Clustering-Driven DGS-Based Micro-Doppler Feature Extraction for Automatic Dynamic Hand Gesture Recognition.

We propose in this work a dynamic group sparsity (DGS) based time-frequency feature extraction method for dynamic hand gesture recognition (HGR) using millimeter-wave radar sensors. Micro-Doppler signatures of hand gestures show both sparse and structured characteristics in time-frequency domain, but previous study only focus on sparsity. We firstly introduce the structured prior when modeling the micro-Doppler signatures in this work to further enhance the features of hand gestures. The time-frequency distributions of dynamic hand gestures are first modeled using a dynamic group sparse model. A DGS-Subspace Pursuit (DGS-SP) algorithm is then utilized to extract the corresponding features. Finally, the support vector machine (SVM) classifier is employed to realize the dynamic HGR based on the extracted group sparse micro-Doppler features. The experiment shows that the proposed method achieved 3.3% recognition accuracy improvement over the sparsity-based method and has a better recognition accuracy than CNN based method in small dataset.

Multi-Scale Capsule Attention Network and Joint Distributed Optimal Transport for Bearing Fault Diagnosis under Different Working Loads.

In recent years, intelligent fault diagnosis methods based on deep learning have developed rapidly. However, most of the existing work performs well under the assumption that training and testing samples are collected from the same distribution, and the performance drops sharply when the data distribution changes. For rolling bearings, the data distribution will change when the load and speed change. In this article, to improve fault diagnosis accuracy and anti-noise ability under different working loads, a transfer learning method based on multi-scale capsule attention network and joint distributed optimal transport (MSCAN-JDOT) is proposed for bearing fault diagnosis under different loads. Because multi-scale capsule attention networks can improve feature expression ability and anti-noise performance, the fault data can be better expressed. Using the domain adaptation ability of joint distribution optimal transport, the feature distribution of fault data under different loads is aligned, and domain-invariant features are learned. Through experiments that investigate bearings fault diagnosis under different loads, the effectiveness of MSCAN-JDOT is verified; the fault diagnosis accuracy is higher than that of other methods. In addition, fault diagnosis experiment is carried out in different noise environments to demonstrate MSCAN-JDOT, which achieves a better anti-noise ability than other transfer learning methods.

iEnhancer-GAN: A Deep Learning Framework in Combination with Word Embedding and Sequence Generative Adversarial Net to Identify Enhancers and Their Strength.

As critical components of DNA, enhancers can efficiently and specifically manipulate the spatial and temporal regulation of gene transcription. Malfunction or dysregulation of enhancers is implicated in a slew of human pathology. Therefore, identifying enhancers and their strength may provide insights into the molecular mechanisms of gene transcription and facilitate the discovery of candidate drug targets. In this paper, a new enhancer and its strength predictor, iEnhancer-GAN, is proposed based on a deep learning framework in combination with the word embedding and sequence generative adversarial net (Seq-GAN). Considering the relatively small training dataset, the Seq-GAN is designed to generate artificial sequences. Given that each functional element in DNA sequences is analogous to a "word" in linguistics, the word segmentation methods are proposed to divide DNA sequences into "words", and the skip-gram model is employed to transform the "words" into digital vectors. In view of the powerful ability to extract high-level abstraction features, a convolutional neural network (CNN) architecture is constructed to perform the identification tasks, and the word vectors of DNA sequences are vertically concatenated to form the embedding matrices as the input of the CNN. Experimental results demonstrate the effectiveness of the Seq-GAN to expand the training dataset, the possibility of applying word segmentation methods to extract "words" from DNA sequences, the feasibility of implementing the skip-gram model to encode DNA sequences, and the powerful prediction ability of the CNN. Compared with other state-of-the-art methods on the training dataset and independent test dataset, the proposed method achieves a significantly improved overall performance. It is anticipated that the proposed method has a certain promotion effect on enhancer related fields.

Ethanol and Cannabinoids Regulate Zebrafish GABAergic Neuron Development and Behavior in a Sonic Hedgehog and Fibroblast Growth Factor-Dependent Mechanism.

BACKGROUND: Ethanol (EtOH) has diverse effects on nervous system development, which includes development and survival of GABAergic neurons in a sonic hedgehog (Shh) and fibroblast growth factor (Fgf)-dependent mechanism. Cannabinoids also function as inhibitors of Shh signaling, raising the possibility that EtOH and cannabinoids may interact to broadly disrupt neuronal function during brain development. METHODS: Zebrafish embryos were exposed to a range of EtOH and/or cannabinoid receptor 1 (CB1R) agonist concentrations at specific developmental stages, in the absence or presence of morpholino oligonucleotides that disrupt shh expression. In situ hybridization was employed to analyze glutamic acid decarboxylase (gad1) gene expression as a marker of GABAergic neuron differentiation, and zebrafish behavior was analyzed using the novel tank diving test as a measure of risk-taking behavior. RESULTS: Combined acute subthreshold EtOH and CB1R agonist exposure results in a marked reduction in gad1 mRNA expression in zebrafish forebrain. Consistent with the EtOH and cannabinoid effects on Shh signaling, fgf8 mRNA overexpression rescues the EtOH- and cannabinoid-induced decrease in gad1 gene expression and also prevents the changes in behavior induced by EtOH and cannabinoids. CONCLUSIONS: These studies provide evidence that forebrain GABAergic neuron development and zebrafish risk-taking behavior are sensitive to both EtOH and cannabinoid exposure in a Shh- and Fgf-dependent mechanism, and provide additional evidence that a signaling pathway involving Shh and Fgf crosstalk is a critical target of EtOH and cannabinoids in FASD.

Exogenous Melatonin Modulates the Physiological and Biochemical Mechanisms of Drought Tolerance in Tartary Buckwheat (Fagopyrum tataricum (L.) Gaertn).

Tartary buckwheat is one of the nutritious minor cereals and is grown in high-cold mountainous areas of arid and semi-arid zones where drought is a common phenomenon, potentially reducing the growth and yield. Melatonin, which is an amphiphilic low molecular weight compound, has been proven to exert significant effects in plants, under abiotic stresses, but its role in the Tartary buckwheat under drought stress remains unexplored. We evaluated the influence of melatonin supplementation on plant morphology and different physiological activities, to enhance tolerance to posed drought stress by scavenging reactive oxygen species (ROS) and alleviating lipid peroxidation. Drought stress decreased the plant growth and biomass production compared to the control. Drought also decreased Chl a, b, and the Fv/Fm ratio by 54%, 70%, and 8%, respectively, which was associated with the disorganized stomatal properties. Under drought stress, H2O2, O2•-, and malondialdehyde (MDA) contents increased by 2.30, 2.43, and 2.22-folds, respectively, which caused oxidative stress. In contrast, proline and soluble sugar content were increased by 84% and 39%, respectively. However, exogenous melatonin (100 µM) could improve plant growth by preventing ROS-induced oxidative damage by increasing photosynthesis, enzymatic antioxidants (superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase), secondary metabolites like phenylalanine ammonialyase, phenolics, and flavonoids, total antioxidant scavenging (free radical DPPH scavenging), and maintaining relative water content and osmoregulation substances under water stress. Therefore, our study suggested that exogenous melatonin could accelerate drought resistance by enhancing photosynthesis and antioxidant defense in Tartary buckwheat plants.

Real-Time Photometric Calibrated Monocular Direct Visual SLAM.

To solve the illumination sensitivity problems of mobile ground equipment, an enhanced visual SLAM algorithm based on the sparse direct method was proposed in this paper. Firstly, the vignette and response functions of the input sequences were optimized based on the photometric formation of the camera. Secondly, the Shi-Tomasi corners of the input sequence were tracked, and optimization equations were established using the pixel tracking of sparse direct visual odometry (VO). Thirdly, the Levenberg-Marquardt (L-M) method was applied to solve the joint optimization equation, and the photometric calibration parameters in the VO were updated to realize the real-time dynamic compensation of the exposure of the input sequences, which reduced the effects of the light variations on SLAM's (simultaneous localization and mapping) accuracy and robustness. Finally, a Shi-Tomasi corner filtered strategy was designed to reduce the computational complexity of the proposed algorithm, and the loop closure detection was realized based on the oriented FAST and rotated BRIEF (ORB) features. The proposed algorithm was tested using TUM, KITTI, EuRoC, and an actual environment, and the experimental results show that the positioning and mapping performance of the proposed algorithm is promising.

Nutrient limitation and enzymolysis of phosphorus in Meiliang Bay, Lake Taihu, during algal blooms.

In this study, algal growth potential tests were performed in water samples collected from six sampling sites in Meiliang Bay, Lake Taihu. The potential release of soluble reactive phosphorus (SRP) by enzymatic hydrolysis of enzymatically hydrolyzable phosphorus (EHP) was simultaneously evaluated. Results show that all studied regions were in highly eutrophic states, with additional nitrogen (N) or phosphorus (P) inputs, inducing negligible further increase in algal growth. EHP in water could be rapidly transformed into SRP, further supporting the proliferation of algal blooms. The shortest EHP mineralization time was calculated as 69 minutes; therefore, limiting specific nutrient inputs alone in extremely eutrophic lakes can have a limited effect on suppressing the proliferation of algal blooms. Methods to establish a suitable environmental fate for excessive nitrogen and phosphorus nutrients may be more effective and provide more significant results. PRACTITIONER POINTS: N and P were no longer serving as the limiting factors in Meiliang Bay. Enzymatically hydrolysable phosphorus could be hydrolyzed into soluble reactive phosphorus in a very short period during algal blooms. Both enzymatically hydrolysable phosphorus and soluble reactive phosphorus are required to be curbed in practical eutrophication control.

Comparison of molecular marker expression in early zebrafish brain development following chronic ethanol or morpholino treatment.

This study was undertaken to ascertain whether defined markers of early zebrafish brain development are affected by chronic ethanol exposure or morpholino knockdown of agrin, sonic hedgehog, retinoic acid, and fibroblast growth factors, four signaling molecules that are suggested to be ethanol sensitive. Zebrafish embryos were exposed to 2% ethanol from 6 to 24 hpf or injected with agrin, shha, aldh1a3, or fgf8a morpholinos. In situ hybridization was employed to analyze otx2, pax6a, epha4a, krx20, pax2a, fgf8a, wnt1, and eng2b expression during early brain development. Our results showed that pax6a mRNA expression was decreased in eye, forebrain, and hindbrain of both chronic ethanol exposed and select MO treatments. Epha4a expression in rhombomere R1 boundary was decreased in chronic ethanol exposure and aldh1a3 morphants, lost in fgf8a morphants, but largely unaffected in agrin and shha morphants. Ectopic pax6a and epha4a expression in midbrain was only found in fgf8a morphants. These results suggest that while chronic ethanol induces obvious morphological change in brain architecture, many molecular markers of these brain structures are relatively unaffected by ethanol exposure.

Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes.

BACKGROUND: Aptamer-protein interacting pairs play a variety of physiological functions and therapeutic potentials in organisms. Rapidly and effectively predicting aptamer-protein interacting pairs is significant to design aptamers binding to certain interested proteins, which will give insight into understanding mechanisms of aptamer-protein interacting pairs and developing aptamer-based therapies. RESULTS: In this study, an ensemble method is presented to predict aptamer-protein interacting pairs with hybrid features. The features for aptamers are extracted from Pseudo K-tuple Nucleotide Composition (PseKNC) while the features for proteins incorporate Discrete Cosine Transformation (DCT), disorder information, and bi-gram Position Specific Scoring Matrix (PSSM). We investigate predictive capabilities of various feature spaces. The proposed ensemble method obtains the best performance with Youden's Index of 0.380, using the hybrid feature space of PseKNC, DCT, bi-gram PSSM, and disorder information by 10-fold cross validation. The Relief-Incremental Feature Selection (IFS) method is adopted to obtain the optimal feature set. Based on the optimal feature set, the proposed method achieves a balanced performance with a sensitivity of 0.753 and a specificity of 0.725 on the training dataset, which indicates that this method can solve the imbalanced data problem effectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous study with a sensitivity of 0.738 and a Youden's Index of 0.451. CONCLUSIONS: These results suggest that the proposed method can be a potential candidate for aptamer-protein interacting pair prediction, which may contribute to finding novel aptamer-protein interacting pairs and understanding the relationship between aptamers and proteins.

Using the SMOTE technique and hybrid features to predict the types of ion channel-targeted conotoxins.

Conotoxins targeting different ion channels play distinct physiological functions and therapeutic potentials in organisms. Accurate identification of types of ion channel-targeted conotoxins will provide significant clues to reveal the physiological mechanism and pharmacological therapeutic potential of conotoxins. In this study, a random forest based predictor called ICTCPred for the types of ion channel-targeted conotoxin prediction is proposed with hybrid features incorporating CTD (Composition, Transition, and Distribution), g-Gap DC (g-Gap Dipeptide Composition), PP (Physicochemical Properties), and SSI (Secondary Structure Information). To deal with the imbalanced benchmark dataset, the SMOTE Technique (Synthetic Minority Over-sampling Technique) is applied. Based on the above-mentioned individual feature spaces, the average accuracy of ICTCPred lies in the range of 0.729-0.886, indicating the discriminative power of these features. In addition, ICTCPred yields the highest average accuracy of 0.895 using the hybrid feature space of CTD, g-Gap DC, PP and SSI. The Relief-IFS (Incremental Feature Selection) method is adopted to further improve the prediction performance of ICTCPred. Based on the training dataset, ICTCPred achieves satisfactory performance with an average accuracy of 0.910. To evaluate the prediction performance objectively, ICTCPred is compared with previous studies on the same independent testing dataset. Encouragingly, our proposed method performs better than previous studies to identify types of ion channel-targeted conotoxins, with the highest sensitivity of 0.919 for Na(+)-targeted conotoxins, the highest sensitivity of 1 for K(+)-targeted conotoxins, and the highest sensitivity of 1 for Ca(2+)-targeted conotoxins. It is anticipated that ICTCPred can be a potential candidate for the ion channel-targeted conotoxin prediction.

A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data.

The Golgi Apparatus (GA) is a major collection and dispatch station for numerous proteins destined for secretion, plasma membranes and lysosomes. The dysfunction of GA proteins can result in neurodegenerative diseases. Therefore, accurate identification of protein subGolgi localizations may assist in drug development and understanding the mechanisms of the GA involved in various cellular processes. In this paper, a new computational method is proposed for identifying cis-Golgi proteins from trans-Golgi proteins. Based on the concept of Common Spatial Patterns (CSP), a novel feature extraction technique is developed to extract evolutionary information from protein sequences. To deal with the imbalanced benchmark dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is adopted. A feature selection method called Random Forest-Recursive Feature Elimination (RF-RFE) is employed to search the optimal features from the CSP based features and g-gap dipeptide composition. Based on the optimal features, a Random Forest (RF) module is used to distinguish cis-Golgi proteins from trans-Golgi proteins. Through the jackknife cross-validation, the proposed method achieves a promising performance with a sensitivity of 0.889, a specificity of 0.880, an accuracy of 0.885, and a Matthew's Correlation Coefficient (MCC) of 0.765, which remarkably outperforms previous methods. Moreover, when tested on a common independent dataset, our method also achieves a significantly improved performance. These results highlight the promising performance of the proposed method to identify Golgi-resident protein types. Furthermore, the CSP based feature extraction method may provide guidelines for protein function predictions.

Analysis of crosstalk between retinoic acid and sonic hedgehog pathways following ethanol exposure in embryonic zebrafish.

BACKGROUND: Ethanol is a teratogen affecting numerous regions of the developing nervous system. The present study was undertaken to ascertain whether ethanol independently disrupts distinct signaling pathways or rather disrupts interactive pathways that regulate development of ethanol-sensitive tissues. METHODS: Zebrafish embryos were exposed to ethanol in the absence or presence of aldh1a3 or Shh morpholino oligonucleotides (MOs), which disrupt retinoic acid (RA) or sonic hedgehog (Shh) function, respectively. Morphological analysis of ocular or midbrain-hindbrain boundary (MHB) development was conducted, and the ability to rescue ethanol and MO-induced phenotypes was assessed. In situ hybridization was used to analyze Pax6a expression during ocular development. RESULTS: Chronic ethanol exposure, or combined ethanol and MO treatment, results in perturbed MHB formation and microphthalmia. While RA can rescue the MHB phenotype following ethanol combined with either MO, Shh mRNA is unable to rescue the disrupted MHB with combined ethanol and aldh1a3 MO treatment. RA also is unable to rescue microphthalmia induced by ethanol and Shh MO. CONCLUSION: These studies demonstrate that while reduction of either RA or Shh signaling produces the same disruption of MHB or ocular development, that can be phenocopied using ethanol combined with either MO, RA overexpression can only rescue disrupted MHB, but not microphthalmia, in combined subthreshold Shh MO and ethanol. Our data suggest that MHB development may involve crosstalk between RA and Shh signaling, while ocular development depends on RA and Shh signaling that both are targets of ethanol in fetal alcohol spectrum disorders but do not depend on a mechanism involving crosstalk.

An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors.

Antifreeze proteins (AFPs) play a pivotal role in the antifreeze effect of overwintering organisms. They have a wide range of applications in numerous fields, such as improving the production of crops and the quality of frozen foods. Accurate identification of AFPs may provide important clues to decipher the underlying mechanisms of AFPs in ice-binding and to facilitate the selection of the most appropriate AFPs for several applications. Based on an ensemble learning technique, this study proposes an AFP identification system called AFP-Ensemble. In this system, random forest classifiers are trained by different training subsets and then aggregated into a consensus classifier by majority voting. The resulting predictor yields a sensitivity of 0.892, a specificity of 0.940, an accuracy of 0.938 and a balanced accuracy of 0.916 on an independent dataset, which are far better than the results obtained by previous methods. These results reveal that AFP-Ensemble is an effective and promising predictor for large-scale determination of AFPs. The detailed feature analysis in this study may give useful insights into the molecular mechanisms of AFP-ice interactions and provide guidance for the related experimental validation. A web server has been designed to implement the proposed method.

An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.

Bacteriophage virion proteins and non-virion proteins have distinct functions in biological processes, such as specificity determination for host bacteria, bacteriophage replication and transcription. Accurate identification of bacteriophage virion proteins from bacteriophage protein sequences is significant to understand the complex virulence mechanism in host bacteria and the influence of bacteriophages on the development of antibacterial drugs. In this study, an ensemble method for bacteriophage virion protein prediction from bacteriophage protein sequences is put forward with hybrid feature spaces incorporating CTD (composition, transition and distribution), bi-profile Bayes, PseAAC (pseudo-amino acid composition) and PSSM (position-specific scoring matrix). When performing on the training dataset 10-fold cross-validation, the presented method achieves a satisfactory prediction result with a sensitivity of 0.870, a specificity of 0.830, an accuracy of 0.850 and Matthew's correlation coefficient (MCC) of 0.701, respectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous studies with a sensitivity of 0.853, a specificity of 0.815, an accuracy of 0.831 and MCC of 0.662 on the independent testing dataset. These results suggest that the proposed method can be a potential candidate for bacteriophage virion protein prediction, which may provide a useful tool to find novel antibacterial drugs and to understand the relationship between bacteriophage and host bacteria. For the convenience of the vast majority of experimental Int. J. Mol. Sci. 2015, 16,21735 scientists, a user-friendly and publicly-accessible web-server for the proposed ensemble method is established.

A fast elitism Gaussian estimation of distribution algorithm and application for PID optimization.

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.

A fast and genotype-independent in planta Agrobacterium-mediated transformation method for soybean.

Efficient genotype-independent transformation and genome editing are highly desirable for plant biotechnology research and product development efforts. We have developed a novel approach to enable fast, high-throughput, and genotype-flexible Agrobacterium-mediated transformation using the important crop soybean as a test system. This new method is called GiFT (genotype-independent fast transformation) and involves only a few simple steps. The method uses germinated seeds as explants, and DNA delivery is achieved through Agrobacterium infection of wounded explants as in conventional in vitro-based methods. Following infection, the wounded explants are incubated in liquid medium with a sublethal level of selection and then transplanted directly into soil. The transplanted seedlings are then selected with herbicide spray for 3 weeks. The time required from initiation to fully established healthy T0 transgenic events is about 35 days. The GiFT method requires minimal in vitro manipulation or use of tissue culture media. Because the regeneration occurs in planta, the GiFT method is highly flexible with respect to genotype, which we demonstrate via successful transformation of elite germplasms from diverse genetic backgrounds. We also show that the soybean GiFT method can be applied to both conventional binary vectors and CRISPR-Cas12a vectors for genome editing applications. Analyses of T1 progeny demonstrate that the events have a high inheritance rate and can be used for genome engineering applications. By minimizing the need for tissue culture, the novel approach described here significantly improves operational efficiency while greatly reducing personnel and supply costs. It is the first industry-scale transformation method to utilize in planta selection in a major field crop.

Forebrain and hindbrain development in zebrafish is sensitive to ethanol exposure involving agrin, Fgf, and sonic hedgehog function.

BACKGROUND: Ethanol is a teratogen that affects numerous developmental processes in the nervous system, which includes development and survival of GABAergic and glutamatergic neurons. Possible molecular mechanisms accounting for ethanol's effects on nervous system development include perturbed fibroblast growth factor (Fgf) and Sonic hedgehog (Shh) signaling. In zebrafish, forebrain GABAergic neuron development is dependent on Fgf19 and Shh signaling. The present study was conducted to test the hypothesis that ethanol affects GABAergic and glutamatergic neuron development by disrupting Fgf, Shh, and agrin function. METHODS: Zebrafish embryos were exposed to varying concentrations of ethanol during a range of developmental stages, in the absence or presence of morpholino oligonucleotides (MOs) that disrupt agrin or Shh function. In situ hybridization was used to analyze glutamic acid decarboxylase (GAD1) gene expression, as well as markers of glutamatergic neurons. RESULTS: Acute ethanol exposure results in marked reduction in GAD1 gene expression in forebrain and hindbrain, and reduction of glutamatergic neuronal markers in hindbrain. Subthreshold ethanol exposure, combined with agrin or Shh MO treatment, produces a similar diminution in expression of markers for GABAergic and glutamatergic neurons. Consistent with the ethanol effects on Fgf and Shh pathways, Fgf19, Fgf8, or Shh mRNA overexpression rescues ethanol-induced decreases in GAD1 and Atonal1a gene expression. CONCLUSIONS: These studies demonstrate that GABAergic and glutamatergic neuron development in zebrafish forebrain or cerebellum is sensitive to ethanol exposure, and provides additional evidence that a signaling pathway involving agrin, Fgfs and Shh may be a critical target of ethanol exposure during zebrafish embryogenesis.

Embryonic Ethanol but Not Cannabinoid Exposure Affects Zebrafish Cardiac Development via Agrin and Sonic Hedgehog Interaction.

Recent studies demonstrate the adverse effects of cannabinoids on development, including via pathways shared with ethanol exposure. Our laboratory has shown that both the nervous system and cardiac development are dependent on agrin modulation of sonic hedgehog (shh) and fibroblast growth factor (Fgf) signaling pathways. As both ethanol and cannabinoids impact these signaling molecules, we examined their role on zebrafish heart development. Zebrafish embryos were exposed to a range of ethanol and/or cannabinoid receptor 1 and 2 agonist concentrations in the absence or presence of morpholino oligonucleotides that disrupt agrin or shh expression. In situ hybridization was employed to analyze cardiac marker gene expression. Exposure to cannabinoid receptor agonists disrupted midbrain-hindbrain boundary development, but had no effect on heart development, as assessed by the presence of cardiac edema or the altered expression of cardiac marker genes. In contrast, exposure to 1.5% ethanol induced cardiac edema and the altered expression of cardiac marker genes. Combined exposure to agrin or shh morpholino and 0.5% ethanol disrupted the cmlc2 gene expression pattern, with the restoration of the normal expression following shh mRNA overexpression. These studies provide evidence that signaling pathways critical to heart development are sensitive to ethanol exposure, but not cannabinoids, during early zebrafish embryogenesis.

Automated, High-Throughput Protoplast Transfection for Gene Editing and Transgene Expression Studies.

In an era of cost-efficient gene synthesis and high-throughput construct assembly, the onus of scientific experimentation is on the rate of in vivo testing for the identification of top performing candidates or designs. Assay platforms that are relevant to the species of interest and in the tissue of choice are highly desirable. A protoplast isolation and transfection method that is compatible with a large repertoire of species and tissues would be the platform of choice. A necessary aspect of this high-throughput screening approach is the need to handle many delicate protoplast samples at the same time, which is a bottleneck for manual operation. Such bottlenecks can be mitigated with the use of automated liquid handlers for the execution of protoplast transfection steps. The method described within this chapter utilizes a 96-well head for simultaneous, high-throughput initiation of transfection. While initially developed and optimized for use with etiolated maize leaf protoplasts, the automated protocol has also been demonstrated to be compatible with other established protoplast systems, such as soybean immature embryo derived protoplast, similarly described within. This chapter also includes instructions for a sample randomization design to reduce the impact of edge effects, which might be present when microplates are used for fluorescence readout following transfection. We also describe a streamlined, expedient, and cost-effective protocol for determining gene editing efficiencies using the T7E1 endonuclease cleavage assay with a publicly available image analysis tool.

Agrin function associated with ocular development is a target of ethanol exposure in embryonic zebrafish.

BACKGROUND: Alcohol (ethanol) is a teratogen known to affect the developing eyes, face, and brain. Among the ocular defects in fetal alcohol spectrum disorder (FASD) are microphthalmia and optic nerve hypoplasia. Employing zebrafish as an FASD model provides an excellent system to analyze the molecular basis of prenatal ethanol exposure-induced defects because embryos can be exposed to ethanol at defined developmental stages and affected genetic pathways can be examined. We have previously shown that disruption of agrin function in zebrafish embryos produces microphthalmia and optic nerve hypoplasia. METHODS: Zebrafish embryos were exposed to varying concentrations of ethanol in the absence or presence of morpholino oligonucleotides (MOs) that disrupt agrin function. In situ hybridization was used to analyze ocular gene expression as a consequence of ethanol exposure and agrin knockdown. Morphologic analysis of zebrafish embryos was also conducted. RESULTS: Acute ethanol exposure induces diminished agrin gene expression in zebrafish eyes and, importantly, combined treatment with subthreshold levels of agrin MO and ethanol produces pronounced microphthalmia, markedly reduces agrin gene expression, and perturbs Pax6a and Mbx gene expression. Microphthalmia produced by combined agrin MO and ethanol treatment was rescued by sonic hedgehog (Shh) mRNA overexpression, suggesting that ethanol-mediated disruption of agrin expression results in disrupted Shh function. CONCLUSIONS: These studies illustrate the strong potential for using zebrafish as a model to aid in defining the molecular basis for ethanol's teratogenic effects. The results of this work suggest that agrin expression and function may be a target of ethanol exposure during embryogenesis.