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Thalassemia is a group of hereditary anemias caused by mutations to the globin gene, which cause significant morbidity and mortality worldwide, especially among populations in Southeast Asia, the Middle East, [...], * Context.--Rare thalassemia subtypes are often undiagnosed because conventional testing methods can only identify 23 common types of [alpha]- and [beta]-thalassemia. Objective.--To assess a comprehensive approach for the screening and diagnosis of rare thalassemia. Design.--The study cohort included 72 individuals with suspected rare thalassemia variants. Screening was conducted by next-generation sequencing (NGS) combined with third-generation sequencing (TGS) and chromosomal microarray analysis (CMA)/copy number variation sequencing. Results.--Of the 72 individuals with suspected rare thalassemia, 49 had rare [alpha]- [beta]-gene variants. NGS combined with gap polymerase chain reaction detected a total of 42 cases, resulting in a positive detection rate of 58.3%. Additionally, 4 [alpha]-globin genetic deletions were identified by TGS, which increased the variant detection rate by 5.6%. Two samples with a microdeletion of chromosome 16 or 11 were detected by CMA, which increased the detection rate by 2.8%. For one sample, reanalysis of the NGS and TGS data confirmed the presence of the [[beta].sup.41-42]/[[beta].sup.N] and [[beta].sup.N]/[[beta].sup.N] mosaic. The HBB:c.315 1 2delT mutation was initially reported in Guangdong Province, China. Two HBB gene mutations (HBB:c.315 1 5G>C and HBB:c.295G>A) and 4 rare HBA gene deletions ([-.sup.11.1], -[[alpha].sup.27.6], -[[alpha].sup.2.4], and -[[alpha].sup.21.9]) were initially identified in the Zhonshan region. The hematologic phenotypes of all rare cases in this study were clarified. Conclusions.--Rare thalassemia variants are more common than previously thought. Despite advancements in TGS, there is still no foolproof method for detection of all types of thalassemia. Thus, a comprehensive approach is necessary for accurate screening and diagnosis of rare thalassemia variants. (Arch Pathol Lab Med. 2025;149:e1-e10; doi: 10.5858/arpa.2023-0382-OA)

Objective To identify pathogenic gene variants of glutaric aciduria typeⅠ (GAⅠ) and provide reference for prenatal diagnosis of this disease. Methods Genomic DNA was extracted from the family members of a suspected case of GAⅠ. Whole exome sequencing was performed on the proband, and DNA Sanger sequencing was performed on the parents to verify the variants and analyze their pathogenicity. Amniotic fluid fetal cells were extracted by amniocentesis at 18 weeks of gestation to detect GCDH gene. Literature review was conducted from PubMed, CNKI, Wanfang Data and Chongqing VIP databases using the keywords of “glutaric aciduria typeⅠ”, “GCDH” and prenatal diagnosis in both Chinese and English. Results Two variants of GCDH gene were detected in the proband： c.206_207delAC(p.Thr70Leufs*117) and c.892G>A(p.Ala298Thr), from the father and mother, respectively. The paternal variant was a novel variant, and the maternal variant was a known variant. And two variants were both pathogenic. GCDH gene variant analysis of amniotic fluid fetal cells showed compound heterozygous c.206_207delAC and c.892G>A variants. The couple decided to terminate the pregnancy thereafter. A total of 9 families with prenatal diagnosis of GAⅠ were retrieved by GCDH gene sequencing analysis. Among them, 5 fetuses from 4 families had prenatal diagnosis of GAⅠ. Four families decided to terminate the pregnancy, and only 1 family insisted on continuing pregnancy until full-term delivery. Conclusions The novel variant of GCDH gene (c.206_207delAC) is detected in this family of GAⅠ. The pathogenic variant sites of GCDH gene are identified in the proband of this GAⅠ family.

Federated learning has rapidly become a research hotspot in the field of security machine learning in recent years because it can train the global optimal model collaboratively without the need for multiple data source aggregation. Firstly, the federated learning framework, algorithm principle and classification were summarized. Then, the main threats and challenges it faced, were analysed indepth the comparative analysis of typical research programs in the three directions of communication efficiency, privacy and security, trust and incentive mechanism was focused on, and their advantages and disadvantages were pointed out. Finally, Combined with application of edge computing, blockchain, 5G and other emerging technologies to federated learning, its future development prospects and research hotspots was prospected.

Phenolic aerogel is a type of nanoporous resin-based material with low density, high specific surface area, excellent thermal insulation performance, and a high carbon residue rate which has been widely used in the field of thermal protection. However, the development of phenolic aerogel is limited by the complex and expensive preparation technique, inadequate oxidation/ablation resistance, and excessive brittleness. As a result, academic research is constantly concentrated on low-cost preparation methods and efficient modification of phenolic aerogel. This review gives a description of the preparation technology, modification techniques, and application fields of phenolic aerogel and summarizes the limitations. Now, phenolic aerogel is not applied widely due to its complex and expensive preparation technique. Moreover, phenolic aerogel shows greater brittleness and insufficient mechanical property. The prospective future developments of the materials were prospected, and the phenolic aerogel with environmental protection, efficient thermal protection, and low cost will be the mainstream research direction.

In recent years, the rapid advancement of drone technology has led to an increasing use of drones equipped with hyperspectral sensors for ground imaging. Hyperspectral data captured via drones offer significantly higher spatial resolution, but this also introduces more complex background details and larger target scales in high-resolution hyperspectral imagery (HRHSI), posing substantial challenges for hyperspectral anomaly detection (HAD). Mainstream reconstruction-based deep learning methods predominantly emphasize spatial local information in hyperspectral images (HSIs), relying on small spatial neighborhoods for reconstruction. As a result, large anomalous targets and background details are often well reconstructed, leading to poor anomaly detection performance, as these targets are not sufficiently distinguished from the background. To address these limitations, we propose a novel HAD network for HRHSI based on large-kernel central block masked convolution and channel attention, termed LKCMCA. Specifically, we first employ the pixel-shuffle technique to reduce the size of anomalous targets without losing image information. Next, we design a large-kernel central block masked convolution to make the network pay more attention to the surrounding background information, enabling better fusion of the information between adjacent bands. This, coupled with an efficient channel attention mechanism, allows the network to capture deeper spectral features, enhancing the reconstruction of the background while suppressing anomalous targets. Furthermore, we introduce an adaptive loss function by down-weighting anomalous pixels based on the mean absolute error. This loss function is specifically designed to suppress the reconstruction of potentially anomalous pixels during network training, allowing our model to be considered an excellent background reconstruction network. By leveraging reconstruction error, the model effectively highlights anomalous targets. Meanwhile, we produced four benchmark datasets specifically for HAD tasks using existing HRHSI data, addressing the current shortage of HRHSI datasets in the HAD field. Extensive experiments demonstrate that our LKCMCA method achieves superior detection performance, outperforming ten state-of-the-art HAD methods on all datasets. [ABSTRACT FROM AUTHOR]

Near-infrared spectroscopy (NIR) has become an essential tool for non-destructive analysis in various fields, including aquaculture. This study presents a pioneering application of portable NIR spectrometers to analyze glycogen content in the gonadal tissues of the Pacific oyster (Crassostrea gigas), marking the first instance of developing quantitative models for glycogen in tetraploid C. gigas. The research also provides a comparative analysis with models for diploid and triploid oysters, underscoring the innovative use of portable NIR technology in aquaculture. Two portable NIR spectrometers were employed: the Micro NIR 1700 (908–1676 nm) and the Micro PHAZIR RX (1624–2460 nm). Near-infrared spectra were acquired from the gonadal tissues of diploid, triploid, and tetraploid C. gigas. Quantitative models for glycogen content were developed and validated using cross-validation methods. Additionally, qualitative models for different ploidies and genders were established. For the Micro NIR 1700, the cross-validation correlation coefficients (Rcv) and cross-validation relative predictive errors (RPDcv) for glycogen were 0.949 and 3.191 for diploids, 0.915 and 2.498 for triploids, and 0.902 and 2.310 for tetraploids. The Micro PHAZIR RX achieved Rcv and RPDcv values of 0.781 and 2.240 for diploids, 0.839 and 2.504 for triploids, and 0.717 and 1.851 for tetraploids. The Micro NIR 1700 demonstrated superior quantitative performance, with RPD values exceeding 2, indicating its effectiveness in predicting glycogen content across different ploidy levels. Qualitative models showed a performance index of 91.6 for diploid and 95 for tetraploid genders using the Micro NIR 1700, while the Micro PHAZIR RX achieved correct identification rates of 99.79% and 100% for diploid and tetraploid genders, respectively. However, differentiation of ploidies was less successful with both instruments. This study's originality lies in establishing the first quantitative models for glycogen content in tetraploid C. gigas using portable NIR spectrometers, highlighting the significant advancements in non-destructive glycogen analysis. The applicability of these findings is substantial for oyster breeding programs focused on enhancing meat quality traits. These models provide a valuable phenotyping tool for selecting oysters with optimal glycogen content, demonstrating the practical utility of portable NIR technology in aquaculture. [ABSTRACT FROM AUTHOR]

Most unsupervised or semisupervised hyperspectral anomaly detection (HAD) methods train background reconstruction models in the original spectral domain. However, due to the noise and spatial resolution limitations, there may be a lack of discrimination between backgrounds and anomalies. This makes it easy for the autoencoder to capture the low‐level features shared between the two, thereby increasing the difficulty of separating anomalies from the backgrounds, which runs counter to the purpose of HAD. To this end, the authors map the original spectrums to the fractional Fourier domain (FrFD) and reformulate it as a mapping task in which restoration errors are employed to distinguish background and anomaly. This study proposes a novel frequency‐to‐spectrum mapping generative adversarial network for HAD. Specifically, the depth separable features of backgrounds and anomalies are enhanced in the FrFD. Due to the semisupervised approach, FTSGAN needs to learn the embedded features of the backgrounds, thus mapping and restoring them from the FrFD to the original spectral domain. This strategy effectively prevents the model from focussing on the numerical equivalence of input and output, and restricts the ability of FTSGAN to restore anomalies. The comparison and analysis of the experiments verify that the proposed method is competitive. [ABSTRACT FROM AUTHOR]