Sudomotor and cardiovascular dysfunction in patients with early untreated Parkinson's disease.

BACKGROUND: According to Braak staging of Parkinson's disease (PD), detection of autonomic dysfunction would help with early diagnosis of PD. OBJECTIVE: To determine whether the autonomic nervous system is involved in the early stage of PD, we evaluated cardiovascular and sudomotor function in early untreated PD patients. METHODS: Orthostatic blood pressure regulation, heart rate variability, skin vasomotor function, and palmar sympathetic sweat responses were examined in 50 early untreated PD patients and 20 healthy control subjects. RESULTS: The mean decrease in systolic blood pressure during head-up tilt in PD patients was mildly but significantly larger than in controls (p = 0.0001). There were no differences between the 2 groups in heart rate variability, with analysis of low frequency (LF; mediated by baroreflex feedback), and high frequency (HF; mainly reflecting parasympathetic vagal) modulation. However, LF/HF, an index of sympatho-parasympathetic balance, was lower in the PD group than in controls (p = 0.02). Amplitudes of palmar sweat responses to deep inspiration (p = 0.004), mental arithmetic (p = 0.01), and exercise (p = 0.01) in PD patients were lower than in controls, with negative correlations with motor severity. Amplitudes of palmar skin vasomotor reflexes in PD patients did not differ from controls. CONCLUSIONS: Our study indicates impairment of sympathetic cardiovascular and sudomotor function with orthostatic dysregulation of blood pressure control, reduced LF/HF and reduction in palm sweat responses even in early untreated PD patients

Chlorophyll d production in crushed algae in aqueous acetone

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Clinicopathological Characteristics of Superficial Type Colorectal Adenomas Obtained by Endoscopic Resection

Colorectal adenomas may be either protruding type or superficial type lesions. To delineate the clinicopathological characteristics of the latter, 153 superficial type adenomas (including the surrounding mucosa) obtained by endoscopic resection were studied morphologically. Superficial type adenomas were defined as flat or flat depressed adenomas with a height of ≤3 mm; histologically, the tubules proliferated horizontally without vertical overlap. The location of tubules in the mucosa was classified as: involvement of the surface layer only (m1), deeper invasion not reaching the muscularis mucosae (m2), or invasion to the muscularis mucosae (m3). The results of analysis indicated: 1) there was no relationship between atypia and size; 2) although macroscopic features (depression, etc.) were associated with the grade of atypia, a closer association was obtained for the location in the mucosa; 3) based on our classification system for tubule location, (m2) and (m3) adenomas had a significantly higher frequency of depressed type lesions than did m1 lesions; and 4) the height of superficial type adenomas was 295 to 413 μm. Height was lowest in the m3 group followed by, in ascending order, the m2 and m1 groups. These morphological and histological characteristics are expected to contribute to improved diagnosis of superficial type adenomas

Physics-Based Human-in-the-Loop Machine Learning Combined with Genetic Algorithm Search for Multi-Criteria Optimization: Electrochemical CO2 Reduction Reaction

Machine learning (ML) can be a powerful tool to expedite materials research, but the deployment for experimental research is often hindered by data scarcity and model uncertainty. An human-in-the-loop procedure to tailor the implementation of ML for multicriteria optimization is described. The effectiveness of this procedure in the development of a nafion-based membrane electrode assembly for electrochemical CO2 reduction reaction (CO2RR) into CO for two targets is demonstrated: energy efficiency (EE) and partial current density for CO2RR (). Model-agnostic nonlinear correlation analyses identify the 11 features relevant to those targets. The three studied decision tree-based ML models yield similar cross-validation errors so an ad hoc feature analysis of the models is done with SHapley Additive exPlanations and nonlinear correlation techniques. The predicted EE- space and the functional dependency of the predictions are investigated to assess model plausibility. A genetic algorithm with CO production cost as the final target with subsequent validation experiments of candidate conditions is devised. The model chosen through ad hoc analysis yields the highest accuracy and the only one that can locate the Pareto front with a single round of experiments, demonstrating how appropriate model selection through careful inspection can greatly accelerate the research cycle

Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification

Text classification must sometimes be applied in a low-resource language with no labeled training data. However, training data may be available in a related language. We investigate whether character-level knowledge transfer from a related language helps text classification. We present a cross-lingual document classification framework (CACO) that exploits cross-lingual subword similarity by jointly training a character-based embedder and a word-based classifier. The embedder derives vector representations for input words from their written forms, and the classifier makes predictions based on the word vectors. We use a joint character representation for both the source language and the target language, which allows the embedder to generalize knowledge about source language words to target language words with similar forms. We propose a multi-task objective that can further improve the model if additional cross-lingual or monolingual resources are available. Experiments confirm that character-level knowledge transfer is more data-efficient than word-level transfer between related languages.Comment: AAAI 202

Analysis and Applications of Cross-Lingual Models in Natural Language Processing

Human languages vary in terms of both typologically and data availability. A typical machine learning-based approach for natural language processing (NLP) requires training data from the language of interest. However, because machine learning-based approaches heavily rely on the amount of data available in each language, the quality of trained model languages without a large amount of data is poor. One way to overcome the lack of data in each language is to conduct cross-lingual transfer learning from resource-rich languages to resource-scarce languages. Cross-lingual word embeddings and multilingual contextualized embeddings are commonly used to conduct cross-lingual transfer learning. However, the lack of resources still makes it challenging to either evaluate or improve such models. This dissertation first proposes a graph-based method to overcome the lack of evaluation data in low-resource languages by focusing on the structure of cross-lingual word embeddings, further discussing approaches to improve cross-lingual transfer learning by using retrofitting methods and by focusing on a specific task. Finally, it provides an analysis of the effect of adding different languages when pretraining multilingual models

A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity

Cross-lingual word embeddings encode the meaning of words from different languages into a shared low-dimensional space. An important requirement for many downstream tasks is that word similarity should be independent of language - i.e., word vectors within one language should not be more similar to each other than to words in another language. We measure this characteristic using modularity, a network measurement that measures the strength of clusters in a graph. Modularity has a moderate to strong correlation with three downstream tasks, even though modularity is based only on the structure of embeddings and does not require any external resources. We show through experiments that modularity can serve as an intrinsic validation metric to improve unsupervised cross-lingual word embeddings, particularly on distant language pairs in low-resource settings.Comment: Accepted to ACL 2019, camera-read