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The sun bear is one of the most endangered ursids, and to date classification of sun bear populations has been based almost exclusively on geographic distribution and morphology. The very few molecular studies focussing on this species were limited in geographic scope. Using archival and non-invasively collected sample material, we have added a substantial number of complete or near-complete mitochondrial genome sequences from sun bears of several range countries of the species' distribution. We here report 32 new mitogenome sequences representing sun bears from Cambodia, Thailand, Peninsular Malaysia, Sumatra, and Borneo. Reconstruction of phylogenetic relationships revealed two matrilines that diverged ~295 thousand years ago: one restricted to portions of mainland Indochina (China, Cambodia, Thailand; "Mainland clade"), and one comprising bears from Borneo, Sumatra, Peninsular Malaysia but also Thailand ("Sunda clade"). Generally recent coalescence times in the mitochondrial phylogeny suggest that recent or historical demographic processes have resulted in a loss of mtDNA variation. Additionally, analysis of our data in conjunction with shorter mtDNA sequences revealed that the Bornean sun bear, classified as a distinct subspecies (), does not harbor a distinctive matriline. Further molecular studies of are needed, which should ideally include data from nuclear loci.
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http://dx.doi.org/10.1002/ece3.9969 | DOI Listing |
Nat Astron
May 2025
Big Bear Solar Observatory, New Jersey Institute of Technology, Big Bear City, CA USA.
Resolving fine structures in the Sun's corona may provide key insights into rapid eruptions and the heating of the corona. Adaptive optics systems have been used for over two decades to reach the diffraction limit of large telescopes, thereby compensating for atmospheric image blur. Current systems, however, are still limited to observations of the solar disk and fail with coronal objects, leaving fundamental coronal dynamics hidden in that blur.
View Article and Find Full Text PDFNature
August 2025
Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Perineural invasion (PNI) is a well-established factor of poor prognosis in multiple cancer types, yet its mechanism remains unclear. Here we provide clinical and mechanistic insights into the role of PNI and cancer-induced nerve injury (CINI) in resistance to anti-PD-1 therapy. Our study demonstrates that PNI and CINI of tumour-associated nerves are associated with poor response to anti-PD-1 therapy among patients with cutaneous squamous cell carcinoma, melanoma and gastric cancer.
View Article and Find Full Text PDFPediatr Infect Dis J
August 2025
From the Institute for Immunization Management, Shandong Center for Disease Control and Prevention, Jinan, China.
Background: Rotavirus vaccine is the most effective measure for preventing rotavirus-related diarrhea. The study aims to identify the coverage and trend of the rotavirus vaccine, providing insights for improving vaccination programs.
Methods: Rotavirus vaccination data from 2005 to 2024 were collected and analyzed using Joinpoint regression to identify trends and significant changes over time.
J Med Virol
August 2025
Phase I Clinical Trial Research Ward, The Second Affiliated Hospital of Xi'an Jiaotong University, Shaanxi Province, PR China.
Hepatitis B virus (HBV) infection remains a significant global public health challenge, contributing to both hepatic and extrahepatic disease burdens. This study aims to estimate the global disease and economic burden of HBV-attributable noncommunicable diseases (NCDs). The study included hepatic NCDs (cirrhosis and other chronic liver diseases [CCLDs] and hepatocellular carcinoma [HCC]) and nonhepatic NCDs (NHNCDs).
View Article and Find Full Text PDFJ Environ Manage
September 2025
School of Public Administration, Hohai University, Nanjing, China.
Satellite evidence indicates a significant phenological changes in alpine vegetation. However, the effect of phenological changes on vegetation gross primary productivity (GPP) is still poorly understood, which limits understanding of climate changes. Here, we analyze the impact of different phenological factors on alpine grassland GPP based on sensitivity coefficient obtained from explainable machine learning.
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