A decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penalty

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem (MOP) into a number of scalar optimization subproblems and optimizes them in a collaborative manner. In MOEA/D, decomposition mechanisms are used to push the population to approach the Pareto optimal front (POF), while a set of uniformly distributed weight vectors are applied to maintain the diversity of the population. Penalty-based boundary intersection (PBI) is one of the approaches used frequently in decomposition. In PBI, the penalty factor plays a crucial role in balancing convergence and diversity. However, the traditional PBI approach adopts a fixed penalty value, which will significantly degrade the performance of MOEA/D on some MOPs with complicated POFs. This paper proposes an angle-based adaptive penalty (AAP) scheme for MOEA/D, called MOEA/D-AAP, which can dynamically adjust the penalty value for each weight vector during the evolutionary process. Six newly designed benchmark MOPs and an MOP in the wastewater treatment process are used to test the effectiveness of the proposed MOEA/D-AAP. Comparison experiments demonstrate that the AAP scheme can significantly improve the performance of MOEA/D

Isoperimetric Problems on the Line with Density |x|^p

On the line with density |x|^p, we prove that the best single bubble is an interval with endpoint at the origin and that the best double bubble is two adjacent intervals that meet at the origin

Label-Free Liver Tumor Segmentation

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors; (II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors -- this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness.Comment: CVPR 202

Rice Xa21 primed genes and pathways that are critical for combating bacterial blight infection

Rice bacterial blight (BB) is a devastating rice disease. The Xa21 gene confers a broad and persistent resistance against BB. We introduced Xa21 into Oryza sativa L ssp indica (rice 9311), through multi-generation backcrossing, and generated a nearly isogenic, blight-resistant 9311/Xa21 rice. Using next-generation sequencing, we profiled the transcriptomes of both varieties before and within four days after infection of bacterium Xanthomonas oryzae pv. oryzae. The identified differentially expressed (DE) genes and signaling pathways revealed insights into the functions of Xa21. Surprisingly, before infection 1,889 genes on 135 of the 316 signaling pathways were DE between the 9311/Xa21 and 9311 plants. These Xa21-mediated basal pathways included mainly those related to the basic material and energy metabolisms and many related to phytohormones such as cytokinin, suggesting that Xa21 triggered redistribution of energy, phytohormones and resources among essential cellular activities before invasion. Counter-intuitively, after infection, the DE genes between the two plants were only one third of that before the infection; other than a few stress-related pathways, the affected pathways after infection constituted a small subset of the Xa21-mediated basal pathways. These results suggested that Xa21 primed critically important genes and signaling pathways, enhancing its resistance against bacterial infection

Endogenous small-noncoding RNAs and their roles in chilling response and stress acclimation in Cassava

BACKGROUND: Small noncoding RNA (sncRNA), including microRNAs (miRNAs) and endogenous small-interfering RNAs (endo-siRNAs) are key gene regulators in eukaryotes, playing critical roles in plant development and stress tolerance. Trans-acting siRNAs (ta-siRNAs), which are secondary siRNAs triggered by miRNAs, and siRNAs from natural antisense transcripts (nat-siRNAs) are two well-studied classes of endo-siRNAs. RESULTS: In order to understand sncRNAs’ roles in plant chilling response and stress acclimation, we performed a comprehensive study of miRNAs and endo-siRNAs in Cassava (Manihot esculenta), a major source of food for the world populations in tropical regions. Combining Next-Generation sequencing and computational and experimental analyses, we profiled and characterized sncRNA species and mRNA genes from the plants that experienced severe and moderate chilling stresses, that underwent further severe chilling stress after chilling acclimation at moderate stress, and that grew under the normal condition. We also included castor bean (Ricinus communis) in our study to understand conservation of sncRNAs. In addition to known miRNAs, we identified 32 (22 and 10) novel miRNAs as well as 47 (26 and 21) putative secondary siRNA-yielding and 8 (7 and 1) nat-siRNA-yielding candidate loci in Cassava and castor bean, respectively. Among the expressed sncRNAs, 114 miRNAs, 12 ta-siRNAs and 2 nat-siRNAs showed significant expression changes under chilling stresses. CONCLUSION: Systematic and computational analysis of microRNAome and experimental validation collectively showed that miRNAs, ta-siRNAs, and possibly nat-siRNAs play important roles in chilling response and chilling acclimation in Cassava by regulating stress-related pathways, e.g. Auxin signal transduction. The conservation of these sncRNA might shed lights on the role of sncRNA-mediated pathways affected by chilling stress and stress acclimation in Euphorbiaceous plants. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-634) contains supplementary material, which is available to authorized users

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem of each dataset, as well as a limited investigation of diverse types of tumors, the resulting models are often limited to segmenting specific organs/tumors and ignore the semantics of anatomical structures, nor can they be extended to novel domains. To address these issues, we propose the CLIP-Driven Universal Model, which incorporates text embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models. This CLIP-based label encoding captures anatomical relationships, enabling the model to learn a structured feature embedding and segment 25 organs and 6 types of tumors. The proposed model is developed from an assembly of 14 datasets, using a total of 3,410 CT scans for training and then evaluated on 6,162 external CT scans from 3 additional datasets. We rank first on the Medical Segmentation Decathlon (MSD) public leaderboard and achieve state-of-the-art results on Beyond The Cranial Vault (BTCV). Additionally, the Universal Model is computationally more efficient (6x faster) compared with dataset-specific models, generalized better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks.Comment: Rank first in Medical Segmentation Decathlon (MSD) Competitio