Hasil untuk "Biochemistry"

Yadong Huang, L. Mucke

G. Patti, Ó. Yanes, G. Siuzdak

M. Hemler

D. Parks, D. Granger

G. Lynch, M. Baudry

Weiting Liu, Jiaxu Cui, Jiao Hu et al.

In science, we are interested not only in forecasting but also in understanding how predictions are made, specifically what the interpretable underlying model looks like. Data-driven machine learning technology can significantly streamline the complex and time-consuming traditional manual process of discovering scientific laws, helping us gain insights into fundamental issues in modern science. In this work, we introduce a pre-trained symbolic foundation regressor that can effectively compress complex data with numerous interacting variables while producing interpretable physical representations. Our model has been rigorously tested on non-network symbolic regression, symbolic regression on complex networks, and the inference of network dynamics across various domains, including physics, biochemistry, ecology, and epidemiology. The results indicate a remarkable improvement in equation inference efficiency, being three times more effective than baseline approaches while maintaining accurate predictions. Furthermore, we apply our model to uncover more intuitive laws of interaction transmission from global epidemic outbreak data, achieving optimal data fitting. This model extends the application boundary of pre-trained symbolic regression models to complex networks, and we believe it provides a foundational solution for revealing the hidden mechanisms behind changes in complex phenomena, enhancing interpretability, and inspiring further scientific discoveries.

John Ashmead

In quantum mechanics time is generally treated as a parameter rather than an observable. For instance wave functions are treated as extending in space, but not in time. But from relativity we expect time and space should be treated on the same basis. What are the effects if time is an observable? Are these effects observable with current technology? In earlier work we showed we should see effects in various high energy scattering processes. We here extend that work to include bound states. The critical advantage of working with bound states is that the predictions are significantly more definite, taking the predictions from testable to falsifiable. We estimate the time dispersion for hydrogen as $.177$ attoseconds, possibly below the current threshold for detection. But the time dispersion should scale as the $3/2$ power of the principle quantum number $n$. Rydberg atoms can have $n$ of order $100$, implying a boost by a factor of $1000$. This takes the the time dispersion to $177$ attoseconds, well within reach of current technology. There are a wide variety of experimental targets: any time-dependent processes should show effects. Falsification will be technically challenging (due to the short time scales) but immediate and unambiguous. Confirmation would have significant implications for attosecond physics, quantum computing and communications, quantum gravity, and the measurement problem. And would suggest practical uses in these areas as well as circuit design, high speed biochemistry, cryptography, fusion research, and any area involving change at attosecond time scales.

Hanane Nour Moussa, Patrick Queiroz Da Silva, Daniel Adu-Ampratwum et al.

As AI tools become increasingly common for research ideation, robust evaluation is critical to ensure the validity and usefulness of generated ideas. We introduce ScholarEval, a retrieval augmented evaluation framework that assesses research ideas based on two fundamental criteria: soundness - the empirical validity of proposed methods based on existing literature, and contribution - the degree of advancement made by the idea across different dimensions relative to prior research. To evaluate ScholarEval, we introduce ScholarIdeas, the first expert-annotated dataset of multi-domain research ideas and reviews, comprised of 117 ideas across four disciplines: artificial intelligence, neuroscience, biochemistry, and ecology. Our evaluation shows that ScholarEval achieves significantly higher coverage of points mentioned in the human expert annotated rubrics in ScholarIdeas compared to all baselines. Furthermore, ScholarEval is consistently preferred over our strongest baseline o4-mini-deep-research, a reasoning and search-enabled agentic system by OpenAI, in terms of evaluation actionability, depth, and evidence support. Our large-scale user study also shows that ScholarEval significantly outperforms deep research in literature engagement, idea refinement, and usefulness. We openly release our code, dataset, and ScholarEval tool for the community to use and build on.

Madison L. Dirks, Joseph Hale, Eric Theiste et al.

Glycomacropeptide (GMP) is isolated from whey and used as an ingredient in phenylketonuria-safe foods because it does not contain phenylalanine. GMP is highly glycosylated and has several sites where <i>N</i>-acetylneuraminic acid (NANA) is bound. In the dairy industry, quantification of NANA from dairy proteins is accomplished by colorimetric, fluorometric, enzymatic, and chromatographic procedures; there is no uniformly accepted industry-wide standard method. In this investigation, NANA quantification methods were evaluated using GMP, and a comparison was made based on the length of time to complete the assay, protein-specificity, linearity, precision, and accuracy. From the methods evaluated, the chromatography protocol was determined to have the greatest benefit for use as a dairy industry standard to measure NANA on GMP. The average mass percent of NANA in 10 statistically independent replicates from a commercial GMP product was measured to be 6.18% ± 0.12%, with a relative standard deviation of 1.94%, which was the lowest of all the methods tested. The accuracy of the chromatographic approach was validated using spike and recovery experiments that provided an average recovery of 90.25%.

Mansour K. Gatasheh, Toqeer Abbas, Shifa shaffique et al.

Abstract Anthropogenic activities such as industrial pollution of water bodies possess threat to floras leading to extinction and endangerment. This study investigates the impact of industrial pollution on vegetation along River Chenab and its associated drains. Rivers and channels transporting industrial effluents have been determined to be significantly contaminated. The contamination was evidenced by the acidic and alkaline nature of industrial effluents, salinity, total dissolved solids, and the sodium absorption ratio. The research revealed that the pollution in the region severely impacts the native vegetation, resulting in a marked decline in density, frequency, relative density, and relative frequency across 10 sites, including three drain sites and one non-polluted site. Four plant species, Calotropis procera, Eclipta alba, Phyla nodiflora, and Ranunculus sceleratus exhibited tolerance to pollution and were present at all sites during all seasons. Anatomical modifications, such as increased root aerenchyma and vascular bundles, enabled these plants to thrive in polluted environments. The study highlights the importance of these species in phytoremediation and their potential for use in restoring degraded ecosystems.

Camille R. Reaux, Shelby A. Meche, Jordan M. Grider et al.

Given the exponential growth of biochemical data and deep effect of computational methods on life sciences, there is a need to rethink undergraduate curricula. A project-oriented learning approach based on the Triangular Spatial Relationship (TSR) algorithm has been developed. The TSR-based method was designed for protein 3D structural comparison, motif discovery and probing molecular interactions. The uniqueness of the method benefits students’ learning of big data and computational methods. Specifically, students learn (i) how to search proteins of interest from the PDB archive, (ii) basic supercomputer skills, (iii) how to prepare datasets, (iv) how to perform protein structure and sequence analyses, (v) how to interpret the results, visualize protein structures and make graphs. Five specific strategies have been developed to achieve students’ highest potentials. (i) This lab exercise is designed as a project-oriented learning approach. (ii) The skills-first and concept-second approach is used. (iii) Students choose the proteins based on their interests. (iv) Students are encouraged to learn from each other to promote student–student interactions. (v) Students are required to write a report and/or present their studies. To assess students’ performance, we have developed an assessment rubric that includes (i) demonstration of supercomputer skills in job script preparation, submission and monitoring, (ii) skills in preparation of datasets, (iii) data analytical skills, (iv) project report, (v) presentation, and (vi) integration of the TSR-based method with other computational methods (e.g., molecular 3D structural visualization and protein sequence analysis). This project has been introduced in undergraduate biochemistry research and teaching labs for 4 years. Most students have learned the basic supercomputer skills as well as structure data analysis skills. Students’ feedback is positive and encouraging. It can be further developed as a module for an integrated computational chemistry lecture course.

M. Levine

K. Saranya, M. Suguna, Salahuddin

The research explores a novel mathematical model for closed loop insulin delivery systems, featuring a glucose sensitive membrane. It employs a sophisticated framework of nonlinear reaction diffusion equations and enzyme kinetics. Central to the study is the development of analytical solutions for the glucose, gluconic acid, and oxygen concentrations, which are meticulously validated against simulation outcomes. This validation underscores the model's accuracy in capturing the complex dynamics inherent in such systems. Additionally, the study leverages Akbar and Ganji's methodology to provide approximate solutions, enabling a comprehensive comparison with analytical results and offering deeper insights into the system's behavior under varying parameters. By integrating both analytical and approximate approaches, the research not only enhances our understanding of biochemical processes but also lays the groundwork for refining closed-loop insulin delivery technology. The findings promise to significantly improve the precision and efficacy of insulin administration, crucial for managing glucose levels in diabetic patients more effectively. Furthermore, the study's implications extend beyond insulin delivery, potentially informing the development of advanced biomedical systems where precise control and understanding of biochemical interactions are paramount. Ultimately, this work represents a significant contribution to both theoretical biochemistry and practical medical applications, setting a foundation for the next generation of closed-loop insulin delivery systems designed to better meet the complex metabolic needs of patients with diabetes.

Dominik Stemer, Stephan Thürmer, Florian Trinter et al.

Amino acids and other small chiral molecules play key roles in biochemistry. However, in order to understand how these molecules behave in vivo, it is necessary to study them under aqueous-phase conditions. Photoelectron circular dichroism (PECD) has emerged as an extremely sensitive probe of chiral molecules, but its suitability for application to aqueous solutions had not yet been proven. Here, we report on our PECD measurements of aqueous-phase alanine, the simplest chiral amino acid. We demonstrate that the PECD response of alanine in water is different for each of alanine's carbon atoms, and is sensitive to molecular structure changes (protonation states) related to the solution pH. For C~1s photoionization of alanine's carboxylic acid group, we report PECD of comparable magnitude to that observed in valence-band photoelectron spectroscopy of gas-phase alanine. We identify key differences between PECD experiments from liquids and gases, discuss how PECD may provide information regarding solution-specific phenomena -- for example the nature and chirality of the solvation shell surrounding chiral molecules in water -- and highlight liquid-phase PECD as a powerful new tool for the study of aqueous-phase chiral molecules of biological relevance.

Shenglin Li, Muneer Ahmed Khoso, Jiabo Wu et al.

The environmental conditions encompassing plants exert a significant impact on their appropriate growth and development. It is of utmost importance to investigate the mechanisms and signaling cascades underlying the tolerance of plants to abiotic stress in order to enhance the quality of crops. Plant growth and development processes are significantly impacted by abiotic stresses, which are intricately linked to their surroundings. Plants exhibit prompt genetic and metabolic network responses, mostly through signaling networks involving transcription factors that respond to stress, including WRKY, MYB, bZIP, AP2/EREBP, and NAC. Among these WRKY TFs transcription factors, fulfill a pivotal function in a diverse range of stress responses and developmental mechanisms. WRKY TFs greatly assist plants in coping with abiotic stress. These transcription factors oversee the control of several target gene categories and active involvement in numerous signaling cascades in plants through their interaction with the W-box cis-acting elements located in the promoters of these target genes.This research provides a comprehensive analysis of the signaling networks linked to WRKY TFs and their response mechanism to abiotic stress. In addition, we have explored the state of knowledge on WRKY TFs' effects on plants' response to a range of abiotic stresses, such as drought, salt, high temperatures, and cold. It elucidates the intricate molecular mechanisms by which WRKY TFs govern signaling pathways and modulate gene expression, thereby conferring stress tolerance upon plants. Moreover, we have summarized the molecular function of WRKY TFs that are involved in tolerance to biotic stress. WRKY TFs, involved in signaling networks and hormonal routes like SA and JA, aid plants in inducing resistance mechanisms and coordinating defense responses against pathogens and environmental challenges. In order to enhance agricultural sustainability and augment crop resilience towards stress, strategies to manipulate the intricate regulatory networks involving WRKY TFs need to be established.

Chawapun Suttinont, Moe Tsutsumi, Tomohiro Numata et al.

Background/Aims: Tactile perception relies on mechanoreceptors and nerve fibers, including c-fibers, Aβ-fibers and Aδ-fibers. Schwann cells (SCs) play a crucial role in supporting nerve fibers, with non-myelinating SCs enwrapping c-fibers and myelinating SCs ensheathing Aβ and Aδ fibers. Recent research has unveiled new functions for cutaneous sensory SCs, highlighting the involvement of nociceptive SCs in pain perception and Meissner corpuscle SCs in tactile sensation. Furthermore, Piezo2, previously associated with Merkel cell tactile sensitivity, has been identified in SCs. The goal of this study was to investigate the channels implicated in SC mechanosensitivity and the release process of neurotrophic factor secretion. Methods: Immortalized IFRS1 SCs and human primary SCs generated two distinct subtypes of SCs: undifferentiated and differentiated SCs. Quantitative PCR was employed to evaluate the expression of differentiation markers and mechanosensitive channels, including TRP channels (TRPV4, TRPM7 and TRPA1) and Piezo channels (Piezo1 and Piezo2). To validate the functionality of specific mechanosensitive channels, Ca2+ imaging and electronic cell sizing experiments were conducted under hypotonic conditions, and inhibitors and siRNAs were used. Protein expression was assessed by Western blotting and immunostaining. Additionally, secretome analysis was performed to evaluate the release of neurotrophic factors in response to hypotonic stimulation, with BDNF, a representative trophic factor, quantified using ELISA. Results: Induction of differentiation increased Piezo2 mRNA expression levels both in IFRS1 and in human primary SCs. Both cell types were responsive to hypotonic solutions, with differentiated SCs displaying a more pronounced response. Gd3+ and FM1-43 effectively inhibited hypotonicity-induced Ca2+ transients in differentiated SCs, implicating Piezo2 channels. Conversely, inhibitors of Piezo1 and TRPM7 (Dooku1 and NS8593, respectively) had no discernible impact. Moreover, Piezo2 in differentiated SCs appeared to participate in regulatory volume decreases (RVD) after cell swelling induced by hypotonic stimulation. A Piezo2 deficiency correlated with reduced RVD and prolonged cell swelling, leading to heightened release of the neurotrophic factor BDNF by upregulating the function of endogenously expressed Ca2+-permeable TRPV4. Conclusion: Our study unveils the mechanosensitivity of SCs and implicates Piezo2 channels in the release of neurotrophic factors from SCs. These results suggest that Piezo2 may contribute to RVD, thereby maintaining cellular homeostasis, and may also serve as a negative regulator of neurotrophic factor release. These findings underscore the need for further investigation into the role of Piezo2 in SC function and neurotrophic regulation.

Yijie Zhang, Zhangyang Gao, Cheng Tan et al.

Predicting protein stability changes induced by single-point mutations has been a persistent challenge over the years, attracting immense interest from numerous researchers. The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry, including drug development, protein evolution analysis, and enzyme synthesis. Despite the proposition of multiple methodologies aimed at addressing this issue, few approaches have successfully achieved optimal performance coupled with high computational efficiency. Two principal hurdles contribute to the existing challenges in this domain. The first is the complexity of extracting and aggregating sufficiently representative features from proteins. The second refers to the limited availability of experimental data for protein mutation analysis, further complicating the comprehensive evaluation of model performance on unseen data samples. With the advent of Large Language Models(LLM), such as the ESM models in protein research, profound interpretation of protein features is now accessibly aided by enormous training data. Therefore, LLMs are indeed to facilitate a wide range of protein research. In our study, we introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations. Furthermore, we have curated a dataset meticulously designed to preclude data leakage, corresponding to two extensively employed test datasets, to facilitate a more equitable model comparison.

Brian Choi, Jie Xu, Trevor Norton et al.

Semi-linear elliptic Partial Differential Equations (PDEs) such as the non-linear Poisson Boltzmann Equation (nPBE) is highly relevant for non-linear electrostatics in computational biology and chemistry. It is of particular importance for modeling potential fields from molecules in solvents or plasmas with stochastic fluctuations. The extensive applications include ones in condensed matter and solid state physics, chemical physics, electrochemistry, biochemistry, thermodynamics, statistical mechanics, and materials science, among others. In this paper we study the complex analytic properties of semi-linear elliptic Partial Differential Equations with respect to random fluctuations on the domain. We first prove the existence and uniqueness of the nPBE on a bounded domain in $\mathbb{R}^3$. This proof relies on the application of a contraction mapping reasoning, as the standard convex optimization argument for the deterministic nPBE no longer applies. Using the existence and uniqueness result we subsequently show that solution to the nPBE admits an analytic extension onto a well defined region in the complex hyperplane with respect to the number of stochastic variables. Due to the analytic extension, stochastic collocation theory for sparse grids predict algebraic to sub-exponential convergence rates with respect to the number of knots. A series of numerical experiments with sparse grids is consistent with this prediction and the analyticity result. Finally, this approach readily extends to a wide class of semi-linear elliptic PDEs.

Rebekah Aduddell, James Fairbanks, Amit Kumar et al.

Regulatory networks depict promoting or inhibiting interactions between molecules in a biochemical system. We introduce a category-theoretic formalism for regulatory networks, using signed graphs to model the networks and signed functors to describe occurrences of one network in another, especially occurrences of network motifs. With this foundation, we establish functorial mappings between regulatory networks and other mathematical models in biochemistry. We construct a functor from reaction networks, modeled as Petri nets with signed links, to regulatory networks, enabling us to precisely define when a reaction network could be a physical mechanism underlying a regulatory network. Turning to quantitative models, we associate a regulatory network with a Lotka-Volterra system of differential equations, defining a functor from the category of signed graphs to a category of parameterized dynamical systems. We extend this result from closed to open systems, demonstrating that Lotka-Volterra dynamics respects not only inclusions and collapsings of regulatory networks, but also the process of building up complex regulatory networks by gluing together simpler pieces. Formally, we use the theory of structured cospans to produce a lax double functor from the double category of open signed graphs to that of open parameterized dynamical systems. Throughout the paper, we ground the categorical formalism in examples inspired by systems biology.

Hong-Gyun Lee, Min-Ji Cho, Je-Min Choi