Accurate medical image segmentation is crucial in clinical applications. The existing Swin-UNet model overcomes the limitations of traditional Transformers in handling local details and high-frequency information by introducing the Swin Transformer. However, there is still room for improvement in feature fusion and multi-scale information processing. U-shaped segmentation models typically fuse features from different levels using element-wise addition or feature concatenation. However, these fusion strategies fail to fully exploit the complementary nature of features at different levels, resulting in blurry lesion boundary segmentation and thus reducing overall segmentation performance. To overcome these limitations, we propose MedSwin-UNet, a general framework for multi-modal medical image segmentation. Specifically, we equip the model with two key modules: Cross-modal Feature Injection (CFI) and Inter-level Multi-scale Perception (IMP). The CFI module effectively merges local and global features by combining CNN encoder layers with the Swin-UNet encoder, utilizing multi-modal information to enhance the model’s performance in complex medical image tasks. The core component of the IMP module is Deep Feature Enhancement (DFE), which employs a pyramid-style feature enhancement strategy to improve the fusion of deep and shallow features in Swin-UNet, thereby strengthening the model’s ability to capture both image details and overall structure. Our study surpasses most existing state-of-the-art methods on four different medical image segmentation task datasets, achieving excellent performance across multiple evaluation metrics.

In this work, we propose a novel motion planning algorithm to facilitate safety-critical navigation for autonomous mobile robots. The proposed algorithm integrates a real-time dynamic obstacle tracking and mapping system that categorizes point clouds into dynamic and static components. For dynamic point clouds, the Kalman filter is employed to estimate and predict their motion states. Based on these predictions, we extrapolate the future states of dynamic point clouds, which are subsequently merged with static point clouds to construct the forward-time-domain (FTD) map. By combining control barrier functions (CBFs) with nonlinear model predictive control, the proposed algorithm enables the robot to effectively avoid both static and dynamic obstacles. The CBF constraints are formulated based on risk points identified through collision detection between the predicted future states and the FTD map. Experimental results from both simulated and real-world scenarios demonstrate the efficacy of the proposed algorithm in complex environments. In simulation experiments, the proposed algorithm is compared with two baseline approaches, showing superior performance in terms of safety and robustness in obstacle avoidance. The source code is released for the reference of the robotics community.

The printability problem of chip making becomes challenging in advanced process nodes. At present, various lithography technologies such as multiple patterning (MP), directed self-assembly (DSA), electron beam (e-beam), and their combinations are being considered. In this paper, the corresponding layout decomposition problems for contact/via generation are studied. In particular, we investigate the simultaneous DSA template and e-beam throughput optimization. First, we present an exact method based on an ILP formulation. Then, a graph-based algorithm is developed. The co-optimization problem for DSA double patterning with e-beam is formulated as a minimum hitting set problem. A primal-dual based algorithm is then derived for solving the problem effectively. Experimental results show that compared with a two-stage method, our method can achieve around 20.6% throughput improvement and 18.7% template cost reduction.

Directed self-assembly (DSA) complemented with multiple patterning (MP) is an attractive next generation lithography (NGL) technique for contact-hole generation. Nevertheless, a high-quality DSA-aware layout decomposer is required to enable the technology. In this article, we introduce an efficient method which incorporates a set packing for generating DSA template candidates and a local search method. Besides, a multi-start strategy is integrated into the framework to prevent the local minima. Our framework encourages the reuse of existing coloring solvers. Hence, the development cost can significantly be reduced. In addition, for DSA multiple patterning where the number of masks is larger than two, we present an efficient iterative partition based method. Experimental results show that compared with the state-of-the-art work, our methods can achieve roughly 100× speedup for double patterning, and 78.8% conflict reduction with 5× speedup for triple patterning on the dense graphs.

As the feature size keeps scaling down and the circuit complexity increases rapidly, a more advanced hybrid lithography, which combines multiple patterning and electron-beam lithography (EBL), is promising to further enhance the pattern resolution. In this paper, we formulate the layout decomposition problem for this hybrid lithography as a minimum vertex deletion${K}$-partition problem, where${K}$is the number of masks in multiple patterning. Stitch minimization and EBL throughput are considered uniformly by adding a virtual vertex between two feature vertices for each stitch candidate during the conflict graph construction phase. For${K} {=} 2$, we propose a primal-dual (PD) method for solving the underlying minimum odd-cycle cover problem efficiently. In addition, a chain decomposition algorithm is employed for removing all “noncyclable” edges. Furthermore, we investigate two versions of the PD method, one with planarization and one without. For${K} {>} 2$, we propose a random-initialized local search method that iteratively applies the PD solver. Experimental results show that compared with a two-stage method, our proposed methods reduce the EBL usage by 65.5% with double patterning and 38.7% with triple patterning on average for the benchmarks.

As the feature size keeps scaling down and the circuit complexity increases rapidly, a more advanced hybrid lithography, which combines multiple patterning and e-beam lithography (EBL), is promising to further enhance the pattern resolution. In this paper, we formulate the layout decomposition problem for this hybrid lithography as a minimum vertex deletion K-partition problem, where K is the number of masks in multiple patterning. Stitch minimization and EBL throughput are considered uniformly by adding a virtual vertex between two feature vertices for each stitch candidate during the conflict graph construction phase. For K = 2, we propose a primal-dual method for solving the underlying minimum odd-cycle cover problem efficiently. In addition, a chain decomposition algorithm is employed for removing all “non-cyclable” edges. For K > 2, we propose a random-initialized local search method that iteratively applies the primal-dual solver. Experimental results show that compared with a two-stage method, our proposed methods reduce the EBL usage by 64.4% with double patterning and 38.7% with triple patterning on average for the benchmarks.

In this article we present a pairwise coloring (PWC) approach to tackle the layout decomposition problem for triple patterning lithography (TPL). The main idea is to reduce the problem to a set of bi-coloring problems. The overall solution is refined by applying a bi-coloring method for pairs of color sets per pass. One obvious advantage of this method is that the existing double patterning lithography (DPL) techniques can be reused effortlessly. Moreover, we observe that each pass can be fulfilled efficiently by integrating an SPQR-tree-graph-division-based bi-coloring method. In addition, to prevent the solution getting stuck in the local minima, an adaptive multi-start (AMS) approach is incorporated. Adaptive starting points are generated according to the vote of previous solutions. The experimental results show that our method is competitive with other works on both solution quality and runtime performance.

BACKGROUND: Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. RESULTS: Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA). CONCLUSIONS: The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

Using genomic DNA as common reference in microarray experiments has recently been tested by different laboratories (2, 3, 5, 7, 9, 20, 24-26). While some reported that experimental results of microarrays using genomic DNA reference conformed nicely to those obtained by cDNA: cDNA co-hybridization method, others acquired poor results. We hypothesized that these conflicting reports could be resolved by biostatistical analyses. To test it, microarray experiments were performed in a gamma-proteobacterium Shewanella oneidensis. Pair-wise comparison of three experimental conditions was obtained either by direct cDNA: cDNA co-hybridization, or by indirect calculation through a Shewanella genomic DNA reference. Several major biostatistical techniques were exploited to reduce the amount of inconsistency between both methods and the results were assessed. We discovered that imposing the constraint of minimal number of replicates, logarithmic transformation and random error analyses significantly improved the data quality. These findings could potentially serve as guidelines for microarray data analysis using genomic DNA as reference.

MOTIVATION: Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules. Identifying these modules is essential to understand the organization of biological systems. RESULT: In this paper, we present a framework to identify modules within biological networks. In this approach, the concept of degree is extended from the single vertex to the sub-graph, and a formal definition of module in a network is used. A new agglomerative algorithm was developed to identify modules from the network by combining the new module definition with the relative edge order generated by the Girvan-Newman (G-N) algorithm. A JAVA program, MoNet, was developed to implement the algorithm. Applying MoNet to the yeast core protein interaction network from the database of interacting proteins (DIP) identified 86 simple modules with sizes larger than three proteins. The modules obtained are significantly enriched in proteins with related biological process Gene Ontology terms. A comparison between the MoNet modules and modules defined by Radicchi et al. (2004) indicates that MoNet modules show stronger co-clustering of related genes and are more robust to ties in betweenness values. Further, the MoNet output retains the adjacent relationships between modules and allows the construction of an interaction web of modules providing insight regarding the relationships between different functional modules. Thus, MoNet provides an objective approach to understand the organization and interactions of biological processes in cellular systems. AVAILABILITY: MoNet is available upon request from the authors.

BACKGROUND: Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. RESULTS: Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the recovery of gene co-expression network. Moreover, the cellular roles of 215 functionally unknown genes from yeast, E. coli and S. oneidensis are predicted by the gene co-expression networks using guilt-by-association principle, many of which are supported by existing information or our experimental verification, further demonstrating the reliability of this approach for gene function prediction. CONCLUSION: Our rigorous analysis of gene expression microarray profiles using RMT has showed that the transition of NNSD of correlation matrix of microarray profile provides a profound theoretical criterion to determine the correlation threshold for identifying gene co-expression networks.

The advance in high-throughput genomic technologies including microarrays has generated a tremendous amount of gene expression data for the entire genome. Deciphering transcriptional networks that convey information on members of gene clusters and cluster interactions is a crucial analysis task in the post-sequence era. Most of the existing analysis methods for large-scale genome-wide gene expression profiles involve several steps that often require human intervention. We propose a random matrix theory-based approach to analyze the cross correlations of gene expression data in an entirely automatic and objective manner to eliminate the ambiguities and subjectivity inherent to human decisions. The correlations calculated from experimental measurements typically contain both "genuine" and "random" components. In the proposed approach, we remove the "random" component by testing the statistics of the eigenvalues of the correlation matrix against a "null hypothesis" - a truly random correlation matrix obtained from mutually uncorrelated expression data series. Our investigation on the components of deviating eigenvectors using varimax orthogonal rotation reveals distinct functional modules. We apply the proposed approach to the publicly available yeast cycle expression data and produce a transcriptional network that consists of interacting functional modules. The experimental results nicely conform to those obtained in previously published literatures