Career decisions are motivated in part by our internal values, but also are influenced strongly by innumerable external forces perceived in the context of our lives. In the research reported here, we explore various social, cultural, economic, and educational factors, as well as personal and professional reasons that influence students in choosing library and information science (LIS) professions as a career. Master of Library and Information Science (MLIS) students from four universities located in four different countries were invited to take part in an online questionnaire survey. The universities were Shanghai University (SHU), the University of British Columbia (UBC), the University of Hong Kong (HKU) and the University of Tsukuba (UT). 175 selfcompleted questionnaires were collected in total. Survey results indicated that students enrolled in MLIS programmes were predominately female. Differences and similarities were encountered for the different sites. For example HKU and UBC had the largest number of students with graduate-level qualifications prior to entering the MLIS programme; and students at HKU and UBC tended to vary widely in terms of their educational and occupational backgrounds. For the majority of the HKU and UBC respondents, the decision to obtain a professional qualification in LIS was driven by the desire to maximize the benefits of a career change or for career advancement, while the majority of respondents at the UT and SHU did not already have a job or much work experience. While the total surveyed populations are small; the study will be of interest and value to LIS educators and administrators responsible for recruiting MLIS graduates and hiring LIS professions.

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The establishment of abundant three‐phase interfaces with accelerated mass transfer in air cathodes is highly desirable for the development of high‐rate and long‐cycling rechargeable zinc–air batteries (ZABs). Covalent organic frameworks (COFs) exhibit tailored nanopore structures, facilitating the rational tuning of their specific properties. Here, by finely tuning the fluorinated nanopores of a COF, a novel air cathode for rechargeable ZABs is unprecedentedly designed and synthesized. COF nanosheets are decorated with fluorinated alkyl chains, which shows high affinity to oxygen (O2), in its nanopores (fluorinated COF). The fluorinated COF nanosheets are stacked into well‐defined O2‐transport channels, which are then assembled into aerophilic “nano‐islands” on the hydrophilic FeNi layered‐double‐hydroxide (FeNi LDH) electrocatalyst surface. Therefore, the mass‐transport “highway” for O2 and water is segregated on the nanoscale, which significantly enlarges the area of three‐phase boundaries and greatly promotes the mass‐transfer therein. ZABs based on the COF‐modified air cathode deliver a small charge/discharge voltage gap (0.64 V at 5 mA cm−2), a peak power density (118 mW cm−2), and a stable cyclability. This work provides a feasible approach for the design of the air cathodes for high‐performance ZABs, and will expand the new application of COFs.

Product Service System (PSS) is introduced as a new concept which means the products and services can be integrated to a package and delivered to customer. From the definition and classification, a PSS can be seen as a system including a set of servicing modes of product. Therefore, designing appropriate PSS to satisfy customer requirements is an important issue in current research. This article reviews current design methodologies and categorized them into three patterns. Each pattern has its advantages and limitations. Hence a new design approach is proposed in this paper. This approach includes integral develop process and focuses on the conceptual design of PSS, which requires innovative thinking to identify possible servicing modes according to customer requirements. Then the evaluation method is provided to help designers to select the principle conceptual solution.

BackgroundFrailty is a state of cumulative degradation of physiological functions that leads to adverse outcomes such as disability or mortality. Currently, there is still little understanding of the prognosis of pre-stroke frailty status with acute cerebral infarction in the elderly.ObjectiveWe investigated the association between pre-stroke frailty status, 28-day and 1-year survival outcomes, and functional recovery after acute cerebral infarction.MethodsClinical data were collected from 314 patients with acute cerebral infarction aged 65–99 years. A total of 261 patients completed follow-up in the survival cohort analysis and 215 patients in the functional recovery cohort analysis. Pre-stroke frailty status was assessed using the FRAIL score, the prognosis was assessed using the modified Rankin Scale (mRS), and disease severity using the National Institutes of Health Stroke Scale (NIHSS).ResultsFrailty was independently associated with 28-day mortality in the survival analysis cohort [hazard ratio (HR) = 4.30, 95% CI 1.35–13.67, p = 0.014]. However, frailty had no independent effect on 1-year mortality (HR = 1.47, 95% CI 0.78–2.79, p = 0.237), but it was independently associated with advanced age, the severity of cerebral infarction, and combined infection during hospitalization. Logistic regression analysis after adjusting for potential confounders in the functional recovery cohort revealed frailty, and the NIHSS score was significantly associated with post-stroke severe disability (mRS > 2) at 28 days [pre-frailty adjusted odds ratio (aOR): 8.86, 95% CI 3.07–25.58, p < 0.001; frailty aOR: 7.68, 95% CI 2.03–29.12, p = 0.002] or 1 year (pre-frailty aOR: 8.86, 95% CI 3.07–25.58, p < 0.001; frailty aOR: 7.68, 95% CI 2.03–29.12, p = 0.003).ConclusionsPre-stroke frailty is an independent risk factor for 28-day mortality and 28-day or 1-year severe disability. Age, the NIHSS score, and co-infection are likewise independent risk factors for 1-year mortality.

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Background: Numerous studies have suggested that programmed cell death (PCD) pathways play vital roles in cerebral ischemia/reperfusion (I/R) injury. However, the specific mechanisms underlying cell death during cerebral I/R injury have yet to be completely clarified. There is thus a need to identify the PCD-related gene signatures and the associated regulatory axes in cerebral I/R injury, which should provide novel therapeutic targets against cerebral I/R injury.Methods: We analyzed transcriptome signatures of brain tissue samples from mice subjected to middle cerebral artery occlusion/reperfusion (MCAO/R) and matched controls, and identified differentially expressed genes related to the three types of PCD(apoptosis, pyroptosis, and necroptosis). We next performed functional enrichment analysis and constructed PCD-related competing endogenous RNA (ceRNA) regulatory networks. We also conducted hub gene analysis to identify hub nodes and key regulatory axes.Results: Fifteen PCD-related genes were identified. Functional enrichment analysis showed that they were particularly associated with corresponding PCD-related biological processes, inflammatory response, and reactive oxygen species metabolic processes. The apoptosis-related ceRNA regulatory network was constructed, which included 24 long noncoding RNAs (lncRNAs), 41 microRNAs (miRNAs), and 4 messenger RNAs (mRNAs); the necroptosis-related ceRNA regulatory network included 16 lncRNAs, 20 miRNAs, and 6 mRNAs; and the pyroptosis-related ceRNA regulatory network included 15 lncRNAs, 18 miRNAs, and 6 mRNAs. Hub gene analysis identified hub nodes in each PCD-related ceRNA regulatory network and seven key regulatory axes in total, namely, lncRNA Malat1/miR-181a-5p/Mapt, lncRNA Malat1/miR-181b-5p/Mapt, lncRNA Neat1/miR-181a-5p/Mapt, and lncRNA Neat1/miR-181b-5p/Mapt for the apoptosis-related ceRNA regulatory network; lncRNA Neat1/miR-181a-5p/Tnf for the necroptosis-related ceRNA regulatory network; lncRNA Malat1/miR-181c-5p/Tnf for the pyroptosis-related ceRNA regulatory network; and lncRNAMalat1/miR-181a-5p for both necroptosis-related and pyroptosis-related ceRNA regulatory networks.Conclusion: The results of this study supported the hypothesis that these PCD pathways (apoptosis, necroptosis, pyroptosis, and PANoptosis) and crosstalk among them might be involved in ischemic stroke and that the key nodes and regulatory axes identified in this study might play vital roles in regulating the above processes. This may offer new insights into the potential mechanisms underlying cell death during cerebral I/R injury and provide new therapeutic targets for neuroprotection.

ObjectiveThis study aimed to develop effective artificial intelligence (AI) diagnostic models based on CT images of pulmonary nodules only, on descriptional and quantitative clinical or image features, or on a combination of both to differentiate benign and malignant ground-glass nodules (GGNs) to assist in the determination of surgical intervention.MethodsOur study included a total of 867 nodules (benign nodules: 112; malignant nodules: 755) with postoperative pathological diagnoses from two centers. For the diagnostic models to discriminate between benign and malignant GGNs, we adopted three different artificial intelligence (AI) approaches: a) an image-based deep learning approach to build a deep neural network (DNN); b) a clinical feature-based machine learning approach based on the clinical and image features of nodules; c) a fusion diagnostic model integrating the original images and the clinical and image features. The performance of the models was evaluated on an internal test dataset (the “Changzheng Dataset”) and an independent test dataset collected from an external institute (the “Longyan Dataset”). In addition, the performance of automatic diagnostic models was compared with that of manual evaluations by two radiologists on the ‘Longyan dataset’.ResultsThe image-based deep learning model achieved an appealing diagnostic performance, yielding AUC values of 0.75 (95% confidence interval [CI]: 0.62, 0.89) and 0.76 (95% CI: 0.61, 0.90), respectively, on both the Changzheng and Longyan datasets. The clinical feature-based machine learning model performed well on the Changzheng dataset (AUC, 0.80 [95% CI: 0.64, 0.96]), whereas it performed poorly on the Longyan dataset (AUC, 0.62 [95% CI: 0.42, 0.83]). The fusion diagnostic model achieved the best performance on both the Changzheng dataset (AUC, 0.82 [95% CI: 0.71-0.93]) and the Longyan dataset (AUC, 0.83 [95% CI: 0.70-0.96]), and it achieved a better specificity (0.69) than the radiologists (0.33-0.44) on the Longyan dataset.ConclusionThe deep learning models, including both the image-based deep learning model and the fusion model, have the ability to assist radiologists in differentiating between benign and malignant nodules for the precise management of patients with GGNs.