274 research outputs found
Fullerenes with the maximum Clar number
The Clar number of a fullerene is the maximum number of independent resonant
hexagons in the fullerene. It is known that the Clar number of a fullerene with
n vertices is bounded above by [n/6]-2. We find that there are no fullerenes
whose order n is congruent to 2 modulo 6 attaining this bound. In other words,
the Clar number for a fullerene whose order n is congruent to 2 modulo 6 is
bounded above by [n/6]-3. Moreover, we show that two experimentally produced
fullerenes C80:1 (D5d) and C80:2 (D2) attain this bound. Finally, we present a
graph-theoretical characterization for fullerenes, whose order n is congruent
to 2 (respectively, 4) modulo 6, achieving the maximum Clar number [n/6]-3
(respectively, [n/6]-2)
Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention
Since radiologists have different training and clinical experiences, they may
provide various segmentation annotations for a lung nodule. Conventional
studies choose a single annotation as the learning target by default, but they
waste valuable information of consensus or disagreements ingrained in the
multiple annotations. This paper proposes an Uncertainty-Guided Segmentation
Network (UGS-Net), which learns the rich visual features from the regions that
may cause segmentation uncertainty and contributes to a better segmentation
result. With an Uncertainty-Aware Module, this network can provide a
Multi-Confidence Mask (MCM), pointing out regions with different segmentation
uncertainty levels. Moreover, this paper introduces a Feature-Aware Attention
Module to enhance the learning of the nodule boundary and density differences.
Experimental results show that our method can predict the nodule regions with
different uncertainty levels and achieve superior performance in LIDC-IDRI
dataset.Comment: 10 pages, 4 figures, 30 reference
The minimum degree of minimal -factor-critical claw-free graphs*
A graph of order is said to be -factor-critical for integers
, if the removal of any vertices results in a graph with a
perfect matching. A -factor-critical graph is minimal if for every edge, the
deletion of it results in a graph that is not -factor-critical. In 1998, O.
Favaron and M. Shi conjectured that every minimal -factor-critical graph has
minimum degree . In this paper, we confirm the conjecture for minimal
-factor-critical claw-free graphs. Moreover, we show that every minimal
-factor-critical claw-free graph has at least
vertices of degree in the case of -connected, yielding further
evidence for S. Norine and R. Thomas' conjecture on the minimum degree of
minimal bricks when .Comment: 17 pages, 12 figure
Editorial: Seeing convergent margin processes through metamorphism
Plate convergence can induce large-scale metamorphism and magmatism, reshape large parts of continental margins, and subsequently change regional climate and biodiversity. Metamorphic rocks in orogenic belts commonly record different metamorphic evolutions and temporal-spatial distributions at the regional scale, which are strongly influenced by convergent processes through time. In some cases, ultrahigh-pressure (UHP) and ultrahigh-temperature (UHT) metamorphic rocks are observed at both ancient and young convergent plate margins, marking the operation of extreme tectonism in the regime of plate tectonics. This Research Topic aims to understand how regional metamorphism operated at convergent plate margins through the study of field and petrographic observations, geochemical and petrological analysis, high-pressure experiments, and thermodynamic modeling. The scope is to gather new ideas and interpretations on the structure and processes of convergent plate margins
Metallothioneins Are Required for Formation of Cross-Adaptation Response to Neurobehavioral Toxicity from Lead and Mercury Exposure in Nematodes
Metallothioneins (MTs) are small, cysteine-rich polypeptides, but the role of MTs in inducing the formation of adaptive response is still largely unknown. We investigated the roles of metallothionein genes (mtl-1 and mtl-2) in the formation of cross-adaptation response to neurobehavioral toxicity from metal exposure in Caenorhabditis elegans. Pre-treatment with mild heat-shock at L2-larva stage effectively prevented the formation of the neurobehavioral defects and the activation of severe stress response in metal exposed nematodes at concentrations of 50 and 100 µM, but pre-treatment with mild heat-shock did not prevent the formation of neurobehavioral defects in 200 µM of metal exposed nematodes. During the formation of cross-adaptation response, the induction of mtl-1 and mtl-2 promoter activity and subsequent GFP gene expression were sharply increased in 50 µM or 100 µM of metal exposed Pmtl-1::GFP and Pmtl-2::GFP transgenic adult animals after mild heat-shock treatment compared with those treated with mild heat-shock or metal exposure alone. Moreover, after pre-treatment with mild heat-shock, no noticeable increase of locomotion behaviors could be observed in metal exposed mtl-1 or mtl-2 mutant nematodes compared to those without mild heat-shock pre-treatment. The defects of adaptive response to neurobehavioral toxicity induced by metal exposure formed in mtl-1 and mtl-2 mutants could be completely rescued by the expression of mtl-1 and mtl-2 with the aid of their native promoters. Furthermore, over-expression of MTL-1 and MTL-2 at the L2-larval stage significantly suppressed the toxicity on locomotion behaviors from metal exposure at all examined concentrations. Therefore, the normal formation of cross-adaptation response to neurobehavioral toxicity induced by metal exposure may need the enough accumulation of MTs protein in animal tissues
Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism
Radiologists possess diverse training and clinical experiences, leading to
variations in the segmentation annotations of lung nodules and resulting in
segmentation uncertainty.Conventional methods typically select a single
annotation as the learning target or attempt to learn a latent space comprising
multiple annotations. However, these approaches fail to leverage the valuable
information inherent in the consensus and disagreements among the multiple
annotations. In this paper, we propose an Uncertainty-Aware Attention Mechanism
(UAAM) that utilizes consensus and disagreements among multiple annotations to
facilitate better segmentation. To this end, we introduce the Multi-Confidence
Mask (MCM), which combines a Low-Confidence (LC) Mask and a High-Confidence
(HC) Mask.The LC mask indicates regions with low segmentation confidence, where
radiologists may have different segmentation choices. Following UAAM, we
further design an Uncertainty-Guide Multi-Confidence Segmentation Network
(UGMCS-Net), which contains three modules: a Feature Extracting Module that
captures a general feature of a lung nodule, an Uncertainty-Aware Module that
produces three features for the the annotations' union, intersection, and
annotation set, and an Intersection-Union Constraining Module that uses
distances between the three features to balance the predictions of final
segmentation and MCM. To comprehensively demonstrate the performance of our
method, we propose a Complex Nodule Validation on LIDC-IDRI, which tests
UGMCS-Net's segmentation performance on lung nodules that are difficult to
segment using common methods. Experimental results demonstrate that our method
can significantly improve the segmentation performance on nodules that are
difficult to segment using conventional methods.Comment: 10 pages, 10 figures. We have reported a preliminary version of this
work in MICCAI 202
Unified Multi-Modal Image Synthesis for Missing Modality Imputation
Multi-modal medical images provide complementary soft-tissue characteristics
that aid in the screening and diagnosis of diseases. However, limited scanning
time, image corruption and various imaging protocols often result in incomplete
multi-modal images, thus limiting the usage of multi-modal data for clinical
purposes. To address this issue, in this paper, we propose a novel unified
multi-modal image synthesis method for missing modality imputation. Our method
overall takes a generative adversarial architecture, which aims to synthesize
missing modalities from any combination of available ones with a single model.
To this end, we specifically design a Commonality- and Discrepancy-Sensitive
Encoder for the generator to exploit both modality-invariant and specific
information contained in input modalities. The incorporation of both types of
information facilitates the generation of images with consistent anatomy and
realistic details of the desired distribution. Besides, we propose a Dynamic
Feature Unification Module to integrate information from a varying number of
available modalities, which enables the network to be robust to random missing
modalities. The module performs both hard integration and soft integration,
ensuring the effectiveness of feature combination while avoiding information
loss. Verified on two public multi-modal magnetic resonance datasets, the
proposed method is effective in handling various synthesis tasks and shows
superior performance compared to previous methods.Comment: 10 pages, 9 figure
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