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ContextDespite the recognition of attention deficit hyperactivity disorder (ADHD) as a multifaceted neurodevelopmental disorder, its core causes are still ambiguous. The objective of this study was to explore if the traits of circulating immune cells contribute causally to susceptibility to ADHD.MethodsBy employing a unified GWAS summary data covering 731 immune traits from the GWAS Catalog (accession numbers from GCST0001391 to GCST0002121), our analysis focused on the flow cytometry of lymphocyte clusters, encompassing 3,757 Sardinians, to identify genetically expected immune cells. Furthermore, we obtained summarized GWAS statistics from the Psychiatric Genomics Consortium to evaluate the genetic forecasting of ADHD. The studies employed ADHD2019 (20,183 cases and 35,191 controls from the 2019 GWAS ADHD dataset) and ADHD2022 (38,691 cases and 275,986 controls from the 2022 GWAS ADHD dataset). Through the examination of genome-wide association signals, we identified shared genetic variances between circulating immune cells and ADHD, employing the comprehensive ADHD2022 dataset. We primarily utilized inverse variance weighted (IVW) and weighted median methods in our Mendelian randomization research and sensitivity assessments to evaluate diversity and pleiotropy.ResultsAfter adjusting for false discovery rate (FDR), three distinct immunophenotypes were identified as associated with the risk of ADHD: CD33 in Im MDSC (OR=1.03, CI: 1.01~1.04, P=3.04×10−5, PFDR=0.015), CD8br NKT %T cell (OR=1.08, 95%CI: 1.04~1.12, P=9.33×10−5, PFDR=0.023), and CD8br NKT %lymphocyte (OR=1.08, 95%CI: 1.03~1.12, P=3.59×10−4, PFDR=0.066). Furthermore, ADHD showed no statistical effects on immunophenotypes. It’s worth noting that 20 phenotypes exist where ADHD’s appearance could diminish 85% of immune cells, including FSC-A in myeloid DC (β= -0.278, 95% CI: 0.616~0.931, P=0.008), CD3 in CD45RA- CD4+ (β= -0.233, 95% CI: 0.654~0.960, P=0.017), CD62L- monocyte AC (β=0.227, 95% CI: 0.038~1.518, P=0.019), CD33 in CD33br HLA DR+ CD14dim (β= -0.331, 95% CI: 0.543~0.950, P=0.020), and CD25 in CD39+ resting Treg (β=0.226, 95% CI: 1.522, P=0.022), and FSC-A in monocytes (β= -0.255, 95% CI: 0.621~0.967, P=0.234), among others.ConclusionStudies indicate that the immune system’s response influences the emergence of ADHD. The findings greatly improve our understanding of the interplay between immune responses and ADHD risk, aiding in the development of treatment strategies from an immunological perspective.

To prove that calcium alginate beads can be used as scaffolds during in vitro culture.

ContextCortisol, a hormone regulated by the hypothalamic-pituitary-adrenal (HPA) axis, has been linked to attention deficit hyperactivity disorder (ADHD). The nature of the relationship between cortisol and ADHD, and whether it is causal or explained by reverse causality, remains a matter of debate.ObjectiveThis study aims to evaluate the bidirectional causal relationship between morning plasma cortisol levels and ADHD.MethodsThis study used a bidirectional 2-sample Mendelian randomization (MR) design to analyze the association between morning plasma cortisol levels and ADHD using genetic information from the authoritative Psychiatric Genomics Collaboration (PGC) database (n = 55,347) and the ADHD Working Group of the CORtisol NETwork (CORNET) Consortium (n = 12,597). MR analyses were employed: inverse variance weighting (IVW), MR-Egger regression, and weighted medians. OR values and 95% CI were used to evaluate whether there was a causal association between morning plasma cortisol levels on ADHD and ADHD on morning plasma cortisol levels. The Egger-intercept method was employed to test for level pleiotropy. Sensitivity analysis was performed using the “leave-one-out” method, MR pleiotropy residual sum, and MR pleiotropy residual sum and outlier (MR-PRESSO).ResultsFindings from bidirectional MR demonstrated that lower morning plasma cortisol levels were associated with ADHD (ADHD-cortisol OR = 0.857; 95% CI, 0.755–0.974; P = 0.018), suggesting there is a reverse causal relationship between cortisol and ADHD. However, morning plasma cortisol levels were not found to have a causal effect on the risk of ADHD (OR = 1.006; 95% CI, 0.909–1.113; P = 0.907), despite the lack of genetic evidence. The MR-Egger method revealed intercepts close to zero, indicating that the selected instrumental variables had no horizontal multiplicity. The “leave-one-out” sensitivity analysis revealed stable results, with no instrumental variables significantly affecting the results. Heterogeneity tests were insignificant, and MR-PRESSO did not detect any significant outliers. The selected single-nucleotide polymorphisms (SNPs) F were all >10, indicating no weak instrumental variables. Thus, the overall MR analysis results were reliable.ConclusionThe study findings suggest a reverse causal relationship between morning plasma cortisol levels and ADHD, with low cortisol levels associated with ADHD. No genetic evidence was found to support a causal relationship between morning plasma cortisol levels and the risk of ADHD. These results suggest that ADHD may lead to a significant reduction in morning plasma cortisol secretion.

Interferometric Synthetic Aperture Radar (InSAR) is a powerful technique for precise topographic mapping. However, owing to the side-looking SAR imaging geometry, geometry distortions appear in mountainous scenarios. Because of phase discontinuities or the absence of a valid phase, it is difficult to recover accurate DEM in such areas with single-aspect InSAR data. Fusion of two or more different aspects of InSAR data can deal with this problem in practice. Experiments using two antiparallel aspects of airborne InSAR data are carried out based on this idea. To decrease the processing error in single-aspect data and fuse them seamlessly, a MOtion COmpensation (MOCO) method using iterative DEM is used to reduce the MOCO error. Besides, phase-unwrapping methods based on terrain characteristics are proposed to avoid phase-unwrapping error owing to phase discontinuities in areas of shadow and layover. Experimental results verify the effectiveness of the processing methods.

Interferometry filtering is one of the key steps in obtain high-precision Digital Elevation Model (DEM) and Digital Orthophoto Map (DOM). In the case of low-correlation or complicated topography, traditional phase filtering methods fail in balancing noise elimination and phase preservation, which leads to inaccurate interferometric phase. This paper proposed an adaptive iterated nonlocal interferometry filtering method to deal with the problem. Based on the thought of nonlocal filtering, the proposed method filters the image with utilization of the image redundancy information. The smoothing parameter of the method is adaptive to the interferometry, and automatic iteration, in which the window size is adjusted, is applied to improve the filtering precision. Validity of the proposed method is verified by simulated and real data. Comparison with existed methods is given at the same time.

When applying to the airborne repeat-pass Interferometric Synthetic Aperture Radar (InSAR), which has long synthetic aperture and large azimuth-dependent errors, the surface assumption used to simply the time-domain algorithm model and the residual motion errors due to the precision of the navigation system will affect the imaging result and the interferometric measurement. This paper analyzes the altitude errors introduced by the surface assumption and the residual motion errors due to the precision of the navigation system. We deduce the range errors model during the single pass and analyze the effects of these errors on the plane location, interferometric phase and DEM precision. Then the accuracy of the theoretical deduction is verified by simulation and real data. The research provides theoretical bases for the system design and signal processing of airborne repeat-pass InSAR.

Phase unwrapping is a key issue in InSAR research. As a critical step of InSAR processing, it affects the accuracy of interferometry measurement directly. The efficiency of the traditional quality-guided phase unwrapping method is low due to a great deal of sorting, espceically for large interferogram. This paper proposes a highly efficient quality-guided phase unwrapping method based on heap sort in order to solve the problem. First, the quality map is caculated according to the interferometric complex data or interferogram. Next, with the max-heap acting as the data structure of sorting, its property is maintained while deleting root node and inserting new node, and thus the sorting of quality map is accomplished and the phase can be unwrapped from high quality areas to low quality areas. The improved algorithm reduces the computational complexity greatly compared with traditional methods, which is significant in large area mapping of InSAR. At the end of the paper, the simulated and experimental results show the accuracy and the efficiency of the algorithm.