Nitrosyl chloride addition to cycloalkenyltrimethylsilanes. An unexpected reversal of regiochemistry of addition, and synthesis of β-​aminosilanes

5-​12-​Membered cycloalkenyltrimethylsilanes and trimethyl(2-​norbornenyl)​silane undergo stereo- and regioselective addn. of nitrosyl chloride, affording silylated chloronitrosocycloalkane intermediates, the corresponding diazene dioxide dimers and eventually α-​chlorocycloalkanones via oxime intermediacy. The addn. is syn, with chlorine and nitrosyl group adding to C-​1 and C-​2 resp. The products dimerize to diazene dioxides, whose stability depends on the conformational flexibility of the ring, the less flexible being more stable. The less stable adducts convert to oximes and further to 1,​2-​dichloro-​1-​nitrosocycloalkanes. The adducts can be hydrolyzed to α-​chloroketones and reduced to β-​aminosilanes. Other interesting observations include transposition of keto group in α-​chloroketones and x-​ray crystal structure of the amide deriv. N-​acetyl-​3-​chloro-​3-​(trimethylsilyl)​norbornane-​2-​amine (6f)​, which reveals -​(CO)​-​HN...H-​CH2-​(CO)​- hydrogen contact

Changes in the midpalatal and pterygopalatine sutures induced by micro-implant-supported skeletal expander, analyzed with a novel 3D method based on CBCT imaging.

BackgroundMini-implant-assisted rapid palatal expansion (MARPE) appliances have been developed with the aim to enhance the orthopedic effect induced by rapid maxillary expansion (RME). Maxillary Skeletal Expander (MSE) is a particular type of MARPE appliance characterized by the presence of four mini-implants positioned in the posterior part of the palate with bi-cortical engagement. The aim of the present study is to evaluate the MSE effects on the midpalatal and pterygopalatine sutures in late adolescents, using high-resolution CBCT. Specific aims are to define the magnitude and sagittal parallelism of midpalatal suture opening, to measure the extent of transverse asymmetry of split, and to illustrate the possibility of splitting the pterygopalatine suture.MethodsFifteen subjects (mean age of 17.2 years; range, 13.9-26.2 years) were treated with MSE. Pre- and post-treatment CBCT exams were taken and superimposed. A novel methodology based on three new reference planes was utilized to analyze the sutural changes. Parameters were compared from pre- to post-treatment and between genders non-parametrically using the Wilcoxon sign rank test. For the frequency of openings in the lower part of the pterygopalatine suture, the Fisher's exact test was used.ResultsRegarding the magnitude of midpalatal suture opening, the split at anterior nasal spine (ANS) and at posterior nasal spine (PNS) was 4.8 and 4.3 mm, respectively. The amount of split at PNS was 90% of that at ANS, showing that the opening of the midpalatal suture was almost perfectly parallel antero-posteriorly. On average, one half of the anterior nasal spine (ANS) moved more than the contralateral one by 1.1 mm. Openings between the lateral and medial plates of the pterygoid process were detectable in 53% of the sutures (P < 0.05). No significant differences were found in the magnitude and frequency of suture opening between males and females. Correlation between age and suture opening was negligible (R 2 range, 0.3-4.2%).ConclusionsMidpalatal suture was successfully split by MSE in late adolescents, and the opening was almost perfectly parallel in a sagittal direction. Regarding the extent of transverse asymmetry of the split, on average one half of ANS moved more than the contralateral one by 1.1 mm. Pterygopalatine suture was split in its lower region by MSE, as the pyramidal process was pulled out from the pterygoid process. Patient gender and age had a negligible influence on suture opening for the age group considered in the study

Resolution of conflicting views on thermodynamics of glass transition: a unified model

Classical description of thermodynamic properties during glass transition has been questioned by the entropy-loss model. The uncompensated loss of entropy at the glass transition temperature and zero residual entropy is at the heart of the controversy. Both the models are critically reviewed. A unified model is presented which incorporates features of both entropy loss and residual entropy. It implies two different types of contributions to the entropy of the supercooled liquid, one of which vanishes at the transition and the other which contributes to residual entropy. Entropy gain during spontaneous relaxation of glass, and the nature of heat capacity ‘hysteresis’ during cooling and heating through the glass transition range support the proposed model. Experiments are outlined for differentiating between the models

Adding New Tasks to a Single Network with Weight Transformations using Binary Masks

Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic forgetting issues. Recent work has shown that masking the internal weights of a given original conv-net through learned binary variables is a promising strategy. We build upon this intuition and take into account more elaborated affine transformations of the convolutional weights that include learned binary masks. We show that with our generalization it is possible to achieve significantly higher levels of adaptation to new tasks, enabling the approach to compete with fine tuning strategies by requiring slightly more than 1 bit per network parameter per additional task. Experiments on two popular benchmarks showcase the power of our approach, that achieves the new state of the art on the Visual Decathlon Challenge

Prognostic factors for lymph node metastasis from upper gingival carcinomas

This study sought to identify tumor characteristics that associate with regional lymph node metastases in squamous cell carcinomas originating in the upper gingiva

Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion

We introduce DeepInversion, a new method for synthesizing images from the image distribution used to train a deep neural network. We 'invert' a trained network (teacher) to synthesize class-conditional input images starting from random noise, without using any additional information about the training dataset. Keeping the teacher fixed, our method optimizes the input while regularizing the distribution of intermediate feature maps using information stored in the batch normalization layers of the teacher. Further, we improve the diversity of synthesized images using Adaptive DeepInversion, which maximizes the Jensen-Shannon divergence between the teacher and student network logits. The resulting synthesized images from networks trained on the CIFAR-10 and ImageNet datasets demonstrate high fidelity and degree of realism, and help enable a new breed of data-free applications - ones that do not require any real images or labeled data. We demonstrate the applicability of our proposed method to three tasks of immense practical importance -- (i) data-free network pruning, (ii) data-free knowledge transfer, and (iii) data-free continual learning. Code is available at https://github.com/NVlabs/DeepInversio