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Wide-field optical coherence tomography (OCT) imaging can enable monitoring of peripheral changes in the retina, beyond the conventional fields of view used in current clinical OCT imaging systems. However, wide-field scans can present significant challenges for retinal layer segmentation. Deep Convolutional Neural Networks (CNNs) have shown strong performance in medical imaging segmentation but typically require large-scale, high-quality, pixel-level annotated datasets to be effectively developed. To address this challenge, we propose an advanced semi-supervised learning framework that combines the detailed capabilities of convolutional networks with the broader perspective of transformers. This method efficiently leverages labelled and unlabelled data to reduce dependence on extensive, manually annotated datasets. We evaluated the model performance on a dataset of 74 volumetric OCT scans, each performed using a prototype swept-source OCT system following a wide-field scan protocol with a 15 × 9 mm field of view, comprising 11,750 labelled and 29,016 unlabelled images. Wide-field retinal layer segmentation using the semi-supervised approach show significant improvements (P-value < 0.001) of up to 11% against a UNet baseline model. Comparisons with a clinical spectral-domain-OCT system revealed significant correlations of up to 0.91 (P-value < 0.001) in retinal layer thickness measurements. These findings highlight the effectiveness of semi-supervised learning with cross-teaching between CNNs and transformers for automated OCT layer segmentation., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Spectral-domain optical coherence tomography (SDOCT) is the gold standard of imaging the eye in clinics. Penetration depth with such devices is, however, limited and visualization of the choroid, which is essential for diagnosing chorioretinal disease, remains limited. Whereas swept-source OCT (SSOCT) devices allow for visualization of the choroid these instruments are expensive and availability in praxis is limited. We present an artificial intelligence (AI)-based solution to enhance the visualization of the choroid in OCT scans and allow for quantitative measurements of choroidal metrics using generative deep learning (DL). Synthetically enhanced SDOCT B-scans with improved choroidal visibility were generated, leveraging matching images to learn deep anatomical features during the training. Using a single-center tertiary eye care institution cohort comprising a total of 362 SDOCT-SSOCT paired subjects, we trained our model with 150,784 images from 410 healthy, 192 glaucoma, and 133 diabetic retinopathy eyes. An independent external test dataset of 37,376 images from 146 eyes was deployed to assess the authenticity and quality of the synthetically enhanced SDOCT images. Experts' ability to differentiate real versus synthetic images was poor (47.5% accuracy). Measurements of choroidal thickness, area, volume, and vascularity index, from the reference SSOCT and synthetically enhanced SDOCT, showed high Pearson's correlations of 0.97 [95% CI: 0.96-0.98], 0.97 [0.95-0.98], 0.95 [0.92-0.98], and 0.87 [0.83-0.91], with intra-class correlation values of 0.99 [0.98-0.99], 0.98 [0.98-0.99], and 0.95 [0.96-0.98], 0.93 [0.91-0.95], respectively. Thus, our DL generative model successfully generated realistic enhanced SDOCT data that is indistinguishable from SSOCT images providing improved visualization of the choroid. This technology enabled accurate measurements of choroidal metrics previously limited by the imaging depth constraints of SDOCT. The findings open new possibilities for utilizing affordable SDOCT devices in studying the choroid in both healthy and pathological conditions., (© 2024. The Author(s).)

Dalbergia is a pantropical genus with more than 250 species, many of which are highly threatened due to overexploitation for their rosewood timber, along with general deforestation. Many Dalbergia species have received international attention for conservation, but the lack of genomic resources for Dalbergia hinders evolutionary studies and conservation applications, which are important for adaptive management. This study produced the first reference transcriptomes for 6 Dalbergia species with different geographical origins and predicted ~ 32 to 49 K unique genes. We showed the utility of these transcriptomes by phylogenomic analyses with other Fabaceae species, estimating the divergence time of extant Dalbergia species to ~ 14.78 MYA. We detected over-representation in 13 Pfam terms including HSP, ALDH and ubiquitin families in Dalbergia. We also compared the gene families of geographically co-occurring D. cochinchinensis and D. oliveri and observed that more genes underwent positive selection and there were more diverged disease resistance proteins in the more widely distributed D. oliveri, consistent with reports that it occupies a wider ecological niche and has higher genetic diversity. We anticipate that the reference transcriptomes will facilitate future population genomics and gene-environment association studies on Dalbergia, as well as contributing to the genomic database where plants, particularly threatened ones, are currently underrepresented.

Plasmodium falciparum resistance to artemisinin derivatives in southeast Asia threatens malaria control and elimination activities worldwide. To monitor the spread of artemisinin resistance, a molecular marker is urgently needed. Here, using whole-genome sequencing of an artemisinin-resistant parasite line from Africa and clinical parasite isolates from Cambodia, we associate mutations in the PF3D7&#95;1343700 kelch propeller domain ('K13-propeller') with artemisinin resistance in vitro and in vivo. Mutant K13-propeller alleles cluster in Cambodian provinces where resistance is prevalent, and the increasing frequency of a dominant mutant K13-propeller allele correlates with the recent spread of resistance in western Cambodia. Strong correlations between the presence of a mutant allele, in vitro parasite survival rates and in vivo parasite clearance rates indicate that K13-propeller mutations are important determinants of artemisinin resistance. K13-propeller polymorphism constitutes a useful molecular marker for large-scale surveillance efforts to contain artemisinin resistance in the Greater Mekong Subregion and prevent its global spread., Competing Interests: The authors declare no competing financial interests. Readers are welcome to comment on the online version of the paper.