Case Report: A case of ALS type 6 associated with a FUS gene variant and right limb muscle weakness and atrophy as the initial symptom

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive degeneration of upper and lower motor neurons. This degeneration results in increasing muscle weakness, ultimately culminating in respiratory failure and death. Mutations in the fused in sarcoma (FUS) gene have been identified as a significant cause of ALS. Here, we present the case of a 40-year-old woman who exhibited right limb muscle weakness and atrophy as her initial symptom. Whole genome sequencing revealed a mutation in the FUS gene, specifically c.1450_1456delinsCCC (p.Tyr484Profs*44), leading to a diagnosis of ALS type 6 (ALS6). The c.1450_1456delinsCCC (p.Tyr484Profs*44) mutation is a frameshift mutation resulting from a non-triplet base deletion in the coding region of the FUS gene. This mutation is novel and has not been previously reported in China or internationally. Furthermore, the onset of muscle weakness and atrophy exclusively in the ipsilateral limb is very rare among ALS patients, and we have found no related reports. This case report aims to enhance medical professionals’ understanding of the complexities associated with ALS caused by FUS gene mutations and the onset of ALS symptoms, thereby facilitating more accurate clinical diagnosis and treatment

Rickettsial Seroepidemiology among Farm Workers, Tianjin, People’s Republic of China

High seroprevalence rates for Anaplasma phagocytophilum (8.8%), Coxiella burnetii (6.4%), Bartonella henselae (9.6%), and Rickettsia typhi (4.1%) in 365 farm workers near Tianjin, People’s Republic of China, suggest that human infections with these zoonotic bacteria are frequent and largely unrecognized. Demographic features of seropositive persons suggest distinct epidemiology, ecology, and risks

Multiple Feature-Based Superpixel-Level Decision Fusion for Hyperspectral and LiDAR Data Classification

The rapid increase in the number of remote sensing sensors makes it possible to develop multisource feature extraction and fusion techniques to improve the classification accuracy of surface materials. It has been reported that light detection and ranging (LiDAR) data can contribute complementary information to hyperspectral images (HSIs). In this article, a multiple feature-based superpixel-level decision fusion (MFSuDF) method is proposed for HSIs and LiDAR data classification. Specifically, superpixel-guided kernel principal component analysis (KPCA) is first designed and applied to HSIs to both reduce the dimensions and compress the noise impact. Next, 2-D and 3-D Gabor filters are, respectively, employed on the KPCA-reduced HSIs and LiDAR data to obtain discriminative Gabor features, and the magnitude and phase information are both taken into account. Three different modules, including the raw data-based feature cube (concatenated KPCA-reduced HSIs and LiDAR data), the Gabor magnitude feature cube, and the Gabor phase feature cube (concatenation of the corresponding Gabor features extracted from the KPCA-reduced HSIs and LiDAR data), can be, thus, achieved. After that, random forest (RF) classifier and quadrant bit coding (QBC) are introduced to separately accomplish the classification task on the aforementioned three extracted feature cubes. Alternatively, two superpixel maps are generated by utilizing the multichannel simple noniterative clustering (SNIC) and entropy rate superpixel segmentation (ERS) algorithms on the combined HSIs and LiDAR data, which are then used to regularize the three classification maps. Finally, a weighted majority voting-based decision fusion strategy is incorporated to effectively enhance the joint use of the multisource data. The proposed approach is, thus, named MFSuDF. A series of experiments are conducted on three real-world data sets to demonstrate the effectiveness of the proposed MFSuDF approach. The experimental results sho...Full Tex