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Substantial evidence has shown that payment delays generate negative effects on suppliers' working capital level and thus can further affect the entire supply chain's working capital efficiency and even result in bankruptcy for capital-constrained suppliers. The adoption of emerging solutions such as supply chain finance (SCF) is considered an innovative approach to deal with this issue. However, the current literature seldom considers the impact of payment term extensions on the supply chain's working capital management (WCM) through the development of applicable SCF methods. Thus, motivated by how SCF can improve a supply chain's WCM in the presence of payment delay, we establish a multi-cycle model and identify the conditions under which extended payments will impact on the supply chain's collaborative cash to cash (CC2C) cycle and the shareholder-value added (SVA). Finally, the numerical analysis not only confirms the major findings of this paper but also provides some additional insights that can assist practitioners in mitigating the adverse effects caused by payment delays. [ABSTRACT FROM AUTHOR]

Supply chain finance (SCF) is an innovative solution dedicated to optimising financial flows in supply chains, and has drawn tremendous attention from academia and industry. Considering the ever-evolving nature of SCF, the existing literature reviews in this field are limited due to a lack of integration of recent findings. Motivated by the limitation, we attempt to fill this gap by investigating the novel achievements that have been reported in the current literature. By conducting a systematic literature review, we selected 99 qualified papers published between 2010 and the first quarter of 2021, and then used descriptive analysis to identify the literature characteristics, followed by in-depth content analysis. We synthesised nine research dimensions in the selected SCF literature. By virtue of comprehensive analysis, we illustrated the embedded mechanisms among all participants in SCF practices, updated the SCF research framework, summarised the most dominant methods applied in current research, made two classifications of the financial service providers (FSPs) and SCF instruments, and provided five future research directions. The significance of this paper lies in providing both a novel theoretical foundation for academic researchers and a practical guide for industrial practitioners. [ABSTRACT FROM AUTHOR]

Abstract Background Mild cognitive impairment (MCI) was recently described as a heterogeneous group with a variety of clinical outcomes and high risk to develop Alzheimer's disease (AD). Regional cerebral blood flow (rCBF) as measured by single photon emission computed tomography (SPECT) was used to study the heterogeneity of MCI and to look for predictors of future development of AD. Methods rCBF was investigated in 54 MCI subjects using Tc-99m hexamethylpropyleneamine oxime (HMPAO). An automated analysis software (BRASS) was applied to analyze the relative blood flow (cerebellar ratios) of 24 cortical regions. After the baseline examination, the subjects were followed clinically for an average of two years. 17 subjects progressed to Alzheimer's disease (PMCI) and 37 subjects remained stable (SMCI). The baseline SPECT ratio values were compared between PMCI and SMCI. Receiver operating characteristic (ROC) analysis was applied for the discrimination of the two subgroups at baseline. Results The conversion rate of MCI to AD was 13.7% per year. PMCI had a significantly decreased rCBF in the left posterior cingulate cortex, as compared to SMCI. Left posterior cingulate rCBF ratios were entered into a logistic regression model for ROC curve calculation. The area under the ROC curve was 74%–76%, which indicates an acceptable discrimination between PMCI and SMCI at baseline. Conclusion A reduced relative blood flow of the posterior cingulate gyrus could be found at least two years before the patients met the clinical diagnostic criteria of AD.

There is an increasing demand for personalization of disease screening based on assessment of patient risk and other characteristics. For example, in breast cancer screening, advanced imaging technologies have made it possible to move away from 'one-size-fits-all' screening guidelines to targeted risk-based screening for those who are in need. Because diagnostic performance of various imaging modalities may vary across subjects, applying the most accurate modality to the patients who would benefit the most requires personalized strategy. To address these needs, we propose novel machine learning methods to estimate personalized diagnostic rules for medical screening or diagnosis by maximizing a weighted combination of sensitivity and specificity across subgroups of subjects. We first develop methods that can be applied when competing modalities or screening strategies that are observed on the same subject (paired design). Next, we present methods for studies where not all subjects receive both modalities (unpaired design). We study theoretical properties including consistency and risk bound of the personalized diagnostic rules and conduct simulation studies to examine performance of the proposed methods. Lastly, we analyze data collected from a brain imaging study of Parkinson's disease using positron emission tomography and diffusion tensor imaging with paired and unpaired designs. Our results show that in some cases, a personalized modality assignment is estimated to improve empirical area under the receiver operating curve compared with a 'one-size-fits-all' assignment strategy. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]