Browse Articles

Amino acid transfer free energies reveal thermodynamic driving forces in biomolecular condensate formation

The self-assembly of intrinsically disordered proteins into biomolecular condensates depends on their primary sequence, leading to sequence-dependent phase separation. Computational methods to study this behavior often rely on residue-level interaction potentials that estimate the propensity of amino acids to partition between the dilute and dense phases. While distribution coefficients would provide the most direct measure of these potentials, their unavailability has led to the use of proxies, most notably, hydropathy. However, recent studies have highlighted limitations in hydropathy-based models. Here, we address this fundamental gap by calculating the transfer free energies for amino acid side chain analogs moving from the dilute phase to the dense phase of biomolecular condensates. We find that, net transfer free energies arise from a balance between favorable protein-mediated and unfavorable water-mediated interactions, with a striking asymmetry between the contributions of positive and negatively charged residues. This asymmetry originates from the stronger solvation of negatively charged species, and extends to modified amino acids. We further demonstrate that the sequence features of the condensate-forming protein modulate these transfer free energies in a context-dependent, but qualitatively similar manner. These findings help explain nontrivial experimental trends and provide a foundation for interpreting the sequence-dependent driving forces underlying condensate formation.

Propensity score matched analysis of nationwide outcomes for intracranial bypass and stenting for treatment of intracranial atherosclerotic disease

Electroreception in treehoppers: How extreme morphologies can increase electrical sensitivity

The link between form and function of an organism’s morphology is usually apparent or intuitive. However, some clades of organisms show remarkable diversity in their form, often exhibiting extreme morphologies, but with no obvious functional explanation. Treehoppers (Membracidae) are a family of insects that exemplify this, displaying an astounding morphological diversity, resulting in a plethora of extreme forms. The function of these morphological extremities and the reasons for their evolution have thus far remained largely enigmatic. However, this mystery can be considered in light of the capacity of many animals to detect electric fields in air via electrostatic actuation of mechanosensory structures on their body. Importantly, the strength of the electric field experienced by these mechanosensory structures is expected by physics to depend on the animal’s geometry, with sharp and elongated features producing the highest electric fields. Therefore, we hypothesize that the extreme morphologies of treehoppers increase their electrical sensitivity. Here, we show that treehoppers, along with their predators and mutualists, produce electric fields and that the treehopper Poppea capricornis can detect electric fields, responding behaviorally. We also demonstrate that predatory wasps and mutualist bees differ significantly in their electrostatic profiles, pointing to the sophistication of electrical information potentially available to treehoppers. Biophysical, computational, and mathematical techniques are then utilized to provide evidence that the pronotum of treehoppers is the site of electroreception and that its extreme shapes may enhance its sensitivity to electricity.

He said, she said: the “accused” and “complainant” in a sexual assault scenario are equally susceptible to misinformation

Lipid residue analysis reveals divergent culinary practices in Japan and Korea at the dawn of intensive agriculture

The dispersal of millet and rice agriculture from Korea to Japan from around 3,000 y ago has been well documented through radiocarbon analysis of botanical remains and surveying seed impressions on pottery. Much less is known about the extent to which these novel crops were consumed and incorporated into everyday culinary practices. In Japan, agriculturalists moving from Korea would have encountered large, sedentary Final Jomon populations who had well-established hunting, foraging, and cultivation strategies for exploiting indigenous fauna and flora. The degree to which these encounters hindered or enhanced the emergence of agriculture is a key question. To investigate potential changes in food exploitation, we analyzed the contents of pottery through lipid residue analysis of 260 vessels from Bronze Age (Mumun) Korea and contemporary Jomon and Yayoi pottery from Northern Kyushu. A lipid biomarker for broomcorn millet was only found in samples from Korea, suggesting that this crop was not routinely prepared in early agricultural pottery from Japan, despite some botanical evidence for its cultivation. Instead, aquatic products continued to be used in early agricultural pottery, pointing to continuity from the Jomon period despite the arrival of new “continental” ceramic forms. Rice remains difficult to identify conclusively, but by modeling carbon isotope values, we were able to determine the maximum extent that rice may have contributed. Overall, we show that there was a change in culinary practices as agriculture dispersed from Korea to Japan, most likely influenced by different long-standing traditions of preparing and cooking foods in each locality.

Sampling zero stability in sampled data control systems with delays using backward triangle sample and hold

Mapping the development of neural structures supporting literacy

Effects of metformin treatment on the risk of acute myocardial infarction

Correction for Gao et al., Subunit specialization in AAA+ proteins and substrate unfolding during transcription complex remodeling

Evaluating the efficacy of using large language models in preoperative prediction of microvascular invasion in HCC: a multicenter study

Abstract Primary liver cancer is the sixth most commonly diagnosed cancer globally and the third leading cause of cancer-related deaths. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and microvascular invasion (MVI) is a significant risk factor affecting postoperative prognosis in HCC. However, accurately predicting MVI preoperatively remains a challenge. This study aims to evaluate the application of large language models (LLMs), specifically ChatGPT 4o, in predicting MVI in HCC and to compare its performance with traditional clinical models. In this retrospective study, 300 HCC patients who underwent curative liver resection between June 2018 and December 2018 were selected at two centers. The collected clinical data included age, gender, HBV infection, liver cirrhosis, AFP levels, and more. ChatGPT 4o were used to process the clinical data of the patients and predict MVI. Subsequently, the predictive results of the ChatGPT 4o were compared with machine learning models, the ROC curves were plotted, and AUC was calculated. The results showed that the AUC of the ChatGPT 4o was 0.755. Machine learning algorithms use Random Forest, Support Vector Machine, Logistic Regression, XGBoost and Decision Tree, the AUC of 5 machine learning algorithms was range from 0.534 to 0.624. ChatGPT 4o achieved the highest AUC and showed statistically significant differences compared to Support Vector Machine, Logistic Regression and Decision Tree. Additionally, the predictive results of the ChatGPT 4o effectively stratified the postoperative overall survival (OS) and recurrence-free survival (RFS) of HCC patients. LLMs have demonstrated significant predictive capabilities for MVI in HCC and for risk stratification regarding postoperative OS and RFS. These advancements possess substantial potential to enhance preoperative management and make surgical planning.

Impacts of climate variability and adaptation strategies on staple crop productivity in Sidama, Ethiopia

Interfacial doping engineering on electronic states and electrical properties of MoS2/Au contact

Imaging the photodissociation dynamics of vibrationless and vibrationally excited CH3S radicals

The photodissociation dynamics of CH3S radicals was studied at different excitation energies within the first absorption band. The CH3S radicals were produced with a broad vibrational energy distribution from the photodissociation of CH3SH at 210 nm, which allowed us to study the effect of the vibrational excitation on the photodissociation dynamics. The photofragments, CH3(ν), S(3PJ), and S(1D) were detected by resonance enhanced multiphoton ionization schemes in slice-imaging experiments, and the corresponding translational energy and angular distributions were obtained for each fragment. Vibrationless CH3S radicals are excited to the Ã2A1 state that predissociates via three possible dissociative states (ã4A2, B̃2A2, and 4E), leading to the formation of CH3(ν = 0), CH3(ν2) with inverted population and S(3PJ) with J = 0, 1, and 2. Instead, vibrationally excited CH3S radicals are excited in the Franck–Condon region of the B̃2A2 dissociative state, where they dissociate directly to produce fast and anisotropic S(3P0) fragments, according to the adiabatic correlation established by the new calculated potential energy curves reported in this work. The B̃2A2 state is crossed by the dissociative 4E state, and this crossing leads to the formation of slower and less anisotropic S(3P1) and S(3P2) fragments that apparently correlate with CH3 populated with one quantum in the C–H stretch (ν1), suggesting that this vibrational mode is involved in the non-adiabatic dynamics associated to the B̃2A2/4E crossing. Finally, S(1D) fragments show a Boltzmann-like kinetic energy distribution with an isotropic angular distribution, associated with slow fragments produced by statistical dissociation from the locally excited Ã2A1 state of CH3S.

Impact of the COVID-19 pandemic on incidence of psychiatric disorders using nationwide cohort data and ARIMA models

Spin–orbit <i>ab initio</i> and density functional theory study of vinyl iodide: Molecular properties and photodissociation dynamics

We present a comprehensive theoretical investigation of vinyl iodide (VI), examining its molecular properties and photodissociation dynamics using high-level ab initio and density functional theory methods explicitly incorporating spin–orbit coupling (SOC). To align with experimental results, accurately determining the bond dissociation energy requires an explicit consideration of SOC. For ab initio calculations, correcting for basis set superposition error proves essential for obtaining quantitatively accurate values consistent with the experimental value. We calculate vertical excitation energies and systematically characterize the potential energy curves (PECs) along the C–I dissociation coordinate. This study establishes explicit excited state assignments for VI for the first time, highlighting the significant role of triplet states, particularly the 4A′ and 4A″ states in photodissociation dynamics. These states are found to contribute the broad UV absorption band around 250 nm mainly through σ* ← n″ and σ* ← n′ excitations rather than the previously proposed π* ← n″ excitation. Our calculated PECs provide theoretical validation and detailed explanations for the experimentally observed wavelength-dependent quantum yields and anisotropy parameters of I(2P3/2) and I*(2P1/2) species.

Doxorubicin causes cognitive impairment and alters gut microbiota in both male and female juvenile rats

The role of the pre-exponential factor on temperature programmed desorption spectra: A computational study of frozen species on interstellar icy grain mantles

Temperature programmed desorption (TPD) is a well-known technique to study gas-surface processes, and it is characterized by two main quantities: the adsorbate binding energy and the pre-exponential factor. While the former has been well addressed in recent years by both experimental and computational methods, the latter remains somewhat ill-defined, and different schemes have been proposed in the literature for its evaluation. In the astrochemistry context, binding energies and pre-exponential factors are key parameters that enter microkinetic models for studying the evolution over time of the chemical species in the universe. In this paper, we studied, by computer simulations, the effect of different pre-exponential factor models using water, ammonia, and methanol adsorbed on amorphous and crystalline ices as test cases: specifically, the one most widely used by the astrochemical community (Herbst–Hasegawa), the models provided by Tait and Campbell, and an extension of the Tait formulation including the calculation of the vibrational partition function. We suggest the methods proposed by Tait and Campbell that provide TPD temperature peaks within 30 K of each other while avoiding demanding quantum mechanical calculations, as they are based on tabulated data. Finally, when the explicit inclusion of the vibrational partition function is needed, we propose a cost-effective strategy to include all the thermal contributions in the partition functions without the need for performing a full vibrational calculation of the whole system.

Advanced satellite-based remote sensing and data analytics for precision water resource management and agricultural optimization

Toward data-driven predictive modeling of electrocatalyst stability and surface reconstruction

Catalyst dissolution and surface restructuring are ubiquitous in electrocatalysis, often leading to formidable activity–stability trade-offs and obscure electrochemically induced surface species that severely hinder the understanding and optimization of electrocatalysts under diverse harsh operating conditions. As even state-of-the-art characterization techniques lack the resolution and efficiency for the unambiguous elucidation of decomposition kinetics and reconstruction dynamics at electrocatalytic interfaces, many atomistic modeling approaches—following the recent advances in physics-driven machine learning—have been widely used to facilitate the atom-by-atom understanding and rational engineering of electrocatalyst stability and dynamics. This Perspective systematically assesses classical and data-driven approaches in theoretical surface science and computational catalysis, recognizing their achievements and highlighting their limitations in throughput, efficiency, accuracy, bias, transferability, and scalability toward enabling realistic and predictive modeling of electrocatalyst degradation and reconstruction. By examining different methods spanning first-principle simulations, surface sampling, neural network interatomic potentials, and generative deep learning models, it is underscored how such data-driven computational techniques help elucidate the precise nature of various key interfacial atomistic processes to address existing technical challenges in surface modeling and provide a new paradigm to optimize dissolution kinetics and restructuring dynamics for electrocatalyst design.

Revolutionizing environment friendly FASnI3 perovskite solar cells with pioneering selenium doped metal chalcogenide charge transport layer unlocking 32% efficiency