Effects of molecular motion on deuteron magic angle spinning NMR spectra

Solid state deuteron NMR experiments, especially magic angle spinning (MAS) and off-magic angle spinning (OMAS), are developed to explore dynamical systems. A theoretical discussion of interactions relevant for spin-1 nuclei is presented. Practical aspects of MAS/OMAS experiments are described an detail. The dominant quadrupolar coupling interaction in deuteron NMR has been simulated and the effects of multiple-frame molecular motions on MAS/OMAS spectra are taken into account in this calculation. Effects of chemical shift anisotropy are also simulated, and shown to be small under conditions of rapid sample spinning.;Two numerical methods, direct integration and an efficient simulation routine based on Floquet thoery, are discussed. Improvements in computational efficiency of the Floquet method in computing solid stae deuteron MAS/OMAS spectrum makes the quantitative analysis of molecular motion possible: complex multiple frame molecular motions, deuteron quadrupolar interactions and chemical shift anisotropy are now included in a single simulation routine and the effects of the multiple-frame molecular motions can be analyzed by comparing the line shapes of simulations with those of experiments.;The enhanced motional sensitivity of deuteron NMR MAS/OMAS makes it possible to detect temperature-dependent motion rates of urea molecules in octanoic acid/urea inclusion compounds. Temperature-dependent deuteron OMAS line shapes have been recorded and fitted through least-square procedures, to provide rates of rotation about both CN and CO bonds. Activation energies have been calculated for these motions. The power and utility of OMAS is demonstrated by this investigation.;The phenyl ring motions in appropriately labeled L-phenylalanine and N-acetyl-L-phenylalanine methyl ester/cyclodextrin inclusion compound have also been studied through high field deuteron MAS experiments. Phenylalanine MAS spectra with ultra-fast ring-flip motion have been simulated and the range of phenyl ring flip rates is obtained by comparing the simulated and experimental spectra. In the studies of phenylalanine/cyclodextrin inclusion compound, an approach to a physically reasonable diffusion model has also been made by increasing the number of jump sites per unit solid angle included in the calculation. These simulations involve repeated diagonalization of very large matrices and demonstrate the capability of the approach to handle complex dynamical systems

Childhood mitochondrial encephalomyopathies: clinical course, diagnosis, neuroimaging findings, mtDNA mutations and outcome in six children

Mitochondrial dysfunction manifests in many forms during childhood. There is no effective therapy for the condition; hence symptomatic therapy is the only option. The effect of symptomatic therapy are not well known. We present clinical course, diagnosis and effect of current treatments for six children suffering from mitochondrial encephalomyopathy identified by clinical demonstrations, brain MRI findings and DNA mutations. Two were male and four were female. Their age ranged between 2 and 17 years. Skeletal muscle biopsies were obtained in three and one showed misshaped and enlarged mitochondria under electron microscope. mtDNA mutation frequency was >30%. Five children were diagnosed with MELAS (mitochondrial encephalopathy, lactic acidosis, and strokelike episodes) and one with Leigh’s syndrome (LS). All were given cocktail and symptomatic treatments. One of the five MELAS children died from severe complications. The other four MELAS children remain alive; four showed improvement, and one remained unresponsive. Of the four who showed improvement, two do not have any abnormal signs and the other two have some degree of motor developmental delay and myotrophy. The LS child is doing well except for ataxia. Until better therapy such as mitochondrial gene therapy is available, cocktail and symptomatic treatments could at least stabilize these children

An Empirical Analysis of Software-as-a-Service Development Mode and Its Impacts on Firm Performance

In this paper, we address the following two research questions: (1) Under what circumstances will firms prefer internal SaaS development to external sourcing; and (2) how does the SaaS development mode affect firm performance? We examine the SaaS development actions in the computer industry (SIC code 737) from 2003 to 2012. Preliminary analysis results demonstrate that firms with large amount of working capital can consider developing SaaS application in-house. However, if firms have high level of R&D capability, they may have better absorptive capability of technology innovation. Firms can grasp SaaS innovation through external sourcing. Firms shall also take into account the market characteristics when making the development choice. Our results indicate that the strategic decision of SaaS development mode will have short-term impact on firm performance (i.e., gross margin and market share), but not for the long-run performance (Tobins’q)

Junior High School Teachers’ Feedback Giving and Perceptions of the CSE Writing Scale

Chinese EFL learners’ deficiencies in writing have become increasingly apparent in their secondary education, especially in the originality of ideas and the logic of arguments. However, they may not be made aware of this as many teachers still use traditional feedback such as a single score to mark and report on students’ performance. The Writing Scale of China’s Standards of English Language Ability (hereinafter referred to as the CSE Writing Scale) released in 2018 provides a framework of reference against which Chinese students’ English writing ability can be assessed. It consists of descriptors of writing ability in different contexts and genres. It makes detailed and analytical feedbacking possible and has far-reaching implications for the development of formative assessment in English teaching. Nevertheless, to date, there has been scarce empirical research as to whether teachers actually use the Writing Scale in teaching and whether they are able to utilize it in an effective way.    This study aims to investigate the current situation of feedback giving in junior high school and teachers’ views of and attitudes towards the application of the CSE Writing Scale. Through a questionnaire and interviews with the teachers, the study revealed three major findings. Firstly, although teachers were aware of the importance of giving feedback on students’ writing, they had problem providing individualized comments on student writing. Secondly, students had difficulty understanding teachers’ feedback. Lastly, teachers were generally not familiar with the CSE Writing Scale and concerned whether it could be effectively used in the process of feedback giving. Conclusions are drawn together with implications and recommendations for teachers to resolve the problems identified.

Identifying Conspiracy Theories News based on Event Relation Graph

Conspiracy theories, as a type of misinformation, are narratives that explains an event or situation in an irrational or malicious manner. While most previous work examined conspiracy theory in social media short texts, limited attention was put on such misinformation in long news documents. In this paper, we aim to identify whether a news article contains conspiracy theories. We observe that a conspiracy story can be made up by mixing uncorrelated events together, or by presenting an unusual distribution of relations between events. Achieving a contextualized understanding of events in a story is essential for detecting conspiracy theories. Thus, we propose to incorporate an event relation graph for each article, in which events are nodes, and four common types of event relations, coreference, temporal, causal, and subevent relations, are considered as edges. Then, we integrate the event relation graph into conspiracy theory identification in two ways: an event-aware language model is developed to augment the basic language model with the knowledge of events and event relations via soft labels; further, a heterogeneous graph attention network is designed to derive a graph embedding based on hard labels. Experiments on a large benchmark dataset show that our approach based on event relation graph improves both precision and recall of conspiracy theory identification, and generalizes well for new unseen media sources.Comment: Accepted to EMNLP 2023 Finding

Discourse Structures Guided Fine-grained Propaganda Identification

Propaganda is a form of deceptive narratives that instigate or mislead the public, usually with a political purpose. In this paper, we aim to identify propaganda in political news at two fine-grained levels: sentence-level and token-level. We observe that propaganda content is more likely to be embedded in sentences that attribute causality or assert contrast to nearby sentences, as well as seen in opinionated evaluation, speculation and discussions of future expectation. Hence, we propose to incorporate both local and global discourse structures for propaganda discovery and construct two teacher models for identifying PDTB-style discourse relations between nearby sentences and common discourse roles of sentences in a news article respectively. We further devise two methods to incorporate the two types of discourse structures for propaganda identification by either using teacher predicted probabilities as additional features or soliciting guidance in a knowledge distillation framework. Experiments on the benchmark dataset demonstrate that leveraging guidance from discourse structures can significantly improve both precision and recall of propaganda content identification.Comment: Accepted to EMNLP 202

Deep Sufficient Representation Learning via Mutual Information

We propose a mutual information-based sufficient representation learning (MSRL) approach, which uses the variational formulation of the mutual information and leverages the approximation power of deep neural networks. MSRL learns a sufficient representation with the maximum mutual information with the response and a user-selected distribution. It can easily handle multi-dimensional continuous or categorical response variables. MSRL is shown to be consistent in the sense that the conditional probability density function of the response variable given the learned representation converges to the conditional probability density function of the response variable given the predictor. Non-asymptotic error bounds for MSRL are also established under suitable conditions. To establish the error bounds, we derive a generalized Dudley's inequality for an order-two U-process indexed by deep neural networks, which may be of independent interest. We discuss how to determine the intrinsic dimension of the underlying data distribution. Moreover, we evaluate the performance of MSRL via extensive numerical experiments and real data analysis and demonstrate that MSRL outperforms some existing nonlinear sufficient dimension reduction methods.Comment: 43 pages, 6 figures and 5 table