Based on Huangdi Neijing (Yellow Emperor's Internal Classic), combined with the Huangdi Mingtang Jing JiJiao (Compilation and Correction of Yellow Emperor's Mingtang Classic) and unearthed Tianhui Yijian (Tianhui Medical Bamboo Slips), it is recognized that the therapeutic indications of Fenglong (ST 40) were recorded repeatedly in many medical works of the Qin and Han dynasties; and the treatments mostly focus on "upward reversion of qi ". In Huangdi Mingtang Jing (Yellow Emperor's Mingtang Classic), a part of symptoms were re-described textually, which affects the understanding on the indications of Fenglong (ST 40) in the medical works of the later generations. On the basis of the construction of phlegm theory in the Sui and Tang dynasties, the scholars of Song, Jin and Yuan dynasties had placed the emphasis on the relationship between phlegm and qi movement. In acupuncture works by Dou Hanqing, Fenglong (ST 40) was selected in treatment of phlegm dampness and phlegm-induced asthma, which is also based on the pathogenesis, "upward reversion of qi ", rather than "phlegm" itself. This view can be understood by the proof of "reducing Zusanli (ST 36) for eliminating wind". The relationship between Fenglong (ST 40) and phlegm was emphasized in Yulong Ge (Jade Dragon Verse) and Zhenfang Liuji (Six Sets of Acupuncture Methods), after which, the understanding, " Fenglong (ST 40), the key point for phlegm disorders", had been formed gradually since the Ming dynasty. The formation and evolution of the therapeutic indications of Fenglong (ST 40) are influenced comprehensively by the errors in textual duplication, cultural background, changes in the term expressions of disorders, and the clinical experience of medical practitioners.

ABSTRACT Recently, interest has grown in the development of dose-finding methods that consider both toxicity and efficacy as endpoints. Along with responses on these, the incorporation of pharmacokinetic (PK) data can be beneficial in terms of patients’ safety and can also increase the efficiency of the design for finding the best dose for the next phase. In this paper, the maximum concentration ( C max ) is used as the PK measure guiding the dose selection. The ethically attractive approach, which is based on the probability of efficacy, is used as a dose optimisation criterion. At each stage of an adaptive trial, that dose is selected for which the criterion is maximised, subject to the constraints imposed on the C max and the probability of toxicity. The inter-patient variability of the PK model parameters is considered, and population D -optimal sampling time points for measuring the concentration of a drug in the blood are calculated. The method is illustrated with a one-compartment PK model with first-order absorption, with the parameters being assumed to be random. The Cox model for bivariate binary responses is employed to model the dose–response outcomes. The results of a simulation study for several plausible dose–response scenarios show a significant gain in the efficiency of the design, as well as a reduction in the proportion of toxic responses.

Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection.

ABSTRACT The Pest Management Attitudes (PMA) scale was developed to provide a unidimensional and versatile tool to assess attitudes toward introduced pests and their management. While the PMA has been tested and shown strong psychometric properties in samples from Aotearoa, New Zealand (NZ), it is only beginning to be used internationally. This study tested the utility and influence of wording of the PMA scale in the United Kingdom (UK), using a 2021 survey (N = 999) distributed via online platform Prolific. Two of the 9 PMA scale items were not appropriate in our UK sample. We posit that despite references to introduced and native species in the PMA wording, many participants completed the survey with human rather than biodiversity pests in mind. While the PMA remains a valuable tool for understanding attitudes toward pests and their management, wording may need modification to ensure that concepts translate cross-culturally to retain meaningful comparisons.

Despite machine learning models being widely used today, the relationship between a model and its training dataset is not well understood. We explore correlation inference attacks, whether and when a model leaks information about the correlations between the input variables of its training dataset. We first propose a model-less attack, where an adversary exploits the spherical parameterization of correlation matrices alone to make an informed guess. Second, we propose a model-based attack, where an adversary exploits black-box model access to infer the correlations using minimal and realistic assumptions. Third, we evaluate our attacks against logistic regression and multilayer perceptron models on three tabular datasets and show the models to leak correlations. We lastly show how extracted correlations can be used as building blocks for attribute inference attacks and enable weaker adversaries. Our results raise fundamental questions on what a model does and should remember from its training set.

ABSTRACT Objectives Interest in teleneuropsychology services increased considerably after the COVID-19 pandemic. However, the utility of unsupervised administration of computerized tests remains largely unexplored. In the present study, we developed a brief computerized battery that assesses self-reported cognitive abilities and performances on executive functioning and verbal memory. We investigated the equivalence of the self-administration online (SAO) procedure and the face-to-face (FTF) administration. Preliminary normative data were developed and the acceptance of the SAO procedure was explored. Methods A community sample of 169 Greek adults [94 women; mean age: 41.95 (SD = 13.40) years, mean years of education: 15.10 (SD = 2.65)] completed the SAO assessment. A subgroup of 40 participants was tested in a counterbalanced way both with SAO and FTF. Participants’ performances were compared with paired sample t-tests and the agreement between the two methods was estimated with intraclass correlation coefficients (ICCs). Multiple linear regression analyses were applied to investigate the effect of demographic characteristics on SAO measures. Results No difference between SAO and FTF scores was observed. ICCs indicated moderate to good agreement (.418–.848) for most measures. Age was positively associated with self-reported cognitive state and negatively with neuropsychological performances and the level of acceptance of the SAO procedure. Approximately 80% of participants reported satisfaction from the SAO assessment, 69% good compliance with the instructions, but less than 30% belief that the FTF assessment could be adequately replaced. Conclusion SAO testing is feasible and well accepted among Greek adults yielding equivalent results with FTF testing. Despite the wide satisfaction, though, notable reluctance was noted for the substitution of FTF with SAO procedures.

ABSTRACT This article investigates the EU’s global potential leadership and actorness in the development and diffusion of Artificial Intelligence (AI). In the EU, AI has raised societal and policy concern due to persisting evidence of the technology disregarding ethical principles and fundamental rights. To address these risks, European institutions and politicians project leadership claims for global norms underpinned by a human-centric and rights-based approach. This article qualitatively assesses the extent to which such leadership claims can be realised through the lens actorness. It concludes that whilst EU official discourse is ambitious in tone, for the EU to realise its potential and exercise effective exemplary and diplomatic leadership in the global AI domain, it must address existing deficiencies in its actorness capabilities internally and externally.

ABSTRACT Tuberculosis (TB) is a communicable, airborne infection caused by the bacillus Mycobacterium tuberculosis. Pulmonary tuberculosis (PTB) is the most common presentation, although infection can spread anywhere to cause extra-pulmonary tuberculosis (EPTB). In this paper, a novel fractional order mathematical model is designed for the transmission dynamics of tuberculosis. Uninfected vulnerable individuals are categorized into the following: susceptible with underline ailment and susceptible without underline ailment. The research seeks to qualitatively and quantitatively analyze the proposed model and suggests comprehensive intervention measures for the control of tuberculosis among individuals with underline ailment. Some of the major highlights from the numerical investigation points out that TB vaccination is key to reducing the spread of TB among individuals with underline ailment. Furthermore, efforts to step down the spread of TB through awareness campaigns could significantly reduce the burden of the disease among individuals with co-morbidity.