BPNN prediction of Au@Au.

The absorption spectrum of Au with a distance from the core to the shell of 5 nm, (a) S = 4 nm, (b) S = 7 nm, (c) S = 9 nm; The diameter of Au nucleus is 6 nm, (d) S = 3 nm, (e) S = 5 nm, (f) S = 7 nm; The diameter of the Au nucleus is 7 nm, (g) C = 5 nm, (h) C = 7 nm, and (i) C = 9 nm.</p

Fig 2 -

Schematic illustration for geometry of the (a) yolk-shell structure.(b) Front view of yolk shell structure.</p

BPNN reverse design architecture.

The yolk shell is widely used in optoelectronic devices due to its excellent optical properties. Compared to single metal nanostructures, yolk shells have more controllable degrees of freedom, which may make experiments and simulations more complex. Using neural networks can efficiently simplify the computational process of yolk shell. In our work, the relationship between the size and the absorption efficiency of the yolk-shell structure is established using a backpropagation neural network (BPNN), significantly simplifying the calculation process while ensuring accuracy equivalent to discrete dipole scattering (DDSCAT). The absorption efficiency of the yolk shell was comprehensively described through the forward and reverse prediction processes. In forward prediction, the absorption spectrum of yolk shell is obtained through its size parameter. In reverse prediction, the size parameters of yolk shells are predicted through absorption spectra. A comparison with the traditional DDSCAT demonstrated the high precision prediction capability and fast computation of this method, with minimal memory consumption.</div

Fig 4 -

(a) Learning curve of the BPNN model with BPNN algorithm prediction results of Au@TiO2, R = 2 nm, C = 4 nm, (b) prediction results, (c) simulation results. (d) Learning curve of the BPNN model with Au@Au, R = 2 nm, C = 4 nm, (e) prediction results, (f) simulation results.</p

Training dataset and prediction results.

The yolk shell is widely used in optoelectronic devices due to its excellent optical properties. Compared to single metal nanostructures, yolk shells have more controllable degrees of freedom, which may make experiments and simulations more complex. Using neural networks can efficiently simplify the computational process of yolk shell. In our work, the relationship between the size and the absorption efficiency of the yolk-shell structure is established using a backpropagation neural network (BPNN), significantly simplifying the calculation process while ensuring accuracy equivalent to discrete dipole scattering (DDSCAT). The absorption efficiency of the yolk shell was comprehensively described through the forward and reverse prediction processes. In forward prediction, the absorption spectrum of yolk shell is obtained through its size parameter. In reverse prediction, the size parameters of yolk shells are predicted through absorption spectra. A comparison with the traditional DDSCAT demonstrated the high precision prediction capability and fast computation of this method, with minimal memory consumption.</div

BP basic network structure.

The yolk shell is widely used in optoelectronic devices due to its excellent optical properties. Compared to single metal nanostructures, yolk shells have more controllable degrees of freedom, which may make experiments and simulations more complex. Using neural networks can efficiently simplify the computational process of yolk shell. In our work, the relationship between the size and the absorption efficiency of the yolk-shell structure is established using a backpropagation neural network (BPNN), significantly simplifying the calculation process while ensuring accuracy equivalent to discrete dipole scattering (DDSCAT). The absorption efficiency of the yolk shell was comprehensively described through the forward and reverse prediction processes. In forward prediction, the absorption spectrum of yolk shell is obtained through its size parameter. In reverse prediction, the size parameters of yolk shells are predicted through absorption spectra. A comparison with the traditional DDSCAT demonstrated the high precision prediction capability and fast computation of this method, with minimal memory consumption.</div

BPNN prediction of Au@TiO₂.

An absorption spectrum with a distance of 5 nm from the Au core to the shell, (a) S = 4 nm, (b) S = 7 nm, and (c) S = 9 nm; The diameter of Au nucleus is 6 nm, (d) S = 3 nm, (e) S = 5 nm, (f) S = 7 nm; The diameter of the Au nucleus is 7 nm, with (g) C = 6 nm, (h) C = 7 nm, and (i) C = 9 nm.</p

Structure diagram of forward prediction neural network.

Structure diagram of forward prediction neural network.</p

Reverse prediction results.

(a) Au@TiO2 Predicted results and actual data, (b) Au@Au Comparison of size parameters between predicted results and real data, reverse prediction and forward prediction, (c) Au@TiO2, (d) Au@Au.</p

Image_2_Role of N-acetylkynurenine in mediating the effect of gut microbiota on urinary tract infection: a Mendelian randomization study.tif

IntroductionThis study explored the causal connections between gut microbiota (GM), urinary tract infection (UTI), and potential metabolite mediators using Mendelian randomization (MR).MethodsWe utilized summary statistics from the most comprehensive and extensive genome-wide association studies (GWAS) available to date, including 196 bacterial traits for GM, 1,091 blood metabolites, 309 metabolite ratios, alongside UTI data from ukb-b-8814 and ebi-a-GCST90013890. Bidirectional MR analyses were conducted to investigate the causal links between GM and UTI. Subsequently, two MR analyses were performed to identify the potential mediating metabolites, followed by a two-step MR analysis to quantify the mediation proportion.ResultsOur findings revealed that out of the total 15 bacterial traits, significant associations with UTI risk were observed across both datasets. Particularly, taxon g_Ruminococcaceae UCG010 displayed a causal link with a diminished UTI risk in both datasets (ukb-b-8814: odds ratio [OR] = 0.9964, 95% confidence interval [CI] = 0.9930–0.9997, P = 0.036; GCST90013890: OR = 0.8252, 95% CI = 0.7217–0.9436, P = 0.005). However, no substantial changes in g_Ruminococcaceae UCG010 due to UTI were noted (ukb-b-8814: β = 0.51, P = 0.87; ebi-a-GCST90013890: β = −0.02, P = 0.77). Additionally, variations in 56 specific metabolites were induced by g_Ruminococcaceae UCG010, with N-acetylkynurenine (NAK) exhibiting a causal correlation with UTI. A negative association was found between g_Ruminococcaceae UCG010 and NAK (OR: 0.8128, 95% CI: 0.6647–0.9941, P = 0.044), while NAK was positively associated with UTI risk (OR: 1.0009; 95% CI: 1.0002–1.0016; P = 0.0173). Mediation analysis revealed that the association between g_Ruminococcaceae UCG010 and UTI was mediated by NAK with a mediation proportion of 5.07%.DiscussionThis MR study provides compelling evidence supporting the existence of causal relationships between specific GM taxa and UTI, along with potential mediating metabolites.</p