Fiber quality is an important economic index and a major breeding goal in cotton, but direct phenotypic selection is often hindered due to environmental influences and linkage with yield traits. A genome-wide association study (GWAS) is a powerful tool to identify genes associated with phenotypic traits. In this study, we identified fiber quality genes in upland cotton (Gossypium hirsutum L.) using GWAS based on a high-density CottonSNP80K array and multiple environment tests. A total of 30 and 23 significant single nucleotide polymorphisms (SNPs) associated with five fiber quality traits were identified across the 408 cotton accessions in six environments and the best linear unbiased predictions, respectively. Among these SNPs, seven loci were the same, and 128 candidate genes were predicted in a 1-Mb region (±500 kb of the peak SNP). Furthermore, two major genome regions (GR1 and GR2) associated with multiple fiber qualities in multiple environments on chromosomes A07 and A13 were identified, and within them, 22 candidate genes were annotated. Of these, 11 genes were expressed [log2(1 + FPKM)>1] in the fiber development stages (5, 10, 20, and 25 dpa) using RNA-Seq. This study provides fundamental insight relevant to identification of genes associated with fiber quality and will accelerate future efforts toward improving fiber quality of upland cotton.

Magnetic dummy molecularly imprinted polymers (MDMIPs) were prepared by combining the surface imprinting technique with computer simulation for selective recognition of phthalate esters (PAEs). A computational study based on the density functional theory was performed to evaluate the template-monomer geometry and interaction energy in the prepolymerization mixture. The MDMIPs were characterized by transmission electron microscopy, scanning electron microscopy, vibrating sample magnetometry, X-ray diffraction, and Fourier transform infrared spectroscopy. They exhibited (a) high saturation magnetization of 53.14 emu g (leading to fast separation), and (b) large adsorption capacity, fast binding kinetics, and high selectivity for PAEs. Subsequently, a molecularly imprinted solid-phase extraction procedure followed by GC-MS was established for selective extraction and determination of 10 PAEs in food samples. Under the optimal experimental conditions, the response (peak area) was linear in the 0.5-100 ng mL concentration range. The limits of detection ranged from 0.15 to 1.64 ng g. The method was applied to the determination of PAEs in spiked real samples. The recoveries for 10 PAEs from various foods were in the range of 73.7%-98.1%, with relative standard deviations of 1.7%-10.2%. Graphical abstract Magnetic dummy molecularly imprinted polymers (MDMIPs) were prepared and successfully were applied as a special sorbent for the selective recognition and fast enrichment of 10 PAEs from different complex matrix.

Background Pediatric astrocytoma constitutes a majority of malignant pediatric brain tumors. Previous studies that investigated pediatric cancer predisposition have primarily been conducted in tertiary referral centers and focused on cancer predisposition genes. In this study, we investigated the contribution of rare germline variants to risk of malignant pediatric astrocytoma on a population level. Methods DNA samples were extracted from neonatal dried bloodspots from 280 pediatric astrocytoma patients (predominantly high grade) born and diagnosed in California and were subjected to whole-exome sequencing. Sequencing data were analyzed using agnostic exome-wide gene-burden testing and variant identification for putatively pathogenic variants in 175 a priori candidate cancer-predisposition genes. Results We identified 33 putatively pathogenic germline variants among 31 patients (11.1%) which were located in 24 genes largely involved in DNA repair and cell cycle control. Patients with pediatric glioblastoma were most likely to harbor putatively pathogenic germline variants (14.3%, N = 9/63). Five variants were located in tumor protein 53 (TP53), of which 4 were identified among patients with glioblastoma (6.3%, N = 4/63). The next most frequently mutated gene was neurofibromatosis 1 (NF1), in which putatively pathogenic variants were identified in 4 patients with astrocytoma not otherwise specified. Gene-burden testing also revealed that putatively pathogenic variants in TP53 were significantly associated with pediatric glioblastoma on an exome-wide level (odds ratio, 32.8, P = 8.04 × 10−7). Conclusion A considerable fraction of pediatric glioma patients, especially those of higher grade, harbor a putatively pathogenic variant in a cancer predisposition gene. Some of these variants may be clinically actionable or may warrant genetic counseling.

BackgroundDetection of lymphovascular space invasion (LVSI) in early cervical cancer (CC) is challenging. To date, no standard clinical markers or screening tests have been used to detect LVSI preoperatively. Therefore, non-invasive risk stratification tools are highly desirable.ObjectiveTo train and validate a multi-parametric magnetic resonance imaging (mpMRI)-based radiomics model to detect LVSI in patients with CC and investigate its potential as a complementary tool to enhance the efficiency of risk assessment strategies.Materials and MethodsThe model was developed from the tumor volume of interest (VOI) of 125 patients with CC. A total of 1037 radiomics features obtained from conventional magnetic resonance imaging (MRI), including a small field-of-view (sFOV) high-resolution (HR)-T2-weighted MRI (T2WI), apparent diffusion coefficient (ADC), T2WI, fat-suppressed (FS)-T2WI, as well as axial and sagittal contrast-enhanced T1-weighted MRI (T1c). We conducted a radiomics-based characterization of each tumor region using pretreatment image data. Feature selection was performed using the least absolute shrinkage and selection operator method on the training set. The predictive performance was compared with single variates (clinical data and single-layer radiomics signatures) analyzed using a receiver operating characteristic (ROC) curve. Three-fold cross-validation performed 20 times was used to evaluate the accuracy of the trained classifiers and the stability of the selected features. The models were validated by using a validation set.ResultsFeature selection extracted the six most important features (3 from sFOV HR-T2WI, 1 T2WI, 1 FS-T2WI, and 1 T1c) for model construction. The mpMRI-combined radiomics model (area under the curve [AUC]: 0.940) reached a significantly higher performance (better than the clinical parameters [AUC: 0.730]), including any single-layer model using sFOV HR-T2WI (AUC: 0.840), T2WI (AUC: 0.770), FS-T2WI (AUC: 0.710), ADC maps (AUC: 0.650), sagittal, and axial T1c values (AUC: 0.710, 0.680) in the validation set.ConclusionBiomarkers using multi-parametric radiomics features derived from preoperative MR images could predict LVSI in patients with CC.