Cigarette smoking combined with genetic variation regulates the m6A methylation of CRNKL1 and is associated with bladder cancer risk.

Cigarette smoking was known to accelerate the occurrence and development of bladder cancer by regulating RNA modification. However, the association between the combination of cigarette smoking and RNA modification-related single nucleotide polymorphisms (RNAm-SNPs) and bladder cancer risk remains unclear. In this study, 1681 participants, including 580 cases and 1101 controls, were recruited for genetic association analysis. In total, 1 287 990 RNAm-SNPs involving nine RNA modifications (m6A, m1A, m6Am, 2'-O-Me, m5C, m7G, A-to-I, m5U, and pseudouridine modification) were obtained from the RMVar database. The interactive effect of cigarette smoking and RNAm-SNPs on bladder cancer risk was assessed through joint analysis. The susceptibility analysis revealed that 89 RNAm-SNPs involving m6A, m1A, and A-to-I modifications were associated with bladder cancer risk. Among them, m6A-related rs2273058 in CRNKL1 was associated with bladder cancer risk (odds ratios (OR) = 1.35, padj = 1.78 × 10-4), and CRNKL1 expression was increased in bladder cancer patients (p = 0.035). Cigarette smoking combined with the A allele of rs2273058 increased bladder cancer risk compared with nonsmokers with the G allele of rs2273058 (OR = 2.40, padj = 3.11 × 10-9). Mechanistically, the A allele of rs2273058 endowed CRNKL1 with an additional m6A motif, facilitating recognition by m6A reader IGF2BP1, thereby promoting CRNKL1 expression under cigarette smoking (r = 0.142, p = 0.017). Moreover, elevated CRNKL1 expression may accelerate cell cycle and proliferation, thereby increasing bladder cancer risk. In summary, our study demonstrated that cigarette smoking combined with RNAm-SNPs contributes to bladder cancer risk, which provides a potential target for bladder cancer prevention.

Improving Needle Tip Tracking and Detection in Ultrasound-Based Navigation System Using Deep Learning-Enabled Approach.

Ultrasound-guided percutaneous interventions have numerous advantages over traditional techniques. Accurate needle placement in the target anatomy is crucial for successful intervention, and reliable visual information is essential to achieve this. However, previous studies have revealed several challenges, such as the variability in needle echogenicity and the common misalignment of the ultrasound beam and the needle. Advanced techniques have been developed to optimize needle visualization, including hardware-based and image-processing-based methods. This paper proposes a novel strategy of integrating ultrasound-based deep learning approaches into an optical navigation system to enhance needle visualization and improve tip positioning accuracy. Both the tracking and detection algorithms are optimized utilizing optical tracking information. The information is introduced into the tracking network to define the search patch update strategy and form a trajectory reference to correct tracking results. In the detection network, the original image is processed according to the needle insertion position and current position given by the optical localization system to locate a coarse region, and the depth-score criterion is adopted to optimize detection results. Extensive experiments demonstrate that our approach achieves promising tip tracking and detection performance with tip localization errors of 1.11 ± 0.59 mm and 1.17 ± 0.70 mm, respectively. Moreover, we establish a paired dataset consisting of ultrasound images and their corresponding spatial tip coordinates acquired from the optical tracking system and conduct real puncture experiments to verify the effectiveness of the proposed methods. Our approach significantly improves needle visualization and provides physicians with visual guidance for posture adjustment.

BAG-Net: a boundary detection and multiple attention-guided network for liver ultrasound image automatic segmentation in ultrasound guided surgery.

Objective.Automated segmentation of targets in ultrasound (US) images during US-guided liver surgery holds the potential to assist physicians in fast locating critical areas such as blood vessels and lesions. However, this remains a challenging task primarily due to the image quality issues associated with US, including blurred edges and low contrast. In addition, studies specifically targeting liver segmentation are relatively scarce possibly since studying deep abdominal organs under US is difficult. In this paper, we proposed a network named BAG-Net to address these challenges and achieve accurate segmentation of liver targets with varying morphologies, including lesions and blood vessels.Approach.The BAG-Net was designed with a boundary detection module together with a position module to locate the target, and multiple attention-guided modules combined with the depth supervision strategy to enhance detailed segmentation of the target area.Main Results.Our method was compared to other approaches and demonstrated superior performance on two liver US datasets. Specifically, the method achieved 93.9% precision, 91.2% recall, 92.4% Dice coefficient, and 86.2% IoU to segment the liver tumor. Additionally, we evaluated the capability of our network to segment tumors on the breast US dataset (BUSI), where it also achieved excellent results.Significance.Our proposed method was validated to effectively segment liver targets with diverse morphologies, providing suspicious areas for clinicians to identify lesions or other characteristics. In the clinic, the method is anticipated to improve surgical efficiency during US-guided surgery.

Detection of Stable Elite Haplotypes and Potential Candidate Genes of Boll Weight Across Multiple Environments via GWAS in Upland Cotton.

Boll weight (BW) is a key determinant of yield component traits in cotton, and understanding the genetic mechanism of BW could contribute to the progress of cotton fiber yield. Although many yield-related quantitative trait loci (QTLs) responsible for BW have been determined, knowledge of the genes controlling cotton yield remains limited. Here, association mapping based on 25,169 single-nucleotide polymorphisms (SNPs) and 2,315 insertions/deletions (InDels) was conducted to identify high-quality QTLs responsible for BW in a global collection of 290 diverse accessions, and BW was measured in nine different environments. A total of 19 significant markers were detected, and 225 candidate genes within a 400 kb region (± 200 kb surrounding each locus) were predicted. Of them, two major QTLs with highly phenotypic variation explanation on chromosomes A08 and D13 were identified among multiple environments. Furthermore, we found that two novel candidate genes (Ghir_A08G009110 and Ghir_D13G023010) were associated with BW and that Ghir_D13G023010 was involved in artificial selection during cotton breeding by population genetic analysis. The transcription level analyses showed that these two genes were significantly differentially expressed between high-BW accession and low-BW accession during the ovule development stage. Thus, these results reveal valuable information for clarifying the genetic basics of the control of BW, which are useful for increasing yield by molecular marker-assisted selection (MAS) breeding in cotton.

Hollow Rodlike MgF2 with an Ultralow Refractive Index for the Preparation of Multifunctional Antireflective Coatings.

Antireflective coatings with superhydrophobic, self-cleaning, and wide-spectrum high-transmittance properties and good mechanical strength have important practical value. In this research, hollow nanorod-like MgF2 sols with different void volumes were prepared by a template-free solvothermal method to further obtain hollow nanorod-like MgF2 crystals with an ultralow refractive index of 1.14. Besides, a MgF2 coating with an adjustable refractive index of 1.10-1.35 was also prepared by the template-free solvothermal method. Then through the combination of base/acid two-step-catalyzed TEOS and hydroxyl modification on the surface of nanosilica spheres, the SiO2 coating with good mechanical strength, a flat surface, and a refractive index of 1.30-1.45 was obtained. Double-layer broadband antireflective coatings with an average transmittance of 99.6% at 400-1400 nm were designed using the relevant optical theory. After the coating thickness was optimized by the dip-coating method, the double-layer antireflective coatings, whose parameters were consistent with those designed by the theory, were obtained. The bottom layer was a SiO2 coating with a refractive index of 1.34 and a thickness of 155 nm, and the top layer was a hollow rodlike MgF2 coating with a refractive index of 1.10 and a thickness of 165 nm. The average transmittance of the obtained MgF2-SiO2 antireflective coatings was 99.1% at 400-1400 nm, which was close to the theoretical value. The hydrophobic angle of the coating surface reached 119° at first, and the angle further reached 152° after conducting surface modification by PFOTES. In addition, because the porosity of the coating surface was only 10.7%, the pencil hardness of the coating surface was 5 H and the critical load Lc was 27.05 N. In summary, the obtained antireflective coatings possessed superhydrophobic, self-cleaning, and wide-spectrum high-transmittance properties and good mechanical strength.