Production performance analysis of sheep MSTN gene C2361T locus.

The myostatin (MSTN) gene exhibits significant nucleotide sequence variations in sheep, impacting growth characteristics and muscular traits of the body. However, its influence on specific growth traits in some sheep remains to be further elucidated. This study utilized single nucleotide polymorphism sequence analysis to investigate the role of the MSTN gene in meat production performance across four sheep breeds: Charolais sheep, Australian White sheep, crossbreeds of Australian White and Small-tailed Han, and crossbreeds of Charolais and Small-tailed Han. At a SNP locus of the MSTN gene, the C2361T site was identified, with three genotypes detected: CC, CT, and TT, among which CC predominated. Gene substitution effect analysis revealed that replacing C with T could elevate the phenotypic value. Comparative analysis of data from different genotypes within the same breed highlighted the superiority of CC and TT genotypes in phenotypic values, underscoring the significance of specific genotypes in influencing key traits. Contrasting the performance of different genotypes across breeds, Charolais sheep and Charolais Han hybrids demonstrated superiority across multiple indicators, offering valuable insights for breeding new sheep varieties. Analysis of gender effects on growth characteristics indicated that ewes exhibited significantly wider chest, waist, and hip widths compared to rams, while rams displayed better skeletal growth and muscle development. Additionally, the MSTN gene also exerted certain effects on lamb growth characteristics, with the CC genotype closely associated with weight. These findings not only contribute crucial insights for sheep breeding but also pave the way for future research exploring the interaction of this gene with others.

Imaging process matched neural network for complex wavefront retrieval with a higher space-bandwidth product.

Recently, deep learning (DL) has shown great potential in complex wavefront retrieval (CWR). However, the application of DL in CWR does not match well with the physical diffraction process. The state-of-the-art DL-based CWR methods crop full-size diffraction patterns down to a smaller size to save computational resources. However, cropping reduces the space-bandwidth product (SBP). In order to solve the trade-off between computational resources and SBP, we propose an imaging process matched neural network (IPMnet). IPMnet accepts full-size diffraction patterns with a larger SBP as inputs and retrieves a higher resolution and a larger field of view of the complex wavefront. We verify the effectiveness of the proposed IPMnet through simulations and experiments.

Extended autofocusing in dual-wavelength digital holography.

In single-wavelength digital holography (DH), the phase wrapping phenomenon limits the total object depth that can be measured due to the requirement for well-resolved phase fringes. To address this limitation, dual-wavelength DH is proposed, enabling measurement of much deeper objects. In single-wavelength DH, because the object depth is limited, the depth of focus (DOF) of DH's optical system at a reconstruction distance is sufficient to cover the object depth. To date, many autofocusing algorithms have been proposed to obtain a correct reconstruction distance. However, in dual-wavelength DH, because the object depth is extended, the DOF at a reconstruction distance cannot cover the extended object depth. The extended object depth can span multiple DOFs, causing partially out of focus object depth. Therefore, in dual-wavelength DH, relying solely on autofocusing algorithms for a single distance is insufficient. But extended autofocusing algorithms, which can autofocus objects through multiple DOFs, are demanded. However, there are no such extended autofocusing algorithms in dual-wavelength DH. Therefore, we propose an extended autofocusing algorithm for dual-wavelength DH based on a correlation coefficient. The proposed algorithm is able to focus the whole object depth when the depth spans multiple DOFs. Through theoretical analysis, simulations, and experiments, the necessity and effectiveness of the proposed algorithm are verified.

Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study.

BACKGROUND: According to a World Health Organization report in 2017, there was almost one patient with depression among every 20 people in China. However, the diagnosis of depression is usually difficult in terms of clinical detection owing to slow observation, high cost, and patient resistance. Meanwhile, with the rapid emergence of social networking sites, people tend to share their daily life and disclose inner feelings online frequently, making it possible to effectively identify mental conditions using the rich text information. There are many achievements regarding an English web-based corpus, but for research in China so far, the extraction of language features from web-related depression signals is still in a relatively primary stage. OBJECTIVE: The purpose of this study was to propose an effective approach for constructing a depression-domain lexicon. This lexicon will contain language features that could help identify social media users who potentially have depression. Our study also compared the performance of detection with and without our lexicon. METHODS: We autoconstructed a depression-domain lexicon using Word2Vec, a semantic relationship graph, and the label propagation algorithm. These two methods combined performed well in a specific corpus during construction. The lexicon was obtained based on 111,052 Weibo microblogs from 1868 users who were depressed or nondepressed. During depression detection, we considered six features, and we used five classification methods to test the detection performance. RESULTS: The experiment results showed that in terms of the F1 value, our autoconstruction method performed 1% to 6% better than baseline approaches and was more effective and steadier. When applied to detection models like logistic regression and support vector machine, our lexicon helped the models outperform by 2% to 9% and was able to improve the final accuracy of potential depression detection. CONCLUSIONS: Our depression-domain lexicon was proven to be a meaningful input for classification algorithms, providing linguistic insights on the depressive status of test subjects. We believe that this lexicon will enhance early depression detection in people on social media. Future work will need to be carried out on a larger corpus and with more complex methods.