Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional “omics” data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.     

南亚热带马尾松-红椎混交林及其纯林土壤细菌群落结构与功能

营建乡土阔叶树种人工纯林和针阔混交林是我国亚热带地区森林经营的发展趋势,但对人工纯林和针阔混交林土壤细菌群落结构及功能知之甚少。本研究以南亚热带乡土针叶树种马尾松、阔叶树种红椎人工纯林及二者的混交林为对象,运用细菌16S rRNA基因高通量测序技术和PICRUSt基因功能预测,分析了3种人工林不同土层(0～20、20～40和40～60 cm)土壤细菌群落的结构与功能。结果表明: 混交林和马尾松林土壤细菌群落结构相似,但与红椎林差异显著,红椎林土壤细菌群落多样性、生物通路代谢功能和氮循环功能低于马尾松林和混交林;土壤全氮、硝态氮和C/N是导致红椎林与马尾松林和混交林土壤细菌群落结构及功能差异的主要土壤理化因子。就土壤细菌群落结构与功能而言,在该地区营造红椎和马尾松针阔混交林要优于红椎纯林。     

In silico annotation of unreviewed acetylcholinesterase (AChE) in some lepidopteran insect pest species reveals the causes of insecticide resistance

Lepidoptera is the second most diverse insect order outnumbered only by the Coeleptera. Acetylcholinesterase (AChE) is the major target site for insecticides. Extensive use of insecticides, to inhibit the function of this enzyme, have resulted in the development of insecticide resistance. Complete knowledge of the target proteins is very important to know the cause of resistance. Computational annotation of insect acetylcholinesterase can be helpful for the characterization of this important protein. Acetylcholinesterase of fourteen lepidopteran insect pest species was annotated by using different bioinformatics tools. AChE in all the species was hydrophilic and thermostable. All the species showed lower values for instability index except L. orbonalis, S. exigua and T. absoluta. Highest percentage of Arg, Asp, Asn, Gln and Cys were recorded in P. rapae. High percentage of Cys and Gln might be reason for insecticide resistance development in P. rapae. Phylogenetic analysis revealed the AChE in T. absoluta, L. orbonalis and S. exigua are closely related and emerged from same primary branch. Three functional motifs were predicted in eleven species while only two were found in L. orbonalis, S. exigua and T. absoluta. AChE in eleven species followed secretory pathway and have signal peptides. No signal peptides were predicted for S. exigua, L. orbonalis and T. absoluta and follow non secretory pathway. Arginine methylation and cysteine palmotylation was found in all species except S. exigua, L. orbonalis and T. absoluta. Glycosylphosphatidylinositol (GPI) anchor was predicted in only nine species.     

Novel artificial intelligence machine learning approaches to precisely predict survival and site-specific recurrence in cervical cancer: A multi-institutional study

BackgroundMachine learning (ML) has been gradually integrated into oncologic research but seldom applied to predict cervical cancer (CC), and no model has been reported to predict survival and site-specific recurrence simultaneously. Thus, we aimed to develop ML models to predict survival and site-specific recurrence in CC and to guide individual surveillance.MethodsWe retrospectively collected data on CC patients from 2006 to 2017 in four hospitals. The survival or recurrence predictive value of the variables was analyzed using multivariate Cox, principal component, and K-means clustering analyses. The predictive performances of eight ML models were compared with logistic or Cox models. A novel web-based predictive calculator was developed based on the ML algorithms.ResultsThis study included 5112 women for analysis (268 deaths, 343 recurrences): (1) For site-specific recurrence, larger tumor size was associated with local recurrence, while positive lymph nodes were associated with distant recurrence. (2) The ML models exhibited better prognostic predictive performance than traditional models. (3) The ML models were superior to traditional models when multiple variables were used. (4) A novel predictive web-based calculator was developed and externally validated to predict survival and site-specific recurrence.ConclusionML models might be a better analytic approach in CC prognostic prediction than traditional models as they can predict survival and site-specific recurrence simultaneously, especially when using multiple variables. Moreover, our novel web-based calculator may provide clinicians with useful information and help them make individual postoperative follow-up plans and further treatment strategies.     

响应面法优化飞龙掌血中总生物碱的提取工艺

以飞龙掌血（Toddalia asiatica（L.）Lam.）的根为实验材料,以总生物碱的含量为检测指标,应用响应面法对飞龙掌血中总生物碱的提取工艺进行了优化。结果显示,当提取条件为乙醇体积分数74.76%、料液比20∶1和提取时间32.87 min时,飞龙掌血提取物中总生物碱的理论含量值可达11.09 mg/g,总生物碱含量达到最高值。按照最佳提取工艺条件进行验证,3次平行实验的均值为（10.74 ±0.18）mg/g,与理论值差异较小,表明利用响应面法来优化飞龙掌血中总生物碱的最佳提取工艺模型拟合良好,具有一定实用价值。     

A method for 3D detection of symmetry line in asymmetric postures

The methods for symmetry line detection presented in the literature are typically suited to analyse symmetric upright postures, both standing and seated. The proposed method focuses on the symmetry line detection in subjects assuming asymmetric postures in which this line falls far outside the sagittal plane. The proposed approach evaluates the symmetry line by means of an autoregressive process in order to determine the set of planes suited to slice the back coherently with its geometric spatial configuration. The method is analysed assuming the cutaneous marking as reference and it is compared with a previous one, also developed by these authors. Results are analysed and critically discussed.     

Development of a multivariate statistical model to predict peroxisome proliferation in the rat,based on urinary 1H-NMR spectral patterns

A previous report of this work (Ringeissen et al. 2003) described the use of nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical data analysis (MVDA) to identify novel biomarkers of peroxisome proliferation (PP) in Wistar Han rats. Two potential biomarkers of peroxisome proliferation in the rat were described, N-methylnicotinamide (NMN) and N-methyl-4-pyridone-3-carboxamide (4PY). The inference from these results was that the tryptophan-nicotinamide adenine dinucleotide (NAD⁺) pathway was altered in correlation with peroxisome proliferation, a hypothesis subsequently confirmed by TaqMan® analysis of the relevant genes encoding two key enzymes in the pathway, aminocarboxymuconate-semialdehyde decarboxylase (EC 4.1.1.45) and quinolinate phosphoribosyltransferase (EC 2.4.2.19). The objective of the present study was to investigate these data further and identify other metabolites in the NMR spectrum correlating equally with PP. MVDA Partial Least Squares (PLS) models were constructed that provided a better prediction of PP in Wistar Han rats than levels of 4PY and NMN alone. The resulting Wistar Han rat predictive models were then used to predict PP in a test group of Sprague Dawley rats following administration of fenofibrate. The models predicted the presence or absence of PP (above on arbitrary threshold of >2-fold mean control) in all Sprague Dawley rats in the test group.