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The SRG rat as a novel host for an orthotopic patient-derived xenograft model of breast cancer brain metastasis
Unified interest point detection and description for perspective and Fisheye images
Promoting collective cooperation through temporal interactions
Collective cooperation maintains the function of many natural and social systems, making understanding the evolution of cooperation a central question of modern science. Although human interactions involve complex contact networks, current explorations are limited to static networks, where social ties are permanent and do not change over time. In reality, human activities often involve temporal interactions, where links are impermanent, and understanding the evolution of cooperation on such temporal networks is an open problem. Here, we systematically analyze how cooperation spreads on arbitrary temporal networks, and we distill our results down to a concise condition, which integrates evolutionary game dynamics with both static and temporal interactions. We find that the emergence of cooperation is facilitated by a simple rule of thumb: Hubs (individuals with many social ties) should be temporally deprioritized in interactions. For empirical applications, we further provide a quantitative metric capturing the priority of hubs, which is validated on empirical datasets based on its effectiveness in orchestrating the ordering of interactions to best promote cooperation. Our findings unveil the fundamental advantages conferred by temporal interactions for promoting collective cooperation, transcending the specific insights gleaned from studying static networks.
Nature-based solutions to climate change
Development and validation of a risk prediction model for unplanned 7-day readmission to PICU
Effects of laser beam profiles on the microstructure and magnetic properties of L-PBF soft magnetic alloys
Analysis of the pooled effect of compression ratio and injection timing variation on conventional diesel engine powered with nano doped biodiesel blend
Green synthesis and toxicological evaluation of zinc oxide nanoparticles utilizing Punica granatum fruit Peel extract: an eco-friendly approach
Impact of low-intensity 463 nm blue light on proliferation and adaptive mutation of Escherichia coli DH5α cells
A comprehensive study based on machine learning models for early identification Mycoplasma pneumoniae infection in segmental/lobar pneumonia
Optimal geometrical selection of skin mesh: experimental analysis and numerical optimization
Nanoscale structural alteration of lung collagen in response to strain and bleomycin injury
Nucleosomes as blueprints of genome architecture
De novo design of D-peptide ligands: Application to influenza virus hemagglutinin
D-peptides hold great promise as therapeutics by alleviating the challenges of metabolic stability and immunogenicity in L-peptides. However, current D-peptide discovery methods are severely limited by specific size, structure, and the chemical synthesizability of their protein targets. Here, we describe a computational method for de novo design of D-peptides that bind to an epitope of interest on the target protein using Rosetta’s hotspot-centric approach. The approach comprises identifying hotspot sidechains in a functional protein–protein interaction and grafting these side chains onto much smaller structured peptide scaffolds of opposite chirality. The approach enables more facile design of D-peptides and its applicability is demonstrated by design of D-peptidic binders of influenza A virus hemagglutinin, resulting in identification of multiple D-peptide lead series. The X-ray structure of one of the leads at 2.38 Å resolution verifies the validity of the approach. This method should be generally applicable to targets with detailed structural information, independent of molecular size, and accelerate development of stable, peptide-based therapeutics.
Comparative analysis of lumbar cerebrospinal fluid drainage versus lumbar puncture effectiveness in patients with aneurysmal subarachnoid hemorrhage
Simulation study of the influence of circular arc vortex generator size on the heat transfer characteristics of fin-and-tube heat exchanger
Whole grain and refined grain consumption and the risk of hypertension: a systematic review and meta-analysis of prospective studies
Abstract A high intake of whole grains has been associated with a reduced risk of hypertension, however, studies have not been entirely consistent. Findings regarding refined grains and hypertension have also been inconsistent. We conducted a systematic review and meta-analysis of prospective cohort studies on whole grain and refined grain consumption and hypertension risk. PubMed and Embase databases were searched up to 25th of July 2024. Random effects models were used to estimate summary relative risks (RRs) and 95% confidence intervals (CIs) for the association between whole grain and refined grain intake and hypertension. Restricted cubic splines were used to investigate potential nonlinear associations. Nine cohort studies were included in the meta-analysis. The summary RR (95% CI) for high vs. low whole grain intake was 0.74 (0.59–0.93, I2 = 97%, pheterogeneity<0.001, n = 9) and per 90 g/d was 0.86 (0.82–0.90, I2 = 63%, pheterogeneity=0.008, n = 8). The summary RR (95% CI) for high vs. low refined grain intake was 0.94 (0.88–1.01, I2 = 7.9%, pheterogeneity=0.36, n = 5) and per 90 g/d was 0.97 (0.93–1.02, I2 = 0%, pheterogeneity=0.42, n = 4). There was no indication of publication bias in either analysis, although the number of studies was low for refined grains. There was no evidence of nonlinearity for whole grains (pnonlinearity=0.31) or refined grains (pnonlinearity=0.21), and for whole grains there was a 22% reduction in risk at 200 vs. 0 g/d. These findings provide further support for a beneficial role of whole grain consumption in relation to hypertension risk and support recommendations to increase whole grain intake in the general population. No clear association was observed between refined grains and risk of hypertension.