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ABSTRACTYouTube plays a vital role in allowing firms to engage with customers and digitally market their products. Many firms operating on major e-commerce platforms (e.g., eBay and Amazon) rely on advertising their products on YouTube by creating video content providing product information. Hence, there is an increasing need for research to examine the various aspects of YouTube videos for better ranking and views. This research develops a framework through machine learning to predict if a particular video will rank in the top 10 on a YouTube search. This research investigates factors affecting video rankings via a post-model agnostic technique called Shapley Additive Explanations (SHAP) and sentiment analysis. The results show that video content creators should optimise video titles and descriptions with the keywords of interest. Creators should consider the sentiment of the description and strive for a positive tone. Finally, creators should solicit views and likes to obtain better rankings.

Artificial intelligence (AI) significantly revolutionizes and transforms the global healthcare industry by improving outcomes, increasing efficiency, and enhancing resource utilization. The applications of AI impact every aspect of healthcare operation, particularly resource allocation and capacity planning. This study proposes a multi-step AI-based framework and applies it to a real dataset to predict the length of stay (LOS) for hospitalized patients. The results show that the proposed framework can predict the LOS categories with an AUC of 0.85 and their actual LOS with a mean absolute error of 0.85 days. This framework can support decision-makers in healthcare facilities providing inpatient care to make better front-end operational decisions, such as resource capacity planning and scheduling decisions. Predicting LOS is pivotal in today's healthcare supply chain (HSC) systems where resources are scarce, and demand is abundant due to various global crises and pandemics. Thus, this research's findings have practical and theoretical implications in AI and HSC management.

ABSTRACTDemand forecasting is critical for energy systems, as energy is difficult to store and should only be supplied as needed. Researchers attempted to improve forecasts of energy consumption. However, they assume independent factors increase at a constant growth rate, which is unrealistic. Existing methods are designed to determine annual consumption, whereas energy-planning organizations rely on short- or medium-term consumption values. Therefore, we propose a new forecasting framework that introduces new models and scenarios. We apply a cohort intelligence-based adaptive neuro-fuzzy inference system (CI-ANFIS) with a subtractive clustering and grid partition approach to forecast net electricity consumption. One challenge in accurately predicting electricity consumption for specific projection intervals is missing values for factors independent of those known for existing net consumption. Then, we utilize a regression equation scenario approach. We test our framework using a real-world energy consumption dataset and show that our proposed framework outperforms the existing methods.

T cells can contribute to clearance of respiratory viruses that cause acute-resolving infections such as SARS-CoV-2, helping to provide long-lived protection against disease. Recent studies have suggested an additional role for T cells in resisting overt infection: pre-existing cross-reactive responses were preferentially enriched in healthcare workers who had abortive infections1, and in household contacts protected from infection2. We hypothesize that such early viral control would require pre-existing cross-reactive memory T cells already resident at the site of infection; such airway-resident responses have been shown to be critical for mediating protection after intranasal vaccination in a murine model of SARS-CoV3. Bronchoalveolar lavage samples from the lower respiratory tract of healthy donors obtained before the COVID-19 pandemic revealed airway-resident, SARS-CoV-2-cross-reactive T cells, which correlated with the strength of human seasonal coronavirus immunity. We therefore demonstrate the potential to harness functional airway-resident SARS-CoV-2-reactive T cells in next-generation mucosal vaccines.

The interoperability of systems is a critical factor for firms to make informed operational and strategic decisions and achieve a competitive edge in the marketplace. As a result, open systems that have a higher level of interoperability with secured and stable operations have significant relevance in today's global economy. Interoperability is accomplished through appropriate system architecture and design. Thus, to achieve the open system interoperability, this paper proposes a framework that looks at the system architecture at various levels of abstraction/implementation and identifies the required attributes at each of these levels. This framework can be used as a reference to analyse and determine interoperability requirements at all levels and prioritise the required aspects of interoperability.

Supplemental Digital Content is available in the text.Antibodies to seasonal human-coronaviruses (sHCoV) may cross-protect against SARS-CoV-2. We investigated antibody responses in biobanked serum obtained before the pandemic from infants with polymerase chain reaction-confirmed sHCoV. Among 141 samples with antibodies to sHCoV, 4 (2.8%) were positive for SARS-CoV-2-S1 and 8 (5.7%) for SARS-CoV-2-S2. Antibodies to sHCoV rarely cross-react with SARS-CoV-2 antigens and are unlikely to account for mild pediatric illness.

Big Data and Artificial Intelligence (BD&AI) have become so pervasive, and the opportunities they present so transformative, that they are viewed as essential for competitive growth. Since the number of firms adopting BD&AI technologies is growing exponentially, the demand for BD&AI practitioners is also growing at a rapid rate. However, several studies indicate that there is a BD&AI talent shortage and skills gap between labor market requirements and expertise available in the current workforce. This talent shortage and skills gap are now recognized as a crucial impediment in leveraging BD&AI for economic growth at the local, national, and global levels. This research aims to identify BD&AI workforce trends, gaps, and opportunities by using bibliometric analysis and extracting insights from job posting data. The study team first conducted bibliometric research and built word co-occurrence diagrams using BD&AI related articles published in high-impact journals to determine technological changes impacting various industry domains. The team then collected job postings data and summarized the skill sets required to be competitive in industries driven by BD&AI. Finally, the study team evaluated the curricula of BD&AI programs at various colleges and universities educating the future workforce and conducted a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis to bridge the gaps between industry needs and academic training. This multi-step research framework forecasts oncoming technological changes in various industry clusters, workforce skills that are and will be needed, and provides recommendations for a workforce development roadmap so that businesses can gain a competitive advantage through the use of BD&AI.

Appointment scheduling is critical to increasing resource utilization and operational performance in various industry domains, especially healthcare. Costs to care for several serious diseases are projected to grow due to the aging population and rising drug prices. Thus, there is an urgent need for efficient operational planning and scheduling to reduce expenses. This research explores ways to effectively schedule outpatient chemotherapy visits with multiple appointments requiring different resources. The study aims to assess the impact of patient no-shows and individual stochastic appointment durations in scheduling performance and determine if overbooking is viable to mitigate the adverse effects of patient no-shows. The study first applies artificial neural networks (ANN) to calculate patient no-show probabilities and individualized appointment durations. Then, it builds several optimization models that use the ANN models’ outcomes to schedule outpatient chemotherapy visits. The performance of patient schedules obtained from these optimization models is assessed using simulation analysis to identify the effectiveness of overbooking to combat patient no-shows and determine if individual stochastic appointment durations produce better key performance indicators.

Disruptions have become more commonplace in the globalised business environment; thus, many firms have been attempting to find risk mitigation strategies (RMS) to create robust and resilient supply chains. To this extent, this study evaluates the performance of various supply chains utilising different RMS to cope with disruptions using optimisation and simulation. In the optimisation phase, mixed integer programming is used to create a three-echelon supply chain design. Then, the initial supply chain is further configured to incorporate two RMS for robustness (i.e., redundancy and multi-sourcing). In the simulation phase, the aforementioned supply chains, with and without RMS for robustness, are exposed to random disruptions of various severity levels, and the effectiveness of four RMS for resiliency (e.g., visibility, flexibility) are tested using key performance indicators such as order fulfilment rates and profits. The results indicate that the RMS for robustness alone is better suited under mild to moderate disruptions while a combination of RMS for robustness and resiliency is essential to handle severe disruptions.

Individuals with potential exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) do not necessarily develop PCR or antibody positivity, suggesting that some individuals may clear subclinical infection before seroconversion. T cells can contribute to the rapid clearance of SARS-CoV-2 and other coronavirus infections1–3. Here we hypothesize that pre-existing memory T cell responses, with cross-protective potential against SARS-CoV-2 (refs. 4–11), would expand in vivo to support rapid viral control, aborting infection. We measured SARS-CoV-2-reactive T cells, including those against the early transcribed replication–transcription complex (RTC)12,13, in intensively monitored healthcare workers (HCWs) who tested repeatedly negative according to PCR, antibody binding and neutralization assays (seronegative HCWs (SN-HCWs)). SN-HCWs had stronger, more multispecific memory T cells compared with a cohort of unexposed individuals from before the pandemic (prepandemic cohort), and these cells were more frequently directed against the RTC than the structural-protein-dominated responses observed after detectable infection (matched concurrent cohort). SN-HCWs with the strongest RTC-specific T cells had an increase in IFI27, a robust early innate signature of SARS-CoV-2 (ref. 14), suggesting abortive infection. RNA polymerase within RTC was the largest region of high sequence conservation across human seasonal coronaviruses (HCoV) and SARS-CoV-2 clades. RNA polymerase was preferentially targeted (among the regions tested) by T cells from prepandemic cohorts and SN-HCWs. RTC-epitope-specific T cells that cross-recognized HCoV variants were identified in SN-HCWs. Enriched pre-existing RNA-polymerase-specific T cells expanded in vivo to preferentially accumulate in the memory response after putative abortive compared to overt SARS-CoV-2 infection. Our data highlight RTC-specific T cells as targets for vaccines against endemic and emerging Coronaviridae.

It is critical for healthcare providers to accurately determine lung cancer patients' prognostics and develop customized treatment plans. However, lung cancer has proven to be a complex disease, and every patient responds differently to treatment options, making survivability predictions highly challenging. This study proposes a holistic machine learning model that can assist healthcare providers in predicting the temporal effects of lung cancer-related factors on one-, five-, and ten-year survival rates. Variable selection algorithms such as genetic algorithm (GA) and Baruta are employed along with data balancing methods to achieve parsimonious models for survival prediction. Classification results are obtained through logistic regression and extreme gradient boosting algorithms followed by an information fusion technique to combine the classification results and identify the temporal effects of lung cancer variables over time. Results demonstrate that the prediction power of the classification models improved as the survival period increased. The models trained using the GA and intersection variable sets generated better average prediction scores. The study contributes to the cancer literature by analyzing the varying temporal impacts of lung cancer variables over varying time periods. Medical professionals can use these findings to understand better the longitudinal characteristics of lung cancer patients’ survival indicators.

Despite the successes of current coronavirus disease 2019 (COVID-19) vaccines, waning immunity, the emergence of variants of concern, and breakthrough infections among vaccinees have begun to highlight opportunities to improve vaccine platforms. Real-world vaccine efficacy studies have highlighted the reduced risk of breakthrough infections and diseases among individuals infected and vaccinated, referred to as hybrid immunity. Thus, we sought to define whether hybrid immunity shapes the humoral immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) following Pfizer/BNT162b2, Moderna mRNA-1273, ChadOx1/AZD1222, and Ad26.COV2.S vaccination. Each vaccine exhibits a unique functional humoral profile in vaccination only or hybrid immunity. However, hybrid immunity shows a unique augmentation of S2-domain-specific functional immunity that was poorly induced for the vaccination only. These data highlight the importance of natural infection in breaking the immunodominance away from the evolutionarily unstable S1 domain and potentially affording enhanced cross-variant protection by targeting the more highly conserved S2 domain of SARS-CoV-2.

ABSTRACTThe role of CD19+CD5+and CD19+CD5−B cell subpopulations in the antibody response to pneumococcal capsular polysaccharides (caps-PSs) is controversial. In the present study, we evaluated the role of human CD19+CD5+and CD19+CD5−cell populations in the serotype-specific antibody response to caps-PS. After vaccination of 5 healthy human adults with Pneumovax (23-valent pneumococcal polysaccharide vaccine [PPV23]), IgG anti-caps-PS serotype 4 antibody-producing cells resided mainly in the CD19+CD5−B cell subset, as assessed by enzyme-linked immunosorbent spot (ELISpot) analysis. Moreover, in a humanized SCID mouse model, CD19+CD5−B cells were more effective than CD19+CD5+cells in producing IgG anti-cap-PS antibodies. Finally, an association was found between the level of IgG anti-caps-PS antibodies and the number of CD19+CD5−B cells in 33 humans vaccinated with PPV23. Taken together, our data suggest that CD5 defines a functionally distinct population of B cells in humans in the anti-caps-PS immune response.

Pneumococcal conjugate vaccine (PCV) induces protective anticapsular IgG, which mediates disease immunity. IgG persistence may influence long-term protection.

Although children have been largely spared from coronavirus disease 2019 (COVID-19), the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) with increased transmissibility, combined with fluctuating mask mandates and school reopenings, has led to increased infections and disease among children. Thus, there is an urgent need to roll out COVID-19 vaccines to children of all ages. However, whether children respond equivalently to adults to mRNA vaccines and whether dosing will elicit optimal immunity remain unclear. Here, we aimed to deeply profile the vaccine-induced humoral immune response in 6- to 11-year-old children receiving either a pediatric (50 μg) or adult (100 μg) dose of the mRNA-1273 vaccine and to compare these responses to vaccinated adults, infected children, and children who experienced multisystem inflammatory syndrome in children (MIS-C). Children elicited an IgG-dominant vaccine-induced immune response, surpassing adults at a matched 100-μg dose but more variable immunity at a 50-μg dose. Irrespective of titer, children generated antibodies with enhanced Fc receptor binding capacity. Moreover, like adults, children generated cross-VOC humoral immunity, marked by a decline of omicron-specific receptor binding domain, but robustly preserved omicron spike protein binding. Fc receptor binding capabilities were also preserved in a dose-dependent manner. These data indicate that both the 50- and 100-μg doses of mRNA vaccination in children elicit robust cross-VOC antibody responses and that 100-μg doses in children result in highly preserved omicron-specific functional humoral immunity.

ABSTRACTMannose-binding lectin (MBL) is an important component of the innate immune system. It binds to the arrays of sugars commonly presented by microorganisms and activates the complement system independently of antibody. Despite detailed knowledge of the stereochemical basis of MBL binding, relatively little is known about how bacterial surface structures influence binding of the lectin. Using flow cytometry, we have measured the binding of MBL to a range of mutants of Salmonella entericaserovar Typhimurium andNeisseria gonorrhoeaewhich differ in the structure of expressed lipopolysaccharide (LPS). For both organisms, the possession of core LPS structures led to avid binding of MBL, which was abrogated by the addition of O antigen (Salmonellaserovar Typhimurium) or sialic acid (N. gonorrhoeae). Truncation of the LPS within the core led to lower levels of MBL binding. It was not possible to predict the magnitude of MBL binding from the identity of the LPS terminal sugar alone, indicating that the three-dimensional disposition of LPS molecules is probably also of importance in determining MBL attachment. These results further support the hypothesis that LPS structure is a major determinant of MBL binding.

Polymorphisms in exon 1 of the MBL-2 gene, resulting in reduced plasma levels of mannose binding lectin, were significantly overrepresented in 23 patients with primary antibody deficiency and culture-proven mycoplasma infections (P = 0.0038). This association persisted with the inclusion of a further nine suspected (doxycycline-responsive) cases (P = 0.0087). The lectin was shown to bind to three strains of mycoplasma.

ABSTRACTPolymorphisms in exon 1 of the MBL-2gene, resulting in reduced plasma levels of mannose binding lectin, were significantly overrepresented in 23 patients with primary antibody deficiency and culture-proven mycoplasma infections (P= 0.0038). This association persisted with the inclusion of a further nine suspected (doxycycline-responsive) cases (P= 0.0087). The lectin was shown to bind to three strains of mycoplasma.