Genetic analysis of phenotypic plasticity identifies BBX6 as the candidate gene for maize adaptation to temperate regions.

Introduction: Climate changes pose a significant threat to crop adaptation and production. Dissecting the genetic basis of phenotypic plasticity and uncovering the responsiveness of regulatory genes to environmental factors can significantly contribute to the improvement of climate- resilience in crops. Methods: We established a BC1F3:4 population using the elite inbred lines Zheng58 and PH4CV and evaluated plant height (PH) across four environments characterized by substantial variations in environmental factors. Then, we quantified the correlation between the environmental mean of PH (the mean performance in each environment) and the environmental parameters within a specific growth window. Furthermore, we performed GWAS analysis of phenotypic plasticity, and identified QTLs and candidate gene that respond to key environment index. After that, we constructed the coexpression network involving the candidate gene, and performed selective sweep analysis of the candidate gene. Results: We found that the environmental parameters demonstrated substantial variation across the environments, and genotype by environment interaction contributed to the variations of PH. Then, we identified PTT(35-48) (PTT is the abbreviation for photothermal units), the mean PTT from 35 to 48 days after planting, as the pivotal environmental index that closely correlated with environmental mean of PH. Leveraging the slopes of the response of PH to both the environmental mean and PTT(35-48), we successfully pinpointed QTLs for phenotypic plasticity on chromosomes 1 and 2. Notably, the PH4CV genotypes at these two QTLs exhibited positive contributions to phenotypic plasticity. Furthermore, our analysis demonstrated a direct correlation between the additive effects of each QTL and PTT(35-48). By analyzing transcriptome data of the parental lines in two environments, we found that the 1009 genes responding to PTT(35-48) were enriched in the biological processes related to environmental sensitivity. BBX6 was the prime candidate gene among the 13 genes in the two QTL regions. The coexpression network of BBX6 contained other genes related to flowering time and photoperiod sensitivity. Our investigation, including selective sweep analysis and genetic differentiation analysis, suggested that BBX6 underwent selection during maize domestication. Discussion: Th is research substantially advances our understanding of critical environmental factors influencing maize adaptation while simultaneously provides an invaluable gene resource for the development of climate-resilient maize hybrid varieties.

Development of SSR molecular markers and genetic diversity analysis of Clematis acerifolia from Taihang Mountains.

Investigating the genetic diversity and population structure is important in conserving narrowly distributed plants. In this study, 90 Clematis acerifolia (C. acerifolia) plants belonging to nine populations were collected from the Taihang Mountains in Beijing, Hebei, and Henan. Twenty-nine simple sequence repeats (SSR) markers developed based on RAD-seq data were used to analyze the genetic diversity and population structure of C. acerifolia. The mean PIC value for all markers was 0.2910, indicating all SSR markers showed a moderate degree of polymorphism. The expected heterozygosity of the whole populations was 0.3483, indicating the genetic diversity of both C. acerifolia var. elobata and C. acerifolia were low. The expected heterozygosity of C. acerifolia var. elobata (He = 0.2800) was higher than that of C. acerifolia (He = 0.2614). Genetic structure analysis and principal coordinate analysis demonstrated that C. acerifolia and C. acerifolia var. elobata showed great genetic differences. Molecular variance analysis (AMOVA) demonstrated that within-population genetic variation (68.31%) was the main contributor to the variation of the C. acerifolia populations. Conclusively, C. acerifolia var. elobata had higher genetic diversity than C. acerifolia, and there are significant genetic differences between C. acerifolia and C. acerifolia var. elobata, and small genetic variations within the C. acerifolia populations. Our results provide a scientific and rational basis for the conservation of C. acerifolia and provide a reference for the conservation of other cliff plants.

Comparison of Operative Time Between Robotic and Laparoscopic Low Anterior Resection for Rectal Cancer:A Systematic Review and Meta-Analysis.

Background. Previous studies have shown that the robotic approach has better perioperative outcomes but longer operative time than the laparoscopic approach for patients undergoing low anterior resection. However, the impact of the learning curve on operative time is controversial. This study aimed to evaluate operative time and associated outcomes by comparing robotic low anterior resection (R-LAR) with laparoscopic low anterior resection (L-LAR). Methods. Pubmed, Embase, Cochrane Library, Ovid, Web of Science, and CNKI databases were interrogated from the inception to April 2021. Two authors screened all records through full-text reading and extracted and synthesized the data using a structured table. A random-effect model was used to evaluate heterogeneity. Meta-analysis was implemented by R 4.1.1 meta-package. Results. Twelve studies (1684 patients) were included in the present review. R-LAR compare to L-LAR approach has significant differences in operative time (min) (MD = 23.14, 95% CI: 6.89-39.40, P < .01), blood loss (mL) (MD = -42.66, 95% CI: [-68.51, -16.81], P < .01), number of lymph nodes harvested (MD = 1.06, 95% CI: [.16; 1.97], P < .05). Sensitivity analysis of the number of lymph nodes harvested indicated that the overall effect might not be stable. Subgroup analysis showed that mean age and sample size of R-LAR were 2 important factors affecting the estimation. Conclusions. Our results presented a prolonged operative time with the robotic approach compared to laparoscopy, but this gap diminished as the sample size increased. It might be more timesaving once surgeons are familiar with surgical robots.

Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography.

BACKGROUND: To develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia. METHODS: A total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC-AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value. RESULTS: The patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful. CONCLUSIONS: The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia.

Physiological and omics analysis of maize inbred lines during late grain development.

BACKGROUND: There were significant differences in the change of moisture content and grain composition at the late stage of grain development among different maize varieties, but the regulation mechanism is not clear. OBJECTIVE: To explore the key genes causing the variation in physiological traits of two typical maize inbred lines in late grain development. METHODS: The grains at different development stages were selected as materials to determine the content of water, sucrose, starch and ABA. Transcriptomic and proteomic analysis of the materials were performed to screen relevant genes. RESULTS: The grain dehydration rate and the content of sucrose, starch and ABA were showed significant differences between two varieties in the late stage of grain development. The enrichment analysis of common differentially expressed genes (proteins) showed that most of the genes (proteins) were enriched in the extracellular region. The downregulated genes were mainly concentrated in carbohydrate metabolism and lipid metabolism, while the upregulated genes were mainly in response to stress. Furthermore, this study also identified many key candidate genes (dehydrin genes, pathogenesis-related genes, sucrose synthase and secondary metabolites related genes) related to late grain development of maize. CONCLUSIONS: The suggested genes related to late grain development of maize can be candidates for further functional study.

Dissection of the Genetic Basis of Yield Traits in Line per se and Testcross Populations and Identification of Candidate Genes for Hybrid Performance in Maize.

Dissecting the genetic basis of yield traits in hybrid populations and identifying the candidate genes are important for molecular crop breeding. In this study, a BC1F3:4 population, the line per se (LPS) population, was constructed by using elite inbred lines Zheng58 and PH4CV as the parental lines. The population was genotyped with 55,000 SNPs and testcrossed to Chang7-2 and PH6WC (two testers) to construct two testcross (TC) populations. The three populations were evaluated for hundred kernel weight (HKW) and yield per plant (YPP) in multiple environments. Marker-trait association analysis (MTA) identified 24 to 151 significant SNPs in the three populations. Comparison of the significant SNPs identified common and specific quantitative trait locus/loci (QTL) in the LPS and TC populations. Genetic feature analysis of these significant SNPs proved that these SNPs were associated with the tested traits and could be used to predict trait performance of both LPS and TC populations. RNA-seq analysis was performed using maize hybrid varieties and their parental lines, and differentially expressed genes (DEGs) between hybrid varieties and parental lines were identified. Comparison of the chromosome positions of DEGs with those of significant SNPs detected in the TC population identified potential candidate genes that might be related to hybrid performance. Combining RNA-seq analysis and MTA results identified candidate genes for hybrid performance, providing information that could be useful for maize hybrid breeding.

Genomic Prediction across Structured Hybrid Populations and Environments in Maize.

Genomic prediction (GP) across different populations and environments should be enhanced to increase the efficiency of crop breeding. In this study, four populations were constructed and genotyped with DNA chips containing 55,000 SNPs. These populations were testcrossed to a common tester, generating four hybrid populations. Yields of the four hybrid populations were evaluated in three environments. We demonstrated by using real data that the prediction accuracies of GP across structured hybrid populations were lower than those of within-population GP. Including relatives of the validation population in the training population could increase the prediction accuracies of GP across structured hybrid populations drastically. G × E models (including main and genotype-by-environment effect) had better performance than single environment (within environment) and across environment (including only main effect) GP models in the structured hybrid population, especially in the environment where yields had higher heritability. GP by implementing G × E models in two cross-validation schemes indicated that, to increase the prediction accuracy of a new hybrid line, it would be better to field-test the hybrid line in at least one environment. Our results would be helpful for designing training population and planning field testing in hybrid breeding.

AI based colorectal disease detection using real-time screening colonoscopy.

Colonoscopy is an effective tool for early screening of colorectal diseases. However, the application of colonoscopy in distinguishing different intestinal diseases still faces great challenges of efficiency and accuracy. Here we constructed and evaluated a deep convolution neural network (CNN) model based on 117 055 images from 16 004 individuals, which achieved a high accuracy of 0.933 in the validation dataset in identifying patients with polyp, colitis, colorectal cancer (CRC) from normal. The proposed approach was further validated on multi-center real-time colonoscopy videos and images, which achieved accurate diagnostic performance on detecting colorectal diseases with high accuracy and precision to generalize across external validation datasets. The diagnostic performance of the model was further compared to the skilled endoscopists and the novices. In addition, our model has potential in diagnosis of adenomatous polyp and hyperplastic polyp with an area under the receiver operating characteristic curve of 0.975. Our proposed CNN models have potential in assisting clinicians in making clinical decisions with efficiency during application.

Preparation, characterization, antioxidant evaluation of new curcumin derivatives and effects of forming HSA-bound nanoparticles on the stability and activity.

Curcumin (CCM) is a well-known active component, which has been studied extensively in food and medicine field since it showed various activities. However, some serious issues limit its application, for example, the extremely low solubility, stability and bioavailability. In this study, 10 Curcumin derivatives were synthesized and characterized by 1H NMR, 13C NMR and HR-MS, then their antioxidant activity was evaluated. Compound 2 and curcumin were further investigated by preparing HSA-bound nanoparticles (NP-2 and NP-CCM) to surmount the difficulties mentioned above. The nanoparticles obtained were about 110 nm in size measured by Dynamic light scattering (DLS), the stability of compound 2 in NP-2 was significantly increased. Above all, NP-2 showed more efficient antioxidant and antitumor activity, which was probably attributed to the introduced isopentenyl groups in 2, it was supposed that the isopentenyl groups increased the interaction between compound 2 and HSA. Overall, NP-2 has great potential for some food and pharmaceutical applications.

The Role of Vitamin D in Combination Treatment for Patients With Rheumatoid Arthritis.

Background: The aim of this study is to evaluate the clinical efficacy of vitamin D (VitD) supplementation in terms of response to treatment and improvement of disease activity in rheumatoid arthritis (RA). Methods: This study analyzed 1180 RA patients' records treated at Mianyang Central Hospital from February 2015 to July 2019. The patients were allocated into VitD group and control group based on their medical regimens. The outcome measures were primary efficacy, defined as treatment response-based EULAR response criteria in RA, and secondary efficacy, defined as improvement in disease activity indicators. Safety was evaluated according to the incidence of all-cause infections. Results: At month 6, the primary efficacy revealed that there were 22.8% good responders and 19.0% moderate responders in the VitD group, and 22.3% good responders and 22.3% moderate responders in the control group; there were no differences between the two groups (p = 0.754). The similar primary efficacy outcomes were observed at months 3, 12, and >12. The secondary efficacy indicated that there were no differences in most indexes between the two groups at months 1, 3, 6, 12, and >12. The subgroups (based on baseline DAS28 (CRP), glucocorticoids use and disease duration) analysis results suggested that VitD group didn't have the advantage for treating RA. The incidence of infections was similar in the two groups. Conclusion: VitD supplementation did not provide additional benefit for anti-rheumatic treatment. These data supported the need for prospective, randomized, controlled trials to evaluate the role of VitD supplementation in treating RA.

Comparison of clinical efficacy between robotic-laparoscopic excision and traditional laparoscopy for rectal cancer: A protocol for systematic review and meta-analysis.

BACKGROUNDS: Laparoscopic surgery, robot-assisted surgery and open surgery are the most commonly consumed surgical techniques in daily living. Considering that in recent years, the situation of choosing laparoscopic surgery and robot-assisted surgery to treat rectal cancer in China is prosperous. Meanwhile, researches lacked in the comparison part between the 2, so we will systematically compare the clinical efficacy of robot-assisted resection and traditional laparoscopic resection for rectal cancer. METHODS AND ANALYSIS: We will search Clinical research literature published before January 2020 in PubMed, Embase, the Cochrane library, Science Network, Wan Fang database, Chinese national knowledge infrastructure, and Chinese biomedicine that evaluate the correlation of rectal cancer with Leonardo's robot and traditional laparoscopy, from inception to July 2019. Weighted mean difference and odds ratio were used to compare the efficacy of robot-assisted resection versus conventional laparoscopic resection for rectal cancer, and the main indicators are operation time, complication rate, conversion rate, blood loss, and length of stay. RESULTS AND CONCLUSION: This study will systematically evaluate the clinical efficacy of robot-assisted resection and traditional laparoscopic resection for rectal cancer, thus providing evidence to the clinical application. The results will be published in a peer-reviewed journal. ETHICS AND DISSEMINATION: No ethical approval and participant consent are required, since this study data is based on published literature. The results of the study will be submitted to a peer-reviewed journal.PROSPERO registration number: CRD42020172161.

Development of a prediction model with serum tumor markers to assess tumor metastasis in lung cancer.

BACKGROUND: This study aimed to explore the possibility of serum tumor markers (TMs) combinations in assessing tumor metastasis in patients with lung cancer. METHODS: We performed a retrospective analysis of 541 patients diagnosed with lung cancer between January 2016 and December 2017 at the Pneumology Department of Dazhou Central Hospital. Serum carcinoembryonic antigen (CEA), carbohydrate antigen (CA)125, CA153, CA199, CA724, cytokeratin 19 fragment (CYFRA), and neuron-specific enolase (NSE) levels were quantified in each patient at the time of lung cancer diagnosis. Metastasis was confirmed by computed tomography, and/or positron emission tomography, and/or surgery or other necessary methods. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the performance of the model. RESULTS: Of the 541 patients eligible for final analysis, 253 were detected with metastasis and 288 were detected without metastasis. Compared with those in nonmetastatic patients, the serum CEA, CA125, CA199, CA153, CYFRA, and NSE levels were notably higher in metastatic patients (P < .05). The ROC curve demonstrated that the CEA-CA125-CA199-CA153-CYFRA-NSE-CA724 combination based on the cut-off value had an optimal area under the curve and specificity in assessing tumor metastasis. The decision tree model is a convenient and valuable tool for guiding the appropriate application of our model to assess metastasis in lung cancer patients. CONCLUSIONS: Our study suggested that the nomogram of the regression model is valuable for assessing tumor metastasis in newly diagnosed lung cancer patients before traditional standard methods are used. These findings could aid in the evaluation of metastasis in the clinic.

Clinical features and multidisciplinary treatment outcome of COVID-19 pneumonia: A report of three cases.

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic threatening global public health. In the current paper, we describe our successful treatment of three COVID-19 pneumonia patients cases including severe cases and cases with mortality risk factors. One 32-year-old male COVID-19 patient was diagnosed with severe COVID-19 pneumonia and moderate ARDS. The second COVID-19 pneumonia patient had a history of diabetes and chronic bronchitis. The third case of COVID-19 pneumonia was an 82-year old female patient. All three cases had severe COVID pneumonia and therefore were aggressively managed with a multidisciplinary and personalized therapeutic approach that included nutritional support, antiviral pharmacotherapy, active control of comorbidities, prevention of complication development and psychological intervention. Our experience highlights the importance of the use of a multidisciplinary therapeutic approach that tailors to the specific condition of the patient in achieving a favorable clinical outcome.

Correction: Enhancing genomic selection by fitting large-effect SNPs as fixed effects and a genotype-by-environment effect using a maize BC1F3:4population.

[This corrects the article DOI: 10.1371/journal.pone.0223898.].

Enhancing genomic selection by fitting large-effect SNPs as fixed effects and a genotype-by-environment effect using a maize BC1F3:4 population.

The popularity of genomic selection (GS) has increased owing to its prospects in commercial breeding. It is necessary to enhance GS to increase its efficiency. In this study, a maize BC1F3:4 population, consisting of 481 families, was evaluated for days to anthesis in four environments, and genotyped with DNA chips including 55,000 single nucleotide polymorphisms (SNPs). This population was used to investigate whether GS could be enhanced by borrowing information from the genetic basis and genotype-by-environment (G × E) interaction. The results showed that: 1) fitting the top four large-effect SNPs as fixed effects could increase prediction accuracy, including three minor-effect SNPs explaining less than 10% phenotypic variance; 2) the increase of prediction accuracy when fitting large-effect SNPs as fixed effects was related to the decrease of genetic variance; 3) generally, the GS model fitting large-effect SNPs as fixed effects and G × E component enhanced GS. Therefore, we propose fitting large-effect markers as fixed effects and G × E effect for crop breeding projects in order to obtain accurately predicted phenotypic data and conduct efficient selection of desired plants.

QTG-Seq Accelerates QTL Fine Mapping through QTL Partitioning and Whole-Genome Sequencing of Bulked Segregant Samples.

Deciphering the genetic mechanisms underlying agronomic traits is of great importance for crop improvement. Most of these traits are controlled by multiple quantitative trait loci (QTLs), and identifying the underlying genes by conventional QTL fine-mapping is time-consuming and labor-intensive. Here, we devised a new method, named quantitative trait gene sequencing (QTG-seq), to accelerate QTL fine-mapping. QTG-seq combines QTL partitioning to convert a quantitative trait into a near-qualitative trait, sequencing of bulked segregant pools from a large segregating population, and the use of a robust new algorithm for identifying candidate genes. Using QTG-seq, we fine-mapped a plant-height QTL in maize (Zea mays L.), qPH7, to a 300-kb genomic interval and verified that a gene encoding an NF-YC transcription factor was the functional gene. Functional analysis suggested that qPH7-encoding protein might influence plant height by interacting with a CO-like protein and an AP2 domain-containing protein. Selection footprint analysis indicated that qPH7 was subject to strong selection during maize improvement. In summary, QTG-seq provides an efficient method for QTL fine-mapping in the era of "big data".

Alteration of the immune status of umbilical cord mesenchymal stem cells stimulated by TLR1/2 agonist, Pam3Csk.

Mesenchymal stem cells (MSCs) have been widely used in clinical trials due to their multiple differentiation ability, low immunogenicity and immunosuppressant effects on immune response. However, accumulating evidence has indicated that MSCs may stimulate in vivo immune responses and result in the disappearance of MSCs following engrafting. Toll­like receptors (TLRs) are important in immune response induction against invaded pathogens, however, the function of TLRs in regulating the immune status of MSCs has been seldom reported. The present stimulated umbilical cord (UC) MSCs by treatment with the TLR1/2 agonist, Pam3Csk, the to determine whether activation of TLR1/2 signaling alters the immune status of UCMSCs. The results indicated that activation of TLR1/2 increased the proliferation of peripheral blood mononuclear cells (PBMCs) and the production of lactate dehydrogenase in a PBMC­MSC co­culture system. The study also demonstrated that Pam3Csk induced the secretion of pro­inflammatory molecules, and increased the expression levels of cytokine and chemokines in UCMSCs. Flow cytometry analysis indicated that the levels of surface co­stimulators, CD80 and CD86, were increased on UCMSCs in the presence of Pam3Csk, whereas activation of TLR1/2 exerted no observable effect on the differentiation abilities of UCMSCs. The results of the current study indicated that activation of TLR1/2 signaling may alter the immune status of UCMSCs, however, further mechanistic research is required in future studies.

Bulked sample analysis in genetics, genomics and crop improvement.

Biological assay has been based on analysis of all individuals collected from sample populations. Bulked sample analysis (BSA), which works with selected and pooled individuals, has been extensively used in gene mapping through bulked segregant analysis with biparental populations, mapping by sequencing with major gene mutants and pooled genomewide association study using extreme variants. Compared to conventional entire population analysis, BSA significantly reduces the scale and cost by simplifying the procedure. The bulks can be built by selection of extremes or representative samples from any populations and all types of segregants and variants that represent wide ranges of phenotypic variation for the target trait. Methods and procedures for sampling, bulking and multiplexing are described. The samples can be analysed using individual markers, microarrays and high-throughput sequencing at all levels of DNA, RNA and protein. The power of BSA is affected by population size, selection of extreme individuals, sequencing strategies, genetic architecture of the trait and marker density. BSA will facilitate plant breeding through development of diagnostic and constitutive markers, agronomic genomics, marker-assisted selection and selective phenotyping. Applications of BSA in genetics, genomics and crop improvement are discussed with their future perspectives.

Insertion/deletion polymorphism in the promoter region of NFKB1 gene increases susceptibility for superficial bladder cancer in Chinese.

Recently, a functional insertion/deletion polymorphism (-94 insertion/deletion ATTG) in the promoter of NFKB1 gene, which encodes the p50 subunit of nuclear factor-kappaB protein complex, was identified. The aim of this study was to explore the association between this polymorphism and bladder cancer in a Chinese population. The NFKB1 polymorphism was assessed in 207 patients with superficial transitional cell carcinomas in bladder and in 228 age-, sex-, and smoking-matched healthy volunteers. The polymerase chain reaction assay was used to determine the NFKB1 genotypes. Genomic DNA used for the assay was extracted from peripheral blood lymphocytes. This study found that the frequency of ATTG(2) allele in bladder cancer patients was significantly higher than that in control subjects (65.2% vs. 56.1%, p = 0.006, odds ratio = 1.465), suggesting that the functional NFKB1 promoter polymorphism is associated with increased risk for superficial transitional cell carcinoma of the bladder.