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LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize lines. LightGBM exhibits superior performance in terms of prediction precision, model stability, and computing efficiency through a series of benchmark tests. We also assess the factors that are essential to ensure the best performance of genomic selection prediction by taking complex scenarios in crop hybrid breeding into account. LightGBM has been implemented as a toolbox, CropGBM, encompassing multiple novel functions and analytical modules to facilitate genomically designed breeding in crops.

Genomic selection (GS) is a promising breeding strategy by which the phenotypes of plant individuals are usually predicted based on genome-wide markers of genotypes. In this study, we present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypes when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional genotypic data. We used a large GS dataset to train DeepGS and compared its performance with other methods. The experimental results indicate that DeepGS can be used as a complement to the commonly used RR-BLUP in the prediction of phenotypes from genotypes. The complementarity between DeepGS and RR-BLUP can be utilized using an ensemble learning approach for more accurately selecting individuals with high phenotypic values, even for the absence of outlier individuals and subsets of genotypic markers. The source codes of DeepGS and the ensemble learning approach have been packaged into Docker images for facilitating their applications in different GS programs.

Background The treatment of large bone defects in lower limbs is a serious challenge for orthopedic surgeons and patients. The bone transport technique using the Ilizarov method has become the main treatment option for the reconstruction of bone defect. However, inevitable difficulties and complications related to bone transport technique have been reported by many studies. The purpose of this study was to evaluate the effectiveness and complications of bone transport technique using Ilizarov method in the treatment of bone defect of lower extremity. Methods The study was conducted on 282 patients who underwent bone transport procedures using Ilizarov method at our institution from January 2007 to June 2017. Patient’s demographic data, complications and clinical outcomes at minimum of 2 years follow-up were collected and retrospectively analyzed. All difficulties that related to bone transport were documented according to Paley’s classification. The clinical outcomes were evaluated using Association for the Study and Application of the Method of Ilizarov criteria (ASAMI) at last clinical visit. Results There were 243 male and 39 females with a mean age of 40 years (range 18–65 years). The mean defect was 6.56 ± 2.15 cm, whereas single level transport in 221 cases and double level transport in 61 cases. There were 189 problems, 166 obstacles and 406 complications (257 minor and 149 major complications), and the average complication rate per patients consists of 0.91 minor and 0.53 major complications. The top five complications were pin-site infection (65.96%), axial deviation (40.78%), joint stiffness (23.76%), soft tissue incarceration (22.34%) and delayed union of the docking site (13.48%).The ASAMI bony result was excellent in 233 patients, good in 32, fair in 5 and poor in 12. The ASAMI functional result was excellent in 136 patients, good in 88, fair in 47, poor in 11. Conclusion Bone transport is a reliable method for reconstruction of bone defects in the femur and tibia. Awareness of predictable complications is beneficial to prevent or early detection of the expected complication which can improve the risk-benefit balance.

Background In maize hybrid breeding, complementary pools of parental lines with reshuffled genetic variants are established for superior hybrid performance. To comprehensively decipher the genetics of heterosis, we present a new design of multiple linked F1 populations with 42,840 F1 maize hybrids, generated by crossing a synthetic population of 1428 maternal lines with 30 elite testers from diverse genetic backgrounds and phenotyped for agronomic traits. Results We show that, although yield heterosis is correlated with the widespread, minor-effect epistatic QTLs, it may be resulted from a few major-effect additive and dominant QTLs in early developmental stages. Floral transition is probably one critical stage for heterosis formation, in which epistatic QTLs are activated by paternal contributions of alleles that counteract the recessive, deleterious maternal alleles. These deleterious alleles, while rare, epistatically repress other favorable QTLs. We demonstrate this with one example, showing that Brachytic2 represses the Ubiquitin3 locus in the maternal lines; in hybrids, the paternal allele alleviates this repression, which in turn recovers the height of the plant and enhances the weight of the ear. Finally, we propose a molecular design breeding by manipulating key genes underlying the transition from vegetative-to-reproductive growth. Conclusion The new population design is used to dissect the genetic basis of heterosis which accelerates maize molecular design breeding by diminishing deleterious epistatic interactions.

In criminal investigations, distinguishing between impact spatters and fly spots presents a challenge due to their morphological similarities. Traditional methods of bloodstain pattern analysis (BPA) rely significantly on the expertise of professional examiners, which can result in limitations including low identification efficiency, high misjudgment rates, and susceptibility to external disturbances. To enhance the accuracy and scientific rigor of identifying impact spatters and fly spots, this study employed artificial intelligence techniques in image recognition and transfer learning. Two types of bloodstains obtained from simulation experiments were utilized as datasets, and a pre-trained neural network, ResNet-18, was employed for feature extraction. The original fully connected layer was replaced, and a new fully connected layer with a dimensionality of 2 was introduced to fulfil the task requirements. The results demonstrate that the transfer learning network model, based on ResNet-18, achieved a maximum accuracy of 93 % in morphologically identifying impact spatters and fly spots. The objective is to assist crime scene investigators and BPA analysts to identify bloodstains at homicide scenes conveniently, rapidly and accurately, thereby furnishing scientific evidence for scene reconstruction and advancing BPA toward intelligent practices. •Objective bloodstain classification: Eliminates experience bias.•Reproducible ResNet-18 model: Adaptable, open-source.•Efficient image processing: Improves accuracy, speed.

Abstract Epigenetic modifications play important roles in genome evolution and innovation. However, most analyses have focused on the evolutionary role of DNA modifications, and little is understood about the influence of posttranscriptional RNA modifications on genome evolution. To explore the evolutionary significance of RNA modifications, we generated transcriptome-wide profiles of N6-methyladenosine (m6A), the most prevalent internal modification of mRNA, for 13 representative plant species spanning over half a billion years of evolution. These data reveal the evolutionary conservation and divergence of m6A methylomes in plants, uncover the preference of m6A modifications on ancient orthologous genes, and demonstrate less m6A divergence between orthologous gene pairs with earlier evolutionary origins. Further investigation revealed that the evolutionary divergence of m6A modifications is related to sequence variation between homologs from whole-genome duplication and gene family expansion from local-genome duplication. Unexpectedly, a significant negative correlation was found between the retention ratio of m6A modifications and the number of family members. Moreover, the divergence of m6A modifications is accompanied by variation in the expression level and translation efficiency of duplicated genes from whole- and local-genome duplication. Our work reveals new insights into evolutionary patterns of m6A methylomes in plant species and their implications, and provides a resource of plant m6A profiles for further studies of m6A regulation and function in an evolutionary context.

Previous efforts on data-based reconstruction focused on complex networks with pairwise or two-body interactions. There is a growing interest in networks with higher-order or many-body interactions, raising the need to reconstruct such networks based on observational data. We develop a general framework combining statistical inference and expectation maximization to fully reconstruct 2-simplicial complexes with two- and three-body interactions based on binary time-series data from two types of discrete-state dynamics. We further articulate a two-step scheme to improve the reconstruction accuracy while significantly reducing the computational load. Through synthetic and real-world 2-simplicial complexes, we validate the framework by showing that all the connections can be faithfully identified and the full topology of the 2-simplicial complexes can be inferred. The effects of noisy data or stochastic disturbance are studied, demonstrating the robustness of the proposed framework. Data-driven recovery of topology is challenging for networks beyond pairwise interactions. The authors propose a framework to reconstruct complex networks with higher-order interactions from time series, focusing on networks with 2-simplexes where social contagion and Ising dynamics generate binary data.

Even though the perovskite solar cell has been so popular for its skyrocketing power conversion efficiency, its further development is still roadblocked by its overall performance, in particular long-term stability, large-area fabrication and stable module efficiency. In essence, the soft component and ionic–electronic nature of metal halide perovskites usually chaperonage large number of anion vacancy defects that act as recombination centers to decrease both the photovoltaic efficiency and operational stability. Herein, we report a one-stone-for-two-birds strategy in which both anion-fixation and associated undercoordinated-Pb passivation are in situ achieved during crystallization by using a single amidino-based ligand, namely 3-amidinopyridine, for metal-halide perovskite to overcome above challenges. The resultant devices attain a power conversion efficiency as high as 25.3% (certified at 24.8%) with substantially improved stability. Moreover, the device without encapsulation retained 92% of its initial efficiency after 5000 h exposure in ambient and the device with encapsulation retained 95% of its initial efficiency after >500 h working at the maximum power point under continuous light irradiation in ambient. It is expected this one-stone-for-two-birds strategy will benefit large-area fabrication that desires for simplicity. Long-term stability and stable efficiency are essential for large-area fabrication of perovskite solar cells. Here, the authors achieve in situ anion-fixation and undercoordinated-Pb passivation using amidino-based ligand, realizing maximum power conversion efficiency of 25.3% with T95 over 500 h.

Crops are often subjected to periods of drought stress during their life cycle. However, how stress response mechanisms contribute to the crosstalk between stress signaling pathways and developmental signaling pathways is still unknown. We built a gene co-expression network from a spatio-temporal transcriptomic map of the drought stress response in maize ( Zea mays ), profiled from three tissues and four developmental stages and characterized hub genes associated with duplication events, selection, and regulatory networks. Co-expression analysis grouped drought-response genes into ten modules, covering 844 highly connected genes (hub genes). Of these, 15.4% hub genes had diverged by whole-genome duplication events and 2.5% might then have been selected during natural domestication and artificial improvement processes, successively. We identified key transcription factor hubs in a transcriptional regulatory network, which may function as a crosstalk mechanism between drought stress and developmental signalling pathways in maize. Understanding the evolutionary biases that have evolved to enhance drought adaptation lays the foundation for further dissection of crosstalk between stress signalling pathways and developmental signalling pathways in maize, towards molecular design of new cultivars with desirable yield and greater stress tolerance.