Fang

This paper introduces a biomolecular Linear Quadratic Regulator (LQR) to investigate the design principles of gene regulatory networks. We show that for fundamental gene regulation network, the bio-controller derived from LQR theory precisely recapitulate natural network motifs, such as auto-regulation and incoherent feedforward loops. This emulation arises from a fundamental principle: the LQR cost function mathematically encodes environmental survival demands, which subsequently drives the selection of both network topology and biochemical parameters. Our work thus establishes a theoretical basis for interpreting biological circuit design, directly linking evolutionary pressures to observable regulatory structures.

Min-Seo Kim, Ju-Hyeon Seong

Wearable device-based personal activity measurement technology provides various personalized services by integrating bio-signals. However, accurately and rapidly estimating energy expenditure (EE) remains challenging due to user movement and the limitations of measurement parameters. In this paper, we propose Real-Time Energy Expenditure (RTEE), a novel real-time and personalized energy expenditure estimation (EEE) method. The proposed RTEE integrates a Deep Q-Network (DQN)-based activity intensity coefficient inference network with a modified energy consumption prediction algorithm to estimate energy expenditure based on real-time variations in the user’s heart rate measurements. Therefore, the proposed algorithm can be applied to various heart rate-based energy consumption prediction methods.

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.

Xinhao Fan, Shreesh P Mysore

Backpropagation (BP) has been pivotal in advancing machine learning and remains essential in computational applications and comparative studies of biological and artificial neural networks. Despite its widespread use, the implementation of BP in the brain remains elusive, and its biological plausibility is often questioned due to inherent issues such as the need for symmetry of weights between forward and backward connections, and the requirement of distinct forward and backward phases of computation. Here, we introduce a novel neuroplasticity rule that offers a potential mechanism for implementing BP in the brain. Similar in general form to the classical Hebbian rule, this rule is based on the core principles of maintaining the balance of excitatory and inhibitory inputs as well as on retrograde signaling, and operates over three progressively slower timescales: neural firing, retrograde signaling, and neural plasticity. We hypothesize that each neuron possesses an internal state, termed credit, in addition to its firing rate. After achieving equilibrium in firing rates, neurons receive credits based on their contribution to the E-I balance of postsynaptic neurons through retrograde signaling. As the network's credit distribution stabilizes, connections from those presynaptic neurons are strengthened that significantly contribute to the balance of postsynaptic neurons. We demonstrate mathematically that our learning rule precisely replicates BP in layered neural networks without any approximations. Simulations on artificial neural networks reveal that this rule induces varying community structures in networks, depending on the learning rate. This simple theoretical framework presents a biologically plausible implementation of BP, with testable assumptions and predictions that may be evaluated through biological experiments.

Damiano Reginato, Daniele Proverbio, Giulia Giordano

The true slime mold \textit{Physarum polycephalum} has the remarkable capability to perform self-organized activities such as network formation among food sources. Despite well reproducing the emergence of slime networks, existing models are limited in the investigation of the minimal mechanisms, at the microscopic scale, that ensure robust problem-solving capabilities at the macroscopic scale. To this end, we develop three progressively more complex multi-agent models to provide a flexible framework to understand the self-organized foraging and network formation behaviors of \textit{Physarum}. The hierarchy of models allows for a stepwise investigation of the minimal set of rules that allow bio-inspired computing agents to achieve the desired behaviors on nutrient-poor substrates. By introducing a quantitative measure of connectedness among food sources, we assess the sensitivity of the model to user-defined and bio-inspired parameters, as well as the robustness of the model to parameter heterogeneity across agents. We ultimately observe the robust emergence of pattern formation, in line with experimental evidence. Overall, our study sheds light onto the basic mechanisms of self-organization and paves the way towards the development of decentralized strategies for network formation in engineered systems, focusing on trade-offs between biological fidelity and computational efficiency.

Binsheng Gong, Dan Li, Yifan Zhang et al.

Abstract Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Giulia Simonetti, Carmela Riccardi, Donatella Pomata et al.

In recent years, due to modern techniques for the distribution, transport, and retail sale of food, the production of large amounts of non-biodegradable and bioaccumulative packaging waste has become a major environmental issue. To address this issue, new food packaging materials based on renewable biomass have been studied as eco-friendly, biodegradable, and biocompatible alternatives to synthetic materials. However, although these materials are not petrochemical derivatives, the presence of contaminants cannot be excluded. This work aims to extend the knowledge on bio-based packaging materials, researching the presence of contaminants potentially able to migrate to food at concentrations of concern. In this study, we focus on two classes of contaminants, organophosphate esters (OPEs) and perfluoroalkyl substances (PFASs), carrying out migration tests toward different simulants, according to the current European regulation. PFAS analysis was performed using high-resolution liquid chromatography coupled to ion trap-tandem mass spectrometry (QTrap). OPE analyses were performed both by gas chromatography–mass spectrometry (GC-MS) and high-resolution liquid chromatography coupled to triple quadrupole mass spectrometry (TQMS). Preliminary findings demonstrate the release of toxic OPEs and PFASs from bio-based food packaging, highlighting the need to investigate the presence of potentially harmful chemicals in these materials.

Hua Zheng, Sarah W. Harcum, Jinxiang Pei et al.

Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity.

Navid Mohammad Mirzaei, Leili Shahriyari

Computational modeling of cancer can help unveil dynamics and interactions that are hard to replicate experimentally. Thanks to the advancement in cancer databases and data analysis technologies, these models have become more robust than ever. There are many mathematical models which investigate cancer through different approaches, from sub-cellular to tissue scale, and from treatment to diagnostic points of view. In this study, we lay out a step-by-step methodology for a data-driven mechanistic model of the tumor microenvironment. We discuss data acquisition strategies, data preparation, parameter estimation, and sensitivity analysis techniques. Furthermore, we propose a possible approach to extend mechanistic ODE models to PDE models coupled with mechanical growth. The workflow discussed in this article can help understand the complex temporal and spatial interactions between cells and cytokines in the tumor microenvironment and their effect on tumor growth.

Maria Kitsara, Gaelle Revet, Jean-Sebastien et al.

As part of applications dealing with cardiovascular tissue engineering, drop-cast polyvinylidene fluoride (PVDF) scaffolds have been treated by cold plasma to enhance their adherence to cardiac cells. The scaffolds were treated in a dielectric barrier device where cold plasma was generated in a gaseous environment combining a carrier gas (helium or argon) with/without a reactive gas (molecular nitrogen). We show that an Ar-N2 plasma treatment of 10 min results in significant hydrophilization of the scaffolds, with contact angles as low as 52.4° instead of 132.2° for native PVDF scaffolds. Correlation between optical emission spectroscopy and X-ray photoelectron spectroscopy shows that OH radicals from the plasma phase can functionalize the surface scaffolds, resulting in improved wettability. For all plasma-treated PVDF scaffolds, the adhesion and maturation of primary cardiomyocytes is increased, showing a well-organized sarcomeric structure (α-actinin immunostaining). The efficacy of plasma treatment was also supported by real-time PCR analysis to demonstrate an increased expression of the genes related to adhesion and cardiomyocyte function. Finally, the biocompatibility of the PVDF scaffolds was studied in a cardiac environment, after implantation of acellular scaffolds on the surface of the heart of healthy mice. Seven and 28 days after implantation, no exuberant fibrosis and no multinucleated giant cells were visible in the grafted area, hence demonstrating the absence of foreign body reaction and the biocompatibility of these scaffolds.

N. D. Kaptein, C. S. Everson, C. S. Everson et al.

<p>Pine plantations are the dominant species currently planted within the South African commercial forestry industry. Improvements in bio-economy markets for dissolving wood pulp products have seen an expansion in fast-growing <i>Eucalyptus</i> plantations due to their higher productivity rates and better pulping properties than pine. This has raised concerns regarding the expansion of <i>Eucalyptus</i> plantations and how they will affect water resources as they have been reported to have higher water use (quantified using transpiration rates) than pine. We measured transpiration rates (mm yr<span class="inline-formula">⁻¹</span>), diameter at breast height (quantified as quadratic mean diameter, <span class="inline-formula"><i>D</i>_q</span>, m) and leaf area index of an 8-year-old <i>Eucalyptus grandis</i> <span class="inline-formula">×</span> <i>Eucalyptus nitens</i> clonal hybrid (<i>GN</i>) and a 20-year-old <i>Pinus elliottii</i>. Transpiration rates were measured for two consecutive hydrological years (2019/20 and 2020/21) using a heat ratio sap-flow method, calibrated against a lysimeter. In the 2019/20 year, annual transpiration for <i>P. elliottii</i> exceeded <i>GN</i> by 28 %, while for the 2020/21 hydrological year, there was no significant difference between the transpiration of the two species, despite a 17 % and 21 % greater leaf area index for <i>P. elliottii</i> than <i>GN</i> in 2019/20 and 2020/21 measurement years respectively. Quadratic mean diameter increments were statistically similar (<span class="inline-formula"><i>p</i></span> <span class="inline-formula">&gt;</span> 0.05) in 2019/20, whereas the 2020/21 year produced significant differences (<span class="inline-formula"><i>p</i>&lt;0.05</span>). Tree transpiration is known to be influenced by climatic variables; therefore, a random forest regression model was used to test the level of influence between tree transpiration and climatic parameters. The soil water content, solar radiation and vapour pressure deficit were found to highly influence transpiration, suggesting these variables can be used in future water-use modelling studies. The profile water content recharge was influenced by rainfall events. After rainfall and soil profile water recharge, there was a rapid depletion of soil water by the <i>GN</i> trees, while the soil profile was depleted more gradually at the <i>P. elliottii</i> site. As a result, trees at the <i>GN</i> site appeared to be water stressed (reduced stem diameters and transpiration), suggesting that there was limited access to alternative water source (such as groundwater). The study concluded that previous long-term paired catchment studies indicate that eucalypts use more water than pine; however, periods of soil water stress and reduced transpiration observed in this study must be accommodated in hydrological models. Long-term total soil water balance studies are recommended in the same region to understand the long-term impact of commercial plantations on water resources.</p>

Gianfranco Piccirillo, Federica Moscucci, Ilaria Di Diego et al.

The head-up/-down tilt test acutely modifies the autonomic nervous system balance throughout a deactivation of the cardiopulmonary reflexes. The present study examines the influence of head-up/-down tilt on a number of ECG segments. A total of 20 healthy subjects underwent a 5 min ECG and noninvasive hemodynamic bio-impedance recording, during free and controlled breathing, lying at (a) 0°; (b) −45°, tilting up at 45°, and tilting up at 90°. Heart rate variability power spectral analysis was obtained throughout some ECG intervals: P-P (P), P-Q (PQ), P_eQ (from the end of P to Q wave), Q-R peak (QR intervals), Q-R-S (QRS), Q-T peak (QT_p), Q-T end (QT_e), ST_p, ST_e, T peak-T end (T_e), and, eventually, the T_eP segments (from the end of T to the next P waves). Results: In all study conditions, the Low Frequency/High Frequency_PP and LF_{PP normalized units (nu)} were significantly lower than the LF/HF_RR and LF_RRnu, respectively. Conversely, the HF_PP and HF_PPnu were significantly higher in all study conditions. ST_e, QT_p, and QT_e were significantly related to the PP and RR intervals, whereas the T wave amplitude was inversely related to the standard deviations of all the myocardial repolarization variables and to the left ventricular end-systolic volume (LVEDV). The T wave amplitude diminished during head-up tilt and significantly correlated with the LVEDV.

Chengjun Zhou, Q. Shi, Weihong Guo et al.

Shengjie Zheng, Wenyi Li, Lang Qian et al.

Brain-computer interfaces (BCIs), transform neural signals in the brain into in-structions to control external devices. However, obtaining sufficient training data is difficult as well as limited. With the advent of advanced machine learning methods, the capability of brain-computer interfaces has been enhanced like never before, however, these methods require a large amount of data for training and thus require data augmentation of the limited data available. Here, we use spiking neural networks (SNN) as data generators. It is touted as the next-generation neu-ral network and is considered as one of the algorithms oriented to general artifi-cial intelligence because it borrows the neural information processing from bio-logical neurons. We use the SNN to generate neural spike information that is bio-interpretable and conforms to the intrinsic patterns in the original neural data. Ex-periments show that the model can directly synthesize new spike trains, which in turn improves the generalization ability of the BCI decoder. Both the input and output of the spiking neural model are spike information, which is a brain-inspired intelligence approach that can be better integrated with BCI in the future.

Debasish Bhattacharjee, Tapasvini Roy, Santanu Acharjee et al.

Dynamic exploration for a predator-prey bio-system of two species with ratio-dependent functional response is carried out, where the capability to predate in both the stages of the predator, the juvenile and the matured, is taken into account. But, only the matured predators are inferred to be efficient in killing the prey without any negative repercussions. The mortality risks for the juvenile predators are attributable to the inefficiency rate of juveniles coupled with habitat complexity which is either in the form of anti-predator behavior of the prey taken with the aid of their habitat or in the form of a territorial generalist mesopredator. So as to avoid extinction of either of the species and to preserve the food chain of the ecological system, the results pertaining to the existence and stability of all the equilibrium points of the bio-system along with permanence, transcritical and Hopf bifurcation has been thoroughly studied. Corroboration of the results along with the dependence of the biosystem on some crucial parameters is done through numerical simulation. It is found that juvenile predators' inefficiency relative to the resistance confronted, plays a crucial role to control each species density of the ecosystem, as an intriguing limit cycle between the trivial and axial equilibriums of the proposed system along with the co-existing periodic point, because of some ineffeciency parametric value of the juvenile predator has been witnessed.

Huabin Wang, Yi Wen, Yu Ding et al.

Cow manure derived biochar (CMBC) can serve as a promising functional material, and CMBC can be regarded as an ecofriendly approach compared to conventional ones. CM bioadsorbent can be employed for heavy metal immobilization (such as for lead) as well as an amendment to increase soil fertility (e.g., phosphorus). Few studies have examined the surface interactions between pollutants and bioadsorbents when inherent nutrient release is present. In this work, CMBC was prepared and applied for Pb(II) removal, and the vital roles of released phosphorus from CMBC were comprehensively disclosed. Furthermore, CMBC could immobilize part of the Pb(II) in soil and promote plant growth. CM400 was an effective adsorbent whose calculated <i>Q_e</i> reached 691.34 mg·g⁻¹, and it rapidly adsorbed 98.36 mg·g⁻¹ of Pb(II) within 1 min. The adsorption mechanisms of Pb(II) by CMBC include ion exchange, physical adsorption, electrostatic attraction, chemical precipitation, surface complexation, and cation–π bond interaction. Based on the residual phosphorus content and adsorption effect, complexation rather than the chemical precipitation had a greater contribution toward adsorption. Besides, as the concentration of Pb(II) increased, the main adsorption mechanisms likely transformed from chemical precipitation to ion exchange and complexation. CMBC not only had a good effect on Pb(II) removal in the solution, but also immobilized the Pb(II) in soil to restrain plant uptake as well as promote plant growth. The main novelty of this work is providing more insights to the cow manure bio adsorbent on Pb immobilization and phosphorus release. This study is expected to serve as a basis and reference for analyzing the release effects of inherent nutrients and the interfacial behaviors with heavy metals when using CMBC and other nutrient–rich carbon–based fertilizers for pollution control.