Hasil untuk "Biochemistry"

G. Skačej, P. Ziherl

H. Berg

This book is a lucid, straightforward introduction to the concepts and techniques of statistical physics that students of biology, biochemistry, and biophysics must know. It provides a sound basis for understanding random motions of molecules, subcellular particles, or cells, or of processes that depend on such motion or are markedly affected by it. Readers do not need to understand thermodynamics in order to acquire a knowledge of the physics involved in diffusion, sedimentation, electrophoresis, chromatography, and cell motility--subjects that become lively and immediate when the author discusses them in terms of random walks of individual particles.

Samuel A. Lambert, A. Jolma, L. Campitelli et al.

Samuel A. Lambert,1,9 Arttu Jolma,2,9 Laura F. Campitelli,1,9 Pratyush K. Das,3 Yimeng Yin,4 Mihai Albu,2 Xiaoting Chen,5 Jussi Taipale,3,4,6,* Timothy R. Hughes,1,2,* and Matthew T. Weirauch5,7,8,* 1Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada 2Donnelly Centre, University of Toronto, Toronto, ON, Canada 3Genome-Scale Biology Program, University of Helsinki, Helsinki, Finland 4Division of Functional Genomics and Systems Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden 5Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA 6Department of Biochemistry, Cambridge University, Cambridge CB2 1GA, United Kingdom 7Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA 8Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA 9These authors contributed equally *Correspondence: ajt208@cam.ac.uk (J.T.), t.hughes@utoronto.ca (T.R.H.), Matthew.Weirauch@cchmc.org (M.T.W.) https://doi.org/10.1016/j.cell.2018.01.029

J. Koza

Arthur H. Smith

J. Jacobsen, L. Smith

G. Bourne

D. J. Bell

F. Mȕller

Irene Hou, Alexander Qin, Lauren Cheng et al.

More scientists are now using AI, but prior studies have examined only how they use it 'at the desk' for computer-based work. However, given that scientific work often happens 'beyond the desk' at lab and field sites, we conducted the first study of how scientific practitioners use AI for embodied physical tasks. We interviewed 12 scientific practitioners doing hands-on lab and fieldwork in domains like nuclear fusion, primate cognition, and biochemistry, and found three barriers to AI adoption in these settings: 1) experimental setups are too high-stakes to risk AI errors, 2) constrained environments make it hard to use AI, and 3) AI cannot match the tacit knowledge of humans. Participants then developed speculative designs for future AI assistants to 1) monitor task status, 2) organize lab-wide knowledge, 3) monitor scientists' health, 4) do field scouting, 5) do hands-on chores. Our findings point toward AI as background infrastructure to support physical work rather than replacing human expertise.

Young-Suk Lee, Ramon Fernandez Astudillo, Radu Florian

Deep research agents increasingly interleave web browsing with multi-step computation, yet existing benchmarks evaluate these capabilities in isolation, creating a blind spot in assessing real-world performance. We introduce DRBENCHER, a synthetic benchmark generator for questions that require both browsing and computation. It enforces four criteria: verifiability (gold answers are computed by executing parameterized code over knowledge-graph values), complexity (multi-hop entity identification, property retrieval, and domain-specific computation), difficulty (a two-stage verification cascade filters out questions solvable by the generating model), and diversity (a greedy max-min embedding filter maximizes coverage). These criteria are realized via a unified answer-first pipeline spanning five domains: biochemistry, financial, geophysical, security, and history. Human evaluation shows 76% validity (84% excluding stale data), with 35% of errors due to outdated knowledge-graph entries, highlighting an inherent limitation of systems that reason over evolving data. Automatic evaluation shows that the strongest frontier model achieves only 20% answer accuracy. Compared to manually constructed benchmarks (BrowseComp+, MATH-500, GPQA), DRBENCHER achieves the highest semantic diversity.

Joshua L. Reynolds, Shaun C. Burd, Tzu-Chieh Yen et al.

Nonlinear optical processes are used in biological microscopy to surpass the diffraction limit on resolution, image deeper into brain tissues, and identify biomolecules without exogenous labels. These techniques typically require high optical intensities to increase the strength of the nonlinear interactions, which can perturb native biochemistry and damage or kill living samples. Stimulated Raman scattering (SRS) microscopy visualizes the spatial distribution of molecules using a nonlinear interaction between light and chemically specific molecular vibrations. However, the detection of biomolecules at low concentrations is limited by the total photon dose that can be applied before photodamage alters the sample, and photon shot noise sets the minimum achievable noise floor for most microscopes. Here we demonstrate a cavity-enhanced SRS microscope that is more sensitive than an equivalent conventional SRS microscope by up to 8.3(7) dB in spectroscopy and 8.6(1) dB in cell imaging. These results approach quantum limits on sensitivity and demonstrate that quantum states of light are sufficient but not necessary to enhance the sensitivity of microscopy techniques that are limited by photodamage.

Belinda Claire Kiam, Aline Gaelle Bouopda-Tuedom, Jean Arthur Mbida Mbida et al.

Abstract Background Assessing vector bionomics and their role in transmission is crucial to improving vector control strategies. Several entomological studies have been conducted to describe malaria transmission in different eco-epidemiological settings in Cameroon; however, data gaps persist, particularly in the highland areas. This study aimed to characterize malaria vectors in three localities along an altitudinal gradient in the western region: Santchou (700 m), Dschang (1400 m) and Penka Michel (1500 m). Methods Human landing catches were conducted from May to June 2023 in 17 villages (including 10 health zones in Dschang, 4 in Santchou and 3 in Penka Michel) from 6:00 p.m. to 9:00 a.m. Mosquitoes were sorted into genera and all Anopheles species were identified using morphological taxonomic keys and species-specific Polymerase Chain reaction (PCR). Entomological indicators, including species composition, abundance, biting behaviour, infection rate and entomological inoculation rate (EIR) were assessed. Genomic DNA from the head and thorax was extracted and tested for Plasmodium infection by real-time PCR. Results A total of 2835 Anopheles mosquitoes were identified, including Anopheles gambiae sensu lato (s.l.) (82.88%), Anopheles funestus s.l. (15.92%), Anopheles nili (0.09%) and Anopheles ziemanni (1.11%), with An. gambiae s.l. being the most prevalent at all sites. Anopheles gambiae s.l. had a significantly higher human-biting rate at Penka Michel (45.25 bites/human/night) compared to Santchou (3.1 bites/human/night [b/h/n]) and Dschang (0.41 bites/human/night) (p-value < 0.001). It was also the main malaria vector, with an entomological inoculation rate (EIR) 13 times higher in Penka Michel than Santchou (1.11 vs. 0.08 infective bites/human/night). The data suggest a very focal distribution of infective An. gambiae s.l. mosquitoes. Plasmodium falciparum was the dominant malaria parasite (67% in Santchou, 62% in Penka Michel), but Plasmodium malariae (33% in Santchou, 31% in Penka Michel) and Plasmodium ovale (1.21% only in Penka Michel) infections were also detected. Conclusion The study highlights a difference in mosquito composition and host-seeking behaviour across altitudes, emphasizing the need for continued surveillance to monitor vector populations. To combat the persistence of malaria in Cameroon, it is crucial to implement additional tools like larviciding, integrated and environmental management, particularly against outdoor-biting mosquitoes, to prevent potential malaria outbreaks in these highland areas.

Sudarshana Laha, Jonathan Bauermann, Frank Jülicher et al.

Phase-separated liquid condensates can spatially organize and thereby regulate chemical processes. However, the physicochemical mechanisms underlying such regulation remain elusive as the intramolecular interactions responsible for phase separation give rise to a coupling between diffusion and chemical reactions at non-dilute conditions. Here, we derive a theoretical framework that decouples the phase separation of scaffold molecules from the reaction kinetics of diluted clients. As a result, phase volume and client partitioning coefficients become control parameters, which enables us to dissect the impact of phase-separated condensates on chemical reactions. We apply this framework to two chemical processes and show how condensates affect the yield of reversible chemical reactions and the initial rate of a simple assembly process. In both cases, we find an optimal condensate volume at which the respective chemical reaction property is maximal. Our work can be applied to experimentally quantify how condensed phases alter chemical processes in systems biology and unravel the mechanisms of how biomolecular condensates regulate biochemistry in living cells.

Jiao Hu, Jiaxu Cui, Bo Yang

Discovering governing equations of complex network dynamics is a fundamental challenge in contemporary science with rich data, which can uncover the mysterious patterns and mechanisms of the formation and evolution of complex phenomena in various fields and assist in decision-making. In this work, we develop a universal computational tool that can automatically, efficiently, and accurately learn the symbolic changing patterns of complex system states by combining the excellent fitting ability from deep learning and the equation inference ability from pre-trained symbolic regression. We conduct intensive experimental verifications on more than ten representative scenarios from physics, biochemistry, ecology, epidemiology, etc. Results demonstrate the outstanding effectiveness and efficiency of our tool by comparing with the state-of-the-art symbolic regression techniques for network dynamics. The application to real-world systems including global epidemic transmission and pedestrian movements has verified its practical applicability. We believe that our tool can serve as a universal solution to dispel the fog of hidden mechanisms of changes in complex phenomena, advance toward interpretability, and inspire more scientific discoveries.

Lorenzo Restaino, Thomas Schnappinger, Markus Kowalewski

Benzophenone serves as a prototype chromophore for studying the photochemistry of aromatic ketones, with applications ranging from biochemistry to organic light-emitting diodes. In particular, its intersystem crossing from the first singlet excited state to triplet states has been extensively studied, but experimental or theoretical studies on the preceding internal conversion within the singlet manifold are very rare. This relaxation mechanism is particularly important because direct population transfer of the first singlet excited state from the ground state is inefficient due to its low oscillator strength. In this work, we aim to fill this gap by employing mixed quantum classical and full quantum dynamics simulations and time-resolved photoelectron spectroscopy for gas-phase benzophenone and meta-methyl benzophenone. Our results show that nonadiabatic relaxation via conical intersections leads to a linear increase in the population of the first singlet excited state. This population transfer due to conical intersections can be directly detected by a bifurcation of the photoelectron signal. In addition, we are able to clarify the role of the third singlet excited state degenerate to the second excited state - a topic that remains largely unexplored in the existing literature on benzophenone.

Aiswarya Premchandar, Ruiji Ming, Abed Baiad et al.

Cystic fibrosis (CF) is a monogenic disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. Premature termination codons (PTCs) represent ∼9% of CF mutations that typically cause severe expression defects of the CFTR anion channel. Despite the prevalence of PTCs as the underlying cause of genetic diseases, understanding the therapeutic susceptibilities of their molecular defects, both at the transcript and protein levels remains partially elucidated. Given that the molecular pathologies depend on the PTC positions in CF, multiple pharmacological interventions are required to suppress the accelerated nonsense-mediated mRNA decay (NMD), to correct the CFTR conformational defect caused by misincorporated amino acids, and to enhance the inefficient stop codon readthrough. The G418-induced readthrough outcome was previously investigated only in reporter models that mimic the impact of the local sequence context on PTC mutations in CFTR. To identify the misincorporated amino acids and their ratios for PTCs in the context of full-length CFTR readthrough, we developed an affinity purification (AP)-tandem mass spectrometry (AP-MS/MS) pipeline. We confirmed the incorporation of Cys, Arg, and Trp residues at the UGA stop codons of G542X, R1162X, and S1196X in CFTR. Notably, we observed that the Cys and Arg incorporation was favored over that of Trp into these CFTR PTCs, suggesting that the transcript sequence beyond the proximity of PTCs and/or other factors can impact the amino acid incorporation and full-length CFTR functional expression. Additionally, establishing the misincorporated amino acid ratios in the readthrough CFTR PTCs aided in maximizing the functional rescue efficiency of PTCs by optimizing CFTR modulator combinations. Collectively, our findings contribute to the understanding of molecular defects underlying various CFTR nonsense mutations and provide a foundation to refine mutation-dependent therapeutic strategies for various CF-causing nonsense mutations.

S. Vasanthakumar, M. Manikandan, Muthu Arumugam

Selenium, an essential micronutrient with potent anticancer and antioxidant properties, the inorganic form of selenium is highly toxic, while organic and elemental nanoforms are more bioavailable and less toxic and have gained attention owing to their dietary and clinical relevance. This study aims to optimize conditions for the biosynthesis and production of elemental selenium nanoparticles for selenium supplements using marine microalgae, Nannochloropsis oceanica CASA CC201. The 10 mM precursor solution treated with 1 % of the algal extract (10:1 ratio of precursor and algal extract, respectively) was shown to be the optimal concentration for synthesizing highly stable selenium nanoparticles with a size of 183 nm and a zeta potential of −38.5 mV. AFM and TEM analysis suggest that the spherical-shaped nanoparticles with smooth surfaces were polydispersely distributed. The nanoparticles are well characterized using various analytical and advanced techniques, including Raman spectroscopy and X-ray photoelectron spectroscopy. FT-IR analyses reveal the presence of microalgae proteins and peptides as stabilizing and fabricating agents of Se-NPs to further understand the mode of bioreduction. The synthesized elemental nanoform (Se0) has been validated for its biological functions, showing enhanced radical scavenging activity (74 % in a concentration-dependent manner). Subsequently, algal-mediated selenite reduction and nanoparticle synthesis is an eco-friendly, non-toxic, and sustainable method for the large-scale production of highly stable Se-NPs for niche applications as dietary and feed supplements.

M. Friedman

Ke Sun, Chao Fang, Mingyu Kang et al.

Electron transfer within and between molecules is crucial in chemistry, biochemistry, and energy science. This study describes a quantum simulation method that explores the influence of light polarization on the electron transfer between two molecules. By implementing precise and coherent control among the quantum states of trapped atomic ions, we can induce quantum dynamics that mimic the electron transfer dynamics in molecules. We use $3$-level systems (qutrits), rather than traditional two-level systems (qubits) to enhance the simulation efficiency and realize high-fidelity simulations of electron transfer dynamics. We treat the quantum interference between the electron coupling pathways from a donor with two degenerate excited states to an acceptor and analyze the transfer efficiency. We also examine the potential error sources that enter the quantum simulations. The trapped ion systems have favorable scalings with system size compared to those of classical computers, promising access to electron-transfer simulations of increasing richness.