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In order to study the structural properties and biological activities of the polysaccharides of Phlebopus portentosus, water-soluble polysaccharides from the fruiting body of P. portentosus were extracted by hot water extraction followed by ethanol precipitation, deproteinized using Sevag reagent, decolorized using macroporous adsorption resin, and purified to homogeneity by successive chromatography on DEAE-52 cellulose and Sephacryl S-400HR columns. The molecular mass of the purified polysaccharides was determined by high performance gel permeation chromatography (HPGPC), and the monosaccharide composition by high performance liquid chromatography (HPLC) following pre-column derivatization with 1-phenyl-3-methyl-5-pyrazolone (PMP). Ultraviolet (UV) absorption spectroscopy was used to examine whether the polysaccharide samples contained proteins or nucleic acids, and the types of functional groups, structure and morphology were characterized by infrared spectroscopy, nuclear magnetic resonance (NMR) spectroscopy and scanning electron microscopy (SEM), respectively. The polysaccharide with high α-glucosidase inhibitory activity was selected to evaluate its inhibition kinetics on α-glucosidase. Furthermore, its in vitro hypoglycemic mechanism and inhibitory type were clarified. The results showed that among four macroporous resins (HP 2MGL, HP 20, SP 850 and SP 825) tested, SP 825 macroporous resin had the best decolorization efficiency. The fraction PPP-0 had a weight average molecular weight (mw) of 31 059 Da. Moreover, it was a heteropolysaccharide mainly composed of galactose, glucose, fucose and mannose that were linked by α- and β-glycosidic bonds. In vitro hypoglycemic experiments showed that the crude polysaccharide and the three purified polysaccharide fractions all exhibited inhibitory activity on α-glucosidase, among which PPP-2 had the highest inhibitory rate, followed by PPP-0. The inhibition was competitive and reversible. This study could provide a theoretical basis for the development of functional foods with hypoglycemic activity and hypoglycemic agents from P. portentosus polysaccharides.

Problem definition: Content promotion policies are crucial for online content platforms to improve content consumption and user engagement. However, traditional promotion policies generally neglect the diffusion effect within a crowd of users. In this paper, we study the candidate generation and promotion optimization (CGPO) problem for an online content platform, emphasizing the incorporation of the diffusion effect. Methodology/results: We propose a diffusion model that incorporates platform promotion decisions to characterize the adoption process of online content. Based on this diffusion model, we formulate the CGPO problem as a mixed-integer program with nonconvex and nonlinear constraints, which is proved to be NP-hard. Additionally, we investigate methods for estimating the diffusion model parameters using available online platform data and introduce novel double ordinary least squares (D-OLS) estimators. We prove the submodularity of the objective function for the CGPO problem, which enables us to find an efficient (1−1/e) -approximation greedy solution. Furthermore, we demonstrate that the D-OLS estimators are consistent and have smaller asymptotic variances than traditional ordinary least squares estimators. By utilizing real data from a large-scale video-sharing platform, we show that our diffusion model effectively characterizes the adoption process of online content. Compared with the policy implemented on the platform, our proposed promotion policy increases total adoptions by 49.90%. Managerial implications: Our research highlights the essential role of diffusion in online content and provides actionable insights for online content platforms to optimize their content promotion policies by leveraging our diffusion model. Funding: R. Zhang is grateful for the financial support from the Hong Kong Research Grants Council General Research Fund [Grants 14502722 and 14504123] and the National Natural Science Foundation of China [Grant 72293560/72293565]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0172. [ABSTRACT FROM AUTHOR]

Abstract. Trade-in remanufacturing is a commonly adopted business practice under which firms collect used products for remanufacturing by allowing repeat customers to trade in used products for upgraded ones at [...]

Meat is an important source of high-value protein providing sustainable nutrition for human health. The discolouration of meat results in significant waste, which threatens the sustainability of meat production in terms of availability, affordability, and utilisation. Advancing the knowledge of factors and underlying mechanisms for meat discolouration supports the sustainability transformation of meat production practices. Previous studies found that colour stability may be associated with signature changes in certain metabolites, including NADH, glutamate, methionine, and testosterone. This study aimed to confirm the effect of these metabolites and sex, sire, and muscle type on lamb meat colour. NADH and glutamate improved colour stability as evidenced by the increased metmyoglobin reductase activity, while methionine and testosterone had detrimental effects. Overall, lamb meat was discoloured with retail display for up to 10 days at 4 °C. The semitendinosus muscle had higher L*, b*, and hue angle and lower a* (p < 0.05) than other muscles, especially in ewes. Lamb meat from rams had a higher L* and hue angle and lower a* than the ewes (p < 0.05), especially in the colour-labile group, suggesting an interaction between sex and sire. The outcomes of this study will help make the production of meat more sustainable by assisting the meat industry in improving the selection of animals for meat production and processing practices to reduce meat waste due to discolouration. [ABSTRACT FROM AUTHOR]

Problem definition: Inventory commitment and monetary compensation are widely recognized as effective strategies in monopoly settings when customers are concerned about stockouts. To attract more customer traffic, a firm reveals its inventory availability information to customers before the sales season or offers monetary compensation to placate customers if the product is out of stock. This paper investigates these two strategies when retailers compete on both price and inventory availability. Methodology/results: We develop a game-theoretic framework to analyze the strategic interactions among the retailers and customers and draw the following insights. First, both inventory commitment and monetary compensation may lead to a prisoner's dilemma. Although these strategies are preferred regardless of the competitor's price and inventory decisions, the equilibrium profit of each retailer could be lower in the presence of inventory commitment or monetary compensation because they intensify the competition between the retailers. Second, we find that market competition may hurt social welfare compared with a centralized setting by reducing the product availability in equilibrium. The inventory commitment and monetary compensation strategies further intensify the competition between the retailers, therefore causing an even lower social welfare. Managerial implications: Our study shows that, although inventory commitment and monetary compensation improve retailers' profit and social welfare under monopoly, these strategies should be used with caution under competition. Funding: F. Zhang is grateful for the financial support from the National Natural Science Foundation of China [Grants 71929201, 72131004]. R. Zhang is grateful for the financial support from the Hong Kong Research Grants Council General Research Fund [Grant 14502722] and the National Natural Science Foundation of China [Grant 72293560/72293565]. Y. Yu is grateful for the financial support from the National Natural Science Foundation of China [Grant 71921001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0411. [ABSTRACT FROM AUTHOR]

Up to 50% of hepatocellular carcinoma (HCC) is caused by hepatitis B virus (HBV) infection, and the surface protein of HBV is essential for the progression of HBV-related HCC. The expression of large HBV surface antigen (LHB) is presented in HBV-associated HCC tissues and is significantly associated with the development of HCC. Gene set enrichment analysis revealed that LHB overexpression regulates the cell cycle process. Excess LHB in HCC cells induced chronic endoplasmic reticulum (ER) stress and was significantly correlated with tumor growth in vivo. Cell cycle analysis showed that cell cycle progression from G1 to S phase was greatly enhanced in vitro. We identified intensive crosstalk between ER stress and cell cycle progression in HCC. As an important regulator of the G1/S checkpoint, p27 was transcriptionally upregulated by transcription factors ATF4 and XBP1s, downstream of the unfolded protein response pathway. Moreover, LHB-induced ER stress promoted internal ribosome-entry-site-mediated selective translation of p27, and E3 ubiquitin ligase HRD1-mediated p27 ubiquitination and degradation. Ultimately, the decrease in p27 protein levels reduced G1/S arrest and promoted the progress of HCC by regulating the cell cycle. [ABSTRACT FROM AUTHOR]

Content-sharing social network platforms rely heavily on user-generated content to attract users and advertisers, but they have limited authority over content provision. We develop an intervention that leverages social interactions between users to stimulate content production. We study social nudges, whereby users connected with a content provider on a platform encourage that provider to supply more content. We conducted a randomized field experiment (N = 993 , 676) on a video-sharing social network platform where treatment providers could receive messages from other users encouraging them to produce more, but control providers could not. We find that social nudges not only immediately boosted video supply by 13.21% without changing video quality but also, increased the number of nudges providers sent to others by 15.57%. Such production-boosting and diffusion effects, although declining over time, lasted beyond the day of receiving nudges and were amplified when nudge senders and recipients had stronger ties. We replicate these results in a second experiment. To estimate the overall production boost over the entire network and guide platforms to utilize social nudges, we combine the experimental data with a social network model that captures the diffusion and over-time effects of social nudges. We showcase the importance of considering the network effects when estimating the impact of social nudges and optimizing platform operations regarding social nudges. Our research highlights the value of leveraging co-user influence for platforms and provides guidance for future research to incorporate the diffusion of an intervention into the estimation of its impacts within a social network. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Funding: H. Dai thanks the University of California, Los Angeles (UCLA) [Hellman Fellowship and Faculty Development Award] for funding support. R. Zhang is grateful for financial support from the Hong Kong Research Grants Council [Grant 16505418]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4622. [ABSTRACT FROM AUTHOR]

Cold start describes a commonly recognized challenge for online advertising platforms: with limited data, the machine learning system cannot accurately estimate the click-through rates (CTR) of new ads and, in turn, cannot efficiently price these new ads or match them with platform users. Traditional cold start algorithms often focus on improving the learning rates of CTR for new ads to improve short-term revenue, but unsuccessful cold start can prompt advertisers to leave the platform, decreasing the thickness of the ad marketplace. To address these issues, we build a data-driven optimization model that captures the essential trade-off between short-term revenue and long-term market thickness on the platform. Based on duality theory and bandit algorithms, we develop the shadow bidding with learning (SBL) algorithms with a provable regret upper bound of O (T 2 / 3 K 1 / 3 (log T) 1 / 3 d 1 / 2) , where K is the number of ads and d captures the error magnitude of the underlying machine learning oracle for predicting CTR. Our proposed algorithms can be implemented in a real online advertising system with minimal adjustments. To demonstrate this practicality, we have collaborated with a large-scale video-sharing platform, conducting a novel, two-sided randomized field experiment to examine the effectiveness of our SBL algorithm. Our results show that the algorithm increased the cold start success rate by 61.62% while compromising short-term revenue by only 0.717%. Our algorithm has also boosted the platform's overall market thickness by 3.13% and its long-term advertising revenue by (at least) 5.35%. Our study bridges the gap between the theory of bandit algorithms and the practice of cold start in online advertising, highlighting the value of well-designed cold start algorithms for online advertising platforms. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: Data and the online appendices are available at https://doi.org/10.1287/mnsc.2022.4550. [ABSTRACT FROM AUTHOR]

We consider a periodic review joint pricing and inventory control model in which a firm faces both stochastic demand and fluctuating procurement costs. To address procurement cost fluctuation, the firm [...]

In this work, we report a controllable green synthesis of gold nanoparticles (AuNPs) using chlorogenic acid (CGA) as the only reducing and stabilizing agent. The well-dispersed AuNPs are obtained due to the protection of CGA against aggregation. The resulting AuNPs are controllable in size and shape mainly by changing the molar ratio of HAuCl4 to CGA in reductive process. With an increase in CGA concentration, the average size of AuNPs increased, which resulted in different morphologies from spherical to flower-like shapes. The dropping speed of gold precursor is also found to influence the size of AuNPs in different trends. Additionally, the advantages of CGA for synergistic biological activities are demonstrated through the enhanced antioxidant activity of AuNPs with CGA for scavenging of DPPH radicals. The controllable size and shape of nanoparticles are potentially important in nanomedical application. [ABSTRACT FROM AUTHOR]

Active packaging (AP) has been developed to improve the safety, quality and integrity of food, and minimise food waste, while its application in meat is scarce. This review aims to describe meat production and consumption culture in China and New Zealand to provide the context for packaging innovation requirements, focusing on the emerging opportunities for AP to be used for the improvement of the shelf-life of pre-rigor, aged, and frozen-thawed meat products. Sustainable polymers utilised in the manufacturing of AP, manufacturing techniques, the release mechanisms of actives, and legal and regulatory constraints are also discussed. Diverse market compositions and consumption cultures in China and New Zealand require different packaging solutions to extend the shelf-life of meat. AP containing antimicrobials, moisture regulating agents, and antioxidants may be used for pre-rigor, dry- and wet-aged products and in improving the quality and shelf-life of frozen-thawed meat. Further innovations using sustainably produced polymers for AP, along with incorporating active compounds of multiple functions for effectively improving meat quality and shelf-life are necessary. Challenges remain to resolve issues with scaling the technology to commercially relevant volumes as well as complying with the rigorous legal and regulatory constraints in various countries. [ABSTRACT FROM AUTHOR]

High accuracy in demand prediction allows retailers to effectively manage their inventory and mitigate stock-outs and excess supply. A typical retail setting involves predicting the demand for hundreds of items simultaneously, some with abundant historical data and others with scarce data. In "Data Aggregation and Demand Prediction," Cohen, Zhang, and Jiao propose a novel practical method, called data aggregation with clustering (DAC), which balances the tradeoff between data aggregation and model flexibility. DAC empowers retailers to predict demand while optimally identifying the features that should be estimated at the item, cluster, and aggregate levels. Theoretically, DAC yields a consistent estimate, along with improved prediction errors relative to the benchmark that estimates a different model for each item. Practically, DAC yields a higher demand prediction accuracy relative to many common benchmarks using a real data set from a large online retailer. We study how retailers can use data aggregation and clustering to improve demand prediction. High accuracy in demand prediction allows retailers to effectively manage their inventory as well as mitigate stock-outs and excess supply. A typical retail setting involves predicting demand for hundreds of items simultaneously. Although some items have a large amount of historical data, others were recently introduced and, thus, transaction data can be scarce. A common approach is to cluster several items and estimate a joint model for each cluster. In this vein, one can estimate some model parameters by aggregating the data from several items and other parameters at the individual-item level. We propose a practical method referred to as data aggregation with clustering (DAC), which balances the tradeoff between data aggregation and model flexibility. DAC allows us to predict demand while optimally identifying the features that should be estimated at the (i) item, (ii) cluster, and (iii) aggregate levels. We show that the DAC algorithm yields a consistent and normal estimate, along with improved prediction errors relative to the decentralized benchmark, which estimates a different model for each item. Using both simulated and real data, we illustrate DAC 's improvement in prediction accuracy relative to a wide range of common benchmarks. Interestingly, the DAC algorithm has theoretical and practical advantages and helps retailers uncover meaningful managerial insights. [ABSTRACT FROM AUTHOR]

We study the joint pricing and inventory management problem in the presence of online customer reviews. Customers who purchase the product may post reviews that would influence future customers' purchasing behaviors. We develop a stochastic joint pricing and inventory management model to characterize the optimal policy in the presence of online reviews. We show that a rating‐dependent base‐stock/list‐price policy is optimal. Interestingly, we can reduce the dynamic program that characterizes the optimal policy to one with a single‐dimensional state space (the aggregate net rating). The presence of online reviews gives rise to the trade‐off between generating current profits and inducing future demands, thus having several important implications for the firm's operations decisions. First, online reviews drive the firm to deliver a better service and attract more customers to post a review. Hence, the safety‐stock and base‐stock levels are higher in the presence of online reviews. Second, the evolution of the aggregate net rating process follows a mean‐reverting pattern: When the current rating is low (respectively, high), it has an increasing (respectively, decreasing) trend in expectation. Third, although myopic profit optimization leads to significant optimality losses in the presence of online reviews, balancing current profits, and near‐future demands suffices to exploit the network effect induced by online reviews. We propose a dynamic look‐ahead heuristic policy that leverages this idea well and achieves small optimality gaps that decay exponentially in the length of the look‐ahead time window. Finally, we develop a general paid‐review strategy, which provides monetary incentives for customers to leave reviews. This strategy facilitates the retailer to (partially) separately generate current profits and induce future demands via the network effect of online reviews. [ABSTRACT FROM AUTHOR]

The effects of the rearing system (artificially vs. naturally milk-fed) and the slaughter age (3-weeks milk-fed vs. 3-months pasture-fed) on consumer liking of East-Friesian-cross dairy lamb Longissimus lumborum muscle and its association with lipid content and composition were evaluated. The artificially reared lambs were removed from their dams at 2–3 days of age and reared with cow milk. Intramuscular fat content (2.8%) was similar between treatments. Only 3 of the 25 fatty acids evaluated were influenced by the rearing system and 15 by the slaughter age. The rearing system had a minor impact (p < 0.10), but the slaughter age had a major (p < 0.01) impact on consumer liking. All consumers preferred on average meat from 3-weeks-old lambs. However, based on overall liking scores, Cluster-1 (60% of consumers) preferred meat from 3-weeks-old lambs driven by all sensory attributes but mostly tenderness, whereas Cluster-2 preferred meat from 3-months-old lambs driven by flavor only, indicating a preference for stronger flavor from older lambs finished on pasture. Meat fatty acid profile and consumer liking were not influenced by the rearing system but by lamb slaughter age, showing a niche product opportunity for the 3-weeks milk-fed lambs. [ABSTRACT FROM AUTHOR]

There has been rapid growth in on‐demand ride‐hailing platforms that serve as an intermediary to match individual service providers (drivers) with consumer demand (riders). Several major players of this market have introduced carpool services that allow passengers heading toward the same direction to share a ride at a discounted fare. In this article, we develop an analytical model to study the pricing issues of ride‐sharing platforms in the presence of carpool services, and their economical and social implications. We show that the carpool service should be provided when its quality and/or the pooling efficiency is high. When the platform finds it optimal to offer the carpool service option, the platform achieves a larger market coverage and the riders are able to enjoy more affordable rides without compromising on service quality. Our analysis reveals that the provision of carpool services benefits the platform and the riders in general, but may hurt the drivers. [ABSTRACT FROM AUTHOR]

Two‐sided platforms have become omnipresent. In this context, firms compete not only for customers but also for flexible self‐scheduling workers who can work for multiple platforms. We consider a setting where two‐sided platforms simultaneously choose prices and wages to compete on both sides of the market. We assume that customers and workers each follow an endogenous generalized attraction model that accounts for network effects. In our model, the behavior of an agent depends not only on the price or wage set by the platforms, but also on the strategic interactions among agents on both sides of the market. We show that a unique equilibrium exists and that it can be computed using a tatônnement scheme. The proof technique for the competition between two‐sided platforms is not a simple extension of the traditional (one‐sided) setting and involves different arguments. Armed with this result, we study the impact of coopetition between two‐sided platforms, that is, the business strategy of cooperating with competitors. Motivated by recent practices in the ride‐sharing industry, we analyze a setting where two competing platforms engage in a profit sharing contract by introducing a new joint service. We show that a well‐designed profit sharing contract (e.g., under Nash bargaining) will benefit every party in the market (platforms, riders, and drivers), especially when the platforms are facing intensive competition on the demand side. However, if the platforms are facing intensive competition on the supply side, the coopetition partnership may hurt the profit of at least one platform. [ABSTRACT FROM AUTHOR]

Recently, many universities apply mobile tools to teaching practices. For instance, some teachers may set up groups on mobile social apps and assign course tasks and advise college students to submit papers online. Nevertheless, how these mobile social apps affect teaching practices, especially the process of students' satisfaction needs to be further explored. To fill this research gap, we build a theoretical model of how mobile social apps' functions affect course satisfaction from the perspective of Media Richness theory and the Uses and Gratifications (U and G) theory. A total of 186 valid questionnaires from college students in China were collected, and a structural equation model was built to test our research model. The results show that as: (1) only the communication function has positive impacts on knowledge sharing, while the impact of the information storing function and information distribution function on knowledge sharing is not significant; (2) knowledge sharing does not affect course satisfaction in a direct way, but it can act indirectly through promoting collaborative learning, which shows the mediating role of collaborative learning. The theoretical implications and practical implications of the study are discussed. [ABSTRACT FROM AUTHOR]

Dry-ageing of fresh meat has been the subject of growing research interest over the past 20 years. To date, inconsistencies in addressing the impact of dry-ageing on the eating qualities of meat are found in the literature, apparently arising from diverse processing conditions. This review aims to critically assess recent work published on the impact of dry-ageing on physical and biochemical changes of meat, mostly on beef muscles, and to establish the mechanisms which impart the development of dry-aged flavour. This review discusses the factors affecting the mechanisms and proposes strategies to tailor dry-aged flavour for industrial applications. The advantage of dry-ageing over commonly used wet-ageing is in flavour development, although the reported results are not consistent. Recent developments in metabolomics and volatile research have advanced the knowledge in unlocking biomolecular signatures for dry-aged meat flavour. The underlying mechanism for flavour development of dry-aged meat is driven by an interplay between microbial activity (mostly yeast and moulds), lipid oxidation and dehydration. Flavour profile of dry-aged meat varies due to factors like animal sources (e.g. using bull beef and lamb), intramuscular fat content, use of novel dry-ageing techniques (e.g. in-bag dry-ageing and combined ageing regimes) and ageing parameters. Tailored flavour profiles and consistent quality can be achieved through manipulating these key elements when designing dry-ageing strategies. Release of lipid-derived volatiles and flavour precursors further contributes to enhancing the flavour of cooked dry-aged meat. • Impacts of dry-ageing on the eating qualities of meat were reviewed and analysed. • Flavour improvement, not tenderness, is the major advantage of dry-over wet-ageing. • Dehydration, microbial growth, and oxidation distinguish dry-ageing from wet-ageing. • Opportunities for value-added meat with tailored dry-aged flavour are presented. [ABSTRACT FROM AUTHOR]

We consider a general joint pricing and inventory management model with delayed differentiation, in which a firm serves a market with multiple products made from a generic one. The firm holds inventory for the generic product which is produced using multiple resources. Moreover, the market size, the attractiveness of each product, the firm's productivity, and the procurement cost of each resource all evolve over the planning horizon driven by an exogenous Markov process. Comparative statics analysis is essential for studying this model, offering insights on the impact of market environment fluctuation upon the firm's optimal pricing and inventory policy. Hence, we propose a new approach that combines the advantages of implicit function theorem (IFT) and monotone comparative statics (MCS) approaches to perform comparative statics analysis in our joint pricing and inventory management model under market environment fluctuation. The new approach applies to our model where the standard IFT and MCS methods are not easily amenable. Using our new comparative statics approach, we characterize the optimal pricing and production policy of the firm, and offer insights on how the firm should adaptively respond to market environment fluctuations. [ABSTRACT FROM AUTHOR]

In-bag dry-aged lean beef was produced using a stepwise ageing process. Lean bull beef striploins were dry-aged at 2°C, 75% RH under three different air velocities: 0.5, 1.5, and 2.5 m·s−1 for 7 days followed by wet-ageing for 14 days. The quality and acceptability of the dry-aged beef were compared with equivalent beef dry-aged for 21 days at 0.5 m·s−1 which served as a control. Two portions of the dry-aged beef (7/21 days) were randomly selected and held frozen at −18°C for 12 months. Shear force, drip, and cook loss decreased significantly (p<0.05) with dry-ageing time. Increased air velocities accelerated dehydration process with no negative impact on the meat quality, microbiological safety, and consumer acceptability compared to the control (p>0.05). Frozen storage for 12 months had little or no effect on the quality and acceptability of the dry-aged lean beef (p>0.05). Dry-aged lean beef of equivalent quality and palatability, with a lower level of surface microorganisms and higher yield compared to the control, could be produced using the stepwise ageing process. [ABSTRACT FROM AUTHOR]