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Relating stress fluctuations to rheology in model biopolymer networks

Cross-linked networks of semiflexible biopolymers are one of the essential building blocks of life as they are the scaffolds providing mechanical strength to biological cells to handle external stress and regulate shape. These protein structures experience strain at different rates often under confinement such as a membrane. Here, we compute the steady-state dynamics of stress and stress fluctuations in a wall-confined, continuously sheared, reversibly cross-linked, sticker–spacer model of a semiflexible biopolymer network. We find that the averages and fluctuations of shear stress and pressure increase by orders of magnitude when the strain rate is increased above a certain regime. The shear viscosity decreases with increasing strain rate except near the critical strain rate regime where it exhibits an inflection. Upon increasing the strain rate, we note a shift from a long time autocorrelation to an oscillatory and then to a sharply dropping autocorrelation function, endorsed by corresponding changes in the power spectrum of the stress. These outcomes indicate a transition from stick to stick-slip (stress buildup and relaxation) and then to slip upon increasing the strain rate, and we posit that this has to be a hallmark intermittent response of a dynamically cross-linked network under continuous shear deformations. We suggest that a fluctuation–dissipation type framework, where the stress is a stochastic process and “resistance to stress” is a function of strain rate, can help us understand the stress dynamics in biopolymer networks.

Influence of co-blending fly ash and ceramic waste powder on the performance and microstructure of cementitious substrates under sulfate dry-wet cycle attack

This study examines the properties of cement-based materials incorporating composite additions of fly ash and ceramic waste powder (CWP) as supplementary cementitious materials (SCM). The resistance of the materials to sulfate erosion under dry-wet cycling conditions was investigated through experimental testing. A Box-Behnken Design was employed to establish a model using three factors: the replacement ratio of cement by SCMs, the mass ratio of CWP to SCMs, and the water-to-binder ratio. The response variable was the mass loss rate due to sulfate erosion after 24 cycles of dry-wet cycling. Significance analysis of single-factor and multiple-factor interactions was conducted based on the response surface model. The research findings indicate that the cement-based materials with combined additions of fly ash and CWP exhibit optimal resistance to sulfate erosion under dry-wet cycling conditions. The water-to-binder ratio was identified as the most significant factor affecting the corrosion resistance of the cement-based materials at 7 days of curing. The dosage of ceramic waste powder influenced the corrosion performance of the cement-based materials at 28 days of curing. The content of SCMs affected the corrosion resistance of the cement-based materials after 56 days of curing. Comparative analysis of the grayscale three-dimensional distribution map and histogram of the cement-based materials with SCMs revealed an increase in the compactness of the matrix.

Advancements in global water and sanitation access (2000–2020)

Abstract Globally equitable access to safely managed drinking water and sanitation is one of the major aims of the United Nations’ Sustainable Development Goals, specifically SDG goal 6. We assessed global-scale progress toward this goal from 2000 to 2020 with access rates to improved drinking water and sanitation services that are adjusted for socioeconomic, political, and hydrological conditions. We found that the adjusted access rates in 2020 were lower than 2000, although not-adjusted access rates had increased. Access rates improved more slowly in higher-GDP countries than in lower-GDP ones during this period. These show that access rates in lower-income countries improved, but were still lower, compared with those in countries with the same social conditions in 2000. This suggests the recent progress toward this goal has not aligned well with social development, emphasizing need for reflecting national management, international cooperation, and investment in water-related infrastructures to achieve the goal by 2030.

Conformational fluxionality of long-chain alkene clusters in the gas phase evidenced from a combined experimental and theoretical approach

Clusters bound by weak, non-covalent forces, such as van der Waals interactions and hydrogen bonds, are ubiquitous in dilute media ranging from aerosols to molecular fluids and biological structures, their interest being not only fundamental as in astrochemistry but also more applied as in organic electronics. Neutral clusters of up to six 1-hexene molecules produced by supersonic expansion of a gas mixture were ionized, mass selected, and spectroscopically characterized using synchrotron-based VUV photoelectron photoion coincidence technique. Ionization energies inferred from these measurements show decreasing trends as the cluster size increases, by about 0.5 eV over the range of 1–6 molecules. Dedicated theoretical DFT-based calculations were performed to unravel the possible structures of these clusters and determine their vertical and adiabatic ionization energies. Our computational search for stable structures considered the possible chirality effects associated with most conformers of the monomer having enantiomers, in an approach with a broad structural sampling employing classical force fields followed by systematic re-optimization using an efficient quantum chemical method. Vertical and adiabatic ionization energies obtained using wavefunction-based methods exhibit significant dispersion due to conformational flexibility already in the monomer, but these effects are magnified in clusters due to their fluxionality at the experimental temperature of about 130 K. Overall, the trends obtained for the calculated vertical ionization energies agree well with the measured data and suggest that possible chiral recognition effects that could stabilize specific structures are likely to be hampered under the present experimental conditions.

LMD-YOLO: A lightweight algorithm for multi-defect detection of power distribution network insulators based on an improved YOLOv8

Insulator defect detection is a critical task in distribution network inspections. To address issues such as low detection accuracy, high model complexity, and large parameter counts caused by the variety of insulator defect types, this study propose a lightweight multi-defect detection network, LMD-YOLO, based on YOLOv8. The network improves the backbone by introducing SCConv module to improve C2f module, which reduces spatial and channel redundancy, lowering both computational complexity and the number of parameters. The SimAM attention mechanism is integrated to suppress irrelevant features and enhance feature extraction capabilities without adding extra parameters. The SIoU loss function is used in place of CIoU to accelerate model convergence and improve detection accuracy. Additionally, this study creates a target detection dataset that encompasses four types of insulators: insulator, absent insulator, broken insulator, and shedding insulator. Experimental results show that LMD-YOLO achieves a 2% higher average accuracy on the insulator dataset compared to YOLOv8n, with a 24.6% reduction in model parameters, offering an effective solution for smart grid inspections.

Author Correction: Association between constipation and incident chronic kidney disease in the UK Biobank study

Drug target affinity prediction based on multi-scale gated power graph and multi-head linear attention mechanism

For the purpose of developing new drugs and repositioning existing ones, accurate drug-target affinity (DTA) prediction is essential. While graph neural networks are frequently utilized for DTA prediction, it is difficult for existing single-scale graph neural networks to access the global structure of compounds. We propose a novel DTA prediction model in this study, MAPGraphDTA, which uses an approach based on a multi-head linear attention mechanism that aggregates global features based on the attention weights and a multi-scale gated power graph that captures multi-hop connectivity relationships of graph nodes. In order to accurately extract drug target features, we provide a gated skip-connection approach in multiscale graph neural networks, which is used to fuse multiscale features to produce a rich representation of feature information. We experimented on the Davis, Kiba, Metz, and DTC datasets, and we evaluated the proposed method against other relevant models. Based on all evaluation metrics, MAPGraphDTA outperforms the other models, according to the results of the experiment. We also performed cold-start experiments on the Davis dataset, which showed that our model has good prediction ability for unseen drugs, unseen proteins, and cases where neither drugs nor proteins has been seen.

Publisher Correction: The associations of cardiovascular health and all-cause mortality among individuals with depression

Correction: Back to normal? The health care situation of home care receivers across Europe during the COVID-19 pandemic and its implications on health

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Inflammation-driven NF-κB signaling represses ferroportin transcription in macrophages via HDAC1 and HDAC3

Abstract Anemia of inflammation is a prevalent comorbidity in patients with chronic inflammatory disorders. Inflammation causes hypoferremia and iron-restricted erythropoiesis by limiting ferroportin (FPN)–mediated iron export from macrophages that recycle senescent erythrocytes. Macrophage cell surface expression of FPN is reduced by hepcidin-induced degradation and/or by repression of FPN (Slc40a1) transcription via cytokine and Toll-like receptor (TLR) stimulation. Although the mechanisms underlying hepcidin-mediated control of FPN have been extensively studied, those inhibiting Slc40a1 messenger RNA (mRNA) expression remain unknown. We applied targeted RNA interference and pharmacological screens in macrophages stimulated with the TLR2/6 ligand FSL1 and identified critical signaling regulators of Slc40a1 mRNA repression downstream of TLRs and NF-κB signaling. Interestingly, the NF-κB regulatory hub is equally relevant for Slc40a1 mRNA repression driven by the TLR4 ligand lipopolysaccharide, the cytokine tumor necrosis factor β/lymphotoxin-alpha (LTA), and heat-killed bacteria. Mechanistically, macrophage stimulation with heat-killed Staphylococcus aureus recruits the histone deacetylases (HDACs) HDAC1 and HDAC3 to the antioxidant response element (ARE) located in the Slc40a1 promoter. Accordingly, pretreatment with a pan-HDAC inhibitor abrogates Slc40a1 mRNA repression in response to inflammatory cues, suggesting that HDACs act downstream of NF-κB to repress Slc40a1 transcription. Consistently, recruitment of HDAC1 and HDAC3 to the Slc40a1 ARE after stimulation with heat-killed S aureus is dependent on NF-κB signaling. These results support a model in which the ARE integrates the transcriptional responses of Slc40a1 triggered by signals from redox, metabolic, and inflammatory pathways. This work identifies the long-sought mechanism of Slc40a1 transcriptional downregulation upon inflammation, paving the way for therapeutic interventions at this critical juncture.