MOTIVATION: Copy-number variants (CNVs) are a form of genetic structural variation with increasing importance in complex human disorders. Both DNA sequencing and microarray data can be used to detect CNVs, which can be used in genetic association tests. Unlike genotypes, CNV detection in microarrays requires the use of observed intensity signals at each probe, which limits the imputability for analyses that span multiple array types. Thus far, a consensus set of probes (those present on all arrays) has been used to circumvent the problem of differing array-specific sensitivities. This has led to excessive reduction in overall sensitivity since arrays can have an undesirably low probe overlap. To overcome this limitation, we developed MarkerMatch, a proximity-based algorithm that matches probes across different genotyping microarrays to maximize the number of probes considered in the CNV calling algorithm, thereby increasing the resolution and sensitivity while preserving precision. RESULTS: By analyzing CNV calls from 4906 individuals genotyped across three different arrays, we show that the MarkerMatch approach improves sensitivity by increasing the density of probes available for CNV calling while maintaining precision or improving it relative to the current practice (e.g. use of consensus probes only). We further demonstrate that MarkerMatch matches the CNV detection from current practice in terms of F1 score and PPV for larger CNVs. We also optimize MarkerMatch parameters, DMAX and Method, and find an optimal DMAX setting at 10 kb, with no clear optimal candidate based on Method, indicating that parameters for this metric should be determined on a use case basis. AVAILABILITY: The R package for MarkerMatch is available at: https://github.com/FranjoIM/MarkerMatch. The code used for analysis and implementation is available at: https://doi.org/10.5281/zenodo.18460979. The live notebook is available at https://fivankovic.notion.site/2026-markermatch.

Zeroing neural networks (ZNNs) play a crucial role in efficiently solving time-varying problems. Recently, ZNNs are integrated with many advanced control theories with a certain convergence time to enhance their performance. On account of the convergence time of the prescribed-time convergence is precise, it is of great significance to investigate ZNN with prescribed-time convergence. In addition, delay is unavoidable in circuit implementation, not only impacting the prescribed-time convergence but also inducing instability and oscillation in ZNN. To evaluate the effectiveness of prescribed-time ZNN under delay environment, prescribed-time delayed zeroing neural network (PTDZNN) is proposed for solving time-varying equations in this article. It is concluded that PTDZNN can obtain the correct real-time solution in prescribed time and the convergence time of PTDZNN is independent of initial conditions. Furthermore, PTDZNN exhibits notable tolerance to delay and distinguishes itself from existing delayed zeroing neural networks by its independence from linear matrix inequality (LMI). Moreover, the LMI-independent stability proof of ZNN under delay environment is also proved. Numerical simulations are presented to demonstrate the prescribed-time convergence and delay tolerance of PTDZNN. Ultimately, PTDZNN is successfully applied in dynamic positioning algorithms and image fusion problems. Notably, PTDZNN stands out as the first ZNN to incorporate both prescribed-time convergence and delay.

Abstract Dental caries, a common oral disease, poses serious risks if untreated, necessitating effective preventive measures like pit and fissure sealing. However, the reliance on experienced dentists for pit and fissures or caries detection limits accessibility, potentially leading to missed treatment opportunities, especially among children. To bridge this gap, we leverage deep learning in object detection to develop a method for autonomously identifying caries and determining pit and fissure sealing requirements using smartphone oral photos. We test several detection models and adopt a tiling strategy to reduce information loss during image pre‐processing. Our implementation achieves 72.3 mAP.5 with the YOLOXs model and tiling strategy. We enhance accessibility by deploying the pre‐trained network as a WeChat applet on mobile devices, enabling in‐home detection by parents or guardians. In addition, our data set of children's first permanent molars will also aid in the broader study of pediatric oral disease.