Pattern formation in oscillatory complex networks consisting of excitable nodes

Oscillatory dynamics of complex networks has recently attracted great attention. In this paper we study pattern formation in oscillatory complex networks consisting of excitable nodes. We find that there exist a few center nodes and small skeletons for most oscillations. Complicated and seemingly random oscillatory patterns can be viewed as well-organized target waves propagating from center nodes along the shortest paths, and the shortest loops passing through both the center nodes and their driver nodes play the role of oscillation sources. Analyzing simple skeletons we are able to understand and predict various essential properties of the oscillations and effectively modulate the oscillations. These methods and results will give insights into pattern formation in complex networks, and provide suggestive ideas for studying and controlling oscillations in neural networks.Comment: 15 pages, 7 figures, to appear in Phys. Rev.

Peridynamic Formulation for Coupled Thermoelectric Phenomena

Modeling of heat and electrical current flow simultaneously in thermoelectric convertor using classical theories do not consider the influence of defects in the material. This is because traditional methods are developed based on partial differential equations (PDEs) and lead to infinite fluxes at the discontinuities. The usual way of solving such PDEs is by using numerical technique, like Finite Element Method (FEM). Although FEM is robust and versatile, it is not suitable to model evolving discontinuities. To avoid such shortcomings, we propose the concept of peridynamic theory to derive the balance of energy and charge equations in the coupled thermoelectric phenomena. Therefore, this paper presents the transport of heat and charge in thermoelectric material in the framework of peridynamic (PD) theory. To illustrate the reliability of the PD formulation, numerical examples are presented and results are compared with those from literature, analytical solutions, or finite element solutions

AP Deployment Research Based on Physical Distance and Channel Isolation

Aiming at the problem of inefficiency of wireless local area networks (WLAN) access point (AP) deployment in urban environment, a new algorithm for AP deployment based on physical distance and channel isolation (DPDCI) is proposed. First, it detects the position information of deployed APs and then calculates the interference penalty factor combined with physical distance and channel isolation, and finally gets the optimal location and channel of the new AP through the genetic algorithm. Comparing with NOOCA algorithm and NOFA-2 algorithm, the results of numerical simulation show that the new algorithm can minimize the mutual interference between basic service sets (BSS), can ensure the maximum of throughput based on quality of service (QoS) in BSS, and can effectively improve the system performance

The role of tripartite motif-containing 28 in cancer progression and its therapeutic potentials

Tripartite motif-containing 28 (TRIM28) belongs to tripartite motif (TRIM) family. TRIM28 not only binds and degrades its downstream target, but also acts as a transcription co-factor to inhibit gene expression. More and more studies have shown that TRIM28 plays a vital role in tumor genesis and progression. Here, we reviewed the role of TRIM28 in tumor proliferation, migration, invasion and cell death. Moreover, we also summarized the important role of TRIM28 in tumor stemness sustainability and immune regulation. Because of the importance of TRIM28 in tumors, TIRM28 may be a candidate target for anti-tumor therapy and play an important role in tumor diagnosis and treatment in the future

Biocontrol of Sugarcane Smut Disease by Interference of Fungal Sexual Mating and Hyphal Growth Using a Bacterial Isolate

Sugarcane smut is a fungal disease caused by Sporisorium scitamineum, which can cause severe economic losses in sugarcane industry. The infection depends on the mating of bipolar sporida to form a dikaryon and develops into hyphae to penetrate the meristematic tissue of sugarcane. In this study, we set to isolate bacterial strains capable of blocking the fungal mating and evaluate their potential in control of sugarcane smut disease. A bacterial isolate ST4 from rhizosphere displayed potent inhibitory activity against the mating of S. scitamineum bipolar sporida and was selected for further study. Phylogenetic analyses and biochemical characterization showed that the isolate was most similar to Pseudomonas guariconensis. Methanol extracts from minimum and potato dextrose agar (PDA) agar medium, on which strain ST4 has grown, showed strong inhibitory activity on the sexual mating of S. scitamineum sporida, without killing the haploid cells MAT-1 or MAT-2. Further analysis showed that only glucose, but not sucrose, maltose, and fructose, could support strain ST4 to produce antagonistic chemicals. Consistent with the above findings, greenhouse trials showed that addition of 2% glucose to the bacterial inoculum significantly increased the strain ST4 biocontrol efficiency against sugarcane smut disease by 77% than the inoculum without glucose. The results from this study depict a new strategy to screen for biocontrol agents for control and prevention of the sugarcane smut disease

SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Previously, the delineation of GTVs and OARs was performed by experienced radiation oncologists. Recently, deep learning has achieved promising results in many medical image segmentation tasks. However, for NPC OARs and GTVs segmentation, few public datasets are available for model development and evaluation. To alleviate this problem, the SegRap2023 challenge was organized in conjunction with MICCAI2023 and presented a large-scale benchmark for OAR and GTV segmentation with 400 Computed Tomography (CT) scans from 200 NPC patients, each with a pair of pre-aligned non-contrast and contrast-enhanced CT scans. The challenge's goal was to segment 45 OARs and 2 GTVs from the paired CT scans. In this paper, we detail the challenge and analyze the solutions of all participants. The average Dice similarity coefficient scores for all submissions ranged from 76.68\% to 86.70\%, and 70.42\% to 73.44\% for OARs and GTVs, respectively. We conclude that the segmentation of large-size OARs is well-addressed, and more efforts are needed for GTVs and small-size or thin-structure OARs. The benchmark will remain publicly available here: https://segrap2023.grand-challenge.orgComment: A challenge report of SegRap2023 (organized in conjunction with MICCAI2023

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Household, community, sub-national and country-level predictors of primary cooking fuel switching in nine countries from the PURE study

Introduction. Switchingfrom polluting (e.g. wood, crop waste, coal)to clean (e.g. gas, electricity) cooking fuels can reduce household air pollution exposures and climate-forcing emissions.While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. Methods.We examined longitudinal survey data from 24 172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology study.We assessed household-level primary cooking fuel switching during a median of 10 years offollow up (∼2005–2015).We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. Results. One-half of study households(12 369)reported changing their primary cookingfuels between baseline andfollow up surveys. Of these, 61% (7582) switchedfrom polluting (wood, dung, agricultural waste, charcoal, coal, kerosene)to clean (gas, electricity)fuels, 26% (3109)switched between different polluting fuels, 10% (1164)switched from clean to polluting fuels and 3% (522)switched between different clean fuels

A criterion utility conversion technique for probabilistic linguistic multiple criteria analysis in emergency management

In multiple criteria decision making (MCDM), the even swaps method uses the relationships of criteria to make trade-offs but the burdens of experts are heavy; the linear programming technique for multidimensional analysis of preference (LINMAP) method cannot deal with the inter-dependencies among criteria but the cognitive burdens of experts are low. Taking the advantages of both these methods, this study proposes a criterion utility conversion (CUC) technique to solve probabilistic linguistic MCDM problems given that the probabilistic linguistic term set (PLTS) can reflect the psychology of experts when making evaluations. The utility conversion process is first proposed based on the marginal utilities of criteria. Then, the criterion preference ratios of experts are refined from the utility conversion process. Based on the criterion preference ratios and the operations of PLTSs, the adjusted probabilistic linguistic expected values of alternatives are calculated. The consistency and inconsistency indexes of alternatives and criteria are defined to set up the linear programming used to work out the criterion preference ratios. An illustration about the selection of emergency logistics supplier is given to validate the proposed method. The comparative analysis indicates the low cognitive burden, high stability, and strong applicability of the proposed method. First published online 05 July 202