KIF21A Mutations in Two Chinese Families with Congenital Fibrosis of the Extraocular Muscles (CFEOM)

Purpose: Two Chinese families (XT and YT) with congenital fibrosis of the extraocular muscles (CFEOM) were identified. The purpose of this study was to determine if previously described Homo sapiens kinesin family member 21A (KIF21A) mutations were responsible for CFEOM in these two Chinese pedigrees. Methods: Clinical characterization and genetic studies were performed. Microsatellite genotyping for linkage to the CFEOM1 and CFEOM3 loci was performed. The probands were screened for KIF21A mutations by bidirectional direct sequencing. Once a mutation was detected in the proband, all other participating family members and 100 unrelated control normal individuals were screened for the mutation. Results: All affected individuals in family XT shared the common manifestations of CFEOM1. Family YT had two affected individuals, a mother and a daughter. The daughter had CFEOM1, while her mother never had congential ptosis but did have limited extraocular movements status post strabismus surgery. Haplotype analysis revealed that pedigree XT was linked to the 12q CFEOM1 locus and the affected memberes harbored the second most common missense mutation in KIF21A (2,861G>A, R954Q). Family YT harbored the most common missense de novo mutation in KIF21A (2,860C>T, R954W). Both of these mutations have been previously described. Conclusions: The observation of these two KIF21A mutations in a Chinese pedigree underscores the homogeneity of these mutations as a cause of CFEOM1 and CFEOM3 across ethnic divisions

A Dynamic and Complex Network Regulates the Heterosis of Yield-Correlated Traits in Rapeseed (Brassica napus L.)

Although much research has been conducted, the genetic architecture of heterosis remains ambiguous. To unravel the genetic architecture of heterosis, a reconstructed F2 population was produced by random intercross among 202 lines of a double haploid population in rapeseed (Brassica napus L.). Both populations were planted in three environments and 15 yield-correlated traits were measured, and only seed yield and eight yield-correlated traits showed significant mid-parent heterosis, with the mean ranging from 8.7% (branch number) to 31.4% (seed yield). Hundreds of QTL and epistatic interactions were identified for the 15 yield-correlated traits, involving numerous variable loci with moderate effect, genome-wide distribution and obvious hotspots. All kinds of mode-of-inheritance of QTL (additive, A; partial-dominant, PD; full-dominant, D; over-dominant, OD) and epistatic interactions (additive × additive, AA; additive × dominant/dominant × additive, AD/DA; dominant × dominant, DD) were observed and epistasis, especially AA epistasis, seemed to be the major genetic basis of heterosis in rapeseed. Consistent with the low correlation between marker heterozygosity and mid-parent heterosis/hybrid performance, a considerable proportion of dominant and DD epistatic effects were negative, indicating heterozygosity was not always advantageous for heterosis/hybrid performance. The implications of our results on evolution and crop breeding are discussed

Some nonlocal elliptic problem involving positive parameter

We consider the following superlinear Kirchhoff type nonlocal problem: \cases \displaystyle -\bigg(a+b\int_\Omega |\nabla u|^2dx\bigg)\Delta u =\lambda f(x,u) & \text{in } \Omega,\ a> 0, \ b> 0, \ \lambda > 0, \\ u=0 &\text{on } \partial\Omega. \endcases Here, $f(x,u)$ does not satisfy the usual superlinear condition, that is, for some $\theta > 0,$ $$0\leq F(x,u)\triangleq \int_0^u f(x,s)ds \leq \frac1{2+\theta}f(x,u)u, \quad \text{for all } (x,u)\in \Omega \times \mathbb{R}^+$$ or the following variant $$0\leq F(x,u)\triangleq \int_0^u f(x,s)ds \leq \frac1{4+\theta}f(x,u)u, \quad \text{for all } (x,u)\in \Omega \times \mathbb{R}^+$$ which is quiet important and natural. But this superlinear condition is very restrictive eliminating many nonlinearities. The aim of this paper is to discuss how to use the mountain pass theorem to show the existence of non-trivial solution to the present problem when we lose the above superlinear condition. To achieve the result, we first consider the existence of a solution for almost every positive parameter $\lambda$ by varying the parameter $\lambda$. Then, it is considered the continuation of the solutions

Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction

Personalized travel itinerary recommendation algorithms are the focus of research in smart tourism and tourism GIS. Aiming to address issues present in travel itinerary recommendations for the increasingly popular “self-drive tour” mode, this study proposes an algorithm based on comprehensive tourist satisfaction to mitigate problems such as the neglect of important relevant factors and low degree of personalization. First, we construct a model of tourist satisfaction for travel itineraries by comprehensively considering factors including time utilization, the attractiveness of attractions, itinerary feasibility, and the diversity of attraction types. Unlike previous studies, we consider dining and accommodation time during the itinerary, the physical condition of tourists, and the diversity of attraction types, and establish penalty functions to flexibly constrain deviations from the expected conditions in itinerary planning. Then, with the optimization of comprehensive tourist satisfaction as the objective, we design a new algorithm to address the itinerary recommendation problem, supporting tourists in selecting must-visit attractions, restaurants, and hotels, as well as personalized preferences such as the sightseeing sequence. The experimental results demonstrate that our proposed algorithm outperforms two baseline algorithms, providing higher comprehensive tourist satisfaction while also exhibiting greater feasibility in itinerary planning. The proposed algorithm effectively addresses the issue of personalized travel itinerary recommendation, presenting an efficient, feasible, and practical solution

An Enhanced Algorithm for Active Fire Detection in Croplands Using Landsat-8 OLI Data

Burning biomass exacerbates or directly causes severe air pollution. The traditional active fire detection (AFD) methods are limited by the thresholds of the algorithms and the spatial resolution of remote sensing images, which misclassify some small-scale fires. AFD for burning straw is interfered with by highly reflective buildings around urban and rural areas, resulting in high commission error (CE). To solve these problems, we developed a multicriteria threshold AFD for burning straw (SAFD) based on Landsat-8 imagery in the context of croplands. In solving the problem of the high CE of highly reflective buildings around urban and rural areas, the SAFD algorithm, which was based on the LightGBM machine learning method (SAFD-LightGBM), was proposed to differentiate active fires from highly reflective buildings with a sample dataset of buildings and active fires and an optimal feature combining spectral features and texture features using the ReliefF feature selection method. The results revealed that the SAFD-LightGBM method performed better than the traditional threshold method, with CE and omission error (OE) of 13.2% and 11.5%, respectively. The proposed method could effectively reduce the interference of highly reflective buildings for active fire detection, and it has general applicability and stability for detecting discrete, small-scale fires in urban and rural areas

Aerosol Evolution and Influencing Factor Analysis during Haze Periods in the Guanzhong Area of China Based on Multi-Source Data

Aerosols suspended in the atmosphere negatively affect air quality and public health and promote global climate change. The Guanzhong area in China was selected as the study area. Air quality data from July 2018 to June 2021 were recorded daily, and 19 haze periods were selected for this study. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to simulate the air mass transport trajectory during this haze period to classify the formation process. The spatial distribution of the aerosol optical depth (AOD) was obtained by processing Moderate-resolution Imaging Spectroradiometer (MODIS) data using the dark target (DT) method. Three factors were used to analyze the AOD spatial distribution characteristics based on the perceptual hashing algorithm (PHA): GDP, population density, and topography. Correlations between aerosols and the wind direction, wind speed, and precipitation were analyzed using weather station data. The research results showed that the haze period in Guanzhong was mainly due to locally generated haze (94.7%). The spatial distribution factors are GDP, population density, and topography. The statistical results showed that wind direction mainly affected aerosol diffusion in Guanzhong, while wind speed (r = −0.63) and precipitation (r = −0.66) had a significant influence on aerosol accumulation and diffusion

Aerosol Evolution and Influencing Factor Analysis during Haze Periods in the Guanzhong Area of China Based on Multi-Source Data

Aerosols suspended in the atmosphere negatively affect air quality and public health and promote global climate change. The Guanzhong area in China was selected as the study area. Air quality data from July 2018 to June 2021 were recorded daily, and 19 haze periods were selected for this study. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to simulate the air mass transport trajectory during this haze period to classify the formation process. The spatial distribution of the aerosol optical depth (AOD) was obtained by processing Moderate-resolution Imaging Spectroradiometer (MODIS) data using the dark target (DT) method. Three factors were used to analyze the AOD spatial distribution characteristics based on the perceptual hashing algorithm (PHA): GDP, population density, and topography. Correlations between aerosols and the wind direction, wind speed, and precipitation were analyzed using weather station data. The research results showed that the haze period in Guanzhong was mainly due to locally generated haze (94.7%). The spatial distribution factors are GDP, population density, and topography. The statistical results showed that wind direction mainly affected aerosol diffusion in Guanzhong, while wind speed (r = −0.63) and precipitation (r = −0.66) had a significant influence on aerosol accumulation and diffusion