The Y locus encodes a REPRESSOR OF PHOTOSYNTHETIC GENES protein that represses carotenoid biosynthesis via interaction with APRR2 in carrot.

Little is known about the factors regulating carotenoid biosynthesis in roots. In this study, we characterized DCAR_032551, the candidate gene of the Y locus responsible for the transition of root color from ancestral white to yellow during carrot (Daucus carota) domestication. We show that DCAR_032551 encodes a REPRESSOR OF PHOTOSYNTHETIC GENES (RPGE) protein, named DcRPGE1. DcRPGE1 from wild carrot (DcRPGE1W) is a repressor of carotenoid biosynthesis. Specifically, DcRPGE1W physically interacts with DcAPRR2, an ARABIDOPSIS PSEUDO-RESPONSE REGULATOR2 (APRR2)-like transcription factor. Through this interaction, DcRPGE1W suppresses DcAPRR2-mediated transcriptional activation of the key carotenogenic genes phytoene synthase 1 (DcPSY1), DcPSY2, and lycopene ε-cyclase (DcLCYE), which strongly decreases carotenoid biosynthesis. We also demonstrate that the DcRPGE1W-DcAPRR2 interaction prevents DcAPRR2 from binding to the RGATTY elements in the promoter regions of DcPSY1, DcPSY2, and DcLCYE. Additionally, we identified a mutation in the DcRPGE1 coding region of yellow and orange carrots that leads to the generation of alternatively spliced transcripts encoding truncated DcRPGE1 proteins unable to interact with DcAPRR2, thereby failing to suppress carotenoid biosynthesis. These findings provide insights into the transcriptional regulation of carotenoid biosynthesis and offer potential target genes for enhancing carotenoid accumulation in crop plants.

Two heat shock cognate 70 genes involved in spermatogenesis regulate the male fertility of Zeugodacus cucurbitae, as potential targets for pest control.

The melon fly Zeugodacus cucurbitae Coquillett (Diptera: Tephritidae) is an agricultural quarantine pest threatening fruit and vegetable production. Heat shock cognate 70 (Hsc70), which is a homolog of the heat shock protein 70 (Hsp70), was first discovered in mice testes and plays an important role in spermatogenesis. In this study, we identified and cloned five Hsc70 genes from melon fly, namely ZcHsc70_1/2/3/4/5. Phylogenetic analysis showed that these proteins are closely related to Hsc70s from other Diptera insects. Spatiotemporal expression analysis showed that ZcHsc70_1 and ZcHsc70_2 are highly expressed in Z. cucurbitae testes. Fluorescence in situ hybridization further demonstrated that ZcHsc70_1 and ZcHsc70_2 are expressed in the transformation and maturation regions of testes, respectively. Moreover, RNA interference-based suppression of ZcHsc70_1 or ZcHsc70_2 resulted in a significant decrease of 74.61% and 63.28% in egg hatchability, respectively. Suppression of ZcHsc70_1 expression delayed the transformation of sperm cells to mature sperms. Meanwhile, suppression of ZcHsc70_2 expression decreased both sperm cells and mature sperms by inhibiting the meiosis of spermatocytes. Our findings show that ZcHsc70_1/2 regulates spermatogenesis and further affects the male fertility in the melon fly, showing potential as targets for pest control in sterile insect technique by genetic manipulation of males.

Exploring and Exploiting Multi-modality Uncertainty for Tumor Segmentation on PET/CT.

Despite the success of deep learning methods in multi-modality segmentation tasks, they typically produce a deterministic output, neglecting the underlying uncertainty. The absence of uncertainty could lead to over-confident predictions with catastrophic consequences, particularly in safety-critical clinical applications. Recently, uncertainty estimation has attracted increasing attention, offering a measure of confidence associated with machine decisions. Nonetheless, existing uncertainty estimation approaches primarily focus on single-modality networks, leaving the uncertainty of multi-modality networks a largely under-explored domain. In this study, we present the first exploration of multi-modality uncertainties in the context of tumor segmentation on PET/CT. Concretely, we assessed four well-established uncertainty estimation approaches across various dimensions, including segmentation performance, uncertainty quality, comparison to single-modality uncertainties, and correlation to the contradictory information between modalities. Through qualitative and quantitative analyses, we gained valuable insights into what benefits multi-modality uncertainties derive, what information multi-modality uncertainties capture, and how multi-modality uncertainties correlate to information from single modalities. Drawing from these insights, we introduced a novel uncertainty-driven loss, which incentivized the network to effectively utilize the complementary information between modalities. The proposed approach outperformed the backbone network by 4.53 and 2.92 Dices in percentages on two PET/CT datasets while achieving lower uncertainties. This study not only advanced the comprehension of multi-modality uncertainties but also revealed the potential benefit of incorporating them into the segmentation network. The code is available at https://github.com/HUST-Tan/MMUE.

Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains.

During the process of computed tomography (CT), metallic implants often cause disruptive artifacts in the reconstructed images, impeding accurate diagnosis. Many supervised deep learning-based approaches have been proposed for metal artifact reduction (MAR). However, these methods heavily rely on training with paired simulated data, which are challenging to acquire. This limitation can lead to decreased performance when applying these methods in clinical practice. Existing unsupervised MAR methods, whether based on learning or not, typically work within a single domain, either in the image domain or the sinogram domain. In this paper, we propose an unsupervised MAR method based on the diffusion model, a generative model with a high capacity to represent data distributions. Specifically, we first train a diffusion model using CT images without metal artifacts. Subsequently, we iteratively introduce the diffusion priors in both the sinogram domain and image domain to restore the degraded portions caused by metal artifacts. Besides, we design temporally dynamic weight masks for the image-domian fusion. The dual-domain processing empowers our approach to outperform existing unsupervised MAR methods, including another MAR method based on diffusion model. The effectiveness has been qualitatively and quantitatively validated on synthetic datasets. Moreover, our method demonstrates superior visual results among both supervised and unsupervised methods on clinical datasets. Codes are available in github.com/DeepXuan/DuDoDp-MAR.

Machine learning predicts heavy metal adsorption on iron (oxyhydr)oxides: A combined insight into the adsorption efficiency and binding configuration.

The adsorption of heavy metal on iron (oxyhydr)oxides is one of the most vital geochemical/chemical processes controlling the environmental fate of these contaminants in natural and engineered systems. Traditional experimental methods to investigate this process are often time-consuming and labor-intensive due to the complexity of influencing factors. Herein, a comprehensive database containing the adsorption data of 11 heavy metals on 7 iron (oxyhydr)oxides was constructed, and the machine learning models was successfully developed to predict the adsorption efficiency. The random forest (RF) models achieved high prediction performance (R2 > 0.9, RMSE < 0.1, and MAE < 0.07) and interpretability. Key factors influencing heavy metal adsorption efficiency were identified as mineral surface area, solution pH, metal concentration, and mineral concentration. Additionally, by integrating our previous binding configuration models, we elucidated the simultaneous effects of input features on adsorption efficiency and binding configuration through partial dependence analysis. Higher pH simultaneously enhanced adsorption efficiency and affinity for cations, whereas lower pH benefited that for oxyanions. While higher mineral surface area improved the metal adsorption efficiency, the adsorption affinity could be weakened. This work presents a data-driven approach for investigating metal adsorption behavior and elucidating the influencing mechanisms from macroscopic to microcosmic scale, thereby offering comprehensive guidance for predicting and managing the environmental behavior of heavy metals.

Portación nasal deStaphylococcus aureus y sus perfiles de resistencia a antibióticos en niños que viven en zonas de gran altitud del sudoeste de China / Staphylococcus aureus nasal carriage and its antibiotic resistance profiles in children in high altitude areas of Southwestern China

Antecedentes/Objetivo. Describir el perfil epidemiológico de la portación nasal de cepas de Staphylococcus aureus (S. aureus), su resistencia a antibióticos y la presencia de los genes de leucocidina de Panton-Valentine (LPV) y mecA en niños en edad escolar que viven en zonas de gran altitud del sudoeste de China. Métodos. En el estudio transversal, se analizaron hisopados nasales de estudiantes a fin de detectar S. aureus. Se realizó la prueba de la reacción en cadena de la polimerasa (RCP) para identificar los genes de LPV y mecA. Resultados. Del total de 314 niños, se detectó S. aureus en el 5,10% (16/314). La resistencia de las cepas aisladas a la penicilina, eritromicina, clindamicina, rifampicina y cefoxitina fue del 100%, 81,3%, 81,3%, 0,0% y 6,3%, respectivamente. Ninguna de las cepas mostró resistencia a la vancomicina. Se detectó la expresión del gen mecA en 3 cepas aisladas, y 10 cepas aisladas dieron resultado positivo para el gen de LPV. Conclusión. Se detectó S. Aureus en el 5,10% (16/314) de la población del estudio; el 0,96% (3 /314) presentó S. Aureus resistente a la meticilina (SARM). Además, se detectó la expresión de los genes de LPV y mecA en 10 y 3 cepas aisladas, respectivamente.

Background/Aim. To describe the epidemiological profile of nasal carriage of Staphylococcus aureus (S. aureus) strains, its antibiotic resistance and mecA and Panton Valentine leukocidin (PVL) genes presence, in school children residing in high altitude areas of Southwestern China. Methods. The cross sectional study screened nasal swabs taken from students for S. aureus. PCR was performed to identify mecA and PVL genes. Results. Of the total 314 children 5.10% (16/314) was detected S. aureus. The resistance of isolated strains to penicillin, erythromycin, clindamycin, rifampicin and cefoxitin was 100%, 81.3%, 81.3%, 0.0%, and 6.3% respectively. No strains demonstrated resistance to vancomycin; expression of mecA gene was detected in 3 isolates and 10 isolates were PVL-positive. Conclusion. S. aureus was detected in 5.10% (16/314) of the study population; 0.96% (3/314) had methicillin resistant S. aureus (MRSA); expression of the mecA and PVL genes were detected in 3 and 10 isolates respectively.