Unusual Removal of T-DNA in T1 Progenies of Rice after Agrobacterium-mediated CRISPR/Cas9 Editing

In the present study, we attempted to knock out the bar gene selection marker in the fixed Bt- and herbicide-resistant transgenic line BT-T07 (T8 generation) to generate a marker-free Bt-resistant rice line. A binary vector containing a CRISPR/Cas9 system targeting the 108–130 bp region of bar was transformed into rice embryonic calli, and plantlets were regenerated under non-selective conditions. Three T0 plants were observed to have non-target point mutations and deletions in the targeted gene and were putatively heterozygous and chimeras. One T0 plant, 130-4, was confirmed to have a 76-nt deletion, from 140 bp to 225 bp, and it showed the segregation of bar in its T1 progenies, with 16 bar-knockout lines and seven normal bar-expressing lines. However, the CRISPR/Cas9 editing vector sequences were not detected in any of the T1 plants. In addition, unusual removal of pre-existing T-DNA was observed in all bar-knockout T1 plants. Illumina sequencing of a bar-knockout line, 130-4-36, revealed a small fraction of read residues of pre-existing T-DNA from the bar sequence to the right border at the original junction site. We speculate that this rare phenomenon might be directed by the homology between pre-existing T-DNA and CRISPR/Cas9 editing vector sequences during meiotic recombination. We report imprecise modifications and unpredictable outcomes of gene-editing techniques, providing valuable perspectives on gene-editing applications.

Emotionally charged text classification with deep learning and sentiment semantic

Text classification is one of the widely used phenomena in different natural language processing tasks. State-of-the-art text classifiers use the vector space model for extracting features. Recent progress in deep models, recurrent neural networks those preserve the positional relationship among words achieve a higher accuracy. To push text classification accuracy even higher, multi-dimensional document representation, such as vector sequences or matrices combined with document sentiment, should be explored. In this paper, we show that documents can be represented as a sequence of vectors carrying semantic meaning and classified using a recurrent neural network that recognizes long-range relationships. We show that in this representation, additional sentiment vectors can be easily attached as a fully connected layer to the word vectors to further improve classification accuracy. On the UCI sentiment labelled dataset, using the sequence of vectors alone achieved an accuracy of 85.6%, which is better than 80.7% from ridge regression classifier—the best among the classical technique we tested. Additional sentiment information further increases accuracy to 86.3%. On our suicide notes dataset, the best classical technique—the Naíve Bayes Bernoulli classifier, achieves accuracy of 71.3%, while our classifier, incorporating semantic and sentiment information, exceeds that at 75% accuracy.

Space vector PWM sequence and harmonic comparison for high power current source rectifier

The space vector PWM (SVPWM) schemes for high power current source drives normally produce low order harmonics due to low switching frequency. To provide a SVPWM with the best harmonic performance, different space vector sequences suitable for a current source rectifier (CSR) are investigated in this project. Details on how to achieve the waveform symmetries with minimum switching frequency for each sequence are discussed. A thorough comparison of the harmonic performance of different space vector sequences based on current source rectifier implementations is carried out. An optimum space vector modulation (SVM) method is proposed to achieve the best line current THD and reduced switching losses. The space vector sequence investigation has been verified in simulation and experimentally using a 10kVA GCT based CSR prototype.

Space vector PWM sequence and harmonic comparison for high power current source rectifier

The space vector PWM (SVPWM) schemes for high power current source drives normally produce low order harmonics due to low switching frequency. To provide a SVPWM with the best harmonic performance, different space vector sequences suitable for a current source rectifier (CSR) are investigated in this project. Details on how to achieve the waveform symmetries with minimum switching frequency for each sequence are discussed. A thorough comparison of the harmonic performance of different space vector sequences based on current source rectifier implementations is carried out. An optimum space vector modulation (SVM) method is proposed to achieve the best line current THD and reduced switching losses. The space vector sequence investigation has been verified in simulation and experimentally using a 10kVA GCT based CSR prototype.

Combined cosine-linear regression model similarity with application to handwritten word spotting

The similarity or the distance measure have been used widely to calculate the similarity or dissimilarity between vector sequences, where the document images similarity is known as the domain that dealing with image information and both similarity/distance has been an important role for matching and pattern recognition. There are several types of similarity measure, we cover in this paper the survey of various distance measures used in the images matching and we explain the limitations associated with the existing distances. Then, we introduce the concept of the floating distance which describes the variation of the threshold’s selection for each word in decision making process, based on a combination of Linear Regression and cosine distance. Experiments are carried out on a handwritten Arabic image documents of Gallica library. These experiments show that the proposed floating distance outperforms the traditional distance in word spotting system.

Almost Sure Convergence for the Maximum and Minimum of Normal Vector Sequences

In this paper, we prove the almost sure convergences for the maximum and minimum of nonstationary and stationary standardized normal vector sequences under some suitable conditions.

ITR-Seq, a next-generation sequencing assay, identifies genome-wide DNA editing sites in vivo following adeno-associated viral vector-mediated genome editing

Background Identifying nuclease-induced double-stranded breaks in DNA on a genome-wide scale is critical for assessing the safety and efficacy of genome editing therapies. We previously demonstrated that after administering adeno-associated viral (AAV) vector-mediated genome-editing strategies in vivo, vector sequences integrated into the host organism’s genomic DNA at double-stranded breaks. Thus, identifying the genomic location of inserted AAV sequences would enable us to identify DSB events, mainly derived from the nuclease on- and off-target activity. Results Here, we developed a next-generation sequencing assay that detects insertions of specific AAV vector sequences called inverted terminal repeats (ITRs). This assay, ITR-Seq, enables us to identify off-target nuclease activity in vivo. Using ITR-Seq, we analyzed liver DNA samples of rhesus macaques treated with AAV vectors expressing a meganuclease. We found dose-dependent off-target activity and reductions in off-target events induced by further meganuclease development. In mice, we identified the genomic locations of ITR integration after treatment with Cas9 nucleases and their corresponding single-guide RNAs. Conclusions In sum, ITR-Seq is a powerful method for identifying off-target sequences induced by AAV vector-delivered genome-editing nucleases. ITR-Seq will help us understand the specificity and efficacy of different genome-editing nucleases in animal models and clinical studies. This information can help enhance the safety profile of gene-editing therapies.