Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features.

BACKGROUND: Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients' survival analysis. METHODS: Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. RESULTS: Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. CONCLUSIONS: The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. TRIAL REGISTRATION: ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.

QTL mapping for the flag leaf-related traits using RILs derived from Trititrigia germplasm line SN304 and wheat cultivar Yannong15 in multiple environments.

BACKGROUND: Developing and enriching genetic resources plays important role in the crop improvement. The flag leaf affects plant architecture and contributes to the grain yield of wheat (Triticum aestivum L.). The genetic improvement of flag leaf traits faces problems such as a limited genetic basis. Among the various genetic resources of wheat, Thinopyrum intermedium has been utilized as a valuable resource in genetic improvement due to its disease resistance, large spikes, large leaves, and multiple flowers. In this study, a recombinant inbred line (RIL) population was derived from common wheat Yannong15 and wheat-Th. intermedium introgression line SN304 was used to identify the quantitative trait loci (QTL) for flag leaf-related traits. RESULTS: QTL mapping was performed for flag leaf length (FLL), flag leaf width (FLW) and flag leaf area (FLA). A total of 77 QTLs were detected, and among these, 51 QTLs with positive alleles were contributed by SN304. Fourteen major QTLs for flag leaf traits were detected on chromosomes 2B, 3B, 4B, and 2D. Additionally, 28 QTLs and 8 QTLs for flag leaf-related traits were detected in low-phosphorus and drought environments, respectively. Based on major QTLs of positive alleles from SN304, we identified a pair of double-ended anchor primers mapped on chromosome 2B and amplified a specific band of Th. intermedium in SN304. Moreover, there was a major colocated QTL on chromosome 2B, called QFll/Flw/Fla-2B, which was delimited to a physical interval of approximately 2.9 Mb and contained 20 candidate genes. Through gene sequence and expression analysis, four candidate genes associated with flag leaf formation and growth in the QTL interval were identified. CONCLUSION: These results promote the fine mapping of QFll/Flw/Fla-2B, which have pleiotropic effects, and will facilitate the identification of candidate genes for flag leaf-related traits. Additionally, this work provides a theoretical basis for the application of Th. intermedium in wheat breeding.

Modified SiP single-polarization CADD receiver.

In cost-sensitive application scenarios, increasing the data rate per channel under a limited receiver bandwidth is critical, and thus, the transceivers with low costs and high electrical spectral efficiencies (ESEs) are highly desirable. In this Letter, we demonstrate a modified silicon photonic (SiP) carrier-assisted differential detection (CADD) receiver with a record ESE for single polarization. The ESE of the conventional CADD is mainly limited by the transfer function that originated from the optical delay and hybrid. We modify the transfer function of the CADD by placing an additional delay in parallel to the original delay path. Consequently, the modified transfer function exhibits a sharper slope around the zero frequency, leading to a higher ESE. Here we employ complementary metal-oxide-semiconductor-compatible SiP integration to further reduce the cost and footprint of the modified CADD receiver. In the experiment, 280-Gb/s raw rate (net 226-Gb/s) 16-QAM OFDM signal after 80-km SMF transmission was detected using a 36.5-GHz SiP modified CADD receiver, with a bit error ratio below the 24% SD-FEC threshold. To our best knowledge, we achieve a record net 6.2-b/s/Hz ESE for an integrated single-polarization DD receiver with a 16-QAM format.

Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data.

OBJECTIVES: MAchine Learning In MyelomA Response (MALIMAR) is an observational clinical study combining "real-world" and clinical trial data, both retrospective and prospective. Images were acquired on three MRI scanners over a 10-year window at two institutions, leading to a need for extensive curation. METHODS: Curation involved image aggregation, pseudonymisation, allocation between project phases, data cleaning, upload to an XNAT repository visible from multiple sites, annotation, incorporation of machine learning research outputs and quality assurance using programmatic methods. RESULTS: A total of 796 whole-body MR imaging sessions from 462 subjects were curated. A major change in scan protocol part way through the retrospective window meant that approximately 30% of available imaging sessions had properties that differed significantly from the remainder of the data. Issues were found with a vendor-supplied clinical algorithm for "composing" whole-body images from multiple imaging stations. Historic weaknesses in a digital video disk (DVD) research archive (already addressed by the mid-2010s) were highlighted by incomplete datasets, some of which could not be completely recovered. The final dataset contained 736 imaging sessions for 432 subjects. Software was written to clean and harmonise data. Implications for the subsequent machine learning activity are considered. CONCLUSIONS: MALIMAR exemplifies the vital role that curation plays in machine learning studies that use real-world data. A research repository such as XNAT facilitates day-to-day management, ensures robustness and consistency and enhances the value of the final dataset. The types of process described here will be vital for future large-scale multi-institutional and multi-national imaging projects. CRITICAL RELEVANCE STATEMENT: This article showcases innovative data curation methods using a state-of-the-art image repository platform; such tools will be vital for managing the large multi-institutional datasets required to train and validate generalisable ML algorithms and future foundation models in medical imaging. KEY POINTS: • Heterogeneous data in the MALIMAR study required the development of novel curation strategies. • Correction of multiple problems affecting the real-world data was successful, but implications for machine learning are still being evaluated. • Modern image repositories have rich application programming interfaces enabling data enrichment and programmatic QA, making them much more than simple "image marts".

Machine learning-based prediction model for distant metastasis of breast cancer.

BACKGROUND: Breast cancer is the most prevalent malignancy in women. Advanced breast cancer can develop distant metastases, posing a severe threat to the life of patients. Because the clinical warning signs of distant metastasis are manifested in the late stage of the disease, there is a need for better methods of predicting metastasis. METHODS: First, we screened breast cancer distant metastasis target genes by performing difference analysis and weighted gene co-expression network analysis (WGCNA) on the selected datasets, and performed analyses such as GO enrichment analysis on these target genes. Secondly, we screened breast cancer distant metastasis target genes by LASSO regression analysis and performed correlation analysis and other analyses on these biomarkers. Finally, we constructed several breast cancer distant metastasis prediction models based on Logistic Regression (LR) model, Random Forest (RF) model, Support Vector Machine (SVM) model, Gradient Boosting Decision Tree (GBDT) model and eXtreme Gradient Boosting (XGBoost) model, and selected the optimal model from them. RESULTS: Several 21-gene breast cancer distant metastasis prediction models were constructed, with the best performance of the model constructed based on the random forest model. This model accurately predicted the emergence of distant metastases from breast cancer, with an accuracy of 93.6 %, an F1-score of 88.9 % and an AUC value of 91.3 % on the validation set. CONCLUSION: Our findings have the potential to be translated into a point-of-care prognostic analysis to reduce breast cancer mortality.

Experiences of doctors and nurses offering fertility guidance to young breast cancer patients: A qualitative study.

PURPOSE: Existing qualitative studies on young breast cancer patients' fertility have explored patients' perspectives rather than those of doctors and nurses. With the goal of presenting a scientific basis for the design of high-quality fertility guidance programs, this study examines the advantages and disadvantages of providing fertility guidance, as well as the obstacles perceived by doctors and nurses. METHODS: Purposive sampling was used to select 16 doctors and nurses from a Class III Grade A cancer hospital in Hunan Province, China, from March to May 2023. Face-to-face in-depth interviews were conducted. The interview data were analyzed and organized using Colaizzi's seven-step analysis method and NVivo software, respectively. RESULTS: Three themes and nine sub-themes were extracted from the data: (1) Attitude: maintain patients' fertility hope, respect patients' fertility wishes; (2) Obstacles: patient factors, family factors, healthcare worker factors; (3) Suggestions: enhance the professional confidence of doctors and nurses, ensure two-way communication between doctors and patients, promote interdisciplinary knowledge sharing, and establish a whole-process fertility guidance model. CONCLUSIONS: When carrying out fertility guidance for young breast cancer patients, doctors and nurses should fully respect and safeguard the patients' fertility wishes and hope, continuously strengthen their own knowledge reserve, promote interdisciplinary cooperation and improve their communication abilities, rationally analyze the hindrances, and provide continuous and personalized fertility guidance considering factors related to the patient and their family.

Genome Analysis of Thinopyrum intermedium and Its Potential Progenitor Species Using Oligo-FISH.

The genome composition of intermediate wheatgrass (IWG) is complex and continues to be a subject of investigation. In this study, molecular cytogenetics were used to investigate the karyotype composition of Th. intermedium and its relative diploid species. St2-80 developed from Pseudowroegneria strigose and pDb12H developed from Dasypyrum breviaristatum were used as probes in fluorescence in situ hybridization (FISH) to classify the chromosomes of Th. intermedium into three groups, expressed as JvsJvsJrJrStSt. A combined multiplex oligonucleotide probe, including pSc119.2-1, (GAA)10, AFA-3, AFA-4, pAs1-1, Pas1-3, pAs1-4, and pAs1-6, was used to establish the FISH karyotype of ten accessions of Th. intermedium. Variability among and within the studied accessions of intermediate wheatgrass was observed in their FISH patterns. Results of this study led to the conclusions that Jvs had largely been contributed from Da. breviaristatum, but not the present-day Da. villosum; IWG had only one J genome, Jr, which was related to either Th. elongatum or Th. bessarabicum; and St was contributed from the genus Pseudoroegneria by hybridization with Th. junceiforme or Th. sartorii.

Development and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients With Lung or Colon Cancer: A Diagnostic Test Accuracy Study.

OBJECTIVES: Whole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times. MATERIALS AND METHODS: A retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013-September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated. RESULTS: Four hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (-1.5% difference; 95% confidence interval [CI], -6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (-4.0% difference; 95% CI, -13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient specificity in both groups was 76.3% (0% difference; 95% CI, -15.0%, 15.0%; P = 0.613), with sensitivity of 73.3% (ML) and 60.0% (non-ML) (13.3% difference; 95% CI, -7.9%, 34.5%; P = 0.313). Per-site specificity was high (>90%) for all metastatic sites and experience levels. There was high sensitivity for the detection of primary tumors (lung cancer detection rate of 98.6% with and without ML [0.0% difference; 95% CI, -2.0%, 2.0%; P = 1.00], colon cancer detection rate of 89.0% with and 90.6% without ML [-1.7% difference; 95% CI, -5.6%, 2.2%; P = 0.65]). When combining all reads from rounds 1 and 2, reading times fell by 6.2% (95% CI, -22.8%, 10.0%) when using ML. Round 2 read-times fell by 32% (95% CI, 20.8%, 42.8%) compared with round 1. Within round 2, there was a significant decrease in read-time when using ML support, estimated as 286 seconds (or 11%) quicker ( P = 0.0281), using regression analysis to account for reader experience, read round, and tumor type. Interobserver variance suggests moderate agreement, Cohen κ = 0.64; 95% CI, 0.47, 0.81 (with ML), and Cohen κ = 0.66; 95% CI, 0.47, 0.81 (without ML). CONCLUSIONS: There was no evidence of a significant difference in per-patient sensitivity and specificity for detecting metastases or the primary tumor using concurrent ML compared with standard WB-MRI. Radiology read-times with or without ML support fell for round 2 reads compared with round 1, suggesting that readers familiarized themselves with the study reading method. During the second reading round, there was a significant reduction in reading time when using ML support.

Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features.

PURPOSE: To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images. METHODS: A training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed. Clinical and radiomic features were extracted to predict: (i) DMI of endometrial cancer patients, (ii) endometrial cancer clinical high-risk level, (iii) histological subtype of tumor, and (iv) presence of LVSI. A classification model with different automatically selected hyperparameter values was created. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, F1 score, average recall, and average precision were calculated to evaluate different models. RESULTS: Based on the independent external testing dataset, the AUCs for DMI, high-risk endometrial cancer, endometrial histological type, and LVSI classification were 0.79, 0.82, 0.91, and 0.85, respectively. The corresponding 95% confidence intervals (CI) of the AUCs were [0.69, 0.89], [0.75, 0.91], [0.83, 0.97], and [0.77, 0.93], respectively. CONCLUSION: It is possible to classify endometrial cancer DMI, risk, histology type, and LVSI using different machine learning methods.

Qualitative study of the fertility information support experiences of young breast cancer patients.

PURPOSE: This qualitative study aimed to deeply understand the current experience of fertility information support for young breast cancer patients and to provide further evidence supporting the development of a fertility information support project. METHODS: Using purposive sampling, 18 young breast cancer patients were selected for in-depth interviews from June to September 2022. Colaizzi's seven step analysis method and NVivo software were used to analyze and organize the interview data. RESULTS: Three themes and 10 subthemes were summarized from the interview data: Information anxiety (strong information demand, insufficient information support, information explosion, and information security); reproductive concerns (desire for fertility, anxiety about their children's health, denial of one's health, multiple burdens of emotional interweaving); and family support (the importance of good family relations, the need for a positive marital relationship). CONCLUSIONS: Medical staff should realize the importance of information support for young breast cancer patients and correctly identify the obstacles to insufficient information support. By establishing a fertility information support program, patients' awareness and the quality of fertility information support can be improved to reduce fertility anxiety in breast cancer patients.

An Integrated Clinical-MR Radiomics Model to Estimate Survival Time in Patients With Endometrial Cancer.

BACKGROUND: Determination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning. PURPOSE: To identify clinical features and imaging signatures on T2-weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects. STUDY TYPE: Retrospective. POPULATION: Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years). FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T scanners with sagittal T2-weighted spin echo sequence. ASSESSMENT: Tumor regions were manually segmented on T2-weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets. STATISTICAL TESTS: A likelihood-ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model. RESULTS: Three radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respectively. DATA CONCLUSION: The proposed CPH model with radiomic signatures may serve as a tool to improve estimated survival time in women with endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Spatiotemporal and Transcriptional Characterization on Tanshinone Initial Synthesis in Salvia miltiorrhiza Roots.

Tanshinones are the bioactive constituents of Danshen (Salvia miltiorrhiza Bunge), which is used in Traditional Chinese Medicine to treat cardiovascular and other diseases, and they synthesize and accumulate in the root periderm of S. miltiorrhiza. However, there is no relevant report on the initial stage of tanshinone synthesis, as well as the root structure and gene expression characteristics. The present study aims to provide new insights into how these bioactive principles begin to synthesize by characterizing possible differences in their biosynthesis and accumulation during early root development from both spatial and temporal aspects. The morphological characteristics and the content of tanshinones in roots of S. miltiorrhiza were investigated in detail by monitoring the seedlings within 65 days after germination (DAGs). The ONT transcriptome sequencing was applied to investigate gene expression patterns. The periderm of the S. miltiorrhiza storage taproot initially synthesized tanshinone on about 30 DAGs. Three critical stages of tanshinone synthesis were preliminarily determined: preparation, the initial synthesis, and the continuous rapid synthesis. The difference of taproots in the first two stages was the smallest, and the differentially expressed genes (DEGs) were mainly enriched in terpene synthesis. Most genes involved in tanshinone synthesis were up regulated during the gradual formation of the red taproot. Plant hormone signal transduction and ABC transport pathways were widely involved in S. miltiorrhiza taproot development. Five candidate genes that may participate in or regulate tanshinone synthesis were screened according to the co-expression pattern. Moreover, photosynthetic ferredoxin (FD), cytochrome P450 reductase (CPR), and CCAAT binding transcription factor (CBF) were predicted to interact with the known downstream essential enzyme genes directly. The above results provide a necessary basis for analyzing the initial synthesis and regulation mechanism of Tanshinones.

Ultracompact topological photonic switch based on valley-vortex-enhanced high-efficiency phase shift.

Topologically protected edge states based on valley photonic crystals (VPCs) have been widely studied, from theoretical verification to technical applications. However, research on integrated tuneable topological devices is still lacking. Here, we study the phase-shifting theory of topological edge modes based on a VPC structure. Benefiting from the phase vortex formed by the VPC structure, the optical path of the topological edge mode in the propagation direction is approximately two-fold that of the conventional optical mode in a strip waveguide. In experiments, we show a 1.57-fold improvement in π-phase tuning efficiency. By leveraging the high-efficiency phase-shifting properties and the sharp-turn features of the topological waveguide, we demonstrate an ultracompact 1 × 2 thermo-optic topological switch (TOTS) operating at telecommunication wavelengths. A switching power of 18.2 mW is needed with an ultracompact device footprint of 25.66 × 28.3 µm in the wavelength range of 1530-1582 nm. To the best of our knowledge, this topological photonic switch is the smallest switch of any dielectric or semiconductor 1 × 2/2 × 2 broadband optical switches, including thermo-optic and electro-optic switches. In addition, a high-speed transmission experiment employing the proposed TOTS is carried out to demonstrate the robust transmission of high-speed data. Our work reveals the phase-shifting mechanism of valley edge modes, which may enable diverse topological functional devices in many fields, such as optical communications, nanophotonics, and quantum information processing.

Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study.

INTRODUCTION: Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness. The MALIMAR study aims to develop and validate a ML algorithm to increase the diagnostic accuracy and reading speed of radiological interpretation of WB-MRI compared with standard methods. METHODS AND ANALYSIS: This phase II/III imaging trial will perform retrospective analysis of previously obtained clinical radiology MRI scans and scans from healthy volunteers obtained prospectively to implement training and validation of an ML algorithm. The study will comprise three project phases using approximately 633 scans to (1) train the ML algorithm to identify active disease, (2) clinically validate the ML algorithm and (3) determine change in disease status following treatment via a quantification of burden of disease in patients with myeloma. Phase 1 will primarily train the ML algorithm to detect active myeloma against an expert assessment ('reference standard'). Phase 2 will use the ML output in the setting of radiology reader study to assess the difference in sensitivity when using ML-assisted reading or human-alone reading. Phase 3 will assess the agreement between experienced readers (with and without ML) and the reference standard in scoring both overall burden of disease before and after treatment, and response. ETHICS AND DISSEMINATION: MALIMAR has ethical approval from South Central-Oxford C Research Ethics Committee (REC Reference: 17/SC/0630). IRAS Project ID: 233501. CPMS Portfolio adoption (CPMS ID: 36766). Participants gave informed consent to participate in the study before taking part. MALIMAR is funded by National Institute for Healthcare Research Efficacy and Mechanism Evaluation funding (NIHR EME Project ID: 16/68/34). Findings will be made available through peer-reviewed publications and conference dissemination. TRIAL REGISTRATION NUMBER: NCT03574454.

Characterization of Two Wheat-Thinopyrum ponticum Introgression Lines With Pyramiding Resistance to Powdery Mildew.

Powdery mildew is one of the most devastating foliar diseases in wheat production. The wild relative Thinopyrum ponticum (2n = 10x = 70) has been widely used in wheat genetic improvement due to its superior resistance to both biotic and abiotic stresses. In the present study, two wheat-Th. ponticum introgression lines named SN0293-2 and SN0293-7 were developed from the progenies of a cross between the octoploid Trititrigia SNTE20 and common wheat, including the elite cultivar Jimai 22. They had a novel powdery mildew resistance gene (temporarily named PmSN0293) putatively from Th. ponticum pyramided with Pm2 and Pm52, exhibiting excellent Pm resistance at both the seedling and adult stages. Sequential GISH-FISH detected no signal of Th. ponticum in these two lines but a pair of T1BL·1RS in SN0293-2. Chromosomal structural variations were also observed obviously in SN0293-2 and SN0293-7. Through the Wheat 660K SNP array, 157 SNPs, 134 of which were on 6A, were found to be specific to Th. ponticum. Based on the data combined with DNA re-sequencing, seven specific markers, including one CAPS marker on 2B and six CAPS and Indel markers on 6A, were developed, confirming their wheat-Th. ponticum introgression nature. Furthermore, the two lines displayed positive plant height and produced more kernels and higher 1,000-grain weight. Excellent resistance with desirable agronomic traits makes them valuable in wheat breeding programs.

Loss of anthocyanidin synthase gene is associated with white flowers of Salvia miltiorrhiza Bge. f. alba, a natural variant of S. miltiorrhiza.

MAIN CONCLUSION: SmANS deletion leads to white flower mutation in Salvia miltiorrhiza. SmANS deletion leads to white flower mutation in Salvia miltiorrhiza. Abstract Salvia miltiorrhiza is an essential traditional Chinese medicine (TCM) with purple flowers, and S. miltiorrhiza Bge. f. alba is a unique intraspecific variation with white flowers. The molecular mechanism of flower color formation in S. miltiorrhiza will provide vital information for the variation and evolution. Here, we performed HPLC, transcriptomic, and re-sequencing analyses of purple-flowered S. miltiorrhiza line 'Zihua105' (ZH105) and white-flowered S. miltiorrhiza Bge. f. alba line 'Baihua18' (BH18). Delphinidin was the most anthocyanidin in ZH105, which become the main different between ZH105 vs. BH18 flowers. Transcriptome analysis revealed 299 differentially expressed genes (DEGs). SmANS, the anthocyanidin synthase gene in the down-stream anthocyanin biosynthesis pathway, was significantly expressed in ZH105 corollas, suggesting it might play a key role in white petal formation. Whole-genome re-sequencing revealed that a 6.75 kb segment located on chromosome 5, which contains the complete sequence of the SmANS genes, was lost in BH18 and another S. miltiorrhiza Bge. f. alba line. In contrast, the other five purple-flowered S. miltiorrhiza lines both possessed this segment. Further molecular marker identification also confirmed that wild S. miltiorrhiza Bge. f. alba lines lost regions that contained a complete or important part of SmANS sequences. Subsequently, the research showed that the deletion mutant of SmANS genes resulted in the natural white flower color variant of S. miltiorrhiza.

Metamaterial-enabled arbitrary on-chip spatial mode manipulation.

On-chip spatial mode operation, represented as mode-division multiplexing (MDM), can support high-capacity data communications and promise superior performance in various systems and numerous applications from optical sensing to nonlinear and quantum optics. However, the scalability of state-of-the-art mode manipulation techniques is significantly hindered not only by the particular mode-order-oriented design strategy but also by the inherent limitations of possibly achievable mode orders. Recently, metamaterials capable of providing subwavelength-scale control of optical wavefronts have emerged as an attractive alternative to manipulate guided modes with compact footprints and broadband functionalities. Herein, we propose a universal yet efficient design framework based on the topological metamaterial building block (BB), enabling the excitation of arbitrary high-order spatial modes in silicon waveguides. By simply programming the layout of multiple fully etched dielectric metamaterial perturbations with predefined mathematical formulas, arbitrary high-order mode conversion and mode exchange can be simultaneously realized with uniform and competitive performance. The extraordinary scalability of the metamaterial BB frame is experimentally benchmarked by a record high-order mode operator up to the twentieth. As a proof of conceptual application, an 8-mode MDM data transmission of 28-GBaud 16-QAM optical signals is also verified with an aggregate data rate of 813 Gb/s (7% FEC). This user-friendly metamaterial BB concept marks a quintessential breakthrough for comprehensive manipulation of spatial light on-chip by breaking the long-standing shackles on the scalability, which may open up fascinating opportunities for complex photonic functionalities previously inaccessible.

SmGDB: genome database of Salvia miltiorrhiza, an important TCM Plant.

BACKGROUND: Salvia miltiorrhiza is an important traditional Chinese medicinal (TCM) plant and a model plant in the genetic study of TCM. A series of omics related to Danshen have been published. Integrating, managing, storing, and sharing data has become an urgent problem to be solved in S. miltiorrhiza genetic studies. OBJECTIVES: The genome database is the link for the exchange, acquisition, and use of different omics data between data producers and users, maximizing value and utilization of data. METHODS: The genome database included DSS3 genome and five RNA-Seq data. The back-end performs data search and retrieval through the LAMP (Linux, Apache, MySQL, PHP) framework. RESULTS: Here, we present SmGDB (S. miltiorrhiza genome database; http://8.140.162.85/ ), which houses the latest version of genome sequence and annotation data for S. miltiorrhiza, combining three unpublished RNA-Seq data from our group and two released RNA-Seq data. We also identified a novel gene cluster including seven CYP71D genes involved in the tanshinone synthesis pathway based on genome sequences and expression data. Besides, SmGDB provides user-friendly web interfaces for querying and browsing gene annotation, structure, location, and expression profiles for concerned genes. Popular bioinformatics tools such as 'BLAST', 'Search', 'Heatmap', 'JBrowse', etc., were also provided in SmGDB. CONCLUSIONS: SmGDB will provide utility for characterizing the structure of the S. miltiorrhiza genome and better understanding gene functions and biological processes underlying complex secondary metabolism in Danshen.

Deep-learning-enabled high-performance full-field direct detection with dispersion diversity.

Data center interconnects require cost-effective photonic integrated optical transceivers to meet the ever-increasing capacity demands. Compared with a coherent transmission system, a complex-valued double-sideband (CV-DSB) direct detection (DD) system can minimize the cost of the photonic circuit, since it replaces two stable narrow-linewidth lasers with only a low-cost un-cooled laser in the transmitter while maintaining a similar spectral efficiency. In the carrier-assisted DD system, the carrier power accounts for a large proportion of the total optical signal power. Reducing the carrier to signal power ratio (CSPR) can improve the information-bearing signal power and thus the achievable system performance. To date, the minimum required CSPR is ∼7 dB for all the reported CV-DSB DD systems having electrical bandwidths of approximately half of baud rates. In this paper, we propose a deep-learning-enabled DD (DLEDD) scheme to recover the full optical field of the transmitted signal at a low CSPR of 2 dB in experiment. Our proposal is based on a dispersion-diversity receiver with an electrical bandwidth of ∼61.0% baud rate and a high tolerance to laser wavelength drift. A deep convolutional neural network enables accurate signal recovery in the presence of a strong signal-signal beat interference. Compared with the conventional method, the proposed DLEDD scheme can reduce the optimum CSPR by ∼8 dB, leading to a significant signal-to-noise ratio improvement of ∼5.8 dB according to simulation results. We experimentally demonstrate the optical field reconstruction for a 28-GBaud 16-ary quadrature amplitude modulation signal after 80-km single-mode fiber transmission based on the proposed DLEDD scheme with a 2-dB optimum CSPR. The results show that the proposed DLEDD scheme could offer a high-performance solution for cost-sensitive applications such as data center interconnects, metro networks, and mobile fronthaul systems.

Carrier-assisted differential detection with reduced guard band and high electrical spectral efficiency.

For high-capacity and short-reach applications, carrier-assisted differential detection (CADD) has been proposed, in which the optical field of a complex-valued double sideband (DSB) signal is reconstructed without using a sharp-edge optical bandpass filter or local oscillator laser. The CADD receiver features a transfer function with periodical nulls in the frequency domain, while the signal-signal beat interference (SSBI) is severely amplified around the frequency nulls of the transfer function. Since the null magnitude at the zero frequency is inevitable, a guard band is required between the carrier and the signal, leading to a higher receiver bandwidth and implementation cost. To reduce the needed guard band, we propose a parallel dual delay-based CADD (PDD-CADD), in which an additional delay is placed parallel to the original delay in the conventional CADD. By this means, the modified transfer function has a sharper roll-off edge around the zero frequency. Consequently, the requirement on the guard band can be relaxed, which maximizes the bandwidth utilization of the system. The parallel delay is first optimized through numerical simulation. We then perform a proof-of-concept experiment to transmit a 100-Gb/s orthogonal frequency division multiplexing (OFDM) 16-ary quadrature amplitude modulation (16-QAM) signal over an 80-km single-mode fiber (SMF). After the fiber transmission, the proposed PDD-CADD can reduce the required guard band from 3 to about 1.2 GHz compared with the single delay-based conventional CADD. To our best knowledge, for the direct detection of a single polarization complex-valued DSB signal without using a sharp-roll-off optical filter, we achieve a record electrical spectral efficiency of 5.9 b/s/Hz.