Hasil untuk "q-bio"

Jia Liu, Zhenkun Sun, Yonghui Deng et al.

Z. Xu, Q. Zeng, G. Lu et al.

Lin Fu, C. Penton, Y. Ruan et al.

S. Ullah, Xiaoqin Yang, Huy Q. Ta et al.

Graphene is a material with unique properties that can be exploited in electronics, catalysis, energy, and bio-related fields. Although, for maximal utilization of this material, high-quality graphene is required at both the growth process and after transfer of the graphene film to the application-compatible substrate. Chemical vapor deposition (CVD) is an important method for growing high-quality graphene on non-technological substrates (as, metal substrates, e.g., copper foil). Thus, there are also considerable efforts toward the efficient and non-damaging transfer of quality of graphene on to technologically relevant materials and systems. In this review article, a range of graphene current transfer techniques are reviewed from the standpoint of their impact on contamination control and structural integrity preservation of the as-produced graphene. In addition, their scalability, cost- and time-effectiveness are discussed. We summarize with a perspective on the transfer challenges, alternative options and future developments toward graphene technology.

Qiang Liu, Tiantian Wang, Binbin Nian et al.

Polyphenols and proteins are essential biomolecules that influence food functionality and, by extension, human health. Their interactions -- hereafter referred to as PhPIs (polyphenol-protein interactions) -- affect key processes such as nutrient bioavailability, antioxidant activity, and therapeutic efficacy. However, these interactions remain challenging due to the structural diversity of polyphenols and the dynamic nature of protein binding. Traditional experimental techniques like nuclear magnetic resonance (NMR) and mass spectrometry (MS), along with computational tools such as molecular docking and molecular dynamics (MD), have offered important insights but face constraints in scalability, throughput, and reproducibility. This review explores how deep learning (DL) is reshaping the study of PhPIs by enabling efficient prediction of binding sites, interaction affinities, and MD using high-dimensional bio- and chem-informatics data. While DL enhances prediction accuracy and reduces experimental redundancy, its effectiveness remains limited by data availability, quality, and representativeness, particularly in the context of natural products. We critically assess current DL frameworks for PhPIs analysis and outline future directions, including multimodal data integration, improved model generalizability, and development of domain-specific benchmark datasets. This synthesis offers guidance for researchers aiming to apply DL in unraveling structure-function relationships of polyphenols, accelerating discovery in nutritional science and therapeutic development.

Amber H. B. Fijn, Casper H. Stiekema, Stijn Boere et al.

Limited availability of inorganic phosphate (Pi) in soil is an important constraint to plant growth. In order to understand better the underlying mechanism of plant response to Pi, the response to phosphate starvation in Arabidopsis thaliana was investigated through use of Petri Nets, a formal language suitable for bio-modeling. A. thaliana displays a range of responses to deal with Pi starvation, but special attention was paid to root hair elongation in this study. A central player in the root hair pathway is the transcription factor ROOT HAIR DEFECTIVE 6-LIKE 4 (RSL4), which has been found to be upregulated during the Pi stress. A Petri Net was created which could simulate the gene regulatory networks responsible for the increase in root hair length, as well as the resulting increase in root hair length. Notably, discrepancies between the model and the literature suggested an important role for RSL2 in regulating RSL4. In the future, the net designed in the current study could be used as a platform to develop hypotheses about the interaction between RSL2 and RSL4.

Parthasarathy Suryanarayanan, Yunguang Qiu, Shreyans Sethi et al.

Quality molecular representations are key to foundation model development in bio-medical research. Previous efforts have typically focused on a single representation or molecular view, which may have strengths or weaknesses on a given task. We develop Multi-view Molecular Embedding with Late Fusion (MMELON), an approach that integrates graph, image and text views in a foundation model setting and may be readily extended to additional representations. Single-view foundation models are each pre-trained on a dataset of up to 200M molecules. The multi-view model performs robustly, matching the performance of the highest-ranked single-view. It is validated on over 120 tasks, including molecular solubility, ADME properties, and activity against G Protein-Coupled receptors (GPCRs). We identify 33 GPCRs that are related to Alzheimer's disease and employ the multi-view model to select strong binders from a compound screen. Predictions are validated through structure-based modeling and identification of key binding motifs.

Qi-Jie Li, Qian Sun, Shao-Qun Zhang

Identifying gene splicing is a core and significant task confronted in modern collaboration between artificial intelligence and bioinformatics. Past decades have witnessed great efforts on this concern, such as the bio-plausible splicing pattern AT-CG and the famous SpliceAI. In this paper, we propose a novel framework for the task of gene splicing identification, named Horizon-wise Gene Splicing Identification (H-GSI). The proposed H-GSI follows the horizon-wise identification paradigm and comprises four components: the pre-processing procedure transforming string data into tensors, the sliding window technique handling long sequences, the SeqLab model, and the predictor. In contrast to existing studies that process gene information with a truncated fixed-length sequence, H-GSI employs a horizon-wise identification paradigm in which all positions in a sequence are predicted with only one forward computation, improving accuracy and efficiency. The experiments conducted on the real-world Human dataset show that our proposed H-GSI outperforms SpliceAI and achieves the best accuracy of 97.20\%. The source code is available from this link.

Rasha A. Al-husseiny, S. Kareem, A. Naje et al.

J. Ko, Jin-Hwi Park, Y. J. Yoo et al.

Fano resonance, known for its unique asymmetric line shape, has gained significant attention in photonics, particularly in sensing applications. However, it remains difficult to achieve controllable Fano parameters with a simple geometric structure. Here, a novel approach of using a thin‐film optical Fano resonator with a porous layer to generate entire spectral shapes from quasi‐Lorentzian to Lorentzian to Fano is proposed and experimentally demonstrated. The glancing angle deposition technique is utilized to create a polarization‐dependent Fano resonator. By altering the linear polarization between s‐ and p‐polarization, a switchable Fano device between quasi‐Lorentz state and negative Fano state is demonstrated. This change in spectral shape is advantageous for detecting materials with a low‐refractive index. A bio‐particle sensing experiment is conducted that demonstrates an enhanced signal‐to‐noise ratio and prediction accuracy. Finally, the challenge of optimizing the film‐based Fano resonator due to intricate interplay among numerous parameters, including layer thicknesses, porosity, and materials selection, is addressed. The inverse design tool is developed based on a multilayer perceptron model that allows fast computation for all ranges of Fano parameters. The method provides improved accuracy of the mean validation factor (MVF = 0.07, q‐q') compared to the conventional exhaustive enumeration method (MVF = 0.37).

M. N, S. Raga, Sanjay Kumar Gowre et al.

Louay Mohammed Musa Jasim, Masoud Homayouni Tabrizi, Elham Darabi et al.

Background Herniarin, a simple coumarin found in chamomile leaf rosettes is known as the oxidative stress protector. In the current study, herniarin was captured into Graphene oxide nanoparticles and coated with chitosan poly-cationic polymer to be used as a novel bio-compatible nano-drug delivery system and investigate its antioxidant, anti-angiogenic and anti-cancer impacts on human lung A549 cancer cells. Method The Chitosan-coated Herniarin-Graphene oxide nanoparticles (CHG-NPs) were designed, produced, and characterized utilizing DLS, FESEM, FTIR, and Zeta-potential analysis. The CHG-NPs’ antioxidant activity was analyzed by conducting ABTS and DPPH antioxidant assays. The CHG-NPs’ anti-angiogenic activity was analyzed by CAM assay and verified by measuring VEGF and VEGFR gene expression levels following their increased treatment doses by applying Q-PCR technique. Finally, the CHG-NPs’ cytotoxicity was studied in the human lung A549 cancer cells. Result The stable (+27.11 mV) 213.6-nm CHG-NPs significantly inhibited the ABTS/DPPH free radicals and exhibited antioxidant activity. The suppressed angiogenesis process in the CAM vessels was observed by detecting the decreased length/number of the vessels. Moreover, the down-regulated VEGF and VEGFR gene expression of the CAM blood vessels following the increased CHG-NPs treatment doses verified the nanoparticles’ anti-angiogenic potential. Finally, the CHG-NPs significantly exhibited a selective cytotoxic impact on human A549 cancer cells compared with the normal HFF cell line. Conclusion The selective cytotoxicity, strong antioxidant activity, and significant anti-angiogenic property of the nano-scaled produced CHG-NPs make it an appropriate anticancer nano-drug delivery system. Therefore, the CHG-NPs have the potential to be used as a selective anti-lung cancer compound.

Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers et al.

Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-invasive bio-signals, which are widely used in human machine interface (HMI) technologies (EEG-HMI and EMG-HMI paradigm) for the rehabilitation of physically disabled people. Successful decoding of EEG and EMG signals into respective control command is a pivotal step in the rehabilitation process. Recently, several Convolutional neural networks (CNNs) based architectures are proposed that directly map the raw time-series signal into decision space and the process of meaningful features extraction and classification are performed simultaneously. However, these networks are tailored to the learn the expected characteristics of the given bio-signal and are limited to single paradigm. In this work, we addressed the question that can we build a single architecture which is able to learn distinct features from different HMI paradigms and still successfully classify them. Approach: In this work, we introduce a single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that is equally useful for EEG-HMI and EMG-HMI paradigms. ConTraNet uses CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the long-range dependencies in the signal, which are crucial for the classification of EEG and EMG signals. Main results: We evaluated and compared the ConTraNet with state-of-the-art methods on three publicly available datasets which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, and 10-class decoding tasks). Significance: The results suggest that ConTraNet is robust to learn distinct features from different HMI paradigms and generalizes well as compared to the current state of the art algorithms.

Mehedi Hasan Sarkar, Adnan Ferdous Ashrafi

We live in a period where bio-informatics is rapidly expanding, a significant quantity of genomic data has been produced as a result of the advancement of high-throughput genome sequencing technology, raising concerns about the costs associated with data storage and transmission. The question of how to properly compress data from genomic sequences is still open. Previously many researcher proposed many compression method on this topic DNA Compression without machine learning and with machine learning approach. Extending a previous research, we propose a new architecture like modified DeepDNA and we have propose a new methodology be deploying a double base-ed strategy for compression of DNA sequences. And validated the results by experimenting on three sizes of datasets are 100, 243, 356. The experimental outcomes highlight our improved approach's superiority over existing approaches for analyzing the human mitochondrial genome data, such as DeepDNA.

Nicolas F. Chaves-de-Plaza, Klaus Hildebrandt, Anna Vilanova

Post-translational modifications (PTMs) affecting a protein's residues (amino acids) can disturb its function, leading to illness. Whether or not a PTM is pathogenic depends on its type and the status of neighboring residues. In this paper, we present the ProtoFold Neighborhood Inspector (PFNI), a visualization system for analyzing residues neighborhoods. The main contribution is a visualization idiom, the Residue Constellation (RC), for identifying and comparing three-dimensional neighborhoods based on per-residue features and spatial characteristics. The RC leverages two-dimensional representations of the protein's three-dimensional structure to overcome problems like occlusion, easing the analysis of neighborhoods that often have complicated spatial arrangements. Using the PFNI, we explored proteins' structural PTM data, which allowed us to identify patterns in the distribution and quantity of per-neighborhood PTMs that might be related to their pathogenic status. In the following, we define the tasks that guided the development of the PFNI and describe the data sources we derived and used. Then, we introduce the PFNI and illustrate its usage through an example of an analysis workflow. We conclude by reflecting on preliminary findings obtained while using the tool on the provided data and future directions concerning the development of the PFNI.

Rana D. Parshad, Sureni Wickramsooriya, Kwadwo Antwi-Fordjour et al.

The literature posits that an introduced predator population, is able to drive it's target pest population extinct, if supplemented with high quality additional food of quantity $ξ> ξ_{critical}$, \cite{SP11, SPV18, SPD17, SPM13}. We show this approach leads to infinite time blow-up of the predator population. We propose an alternate model in which the additional food induces predator competition. Analysis herein indicates that there are threshold values $c^{*}_{1} < c^{*}_{2} < c^{*}_{3}$ of the competition parameter $c$, s.t. when $c < c^{*}_{1}$, the pest free state is globally stable, when $c^{*}_{2} < c < c^{*}_{3}$, bi-stability is possible, and when $c^{*}_{3} < c$, up to three interior equilibriums could exist. As $c$ and $ξ$-$c$ are varied, standard co-dimension one and co-dimension two bifurcations are observed. The recent dynamical systems literature involving predator competition, report several non-standard bifurcations such as the saddle-node-transcritical bifurcation (SNTC) occurring in co-dimension two \cite{KSV10, BS07}, and cusp-transcritical bifurcation (CPTC) in co-dimension three, \cite{D20, BS07}. We show that in our model structural symmetries can be exploited to construct a SNTC in co-dimension two, and a CPTC also in co-dimension two. We further use these symmetries to construct a novel pitchfork-transcritical bifurcation (PTC) in co-dimension two, thus explicitly characterizing a new organizing center of the model. Dynamics such as homoclinic orbits, concurrently occurring limit cycles, and competition driven Turing patterns are also observed. Our findings indicate that increasing additional food in predator-pest models, can hinder bio-control, contrary to some of the literature. However, additional food that also induces predator competition, leads to novel bio-control scenarios, and complements the work in \cite{H21, B98, K04, D20, BS07, VH19}.

Oleg Nikitin, Olga Lukyanova, Alex Kunin

Parallels between the signal processing tasks and biological neurons lead to an understanding of the principles of self-organized optimization of input signal recognition. In the present paper, we discuss such similarities among biological and technical systems. We propose adding the well-known STDP synaptic plasticity rule to direct the weight modification towards the state associated with the maximal difference between background noise and correlated signals. We use the principle of physically constrained weight growth as a basis for such weights' modification control. It is proposed that the existence and production of bio-chemical 'substances' needed for plasticity development restrict a biological synaptic straight modification. In this paper, the information about the noise-to-signal ratio controls such a substances' production and storage and drives the neuron's synaptic pressures towards the state with the best signal-to-noise ratio. We consider several experiments with different input signal regimes to understand the functioning of the proposed approach.

Yair Zarmi, Jeffrey M. Gordon, Amit Mahulkar et al.

We present experimental results demonstrating that, relative to continuous illumination, an increase of a factor of 3-10 in the photon efficiency of algal photo-synthesis is attainable via the judicious application of pulsed light for light intensities of practical interest (e.g., average-to-peak solar photon flux). We also propose a simple model that can account for all the measurements. The model (1) reflects the essential rate-limiting elements in bio-productivity, (2) incorporates the impact of photon arrival-time statistics and (3) accounts for how the enhancement in photon efficiency depends on the timescales of light pulsing and photon flux density. The key is avoiding clogging of the photosynthetic pathway by properly timing the light-dark cycles experienced by algal cells. We show how this can be realized with pulsed light sources, or by producing pulsed-light effects from continuous illumination via turbulent mixing in dense algal cultures in thin photo-bioreactors.

Alireza Nadafian, Mohammad Ganjtabesh

The plasticity of the conduction delay between neurons plays a fundamental role in learning. However, the exact underlying mechanisms in the brain for this modulation is still an open problem. Understanding the precise adjustment of synaptic delays could help us in developing effective brain-inspired computational models in providing aligned insights with the experimental evidence. In this paper, we propose an unsupervised biologically plausible learning rule for adjusting the synaptic delays in spiking neural networks. Then, we provided some mathematical proofs to show that our learning rule gives a neuron the ability to learn repeating spatio-temporal patterns. Furthermore, the experimental results of applying an STDP-based spiking neural network equipped with our proposed delay learning rule on Random Dot Kinematogram indicate the efficacy of the proposed delay learning rule in extracting temporal features.

Gabriel O. Fosu, Emmanuel K. Mintah

The dynamics of a SIVR model with power relationship incidence rates $(βI^p S^q)$ is investigated. It is assumed an individual can be susceptible after receiving the first dose of the vaccine, hence a second dose is required to attain permanent immunity. The steady states conditions of the disease-free equilibrium and the endemic equilibrium are critically presented. Numerical simulations are carried out to determine the impact of the exponential parameters $(p;q)$ on infection.