Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation

One challenge of unsupervised MRI brain image segmentation is the central gray matter due to the faint contrast with respect to the surrounding white matter. In this paper, the necessity of supervised image segmentation is addressed, and a soft Mumford-Shah model is introduced. Then, a framework of semisupervised image segmentation based on soft Mumford-Shah model is developed. The main contribution of this paper lies in the development a framework of a semisupervised soft image segmentation using both Bayesian principle and the principle of soft image segmentation. The developed framework classifies pixels using a semisupervised and interactive way, where the class of a pixel is not only determined by its features but also determined by its distance from those known regions. The developed semisupervised soft segmentation model turns out to be an extension of the unsupervised soft Mumford-Shah model. The framework is then applied to MRI brain image segmentation. Experimental results demonstrate that the developed framework outperforms the state-of-the-art methods of unsupervised segmentation. The new method can produce segmentation as precise as required

Disorder Improves Light Absorption in Thin Film Silicon Solar Cells with Hybrid Light Trapping Structure

We present a systematic simulation study on the impact of disorder in thin film silicon solar cells with hybrid light trapping structure. For the periodical structures introducing certain randomness in some parameters, the nanophotonic light trapping effect is demonstrated to be superior to their periodic counterparts. The nanophotonic light trapping effect can be associated with the increased modes induced by the structural disorders. Our study is a systematic proof that certain disorder is conceptually an advantage for nanophotonic light trapping concepts in thin film solar cells. The result is relevant to the large field of research on nanophotonic light trapping which currently investigates and prototypes a number of new concepts including disordered periodic and quasiperiodic textures. The random effect on the shape of the pattern (position, height, and radius) investigated in this paper could be a good approach to estimate the influence of experimental inaccuracies for periodic or quasi-periodic structures

Integrated Metabonomic-Proteomic Analysis of an Insect-Bacterial Symbiotic System

The health of animals, including humans, is dependent on their resident microbiota, but the complexity of the microbial communities makes these associations difficult to study in most animals. Exceptionally, the microbiology of the pea aphid Acyrthosiphon pisum is dominated by a single bacterium Buchnera aphidicola (B. aphidicola). A 1H NMR-based metabonomic strategy was applied to investigate metabolic profiles of aphids fed on a low essential amino acid diet and treated by antibiotic to eliminate B. aphidicola. In addition, differential gel electrophoresis (DIGE) with mass spectrometry was utilized to determine the alterations of proteins induced by these treatments. We found that these perturbations resulted in significant changes to the abundance of 15 metabolites and 238 proteins. Ten (67%) of the metabolites with altered abundance were amino acids, with nonessential amino acids increased and essential amino acids decreased by both perturbations. Over-represented proteins in the perturbed treatments included catabolic enzymes with roles in amino acid degradation and glycolysis, various cuticular proteins, and a C-type lectin and regucalcin with candidate defensive roles. This analysis demonstrates the central role of essential amino acid production in the relationship and identifies candidate proteins and processes underpinning the function and persistence of the association

Developing artificial solid-state interphase for Li metal electrodes: recent advances and perspective

The failure of Li metal anodes can be attributed to their unstable electrode/electrolyte interface, especially the continuous formation of solid electrolyte interphase (SEI) and dendrite growth. To address this challenge, scholars proposed the construction of artificial SEI (ASEI) as a promising strategy. The ASEI mainly homogenizes the distribution of Li+, mitigates dendrite growth, facilitates Li+ diffusion, and protects the Li metal anode from electrolyte erosion. This review comprehensively summarizes the recent progress in the construction of ASEI layers in terms of their chemical composition. Fundamental understanding of the mechanisms, design principles, and functions of the main components are analyzed. We also propose future research directions to facilitate the in-depth study of ASEI and its practical applications in Li metal batteries. This review offers perspectives that will greatly contribute to the design of practical Li metal electrodes

Penaeid shrimp genome provides insights into benthic adaptation and frequent molting

Crustacea, the subphylum of Arthropoda which dominates the aquatic environment, is of major importance in ecology and fisheries. Here we report the genome sequence of the Pacific white shrimp Litopenaeus vannamei, covering similar to 1.66 Gb (scaffold N50 605.56 Kb) with 25,596 protein-coding genes and a high proportion of simple sequence repeats (>23.93%). The expansion of genes related to vision and locomotion is probably central to its benthic adaptation. Frequent molting of the shrimp may be explained by an intensified ecdysone signal pathway through gene expansion and positive selection. As an important aquaculture organism, L. vannamei has been subjected to high selection pressure during the past 30 years of breeding, and this has had a considerable impact on its genome. Decoding the L. vannamei genome not only provides an insight into the genetic underpinnings of specific biological processes, but also provides valuable information for enhancing crustacean aquaculture

A Novel Candidate Gene Associated With Body Weight in the Pacific White Shrimp Litopenaeus vannamei

Improvements of growth traits are always the focus in selective breeding programs for the Pacific white shrimp Litopenaeus vannamei (L. vannamei). Identification of growth-related genes or markers can contribute to the application of modern breeding technologies, and thus accelerate the genetic improvement of growth traits. The aim of this study was to identify the genes and molecular markers associated with the growth traits of L. vannamei. A population of 200 individuals was genotyped using 2b-RAD techniques for genome-wide linkage disequilibrium (LD) analysis and genome-wide association study (GWAS). The results showed that the LD decayed fast in the studied population, which suggest that it is feasible to fine map the growth-related genes with GWAS in L. vannamei. One gene designated as LvSRC, encoding the class C scavenger receptor (SRC), was identified as a growth-related candidate gene by GWAS. Further targeted sequencing of the candidate gene in another population of 322 shrimps revealed that several non-synonymous mutations within LvSRC were significantly associated with the body weight (P < 0.01), and the most significant marker (SRC_24) located in the candidate gene could explain 13% of phenotypic variance. The current results provide not only molecular markers for genetic improvement in L. vannamei, but also new insights for understanding the growth regulation mechanism in penaeid shrimp

Predictors of lung adenocarcinoma with leptomeningeal metastases: A 2022 targeted-therapy-assisted molGPA model

Objective: To explore prognostic indicators of lung adenocarcinoma with leptomeningeal metastases (LM) and provide an updated graded prognostic assessment model integrated with molecular alterations (molGPA). Methods: A cohort of 162 patients was enrolled from 202 patients with lung adenocarcinoma and LM. By randomly splitting data into the training (80%) and validation (20%) sets, the Cox regression and random survival forest methods were used on the training set to identify statistically significant variables and construct a prognostic model. The C-index of the model was calculated and compared with that of previous molGPA models. Results: The Cox regression and random forest models both identified four variables, which included KPS, LANO neurological assessment, TKI therapy line, and controlled primary tumor, as statistically significant predictors. A novel targeted-therapy-assisted molGPA model (2022) using the above four prognostic factors was developed to predict LM of lung adenocarcinoma. The C-indices of this prognostic model in the training and validation sets were higher than those of the lung-molGPA (2017) and molGPA (2019) models. Conclusions: The 2022 molGPA model, a substantial update of previous molGPA models with better prediction performance, may be useful in clinical decision making and stratification of future clinical trials