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Wikstroemiafragrans (Thymelaeaceae, Daphneae), a new species from Mount Danxia, China based on morphological and molecular evidence

Chen JR, Lee SY, Guo JQ, Jin JH, Fan Q, Liao WB

PhytoKeys, 2022;213:67-78.
PMID: 36762252 DOI: 10.3897/phytokeys.213.91116

A new species, Wikstroemiafragrans (Thymelaeaceae, Daphneae), from Danxiashan National Park, Shaoguan, Guangdong of China is described and illustrated. It is similar to the sympatric W.trichotoma, but can be differentiated easily from the latter by its shorter racemose inflorescences, yellowish green calyx tube, and smaller leaves. It also resembles the allopatric W.fargesii, but differs from it by its strigose-pubescent ovary and disk scale that is 2- or 3-dentate apically. Phylogenetic analysis using the nuclear DNA internal transcribed spacer (ITS) region revealed that W.fragrans falls within the Wikstroemia clade; based on current sampling, W.fragrans is closely-related to W.capitata. It is also the first species of Wikstroemia known to be endemic to the Danxia landform and is classified provisionally as Critically Endangered according to the IUCN Red List Categories and Criteria.
Fulltext Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model Characteristics

Hsu HY, Keoy KH, Chen JR, Chao HC, Lai CF

Sensors (Basel), 2023 Nov 07;23(22).
PMID: 38005404 DOI: 10.3390/s23229016

The proliferation of IoT devices has led to an unprecedented integration of machine learning techniques, raising concerns about data privacy. To address these concerns, federated learning has been introduced. However, practical implementations face challenges, including communication costs, data and device heterogeneity, and privacy security. This paper proposes an innovative approach within the context of federated learning, introducing a personalized joint learning algorithm for Non-IID IoT data. This algorithm incorporates multi-task learning principles and leverages neural network model characteristics. To overcome data heterogeneity, we present a novel clustering algorithm designed specifically for federated learning. Unlike conventional methods that require a predetermined number of clusters, our approach utilizes automatic clustering, eliminating the need for fixed cluster specifications. Extensive experimentation demonstrates the exceptional performance of the proposed algorithm, particularly in scenarios with specific client distributions. By significantly improving the accuracy of trained models, our approach not only addresses data heterogeneity but also strengthens privacy preservation in federated learning. In conclusion, we offer a robust solution to the practical challenges of federated learning in IoT environments. By combining personalized joint learning, automatic clustering, and neural network model characteristics, we facilitate more effective and privacy-conscious machine learning in Non-IID IoT data settings.
Fulltext Genetic risk of extranodal natural killer T-cell lymphoma: a genome-wide association study

Li Z, Xia Y, Feng LN, Chen JR, Li HM, Cui J, et al.

Lancet Oncol, 2016 Sep;17(9):1240-7.
PMID: 27470079 DOI: 10.1016/S1470-2045(16)30148-6

BACKGROUND: Extranodal natural killer T-cell lymphoma (NKTCL), nasal type, is a rare and aggressive malignancy that occurs predominantly in Asian and Latin American populations. Although Epstein-Barr virus infection is a known risk factor, other risk factors and the pathogenesis of NKTCL are not well understood. We aimed to identify common genetic variants affecting individual risk of NKTCL.
METHODS: We did a genome-wide association study of 189 patients with extranodal NKTCL, nasal type (WHO classification criteria; cases) and 957 controls from Guangdong province, southern China. We validated our findings in four independent case-control series, including 75 cases from Guangdong province and 296 controls from Hong Kong, 65 cases and 983 controls from Guangdong province, 125 cases and 1110 controls from Beijing (northern China), and 60 cases and 2476 controls from Singapore. We used imputation and conditional logistic regression analyses to fine-map the associations. We also did a meta-analysis of the replication series and of the entire dataset.
FINDINGS: Associations exceeding the genome-wide significance threshold (p<5 × 10(-8)) were seen at 51 single-nucleotide polymorphisms (SNPs) mapping to the class II MHC region on chromosome 6, with rs9277378 (located in HLA-DPB1) having the strongest association with NKTCL susceptibility (p=4·21 × 10(-19), odds ratio [OR] 1·84 [95% CI 1·61-2·11] in meta-analysis of entire dataset). Imputation-based fine-mapping across the class II MHC region suggests that four aminoacid residues (Gly84-Gly85-Pro86-Met87) in near-complete linkage disequilibrium at the edge of the peptide-binding groove of HLA-DPB1 could account for most of the association between the rs9277378*A risk allele and NKTCL susceptibility (OR 2·38, p value for haplotype 2·32 × 10(-14)). This association is distinct from MHC associations with Epstein-Barr virus infection.
INTERPRETATION: To our knowledge, this is the first time that a genetic variant conferring an NKTCL risk is noted at genome-wide significance. This finding underlines the importance of HLA-DP antigen presentation in the pathogenesis of NKTCL.
FUNDING: Top-Notch Young Talents Program of China, Special Support Program of Guangdong, Specialized Research Fund for the Doctoral Program of Higher Education (20110171120099), Program for New Century Excellent Talents in University (NCET-11-0529), National Medical Research Council of Singapore (TCR12DEC005), Tanoto Foundation Professorship in Medical Oncology, New Century Foundation Limited, Ling Foundation, Singapore National Cancer Centre Research Fund, and the US National Institutes of Health (1R01AR062886, 5U01GM092691-04, and 1R01AR063759-01A1).

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