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Large retail chains routinely make replenishment decisions for inventory at the store Stock-Keeping-Unit (SKU) level, relying heavily on accurate demand forecasting to meet target service levels. We critically examine the current state-of-the-art in demand forecasting, revealing a notable gap in existing models, specifically the neglect of intricate cross-SKU promotional effects. To address this gap, we introduce an innovative solution: the Attributed Space Graph Recurrent Neural Network (ASG-RNN), which encodes demand history and drivers' history using a recurrent neural network, and captures cross-SKU promotional interactions through a graph convolution network. The graph convolutions operate on an attribute space graph constructed using K -nearest neighbor algorithm based on SKU-level static attributes. Initial empirical tests were conducted on a dataset comprising 200 SKUs in the Coffee category from a retail chain with 93 stores. The analysis demonstrates that ASG-RNN, on average, reduces loss by 4.1% compared to the best benchmark, resulting in average inventory cost savings of 4.94%. Notably, for promoted SKUs, ASG-RNN reduces loss by 13% on average, and it can achieve a 30% reduction in loss when the target service rate is high. Further experiments conducted on four additional product categories, including Beer, Laundry Detergent, Milk, and Mayo, yield similar results, showcasing the robustness of ASG-RNN's performance. These findings underscore the practical significance and cost-effectiveness of our innovative approach in enhancing demand forecasting and optimizing replenishment decisions for large retail chains. [ABSTRACT FROM AUTHOR]

Numerous studies have purported to show that using online investor sentiment measures can enhance the accuracy of forecasting stock return volatility. However, many of these studies did not validate their findings with out-of-sample data, had insufficient validation samples, or failed to incorporate appropriate controls in their predictive models. None of these studies compared the predictive power of online sentiment measures with commonly used technical sentiment measures, nor did they investigate whether online sentiment measures offer any additional predictive value for volatility forecasting when controlling for technical measures. Using a large stock sample in China A-shares, we contribute to examining the additional predictive power of online sentiment measures for predicting stock volatility for different forecast horizons and different time windows, while controlling for technical measures. Methodologically, through using rolling origins and moving windows evaluation framework, we consider the benefits of using global forecasting models compared to local stock-specific models, and the robustness of our appraisal of the use of sentiment measures. We found that technical sentiment measures generally perform better than online sentiments. When controlling for technical measures, online sentiment measures have extra predictive value only when the forecasting horizon is long (e.g., one week or month), but the magnitudes of the improvement are relatively limited. [ABSTRACT FROM AUTHOR]

Enterovirus 71 (EV71) is often identified as the primary pathogen that directly leads to severe cases of HFMD, whereas the association between other enteroviruses and EV71 infection remains largely unclear. Here we report a rare case of a 5-year-old boy co-infected with EV71 and vaccine-derived Poliovirus (VDPV) type II, which were identified based on PCR and sequence analysis results and clinical symptoms and were characterized on CT. We determined that the EV71 strain belongs to the C4 subtype, and the VDPV II strain was closely genetically related to the reference Sabin type II strain. This report may improved our understanding of the clinical significance of the associations between clinical signs and the infectious properties of the involved pathogens. [ABSTRACT FROM AUTHOR]

Abstract: Mumps epidemics are usually caused by airborne transmission of mumps virus (MuV) and have high morbidity in non-immunized children. Epidemiological studies in many regions of China show that the genotype F viral strain is the most prevalent. However, the genotype A strain is currently used to prepare vaccines. Regional epidemiological MuV data suggest a significant application for the development of live attenuated mumps vaccines targeting specific genotypes. This article reports the isolation and culture of a genotype F MuV candidate strain that could be used to prepare a live attenuated mumps vaccine. This strain is shown to have good immunological efficacy and stability in neurovirulence evaluations. This work should facilitate the implementation of mumps vaccination in mainland China by targeting the most prevalent MuV genotype, genotype F. [ABSTRACT FROM AUTHOR]

Abstract: While an attenuated vaccine against mumps has played a role in controlling the epidemic of this disease worldwide, some problems with efficacy and safety of the vaccine are still present. In the work described here, a novel mumps vaccine with good immunity and safety was developed by selecting an antigen component of the mumps virus. The results suggest that this purified antigen vaccine is immunogenic in animals and is capable of inducing a specific neutralizing antibody response against viral HN, but not against other viral proteins. The clinical and pathological observations after challenge of vaccinated rhesus monkeys indicated further that the immune response induced by this vaccine provided complete protection from wild-type virus infection. Furthermore, the immunological analysis and clinical observation of experimental monkeys that were immunized with the vaccine, followed by infection with wild-type virus, suggest that no abnormal changes in expression of inflammatory cytokines and no clinical allergic reaction were found at 1 month after challenge. [Copyright &y& Elsevier]

The primary focus of this research is the proposition of a methodological framework for the clinical application of the long COVID symptoms and severity score (LC-SSS). This tool is not just a self-reported assessment instrument developed and validated but serves as a standardized, quantifiable means to monitor the diverse and persistent symptoms frequently observed in individuals with long COVID. A 3-stage process was used to develop, validate, and establish scoring standards for the LC-SSS. Validation measures included correlations with other patient-reported measures, confirmatory factor analysis, Cronbach's α for internal consistency, and test-retest reliability. Scoring standards were determined using K-means clustering, with comparative assessments made against hierarchical clustering and the Gaussian Mixture Model. The LC-SSS showed correlations with EuroQol 5-Dimension 5-Level (r s = −0.55), EuroQol visual analog scale (r s = −0.368), Patient Health Questionnaire-9 (r s = 0.538), Beck Anxiety Inventory (r s = 0.689), and Insomnia Severity Index (r s = 0.516), confirming its construct validity. Structural validity was good with a comparative fit index of 0.969, with Cronbach's α of 0.93 indicating excellent internal consistency. Test-retest reliability was also satisfactory (intraclass correlation coefficient 0.732). K-means clustering identified 3 distinct severity categories in individuals living with long COVID, providing a basis for personalized treatment strategies. The LC-SSS provides a robust and valid tool for assessing long COVID. The severity categories established via K-means clustering demonstrate significant variation in symptom severity, informing personalized treatment and improving care quality for patients with long COVID. • This study introduces the long COVID symptoms and severity score (LC-SSS), a novel, validated tool incorporating unsupervised machine learning to categorize symptom severity. This addresses a critical gap in standardized and quantifiable monitoring of long COVID, going beyond the limitations of traditional patient-reported outcome measures and offering a refined, personalized assessment framework. • The research demonstrates that the LC-SSS is strongly correlated with existing health quality measures, showing high construct and structural validity, excellent internal consistency, and satisfactory test-retest reliability. K-means clustering further identified 3 distinct severity categories among patients with long COVID, which can guide personalized treatment strategies, highlighting the tool's efficacy in capturing the complexity of long COVID symptoms. • By enabling precise classification of symptom severity, the LC-SSS facilitates tailored treatment plans and informs healthcare resource allocation and policy formulation for long COVID management. Its integration into clinical practice promises to improve diagnostic accuracy, enhance care quality, and potentially uplift patient outcomes in the context of health economics and outcomes research, making a significant contribution to the field. [ABSTRACT FROM AUTHOR]

Objective To compare cross-sectional area (CSA) and volume (V) between the trigeminal nerves (TGNs) of the affected side and the unaffected side in patients with idiopathic trigeminal neuralgia (ITN), and both nerves in normal controls, and to correlate these morphological data with degree of facial pain and emotional disorder severity in ITN patients. Methods Forty ITN patients and 40 matched healthy volunteers underwent three-dimensional fast imaging employing steady state acquisition (3D-FIESTA) and time-of-flight magnetic resonance angiography (TOF-MRA) focusing on CSA and V of the TGN cisternal segment. Correlations between the morphological results and scores of visual analogue scale (VAS), Hamilton Depression Rating Scale (HAMD), and Hamilton Anxiety Rating Scale (HAMA) were analyzed in two groups. Results CSA and V of the affected TGN were significantly smaller than in the unaffected TGN and both sides in controls. No statistical differences were observed between morphological data of the unaffected TGN and control TGNs. CSA and V of the affected TGN were significantly associated with VAS scores in ITN patients, and intermediate correlations were detected with HAMD and HAMA scores. Conclusion Degree of atrophy in the affected TGN can be effective for evaluating facial pain and assessing emotional deficits in ITN patients. [ABSTRACT FROM AUTHOR]

Highlights: [•] We investigated the prevalence and E6/E7 variants of HPV-58 in Southwest China. [•] 4 single nucleotide changes were observed in HPV-58 E6 sequences. [•] 8 single nucleotide changes were observed in HPV-58 E7 sequences. [•] HPV-58 E6/E7 variants identified belonged to the Southeast Asia lineage. [•] There was no evidence of positive selection in sequence alignment of HPV-58 E6/E7. [ABSTRACT FROM AUTHOR]

Abstract: Recent developments in depression studies have heightened the need for investigating adolescent major depressive disorder (MDD). Many previous neuroimaging studies used task designs and found consistent results in the dysfunction of brain regions in depressed adolescent patients. In this study, we aimed to evaluate the topological properties of brain functional networks of adolescents with MDD from an integrated view. Using resting state functional magnetic resonance imaging (fMRI), graph theory was applied to construct the resting networks in 16 first-episode and unmedicated adolescents with MDD and 16 healthy controls (HC). Our results showed that the topological properties of depressed adolescents’ networks were significantly disrupted compared with HC. Dysregulation of brain regions were found in the anterior cingulate cortex, dorsolateral, medial and inferior prefrontal cortex, insula, amygdala, and the temporal cortices. Furthermore, the connectivity degree of amygdala related functional connection was positively correlated with the duration of depression. Detection and estimation of these functional impairments may advance our current understanding of the pathophysiological mechanism of adolescent MDD. [Copyright &y& Elsevier]