SoySNP618K array: A high-resolution single nucleotide polymorphism platform as a valuable genomic resource for soybean genetics and breeding.

Innovations in genomics have enabled the development of low-cost, high-resolution, single nucleotide polymorphism (SNP) genotyping arrays that accelerate breeding progress and support basic research in crop science. Here, we developed and validated the SoySNP618K array (618,888 SNPs) for the important crop soybean. The SNPs were selected from whole-genome resequencing data containing 2,214 diverse soybean accessions; 29.34% of the SNPs mapped to genic regions representing 86.85% of the 56,044 annotated high-confidence genes. Identity-by-state analyses of 318 soybeans revealed 17 redundant accessions, highlighting the potential of the SoySNP618K array in supporting gene bank management. The patterns of population stratification and genomic regions enriched through domestication were highly consistent with previous findings based on resequencing data, suggesting that the ascertainment bias in the SoySNP618K array was largely compensated for. Genome-wide association mapping in combination with reported quantitative trait loci enabled fine-mapping of genes known to influence flowering time, E2 and GmPRR3b, and of a new candidate gene, GmVIP5. Moreover, genomic prediction of flowering and maturity time in 502 recombinant inbred lines was highly accurate (>0.65). Thus, the SoySNP618K array is a valuable genomic tool that can be used to address many questions in applied breeding, germplasm management, and basic crop research.

Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data.

BACKGROUND: Feature selection in class-imbalance learning has gained increasing attention in recent years due to the massive growth of high-dimensional class-imbalanced data across many scientific fields. In addition to reducing model complexity and discovering key biomarkers, feature selection is also an effective method of combating overlapping which may arise in such data and become a crucial aspect for determining classification performance. However, ordinary feature selection techniques for classification can not be simply used for addressing class-imbalanced data without any adjustment. Thus, more efficient feature selection technique must be developed for complicated class-imbalanced data, especially in the context of high-dimensionality. RESULTS: We proposed an algorithm called sssHD to achieve stable sparse feature selection applied it to complicated class-imbalanced data. sssHD is based on the Hellinger distance (HD) coupled with sparse regularization techniques. We stated that Hellinger distance is not only class-insensitive but also translation-invariant. Simulation result indicates that HD-based selection algorithm is effective in recognizing key features and control false discoveries for class-imbalance learning. Five gene expression datasets are also employed to test the performance of the sssHD algorithm, and a comparison with several existing selection procedures is performed. The result shows that sssHD is highly competitive in terms of five assessment metrics. In addition, sssHD presents limited differences between performing and not performing re-balance preprocessing. CONCLUSIONS: sssHD is a practical feature selection method for high-dimensional class-imbalanced data, which is simple and can be an alternative for performing feature selection in class-imbalanced data. sssHD can be easily extended by connecting it with different re-balance preprocessing, different sparse regularization structures as well as different classifiers. As such, the algorithm is extremely general and has a wide range of applicability.

Tuning model parameters in class-imbalanced learning with precision-recall curve.

An issue for class-imbalanced learning is what assessment metric should be employed. So far, precision-recall curve (PRC) as a metric is rarely used in practice as compared with its alternative of receiver operating characteristic (ROC). This study investigates the performance of PRC as the evaluating criterion to address the class-imbalanced data and focuses on the comparison of PRC with ROC. The advantages of PRC over ROC on assessing class-imbalanced data are also investigated and tested on our proposed algorithm by tuning the whole model parameters in simulation studies and real data examples. The result shows that PRC is competitive with ROC as performance measurement for handling class-imbalanced data in tuning the model parameters. PRC can be considered as an alternative but effective assessment for preprocessing (such as variable selection) skewed data and building a classifier in class-imbalanced learning.

Response of growth and development of the Pacific oyster (Crassostrea gigas) to thermal discharge from a nuclear power plant.

BACKGROUND: During electricity generation of nuclear power plant, heat energy cannot be completely converted into electrical energy, and a part of it is lost in the form of thermal discharge into the environment. The thermal discharge is harmful to flora and fauna leading to environmental deterioration, biological diversity decline, and even biological extinction. RESULTS: The present study investigated the influence of thermal discharge from a nuclear power plant on the growth and development of Pacific oyster Crassostrea gigas which is widely used as bio indicator to monitor environmental changes. The growth of soft part and the gonad development of oysters were inhibited due to thermal discharge. During winter season, temperature elevation caused by thermal discharge promoted the growth of oyster shells. During summer season, the growth rate of oysters in thermal discharge area was significantly lower than that of the natural sea area. CONCLUSIONS: The results of this study provided a better understanding of assessing the impact of thermal discharge on the marine ecological environment and mariculture industry. It also provided a scientific basis for defining a safe zone for aquaculture in the vicinity of nuclear power plants.

Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance.

Feature screening is an important and challenging topic in current class-imbalance learning. Most of the existing feature screening algorithms in class-imbalance learning are based on filtering techniques. However, the variable rankings obtained by various filtering techniques are generally different, and this inconsistency among different variable ranking methods is usually ignored in practice. To address this problem, we propose a simple strategy called rank aggregation with re-balance (RAR) for finding key variables from class-imbalanced data. RAR fuses each rank to generate a synthetic rank that takes every ranking into account. The class-imbalanced data are modified via different re-sampling procedures, and RAR is performed in this balanced situation. Five class-imbalanced real datasets and their re-balanced ones are employed to test the RAR's performance, and RAR is compared with several popular feature screening methods. The result shows that RAR is highly competitive and almost better than single filtering screening in terms of several assessing metrics. Performing re-balanced pretreatment is hugely effective in rank aggregation when the data are class-imbalanced.

A potential association of RNF219-AS1 with ADHD: Evidence from categorical analysis of clinical phenotypes and from quantitative exploration of executive function and white matter microstructure endophenotypes.

AIMS: Attention-deficit/hyperactivity disorder (ADHD) is a neuropsychiatric disorder of substantial heritability, yet emerging evidence suggests that key risk variants might reside in the noncoding regions of the genome. Our study explored the association of lncRNAs (long noncoding RNAs) with ADHD as represented at three different phenotypic levels guided by the Research Domain Criteria (RDoC) framework: (i) ADHD caseness and symptom dimension, (ii) executive functions as functional endophenotype, and (iii) potential genetic influence on white matter architecture as brain structural endophenotype. METHODS: Genotype data of 107 tag single nucleotide polymorphisms (SNP) from 10 candidate lncRNAs were analyzed in 1040 children with ADHD and 630 controls of Chinese Han descent. Executive functions including inhibition and set-shifting were assessed by STROOP and trail making tests, respectively. Imaging genetic analyses were performed in a subgroup of 33 children with ADHD and 55 controls using fractional anisotropy (FA). RESULTS: One SNP rs3908461 polymorphism in RNF219-AS1 was found to be significantly associated with ADHD caseness: with C-allele detected as the risk genotype in the allelic model (P = 8.607E-05) and dominant genotypic model (P = 9.628E-05). Nominal genotypic effects on inhibition (p = 0.020) and set-shifting (p = 0.046) were detected. While no direct effect on ADHD core symptoms was detected, mediation analysis suggested that SNP rs3908461 potentially exerted an indirect effect through inhibition function [B = 0.21 (SE = 0.12), 95% CI = 0.02-0.49]. Imaging genetic analyses detected significant associations between rs3908461 genotypes and FA values in corpus callosum, left superior longitudinal fasciculus, left posterior limb of internal capsule, left posterior thalamic radiate (include optic radiation), and the left anterior corona radiate (P FWE corrected  < 0.05). CONCLUSION: Our present study examined the potential roles of lncRNA in genetic etiological of ADHD and provided preliminary evidence in support of the potential RNF219-AS1 involvement in the pathophysiology of ADHD in line with the RDoC framework.

A Comparison of Sparse Partial Least Squares and Elastic Net in Wavelength Selection on NIR Spectroscopy Data.

Elastic net (Enet) and sparse partial least squares (SPLS) are frequently employed for wavelength selection and model calibration in analysis of near infrared spectroscopy data. Enet and SPLS can perform variable selection and model calibration simultaneously. And they also tend to select wavelength intervals rather than individual wavelengths when the predictors are multicollinear. In this paper, we focus on comparison of Enet and SPLS in interval wavelength selection and model calibration for near infrared spectroscopy data. The results from both simulation and real spectroscopy data show that Enet method tends to select less predictors as key variables than SPLS; thus it gets more parsimony model and brings advantages for model interpretation. SPLS can obtain much lower mean square of prediction error (MSE) than Enet. So SPLS is more suitable when the attention is to get better model fitting accuracy. The above conclusion is still held when coming to performing the strongly correlated NIR spectroscopy data whose predictors present group structures, Enet exhibits more sparse property than SPLS, and the selected predictors (wavelengths) are segmentally successive.

Bone formation in subcutaneous pocket after bone flap preservation.

Residual periosteum developed periosteal bone formation in the pocket 10 years after cranioplasty, lumpectomy was conducted on the left lower abdomen under local anesthesia. Pathological sections revealed abundant osteocytes and mature bone matrix, and confirmed the bone formation on the residual periosteum.

Early care of acute hyperglycemia benefits the outcome of traumatic brain injury in rats.

OBJECTIVE: Previous animal studies showed contradictory clinical observations on whether acute hyperglycemia contributes to poor outcome in traumatic brain injury (TBI). Herein, we tried to clarify this issue. METHODS: Striking with depths of 3.0-4.25mm at right occipitoparietal brain region and with depth of 3.75mm at right/left occipitoparietal or right/left frontoparietal brain region were performed, respectively. Blood glucose and insulin levels were traced every four hours from 1 to 72h after striking. HOMA2-%S and HOMA2-%ß were calculated. Modified neurological severity scores (mNSS) were used to evaluate neurological deficit within 72h. RESULTS: Striking with depths of 3.5-4.25mm induced increase in blood glucose lasting up to 24h after striking. The levels of blood glucose after striking with depths of 3.75-4.25mm were significantly different from that of striking with the depth of 3.0mm. Striking with depth of 3.75mm at right/left occipitoparietal region induced higher blood glucose in 24h than that at right/left frontoparietal region. Insulin concentration increased slowly during 72h after striking. Striking also induced decrease in insulin sensitivity and secretion lasting 72h. Evaluation of mNSS revealed that severe striking (beyond 3.75mm) worsened nerve function than slight striking (<3.0mm). Intervention of acute hyperglycemia could decrease the mNSS from 2 to 7 days after TBI. CONCLUSION: Our results suggested that only severe TBI could induce acute hyperglycemia by itself, and early care of acute hyperglycemia could benefit the outcome of TBI patients.

Elastic net grouping variable selection combined with partial least squares regression (EN-PLSR) for the analysis of strongly multi-collinear spectroscopic data.

In this paper a novel wavelength region selection algorithm, called elastic net grouping variable selection combined with partial least squares regression (EN-PLSR), is proposed for multi-component spectral data analysis. The EN-PLSR algorithm can automatically select successive strongly correlated prediction variable groups related to the response variable using two steps. First, a portion of the correlated predictors are selected and divided into subgroups by means of the grouping effect of elastic net estimation. Then, a recursive leave-one-group-out strategy is employed to further shrink the variable groups in terms of the root mean square error of cross-validation (RMSECV) criterion. The performance of the algorithm with real near-infrared (NIR) spectroscopic data sets shows that the EN-PLSR algorithm is competitive with full-spectrum PLS and moving window partial least squares (MWPLS) regression methods and it is suitable for use with strongly correlated spectroscopic data.