Synthetic and Mechanistic Aspects of Free-Radical Reactions in Solution

This thesis is divided into three chapters. Chapter 1. Radical cyclisation of ortho-(2-propenyloxy)benzenediazonium tetrafluoroborate in aqueous solution in the presence of a reducing reagent and a hydrogen-atom donor gives a mixture products containing 3-methyl-2,3-dihydrobenzofuran and uncyclised allyloxybenzene. Enantioselective radical cyclisation of the diazonium salt was found in the presence of cyclodextrins. The enantiomeric excess of the resulting 3-methyl-2,3-dihydrobenzofuran is ca. 7% in the presence of hydroxypropyl-β-cyclodextrin and ca. 13% in the presence of hydroxypropyl-α-cyclodextrin. The results suggest that higher enantioselectivities for radical reactions in aqueous solution might be achieved by modifying the structures of the guest radicals and cyclodextrins. Chapter 2. Thiols act as polarity-reversal catalysts and promote the radical-chain cyclisation of alkenyloxysilanes at 60-65 °C, in the presence of di-tert-butyl hyponitrite as initiator. Allyloxysilanes give five-membered-ring products via 5-endo-trig cyclisation of the intermediate allyloxysilyl radical. Homoallyloxysilanes give mixtures of five-and six-membered heterocycles, but the intermediate silyl radicals undergo predominantly 6-endo cyclisation, in contrast to the corresponding carbon-centred radicals which cyclise preferentially in the 5-exo mode. An analogous pentenyloxysilane gives only the seven-membered-ring product via a 7-endo radical cyclisation. Steric effects play an important part in influencing the final-product stereochemistry when this is determined in the hydrogen-atom transfer reaction between the cyclic adduct radical and the thiol catalyst. An unsuccessful thiol-catalysed tandem cyclisation shows that it is important for the addition of the thiyl radical to the C=CH2 group to be reversible under the reaction conditions. Complementary EPR spectroscopic studies of the short-lived intermediate cyclic adduct radicals have been carried out in the absence of thiol and the structures and conformations of these species have been determined. Chapter 3. Alkanethiols with electron withdrawing S-alkyl groups and silanethiols act as polarity-reversal catalysts to promote the radical-chain racemisation of (R)-tetrahydrofurfuryl acetate and the cis-trans-isomerisation of 2,5-dimethyltetrahydro- furan at 60 °C, while simple alkanethiols are ineffective. The a-alkoxyalkyl radical derived from (R)-tetrahydrofurfuryl acetate has been studied by EPR spectroscopy and its conformation has been determined. The rate constant for hydrogen-atom abstraction by tert-butoxyl radicals from the tertiary CH group in 2,5-dimethyltetrahydrofuran is ca. 7.5 times greater than that for abstraction from the tertiary CH group in tetrahydrofurfuryl acetate at -30 °

CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, 201

Demonstration of two novel methods for predicting functional siRNA efficiency

BACKGROUND: siRNAs are small RNAs that serve as sequence determinants during the gene silencing process called RNA interference (RNAi). It is well know that siRNA efficiency is crucial in the RNAi pathway, and the siRNA efficiency for targeting different sites of a specific gene varies greatly. Therefore, there is high demand for reliable siRNAs prediction tools and for the design methods able to pick up high silencing potential siRNAs. RESULTS: In this paper, two systems have been established for the prediction of functional siRNAs: (1) a statistical model based on sequence information and (2) a machine learning model based on three features of siRNA sequences, namely binary description, thermodynamic profile and nucleotide composition. Both of the two methods show high performance on the two datasets we have constructed for training the model. CONCLUSION: Both of the two methods studied in this paper emphasize the importance of sequence information for the prediction of functional siRNAs. The way of denoting a bio-sequence by binary system in mathematical language might be helpful in other analysis work associated with fixed-length bio-sequence

BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation

Accurate medical image segmentation is essential for clinical quantification, disease diagnosis, treatment planning and many other applications. Both convolution-based and transformer-based u-shaped architectures have made significant success in various medical image segmentation tasks. The former can efficiently learn local information of images while requiring much more image-specific inductive biases inherent to convolution operation. The latter can effectively capture long-range dependency at different feature scales using self-attention, whereas it typically encounters the challenges of quadratic compute and memory requirements with sequence length increasing. To address this problem, through integrating the merits of these two paradigms in a well-designed u-shaped architecture, we propose a hybrid yet effective CNN-Transformer network, named BRAU-Net++, for an accurate medical image segmentation task. Specifically, BRAU-Net++ uses bi-level routing attention as the core building block to design our u-shaped encoder-decoder structure, in which both encoder and decoder are hierarchically constructed, so as to learn global semantic information while reducing computational complexity. Furthermore, this network restructures skip connection by incorporating channel-spatial attention which adopts convolution operations, aiming to minimize local spatial information loss and amplify global dimension-interaction of multi-scale features. Extensive experiments on three public benchmark datasets demonstrate that our proposed approach surpasses other state-of-the-art methods including its baseline: BRAU-Net under almost all evaluation metrics. We achieve the average Dice-Similarity Coefficient (DSC) of 82.47, 90.10, and 92.94 on Synapse multi-organ segmentation, ISIC-2018 Challenge, and CVC-ClinicDB, as well as the mIoU of 84.01 and 88.17 on ISIC-2018 Challenge and CVC-ClinicDB, respectively.Comment: 12 pages, 6 figures, 9 tables code: https://github.com/Caipengzhou/BRAU-Netplusplu

Calibrating ultrasonic sensor measurements of crop canopy heights: a case study of maize and wheat

Canopy height serves as an important dynamic indicator of crop growth in the decision-making process of field management. Compared with other commonly used canopy height measurement techniques, ultrasonic sensors are inexpensive and can be exposed in fields for long periods of time to obtain easy-to-process data. However, the acoustic wave characteristics and crop canopy structure affect the measurement accuracy. To improve the ultrasonic sensor measurement accuracy, a four-year (2018−2021) field experiment was conducted on maize and wheat, and a measurement platform was developed. A series of single-factor experiments were conducted to investigate the significant factors affecting measurements, including the observation angle (0−60°), observation height (0.5−2.5 m), observation period (8:00−18:00), platform moving speed with respect to the crop (0−2.0 m min−1), planting density (0.2−1 time of standard planting density), and growth stage (maize from three−leaf to harvest period and wheat from regreening to maturity period). The results indicated that both the observation angle and planting density significantly affected the results of ultrasonic measurements (p-value< 0.05), whereas the effects of other factors on measurement accuracy were negligible (p-value > 0.05). Moreover, a double-input factor calibration model was constructed to assess canopy height under different years by utilizing the normalized difference vegetation index and ultrasonic measurements. The model was developed by employing the least-squares method, and ultrasonic measurement accuracy was significantly improved when integrating the measured value of canopy heights and the normalized difference vegetation index (NDVI). The maize measurement accuracy had a root mean squared error (RMSE) ranging from 81.4 mm to 93.6 mm, while the wheat measurement accuracy had an RMSE from 37.1 mm to 47.2 mm. The research results effectively combine stable and low-cost commercial sensors with ground-based agricultural machinery platforms, enabling efficient and non-destructive acquisition of crop height information

Correlation and combining ability analysis of physiological traits and some agronomic traits in maize

Combining ability information on the physiological traits in maize (Zea mays L) and the relationship between physi¬ological traits and biomass, grain yield (GY) and yield components (YC) can help maize breeders design experi¬ments for improving inbred lines and/or developing hybrids with improved GY or YC (GYYC). A six-parent diallel experiment (Griffing method 3) was conducted for combining ability and correlation analyses. The objectives of this study were to 1) study the correlation between physiological traits and biomass at seedling stage; 2) study which physiological traits at seedling stage have significant correlation with biomasses at both seedling and later growth stages and GYYCs; 3) evaluate combining ability of the physiological traits that are significantly correlated with either GY or one of the YCs. Results showed plant heights at 20 day, 40 day, and leaf area were highly corre¬lated with both dry weights of shoots and roots. All chlorophyll-related organelles were significantly correlated with only dry weights of shoots. However, dry matter at seedling stage seemed not to be related to dry matter in later growth stages. Five physiological traits (stomatal conductance, transpiration rate, net photosynthetic rate, two quantum yield related traits) at seedling stage were identified to greatly impact dry matter at later growth stages. Results also showed that 13 out of 35 physiological traits studied were significantly correlated with GYYCs. Differ¬ent germplasms for improving GYYCs could be used based on both correlation between the 13 traits and GYYCs and combining ability effects of each line for the 13 selected traits

Real-time face view correction for front-facing cameras

Face view is particularly important in person-to-person communication. Disparity between the camera location and the face orientation can result in undesirable facial appearances of the participants during video conferencing. This phenomenon becomes particularly notable on devices where the front-facing camera is placed at unconventional locations such as below the display or within the keyboard. In this paper, we takes the video stream from a single RGB camera as input, and generates a video stream that emulates the view from a virtual camera at a designated location. The most challenging issue of this problem is that the corrected view often needs out-of-plane head rotations. To address this challenge, we reconstruct 3D face shape and re-render it into synthesized frames according to the virtual camera location. To output the corrected video stream with natural appearance in real-time, we propose several novel techniques including accurate eyebrow reconstruction, high-quality blending between corrected face image and background, and a template-based 3D reconstruction of glasses. Our system works well for different lighting conditions and skin tones, and is able to handle users wearing glasses. Extensive experiments and user studies demonstrate that our proposed method can achieve high-quality results