Erratum to Traditional Chinese medicine and new concepts of predictive, preventive and personalised medicine in diagnosis and treatment of sub-optimal health [The EPMA Journal 5, (2014) 12]

The premise of disease-related phenotypes is the definition of the counterpart normality in medical sciences. Contrary to clinical practices that can be carefully planned according to clinical needs, heterogeneity and uncontrollability is the essence of humans in carrying out health studies. Full characterization of consistent phenotypes that define the general population is the basis to individual difference normalization in personalized medicine. Self-claimed normal status may not represent health because asymptomatic subjects may carry chronic diseases at their early stage, such as cancer, diabetes mellitus and atherosclerosis. Currently, treatments for non-communicable chronic diseases (NCD) are implemented after disease onset, which is a very much delayed approach from the perspective of predictive, preventive and personalized medicine (PPPM). A NCD pandemic will develop and be accompanied by increased global economic burden for healthcare systems throughout both developed and developing countries. This paper examples the characterization of the suboptimal health status (SHS) which represents a new PPPM challenge in a population with ambiguous health complaints such as general weakness, unexplained medical syndrome (UMS), chronic fatigue syndrome (CFS), myalgic encephalomyelitis (ME), post-viral fatigue syndrome (PVFS) and chronic fatigue immune dysfunction syndrome (CFIDS). Methods: We applied clinical informatic approaches and developed a questionnaire-suboptimal health status questionnaire-25 (SHSQ-25) for measuring SHS. The validity and reliability of this approach were evaluated in a small pilot study and then in a cross-sectional study of 3,405 participants in China. Results: We found a correlation between SHS and systolic blood pressure, diastolic blood pressure, plasma glucose, total cholesterol and high-density lipoprotein (HDL) cholesterol among men, and a correlation between SHS and systolic blood pressure, diastolic blood pressure, total cholesterol, triglycerides and HDL cholesterol among women. Conclusions: The SHSQ-25 is a self-rated questionnaire of perceived health complaints, which can be used as a new instrument for PPPM. An ongoing longitudinal SHS cohort survey (China Sub-optimal Health Cohort Study, COACS) consisting of 50,000 participants will provide a powerful health trial to use SHSQ-25 for its application to PPPM through patient stratification and therapy monitoring using innovative technologies of predictive diagnostics and prognosis: an effort of paradigm shift from reactive to predictive medicine

Method to determine test profile in accelerated reliability demonstration test under Type-I censoring

Conventional reliability demonstration test (RDT) based on statistical method is widely used in industry as it is simple and convenient to apply. But for products with high reliability and long life, this test method fails to satisfy the demand for short cycle and low cost, and is liable to cause the phenomenon of over-test and short-test. This paper gives a method to determine the accelerated stress profile for RDT under multiple stresses and mechanisms, making it faster to make decision of accept or reject. By raising the levels of sensitive stresses that the product would experience, the test time can be cut down remarkably. We can derive the overall acceleration factor based on the narrow reliability bounds theory. Then we choose the test plan referring to GJB 899A. Furthermore, combined with the reliability qualification test (RQT) profile, the accelerated test profile is acquired. An example is given to illustrate the superior performance of the proposed method over traditional methods

Ambiguity and entropy in the process of translation and post-editing

This thesis analyses the way in which ambiguity is cognitively processed, in translation in general and post-editing in particular, drawing inferences from psycholinguistics, bilingualism, and entropy-based models of translation cognition. Conceptually, it assumes non-selective activation of both languages (source and target) in the translation process, and explores how entropy and entropy reduction can theoretically describe assumed mental states during disambiguation. Empirically, it uses a product-based metric of word translation entropy (HTra), and eye-movement and keystroke data from the CRITT Translation Process Research Database, to shed light on how the conceptual understanding of lexical and structural ambiguity may be manifested by observable behaviour. At the lexical level, examination of behavioural data pertaining to a high-HTra item from 217 participants translating/post-editing from English into multiple languages shows that the item tends to result in pauses in production and regression of eye movements, and that the translators’/post-editors’ corresponding scrutinization of the source text (ST) tends to involve a visual search for lower-HTra words in the co-text and, accordingly, a decrease in the average entropy of the activity unit. Regarding syntax, a Chinese relative clause in the machine translation output, which can involve a garden-path effect, is examined in terms of eye movements from 18 participants. Results show that, contrary to monolingual reading, disruptions of processing tend to occur not in the later part of the sentence where the wrong parse is disconfirmed, but in the earlier regions where the most quickly-built analysis is semantically inconsistent with the ST. Structural disambiguation and re-analysis seem to be bypassed. This suggests that, on the one hand, reading for post-editing receives a strong biasing effect from the ST, and on the other, argument integration is more appropriately explained from an incremental processing perspective rather than a head-driven approach, as thematic roles seem to be assigned immediately in reading for post-editing. While the lexical analysis supports a parallel disambiguation model, the structural analysis seems to support a serial one. In terms of translation models, both emphasize the impact of cross-linguistic priming and the presence of considerable horizontality in the translation process

Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair

During Automated Program Repair (APR), it can be challenging to synthesize correct patches for real-world systems in general-purpose programming languages. Recent Large Language Models (LLMs) have been shown to be helpful "copilots" in assisting developers with various coding tasks, and have also been directly applied for patch synthesis. However, most LLMs treat programs as sequences of tokens, meaning that they are ignorant of the underlying semantics constraints of the target programming language. This results in plenty of statically invalid generated patches, impeding the practicality of the technique. Therefore, we propose Repilot, a framework to further copilot the AI "copilots" (i.e., LLMs) by synthesizing more valid patches during the repair process. Our key insight is that many LLMs produce outputs autoregressively (i.e., token by token), resembling human writing programs, which can be significantly boosted and guided through a Completion Engine. Repilot synergistically synthesizes a candidate patch through the interaction between an LLM and a Completion Engine, which 1) prunes away infeasible tokens suggested by the LLM and 2) proactively completes the token based on the suggestions provided by the Completion Engine. Our evaluation on a subset of the widely-used Defects4j 1.2 and 2.0 datasets shows that Repilot fixes 66 and 50 bugs, respectively, surpassing the best-performing baseline by 14 and 16 bugs fixed. More importantly, Repilot is capable of producing more valid and correct patches than the base LLM when given the same generation budget

Traditional Chinese Medicine And New Concepts Of Predictive, Preventive And Personalized Medicine In Diagnosis And Treatment Of Suboptimal Health

The premise of disease-related phenotypes is the definition of the counterpart normality in medical sciences. Contrary to clinical practices that can be carefully planned according to clinical needs, heterogeneity and uncontrollability is the essence of humans in carrying out health studies. Full characterization of consistent phenotypes that define the general population is the basis to individual difference normalization in personalized medicine. Self-claimed normal status may not represent health because asymptomatic subjects may carry chronic diseases at their early stage, such as cancer, diabetes mellitus and atherosclerosis. Currently, treatments for non-communicable chronic diseases (NCD) are implemented after disease onset, which is a very much delayed approach from the perspective of predictive, preventive and personalized medicine (PPPM). A NCD pandemic will develop and be accompanied by increased global economic burden for healthcare systems throughout both developed and developing countries. This paper examples the characterization of the suboptimal health status (SHS) which represents a new PPPM challenge in a population with ambiguous health complaints such as general weakness, unexplained medical syndrome (UMS), chronic fatigue syndrome (CFS), myalgic encephalomyelitis (ME), post-viral fatigue syndrome (PVFS) and chronic fatigue immune dysfunction syndrome (CFIDS). Methods: We applied clinical informatic approaches and developed a questionnaire-suboptimal health status questionnaire-25 (SHSQ-25) for measuring SHS. The validity and reliability of this approach were evaluated in a small pilot study and then in a cross-sectional study of 3,405 participants in China. Results: We found a correlation between SHS and systolic blood pressure, diastolic blood pressure, plasma glucose, total cholesterol and high-density lipoprotein (HDL) cholesterol among men, and a correlation between SHS and systolic blood pressure, diastolic blood pressure, total cholesterol, triglycerides and HDL cholesterol among women. Conclusions: The SHSQ-25 is a self-rated questionnaire of perceived health complaints, which can be used as a new instrument for PPPM. An ongoing longitudinal SHS cohort survey (China Sub-optimal Health Cohort Study, COACS) consisting of 50,000 participants will provide a powerful health trial to use SHSQ-25 for its application to PPPM through patient stratification and therapy monitoring using innovative technologies of predictive diagnostics and prognosis: an effort of paradigm shift from reactive to predictive medicine

Ionization Induced by the Ponderomotive Force in Intense and High-Frequency Laser Fields

Atomic stabilization is a universal phenomenon that occurs when atoms interact with intense and high-frequency laser fields. In this work, we systematically study the influence of the ponderomotive (PM) force, present around the laser focus, on atomic stabilization. We show that the PM force could induce tunneling and even over-barrier ionization to the otherwise stabilized atoms. Such effect may overweight the typical multiphoton ionization under moderate laser intensities. Our work highlights the importance of an improved treatment of atomic stabilization that includes the influence of the PM force

Data augmentation and semi-supervised learning for deep neural networks-based text classifier

User feedback is essential for understanding user needs. In this paper, we use free-text obtained from a survey on sleep-related issues to build a deep neural networks-based text classifier. However, to train the deep neural networks model, a lot of labelled data is needed. To reduce manual data labelling, we propose a method which is a combination of data augmentation and pseudo-labelling: data augmentation is applied to labelled data to increase the size of the initial train set and then the trained model is used to annotate unlabelled data with pseudo-labels. The result shows that the model with the data augmentation achieves macro-averaged f1 score of 65.2% while using 4,300 training data, whereas the model without data augmentation achieves macro-averaged f1 score of 68.2% with around 14,000 training data. Furthermore, with the combination of pseudo-labelling, the model achieves macro-averaged f1 score of 62.7% with only using 1,400 training data with labels. In other words, with the proposed method we can reduce the amount of labelled data for training while achieving relatively good performance

Effect of soil particle-size distribution (PSD) on soil-subsoiler interactions in the discrete element model

Aim of study: This work investigated the significance and mechanism for the effect of particle-size distribution (PSD) under different nominal radii using the discrete element method (DEM) and validated using the laboratory soil-bin results to accurately determine PSD.Area of study: Yangling, ChinaMaterial and methods: The experimental soil was Lou soil. Soil disturbance characteristics (soil rupture distance ratio, height of accumulated soil, soil density change rate) and cutting forces (draft and vertical) under different treatments were predicted and measured respectively.Main results: The ANOVA outputs showed that PSD significantly affected draft and vertical forces (