Browse Articles

Impact of gas background on XFEL single-particle imaging

Abstract Single-particle imaging (SPI) using X-ray free-electron Lasers (XFELs) offers the potential to determine protein structures at high spatial and temporal resolutions without the need for crystallization or vitrification. However, the technique faces challenges due to weak diffraction signals from single proteins and significant background scattering from gases used for sample delivery. A recent observation of a diffraction pattern from an isolated GroEL protein complex Ekeberg T et al. (Light Sci Appl 13:15, 2024. https://doi.org/10.1038/274s41377-023-01352-7) had similar numbers of signal and background photons. Ongoing efforts aim to reduce the background created by sample delivery, with one approach replacing most of the used gas with helium Yenupuri T et al. (Sci Rep 14:4401, 2024. https://doi.org/10.1038/s41598-024-54605-9). In this study, we investigate the effects of a reduced background on the resolution limits for SPI of isolated proteins under different experiment conditions. As a test case, we used GroEL, and we used experimentally derived parameters for our simulations. We observe that background significantly impacts the achievable resolution, particularly when the signal strength is comparable to the background. This is best exemplified at 6.0 keV, where a background reduction by a factor of 10 leads to a resolution improvement from 1.9 to 1.2 nm, for a dataset of $$10^4$$ patterns.

Thrombomodulin resistance as a novel prothrombotic pathway in COVID-19

Multi-strategy collaborative optimization of gravitational search algorithm

Reduced graphene oxide-based absorbance biosensors for detecting Escherichia coli DNA

Detailed petrophysical analysis and insights into the Alam El Bueib 3E reservoir from the berenice field, Western desert, Egypt

Abstract This study presents a comprehensive petrophysical assessment of the Alam El Bueib 3E (AEB-3E) sandstone reservoir in the Berenice Oil Field, located within the Faghur Basin, Western Desert, Egypt. The main objective is to evaluate reservoir quality, hydrocarbon potential, and lateral continuity to support effective field development strategies. The analysis is based on wireline log data from four wells: Berenice-TD-1X, Berenice-03, Berenice-08, and Berenice-09. Lithological analysis using M–N and RHOB–NPHI crossplots confirms that the reservoir is predominantly composed of clean sandstone, with limited shale and siltstone interbeds. Hydrocarbon-bearing intervals were identified between 11,150 and 11,190 feet based on neutron-density separation, resistivity log responses, and indicators of movable hydrocarbons. Formation water resistivity (Rw = 0.0378 Ω·m) and Archie parameters (a = 1, m = 1.9, n = 2) were derived from Pickett plot analysis in the Berenice-08 well, providing a basis for water saturation estimation in the absence of core data. The reservoir exhibits low shale content (3–8%), with effective porosity reaching up to 18%, particularly in the southeastern part of the field. Water saturation ranges between 28% and 54%, and the net pay intervals align well with hydrocarbon-bearing zones. Structural mapping and well correlation indicate consistent reservoir thickness, with central thickening influenced by ENE–WSW trending normal faults. Seismic interpretation reveals horst and graben structures that contribute to reservoir compartmentalization. The petroleum system is supported by mature source rocks of the Safa Formation, in addition to effective intraformational and regional seals, which enhance hydrocarbon entrapment. The results of this study contribute to a clearer understanding of the petrophysical and structural characteristics of the AEB-3E reservoir, offering valuable insights for future development and exploration efforts in the region.

Severity-specific immune landscape of COVID-19 revealed by single-cell sequencing

On the Baltimore Light RailLink into the quantum future

Abstract In the current era of noisy intermediate-scale quantum (NISQ) technology, quantum devices present new avenues for addressing complex, real-world challenges including potentially NP-hard optimization problems. Acknowledging the fact that quantum methods underperform classical solvers, the primary goal of our research is to demonstrate how to leverage quantum noise as a computational resource for optimization. This work aims to showcase how the inherent noise in NISQ devices can be leveraged to solve such real-world problems effectively. Utilizing a D-Wave quantum annealer and IonQ’s gate-based NISQ computers, we generate and analyze solutions for managing train traffic under stochastic disturbances. Our case study focuses on the Baltimore Light RailLink, which embodies the characteristics of both tramway and railway networks. We explore the feasibility of using NISQ technology to model the stochastic nature of disruptions in these transportation systems. Our research marks the inaugural application of both quantum computing paradigms to tramway and railway rescheduling, highlighting the potential of quantum noise as a beneficial resource in complex optimization scenarios.

Morphological profiling reveals neuroprotection via mitochondrial uncoupling in human dopaminergic neurons

A dynamic early warning model based on LSTM-AM for property-based elderly care resource shortages in urban-rural integrated counties in China

The proportion of genetic similarity for liability for neuroticism in mother–child and mother–father dyads is associated with reported relationship quality

Abstract This study aims to explore the influence of genetic similarity for neuroticism liability in mother’s reported quality of relationship with her child and partner. Such understanding could provide insight into the role of genetic similarity in neuroticism liability in close relationships. Molecular genetic data in 4,704 mothers, partners, and children in the Avon Longitudinal Study Parents And Children (ALSPAC) study were used to derive the proportion of genetic similarity in neuroticism liability between mother and child, and mother and partner, for genetic variants associated with neuroticism. Associations between genetic similarity in neuroticism liability scores and mothers’ reported enjoyment and conflict in the parenting relationship (child ages 0–3) and her reported partner relationship were examined. For a one standard deviation (SD) increase in similarity in mother and child genetic variants associated with neuroticism, there was a 0.15SD (95%CI = 0.003 to 0.500, p = 0.046) increase in maternal reported enjoyment in their relationship. This association was greater where mother and child were both in the top quartile for high neuroticism (standardised beta = 0.29, 95%CI = 0.02 to 0.56, p = 0.034). Similar patterns of results emerged for similarity for genetic variants associated with neuroticism between mothers and partners, and the quality of the mother-partner relationship. These results highlight how phenotypic variation (i.e. the link between PGS scores and mothers reported enjoyment) linked to genetic liability in one individual may be linked with the genetic liability of those around them (i.e. the genetic liability of the infant). In other words, parenting and intimate partner relationships as perceived by the mother were explained not by one or the other’s genetic score, but by the similarity between them These exploratory findings present an intriguing mechanism by which similarity between genetic liability might be linked to family relationships.

Association between life satisfaction and fall severity among hospitalized stroke patients

Advanced food waste quantification at municipal level to strengthen the assessment of prevention actions

Curcumin preserves bone health compromised by diabetes by inhibiting osteoporosis through regulation of the SIRT3/FoxO3a signalling pathway

Bayesian inference for dependent stress–strength reliability of series–parallel system based on copula

Abstract In this paper, we investigate the inferential procedures for dependent stress-strength reliability within a series-parallel system, utilizing the Clayton copula to characterize the dependence structure between stress and strength variables, which follow proportional reversed hazard rate model. We establish maximum likelihood estimations for model parameters and system reliability, along with improved approximate confidence intervals based on Fisher information. Bayesian estimations are performed using the highly flexible Gamma-Beta prior distribution under different loss functions, and the highest posterior density interval is obtained via the Metropolis-Hastings algorithm. To assess the performance of the proposed methods, Monte Carlo simulations are conducted. Finally, an original data set, the general dam occupancy rate of Istanbul, is analyzed for illustrative purposes.

Abundant fluids in southern Kumano Basin linked to fluid source and slow earthquakes at the plate boundary in the Nankai Trough

The Nankai Trough is a major subduction zone in southern Japan capable of generating the next M7 or larger earthquake off Kii Peninsula. In addition to earthquakes characterized by sharp P and S arrivals, there are numerous slow earthquakes without clear P phases at the subducting plate boundary corresponding to transient slips that are related to fluids at the plate boundary and high pore pressure. In this study, we perform ambient noise differential adjoint tomography to derive the S-wave velocity model beneath a linear Ocean Bottom Seismometer (OBS) array, which was previously difficult based on airgun active source study. We find that S-wave velocities in the southern (seaward) Kumano Basin are significantly lower than in the northern (landward) Kumano Basin, suggesting weak upper plate and abundant fluids in the southern Kumano Basin. The fluid-rich southern Kumano Basin lies above a weak interplate coupling zone with shallow slow earthquakes and pressurized fluids due to plate interface dehydration. Conversely, the northern Kumano Basin overlies a strong interplate coupling zone with few slow earthquakes, suggesting fluid migration from the weak-coupling interface to the forearc basin above.

Transcriptome analysis reveals that regulation network of the genes related to unique double flowers in tropical viviparous water lily

Gaming the cancer–immunity cycle by synchronizing the dose schedules

We introduce a mathematical model of the cancer–immunity cycle and use it to test several hypotheses regarding the combination, timing, and optimization associated with chemotherapy and immunotherapy dosing schedules in the context of competition and time-dependent selection pressure. A key idea is the value of synchronizing the dosing schedules with the fundamental period of the cancer–immunity cycle. The competitors in the population dynamics evolutionary game are the cancer cells, healthy (normal) cells, and T cells, which conceptually form a nontransitive rock-paper-scissor chain. The chemotherapy and immunotherapy dosing schedules each act as control functions whose timing and magnitudes we synchronize with the fundamental period of the underlying nonlinear dynamical system. With the model, we show among other more detailed results, that chemotherapy and immunotherapy pulse-dosing schedules do not commute; the best duration of the chemotherapy is one-quarter of the cancer–immunity cycle, whereas for immunotherapy it is one-half cycle; immunotherapy dosing should precede chemotherapy dosing and last twice as long. A general conclusion is that optimized timing of the dosing schedules can make up for lower total dose, opening up new possibilities for designing less toxic and more efficacious dosing regimens with drugs currently in use. Obtaining and calibrating more accurate measurements of the cycle-period across patient populations would be an important step in making some of these ideas clinically actionable.

Exosomal LncRNA SCAMP1-AS1 enhances osteosarcoma malignancy by regulating the LKB1-AMPK signaling pathway

Phosphatidic acid production on the vacuole harboring <i>Legionella pneumophila</i> is a signal for recognition of interferon-induced GTPases

The interferon (IFN)-inducible GTPases play a crucial role in cell autonomous immunity against intracellular pathogens. Particularly, these GTPases directly recognize the host membrane-derived vacuole containing pathogens and subsequently destroy it. Although it has been revealed that these GTPases target the membrane of Legionella pneumophila ( L. pneumophila )-containing vacuole (LCV), molecular mechanism has been totally uncleared. Here, we show that mouse guanylate-binding protein 2 (mGBP2) is specifically recruited to the LCV and subsequently ruptures it. Furthermore, we also show that mGBP2 recognizes phosphatidic acid (PA) produced by the Legionella effector Lpg2552 and fails to target the vacuole harboring Lpg2552-depleted L. pneumophila . Consistently, this strain successfully grows in cells expressing mGBP2. We additionally identified lysine 585 (K585) of mGBP2 is required for the binding to PA and K585-mutated mGBP2 fails to recognize LCV. Interestingly, this lysine residue is only conserved in GBP1 among human GBPs and the conserved lysine residue is important for PA recognition and subsequent LCV distribution. Importantly, L. pneumophila lacking Lpg2552 exhibits high resistance against IFN stimulation in THP-1-derived human macrophage.

Development and validation of large language model rating scales for automatically transcribed psychological therapy sessions

Abstract Rating scales have shaped psychological research, but are resource-intensive and can burden participants. Large Language Models (LLMs) offer a tool to assess latent constructs in text. This study introduces LLM rating scales, which use LLM responses instead of human ratings. We demonstrate this approach with an LLM rating scale measuring patient engagement in therapy transcripts. Automatically transcribed videos of 1,131 sessions from 155 patients were analyzed using DISCOVER, a software framework for local multimodal human behavior analysis. Llama 3.1 8B LLM rated 120 engagement items, averaging the top eight into a total score. Psychometric evaluation showed a normal distribution, strong reliability (ω = 0.953), and acceptable fit (CFI = 0.968, SRMR = 0.022), except RMSEA = 0.108. Validity was supported by significant correlations with engagement determinants (e.g., motivation, r = .413), processes (e.g., between-session efforts, r = .390), and outcomes (e.g., symptoms, r = − .304). Results remained robust across bootstrap resampling and cross-validation, accounting for nested data. The LLM rating scale exhibited strong psychometric properties, demonstrating the potential of the approach as an assessment tool. Importantly, this automated approach uses interpretable items, ensuring clear understanding of measured constructs, while supporting local implementation and protecting confidential data.