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Background Currently, whole exome sequencing has been performed as a helpful complement in the prenatal setting in case of fetal anomalies. However, data on its clinical utility remain limited in practice. Herein, we reported our data of fetal exome sequencing in a cohort of 512 trios to evaluate its diagnostic yield. Methods In this retrospective cohort study, the couples performing prenatal exome sequencing were enrolled. Fetal phenotype was classified according to ultrasound and magnetic resonance imaging findings. Genetic variants were analyzed based on a phenotype-driven followed by genotype-driven approach in all trios. Results A total of 97 diagnostic variants in 65 genes were identified in 69 fetuses, with an average detection rate of 13.48%. Skeletal and renal system were the most frequently affected organs referred for whole exome sequencing, with the highest diagnostic rates. Among them, short femur and kidney cyst were the most common phenotype. Fetal growth restriction was the most frequently observed phenotype with a low detection rate (4.3%). Exome sequencing had limited value in isolated increased nuchal translucency and chest anomalies. Conclusions This study provides our data on the detection rate of whole exome sequencing in fetal anomalies in a large cohort. It contributes to the expanding of phenotypic and genotypic spectrum.

Genomic selection (GS) has created a lot of excitement and expectations in the animal‐ and plant‐breeding research communities. In this review, we briefly describe how genomic prediction can be integrated into breeding efforts and point out achievements and areas where more research is needed. Genomic selection provides many opportunities to increase genetic gain in plant breeding per unit time and cost. Early empirical and simulation results are promising, but for GS to deliver genetic gains, careful consideration of the problem of optimal resource allocation is needed. Consideration of the cost‐benefit balance of using markers for each trait and stage of the breeding cycle is needed, moving beyond only focusing on recurrent selection with GS on a few complex traits, using prediction on unphenotyped individuals. With decreasing marker cost, phenotype data is quickly becoming the most valuable asset and marker‐assisted selection strategies should focus on making the most of scarce and expensive phenotypes. It is important to realize that markers can also improve accuracy of selection for phenotyped individuals. Use of markers as an aid to phenotype analysis suggests a number of new strategies in terms of experimental design and multi‐trait models. GS also provides new ways to analyze and deal with genotype by environment interactions. Lastly, we point to some recent results showing that new models are needed to improve predictions particularly with respect to the use of distantly related individuals in the training population.

Background High resolution and high throughput genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks and Long-Short Term Memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. More recently, CNNs have been used for plant classification and phenotyping, using individual static images of the plants. On the other hand, dynamic behavior of the plants as well as their growth has been an important phenotype for plant biologists, and this motivated us to study the potential of LSTMs in encoding these temporal information for the accession classification task, which is useful in automation of plant production and care. Methods In this paper, we propose a CNN-LSTM framework for plant classification of various genotypes. Here, we exploit the power of deep CNNs for automatic joint feature and classifier learning, compared to using hand-crafted features. In addition, we leverage the potential of LSTMs to study the growth of the plants and their dynamic behaviors as important discriminative phenotypes for accession classification. Moreover, we collected a dataset of time-series image sequences of four accessions of Arabidopsis, captured in similar imaging conditions, which could be used as a standard benchmark by researchers in the field. We made this dataset publicly available. Conclusion The results provide evidence of the benefits of our accession classification approach over using traditional hand-crafted image analysis features and other accession classification frameworks. We also demonstrate that utilizing temporal information using LSTMs can further improve the performance of the system. The proposed framework can be used in other applications such as in plant classification given the environment conditions or in distinguishing diseased plants from healthy ones.

A successful pregnancy requires a fine-tuned and highly regulated balance between immune activation and embryonic antigen tolerance. Since the fetus is semi-allogeneic, the maternal immune system should exert tolerant to the fetus while maintaining the defense against infection. The maternal-fetal interface consists of different immune cells, such as decidual natural killer (dNK) cells, macrophages, T cells, dendritic cells, B cells, and NKT cells. The interaction between immune cells, decidual stromal cells, and trophoblasts constitute a vast network of cellular connections. A cellular immunological imbalance may lead to adverse pregnancy outcomes, such as recurrent spontaneous abortion, pre-eclampsia, pre-term birth, intrauterine growth restriction, and infection. Dynamic changes in immune cells at the maternal-fetal interface have not been clearly stated. While many studies have described changes in the proportions of immune cells in the normal maternal-fetus interface during early pregnancy, few studies have assessed the immune cell changes in mid and late pregnancy. Research on pathological pregnancy has provided clues about these dynamic changes, but a deeper understanding of these changes is necessary. This review summarizes information from previous studies, which may lay the foundation for the diagnosis of pathological pregnancy and put forward new ideas for future studies.

Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for crop yield prediction based on environmental data and management practices. The proposed CNN-RNN model, along with other popular methods such as random forest (RF), deep fully connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt (including 13 states) in the United States for years 2016, 2017, and 2018 using historical data. The new model achieved a root-mean-square-error (RMSE) 9% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN has three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information. (2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields.

The 2019 Havemeyer Workshop brought together researchers and clinicians to discuss the latest information on Equine Asthma and provide future research directions. Current clinical and molecular asthma phenotypes and endotypes in humans were discussed and compared to asthma phenotypes in horses. The role of infectious and non-infectious causes of equine asthma, genetic factors and proposed disease pathophysiology were reviewed. Diagnostic limitations were evident by the limited number of tests and biomarkers available to field practitioners. The participants emphasized the need for more accessible, standardized diagnostics that would help identify specific phenotypes and endotypes in order to create more targeted treatments or management strategies. One important outcome of the workshop was the creation of the Equine Asthma Group that will facilitate communication between veterinary practice and research communities through published and easily accessible guidelines and foster research collaboration.

Recurrent spontaneous abortion (RSA) is defined as two or more pregnancy loss, affecting the happiness index of fertility couples. The mechanisms involved in the occurrence of RSA are not clear to date. The primary problem for the maternal immune system is how to establish and maintain the immune tolerance to the semi-allogeneic fetuses. During the pregnancy, decidual macrophages mainly play an important role in the immunologic dialogue. The purpose of this study is to explore decidual macrophages, and to understand whether there is a connection between these cells and RSA by analyzing their phenotypes and functions. Pubmed, Web of Science and Embase were searched. The eligibility criterion for this review was evaluating the literature about the pregnancy and macrophages. Any disagreement between the authors was resolved upon discussion and if required by the judgment of the corresponding author. We summarized the latest views on the phenotype, function and dysfunction of decidual macrophages to illuminate its relationship with RSA.

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

Neurofibromatosis type 1 (NF1) is one of the most common genetic tumor predisposition syndrome, caused by mutations in the NF1. To date, few genotype-phenotype correlations have been discerned in NF1, due to a highly variable clinical presentation. We aimed to study the molecular spectrum of NF1 and genotype-phenotype correlations in a monocentric study cohort of 85 NF1 patients (20 relatives, 65 sporadic cases). Clinical data were collected at the time of the mutation analysis and reviewed for accuracy in this investigation. An internal phenotypic categorization was applied. The 94% of the patients enrolled showed a severe phenotype with at least one systemic complication and a wide range of associated malignancies. Spine deformities were the most common complications in this cohort. We also reported 66 different NF1 mutations, of which 7 are novel mutations. Correlation analysis identified a slight significant inverse correlation between age at diagnosis and delayed acquisition of psychomotor skills with residual multi-domain cognitive impairment. Odds ratio with 95% confidence interval showed a higher prevalence of learning disabilities in patients carrying frameshift mutations. Overall, our results aim to offer an interesting contribution to studies on the genotype–phenotype of NF1 and in genetic management and counselling.

Attention-deficit hyperactivity disorder (ADHD) is associated with later depression and there is considerable genetic overlap between them. This study investigated if ADHD and ADHD genetic liability are causally related to depression using two different methods. First, a longitudinal population cohort design was used to assess the association between childhood ADHD (age 7 years) and recurrent depression in young-adulthood (age 18-25 years) in N = 8310 individuals in the Avon Longitudinal Study of Parents and Children (ALSPAC). Second, two-sample Mendelian randomization (MR) analyses examined relationships between genetic liability for ADHD and depression utilising published Genome-Wide Association Study (GWAS) data. Childhood ADHD was associated with an increased risk of recurrent depression in young-adulthood (OR 1.35, 95% CI 1.05-1.73). MR analyses suggested a causal effect of ADHD genetic liability on major depression (OR 1.21, 95% CI 1.12-1.31). MR findings using a broader definition of depression differed, showing a weak influence on depression (OR 1.07, 95% CI 1.02-1.13). Our findings suggest that ADHD increases the risk of depression later in life and are consistent with a causal effect of ADHD genetic liability on subsequent major depression. However, findings were different for more broadly defined depression.