Preference Structure Research Articles

Establishing a biologics and biosimilars committee/network: assessing educational requirements of pharmacy and other healthcare professionals

Abstract Introduction Successful professional networking involves shared, mutually beneficial interactions between individuals and/or individuals and groups1. Biologics and biosimilar medicines prescribing is rapidly evolving with an increasing number of clinical indications in healthcare2,3. Despite their importance, the absence of a structured national network dedicated to these complex therapies has left a gap in professional education and resource sharing among pharmacists. Recognising the necessity for a cohesive support system, this work explores the learning and professional needs of pharmacists regarding biologics and biosimilars and the potential development of a national pharmacy network to address these gaps. Aim The survey aimed to explore the educational needs and preference for a network and/or committee for pharmacy and other healthcare professionals focused on biologics and biosimilars medicines. Methods An electronic survey was developed and deployed for pharmacy and other healthcare professionals involved in the management of biologics and biosimilars via email cascade and social media, 12th February - 6th March 2023. The survey explored the preferred structure of the proposed group; formal committee and/or informal network, what the group would deliver, and any educational needs for members. The survey included questions about the respondent’s roles, sectors of work, regions and their clinical area(s). The open text responses were themed. The NHS health Research Authority tool was used to identify this project as service improvement and development, therefore did not require ethical approval. Results The responses received were from 46 pharmacists, 1 pharmacy technician, 15 doctors and nurses and 4 participants whose roles were unknown, working across multiple sectors; hospital, community, industry, and academia with representation from the devolved nations (n=3) and internationally (n=1). Most respondents expressed a need for a dedicated network and committee n=52 (79%), n=10 (15%) formal network, and n=4 (6%) informal committee. Common themes in response to what the group should deliver and educational needs included; harmonisation of approaches to guidelines and policies, pathway design, reduction in unwarranted variation (68%); platform to share new ideas, knowledge and peer advice (44%); strategy and leadership in service delivery and promotion of best practice and cost training, horizon scanning (29%); working collaboratively to produce multi-centre research projects (24%); to create a structured training programme for health care professionals (9%). Discussion/Conclusion The survey results highlight a gap in the current educational and professional support systems for PP’s concerning biologics and biosimilar medicines. The identified learning needs point to a strong demand for a structured network that can provide targeted education, share best practices and facilitate professional networking. The expertise within the group can also create formal eLearning programmes for healthcare professionals. This work serves as a foundational step towards the development this network, highlighting the critical areas of focus and the collective interest of the pharmacy community in bridging these gaps. The network should also facilitate communication and collaboration across different sectors, fostering a more cohesive and informed pharmacy community. Such an innovative network would not only enhance individual competency but also contribute to the overall advancement of pharmaceutical care in this specialised area.

Relationship between personality and music preference: Psychometric adaptation of the music preference scales in a Chinese sample

Individuals evidently have different tastes in and preferences for music. The purpose of this study was to develop two scales for measuring music preference based on genres (text-based) and excerpts (clip-based), using a Chinese sample to explore the relationship between music preference and personality. Specifically, we collected measures of people’s self-reported preferences for a wide range of music genres for further analysis in Study 1. In Study 2, we developed two music preference scales based on the genres identified in Study 1, one text- and the other clip-based. The results of two principal component analyses suggest three-dimensional psychological structures of music preference using both versions of the scale: (1) Traditional and Retro, (2) Exotic and Complex, and (3) Intense and Rebellious. Correlation and regression analyses of music preferences and personality also indicated that music preference is a relatively stable construct that reflects personality traits. Moreover, the three factors identified from the results of studying music preferences in a Chinese sample differ from those identified in studies of music preference in Western countries, suggesting that music preference is influenced by culture. The two scales yielded similar but slightly different outcomes reflecting their discrete emphases and characteristics; they can be used separately or together, depending on the research topic and question. In this study we verified the structure of music preference among a Chinese sample. It provides a reference point for theoretical research on music preference and personality and has practical applications.

Structural projections to the nucleus accumbens link to impulsive components of human risk preference

Abstract Functional responses in the Nucleus Accumbens (NAcc) to risk- and reward-related cues can predict real-life risk-taking behavior. Since NAcc activity depends on neurotransmission from connected brain regions, projections to the NAcc may also predict risk preference. To quantify risk preference, we employed latent variables previously derived in a comprehensive, independent study examining the psychometric structure of risk preference, which yielded a general risk preference factor as well as several specific factors, including a factor capturing impulsivity. Informed by previous work, we preregistered a set of hypotheses concerning the association between different risk preference factors and fractional anisotropy (or FA, which is sensitive to fiber coherence) for projections to the NAcc from Medial PreFrontal Cortex (MPFC), Anterior Insula, Amygdala, and an inferior tract from the Ventral Tegmental Area (iVTA). We tested our hypotheses in a community sample of 125 healthy human adults. As predicted, bilateral iVTA-NAcc tract FA showed a negative correlation with a psychometric factor that captured impulsivity, generalizing findings from prior research. Also as predicted, FA of the bilateral Amygdala-NAcc tract was positively associated with the impulsivity factor. Contrary to predictions, however, we observed no robust associations between the general risk preference factor and FA for projections from bilateral MPFC, right Anterior Insula, or bilateral Amygdala to the NAcc. Notably, exploratory unilateral analyses revealed an association between the general risk preference factor and left MPFC-NAcc tract FA. Taken together, these findings suggest that impulse control as a facet of risk preference maps onto specific neurobiological targets, while more general facets of risk preference may be supported by structural properties of lateral fronto-striatal projections. Although the exact associated functional mechanisms remain to be fully clarified, conNAcctomic approaches like the one presented here could pave the way for further research into the physiological foundations of risk preference and related constructs.

In Silico Structural Comparison of Aromatic and Aliphatic Chiral Peptoid Oligomers.

Atomistic simulations of peptoids have the capability to predict structure-property relationships, depending on the accuracy of the associated force field. This work presents an addendum to the CGenFF-NTOID peptoid force field for aliphatic side chains. We develop parameters for two aliphatic side chains, RN1-tertiary butylethyl glycine (r1tbe) and SN1-tertiary butylethyl glycine (s1tbe). Enhanced sampled (well-tempered metadynamics) atomistic simulations are performed using CGenFF-NTOID to determine the monomer structural preferences for these side chains. The free energy minima attained through these simulations are compared with structural observations obtained from experiments. We also compare the structural preferences of aliphatic s1tbe and aromatic SN1-naphthylethyl glycine (s1ne). This is done through parallel bias metadynamics on monomers and pentamers of s1tbe and s1ne. The structural observations through simulations are also compared with available experimental metrics of the dihedral angles and pitch. The pentamer minima structures are also compared with ab initio optimized structures, which show excellent agreement. This comparison illustrates alternatives to aromatic side chains that can be used to stabilize peptoid secondary structures. The developed parameters help to increase the diversity of peptoid side chains available for materials discovery through computational studies.

Carbon emission reducing and financing strategies of capital-constrained supply chains using equity financing under different power structures

AbstractThis study considers a low-carbon supply chain model comprised of a capital-constrained manufacturer and a retailer under a cap-and-trade system. These two parties can choose from two financing modes: bank financing or mixed financing, in which the latter combines bank financing with equity financing. Two decision-making power structures are formulated, namely the Nash game model and the retailer-led Stackelberg game model. Subsequently, a comparative analysis of equilibrium outcomes for both parties within these power structures is performed, yielding the following insights: (1) In the bank financing mode, retailers find greater profitability in adopting dominance. When emission reduction efficiency is high, manufacturers obtain more profit within a Nash game context. However, when emission reduction efficiency is low, manufacturers accrue more profit in a Stackelberg game context. In the mixed financing mode without cap-and-trade regulations, supply chain participants do not express a preference for any specific game structure. (2) Without cap-and-trade regulation, in both the Nash and Stackelberg game models, the supply chain participants find more profitability in mixed financing when carbon emission reduction efficiency is high. Meanwhile, bank financing proves more profitable when the carbon emission reduction is low. Moreover, this study investigates the impact of wholesale and retail prices of low-carbon products on the financing strategies of the supply chain participants under different power structures and financing modes. Additionally, extended models are considered, including the manufacturer-led Stackelberg game structure and the capital-constrained manufacturer with a certain amount of initial capital.

A framework for generating recommendations based on trust in an informal e-learning environment

Rapid advancement in information technology promotes the growth of new online learning communities in an e-learning environment that overloads information and data sharing. When a new learner asks a question, how a system recommends the answer is the problem of the learner’s cold start. In this article, our contributions are: (i) We proposed a Trust-aware Deep Neural Recommendation (TDNR) framework that addresses learner cold-start issues in informal e-learning by modeling complex nonlinear relationships. (ii) We utilized latent Dirichlet allocation for tag modeling, assigning tag categories to newly posted questions and ranking experts related to specific tags for active questioners based on hub and authority scores. (iii) We enhanced recommendation accuracy in the TDNR model by introducing a degree of trust between questioners and responders. (iv) We incorporated the questioner-responder relational graph, derived from structural preference information, into our proposed model. We evaluated the proposed model on the Stack Overflow dataset using mean absolute precision (MAP), root mean squared error (RMSE), and F-measure metrics. Our significant findings are that TDNR is a hybrid approach that provides more accurate recommendations compared to rating-based and social-trust-based approaches, the proposed model can facilitate the formation of informal e-learning communities, and experiments show that TDNR outperforms the competing methods by an improved margin. The model’s robustness, demonstrated by superior MAE, RMSE, and F-measure metrics, makes it a reliable solution for addressing information overload and user sparsity in Stack Overflow. By accurately modeling complex relationships and incorporating trust degrees, TDNR provides more relevant and personalized recommendations, even in cold-start scenarios. This enhances user experience by facilitating the formation of supportive learning communities and ensuring new learners receive accurate recommendations.

Strategy and dynamics in sub-regional governance and planning: Lessons from Eastern England 2010 to 2020

This article extends analysis of sub-regional governance dynamics, focusing on local institutions in Eastern England in the period from 2010 to 2020. Building on Third Phase Institutionalism and the growing interest in the impact of agency, the paper shows how the narratives, practices and rules of governance institutions both shape and are shaped by the varied strategic ambitions of local actors. Thus when particular objectives of ‘recognition’, ‘investment’ and ‘control’ were pursued by local actors there was a reinforcing relationship with positive narratives, collaborative practices and stable rules. Alternatively, though, institutional constraints may emphasise other strategic objectives such as preserving or advancing the power of preferred institutional structures, together with an associated limiting of narratives and practices.

Two ubiquitous aldo-keto reductases in the genus Papaver support a patchwork model for morphine pathway evolution

The evolution of morphinan alkaloid biosynthesis in plants of the genus Papaver includes permutation of several processes including gene duplication, fusion, neofunctionalization, and deletion resulting in the present chemotaxonomy. A critical gene fusion event resulting in the key bifunctional enzyme reticuline epimerase (REPI), which catalyzes the stereochemical inversion of (S)-reticuline, was suggested to precede neofunctionalization of downstream enzymes leading to morphine biosynthesis in opium poppy (Papaver somniferum). The ancestrally related aldo-keto reductases 1,2-dehydroreticuline reductase (DRR), which occurs in some species as a component of REPI, and codeinone reductase (COR) catalyze the second and penultimate steps, respectively, in the pathway converting (S)-reticuline to morphine. Orthologs for each enzyme isolated from the transcriptomes of 12 Papaver species were shown to catalyze their respective reactions in species that capture states of the metabolic pathway prior to key evolutionary events, including the gene fusion event leading to REPI, thus suggesting a patchwork model for pathway evolution. Analysis of the structure and substrate preferences of DRR orthologs in comparison with COR orthologs revealed structure-function relationships underpinning the functional latency of DRR and COR orthologs in the genus Papaver, thus providing insights into the molecular events leading to the evolution of the pathway.

Expanding the biosynthesis spectrum of hydroxy fatty acids: unleashing the potential of novel bacterial fatty acid hydratases

BackgroundHydroxy fatty acids represent an emerging class of compounds with promising applications in the chemical, medicinal and functional food sectors. The challenges associated with their chemical synthesis have spurred exploration of biological synthesis as an alternative route, particularly through the use of fatty acid hydratases. Fatty acid hydratases catalyse the regioselective addition of a hydrogen atom and a hydroxyl group from a water molecule to the carbon–carbon cis-double bond of unsaturated fatty acids to form hydroxy fatty acids. Despite having been discovered in the early 1960s, previous research has primarily focused on characterizing single fatty acid hydratase variants with a limited range of substrates. Comprehensive studies that systematically examine and compare the characteristics of multiple variants of fatty acid hydratases are still lacking.ResultsIn this study, we employed an integrated bioinformatics workflow to identify 23 fatty acid hydratases and characterized their activities against nine unsaturated fatty acid substrates using whole-cell biotransformation assays. Additionally, we tested a dual-protein system involving two fatty acid hydratases of distinct regioselectivity and demonstrated its suitability in enhancing the biosynthesis of di-hydroxy fatty acids.ConclusionsOur study demonstrates that fatty acid hydratases can be classified into three subtypes based on their regioselectivity and provides insights into their preferred substrate structures. These understandings pave ways for the design of optimal fatty acid hydratase variants and bioprocesses for the cost-efficient biosynthesis of hydroxy fatty acids.

Optimization of a finned multi-tube latent heat storage system using new structure evaluation indexes

The latent heat thermal energy storage (LHTES) is one of the most promising ways of storing solar thermal energy. Since the thermal conductivity of phase change materials are low, traditional shell and tube heat exchangers tend to develop dead zones. Therefore, structural optimization is essential, and a finned multi-tube design is recommended. In this work, nineteen structures for a heat storage tank are designed to explore the influence of different specifications of heat exchange tubes and fins on heat storage/release performance. After establishing 3D physical models, ANSYS/Fluent simulation and a two-step optimization for charging and discharging processes were conducted. Among these structures, N3-M3-D10.2 emerged as the most efficient during charging process in terms of reducing the complete melting time by 25 % and increasing the 3-h heat storage volume by 2.95 times; however, it shows low performance during discharging process, especially with a 2.24 times non-uniformity factor. In the four preferred structures, the benefit-to-cost ratio of N3-M3-D10.2 is 48 %–86.6 % higher than that of the others. The results also show that the number of tubes and fins are negatively related to the melting rate, and positively related to the solidification rate. Moreover, the influence of fin diameter is greater than that of fin number and tube diameter on heat transfer rate. The novelty of this work is not only lies in both considering the charging and discharging processes, but also in using dimensionless normalized indexes and different weights based on customers’ actual requirements for optimization.

MOF-on-MOF Growth: Inducing Naturally Nonpreferred MOFs and Atypical MOF Growth.

ConspectusOverflowing metal-organic frameworks (MOFs) have been synthesized from a wide range of metal and organic components for specific purposes and intellectual curiosity. Each MOF has unique chemical and structural characteristics directed by the incorporated components, metal ions (or clusters), organic linkers, and their intrinsic coordination interactions. These incorporated components and structural characteristics are two pivotal factors influencing MOFs' fundamental properties and subsequent applications. Therefore, selecting the appropriate metal and organic components, considering their innate chemical and structural properties, is crucial to endow the final MOFs with the desired properties. Ultimately, producing MOFs with a desired structure using ideal components is the best approach to achieving the best MOFs tailored for specific purposes with desired properties. However, achieving MOFs with the intended structure from chosen components remains underdeveloped. In many cases, the resulting MOF structure is governed by the thermodynamically and/or kinetically preferred configuration (refers to a naturally preferred structure) of the chosen components and given reaction conditions. Additionally, producing hybrid MOFs with complex components, structures, and morphologies presents a great opportunity to obtain special MOFs with advanced properties and functions. In this Account, we outline our group's efforts over the past few years to develop naturally nonpreferred MOFs through the induced MOF-on-MOF growth process and atypical hybrid MOFs via nonstandard MOF-on-MOF growth. First, we highlight the prime strategy for producing naturally nonpreferred MOFs based on template-induced MOF-on-MOF growth. In this section, we discuss the two basic growth behaviors, isotropic and anisotropic growth of naturally nonpreferred MOFs, determined by the degree of matching between the cell lattices of the two MOFs. Second, we introduce the MOF farming concept for the productive cultivation and effective harvesting of naturally nonpreferred MOFs made by MOF-on-MOF growth. Here we discuss the importance of selecting the ideal MOF template for productive growth and developing an efficient method for harvesting cultivated MOFs. Next, we describe atypical anisotropic MOF-on-MOF growths between two MOFs with mismatched cell lattices. In this section, we introduce tip-to-middle MOF-on-MOF growth involving self-structural adjustment of the secondary MOF, logical inference of unidentified MOF structures based on MOF-on-MOF growth behavior and morphological features, and MOF-on-MOF growth accompanied by etching and transformation of the template. Finally, we discuss the perspectives and challenges of MOF-on-MOF growth and the synthesis of naturally nonpreferred MOFs. We hope that this Account offers valuable insights into the rational design and development of MOFs with desired structural and compositional characteristics, leading to the creation of ideal MOFs.

MELD-Adapt: On-the-Fly Belief Updating in Integrative Molecular Dynamics.

Integrative structural biology synergizes experimental data with computational methods to elucidate the structures and interactions within biomolecules, a task that becomes critical in the absence of high-resolution structural data. A challenging step for integrating the data is knowing the expected accuracy or belief in the dataset. We previously showed that the Modeling Employing Limited Data (MELD) approach succeeds at predicting structures and finding the best interpretation of the data when the initial belief is equal to or slightly lower than the real value. However, the initial belief might be unknown to the user, as it depends on both the technique and the system of study. Here we introduce MELD-Adapt, designed to dynamically evaluate and infer the reliability of input data while at the same time finding the best interpretation of the data and the structures compatible with it. We demonstrate the utility of this method across different systems, particularly emphasizing its capability to correct initial assumptions and identify the correct fraction of data to produce reliable structural models. The approach is tested with two benchmark sets: the folding of 12 proteins with coarse physical insights and the binding of peptides with varying affinities to the extraterminal domain using chemical shift perturbation data. We find that subtle differences in data structure (e.g., locally clustered or globally distributed), starting belief, and force field preferences can have an impact on the predictions, limiting the possibility of a transferable protocol across all systems and data types. Nonetheless, we find a wide range of initial setup conditions that will lead to successful sampling and identification of native states, leading to a robust pipeline. Furthermore, disagreements about how much data is enforced and satisfied rapidly serve to identify incorrect setup conditions.

Oriented Crystal Growth of Li0.33La0.557TiO3 Nanowire Induced by One-Dimensional Polymer Sheath toward Rapid Lithium-Ion Transfer.

Superionic conductor-based solid-state electrolytes with preferred crystal structures hold great promise for realizing ultrafast lithium-ion (Li+) transfer, which is urgently desired for all-solid-state lithium batteries. However, the precise control of crystal growth of superionic conductors is still challenging since the crystals always spontaneously grow to disordered structures with the lowest internal energy to ensure thermodynamic stability. Herein, a coaxial nanowire with a polyvinylpyrrolidone (PVP) sheath and a Li0.33La0.557TiO3 (LLTO) precursor core (PVP/LLTO-caNW) is prepared through coaxial electrospinning, followed by sintering into LLTO nanowire with an oriented crystal structure (LLTO-caNW). We demonstrate that the one-dimensional PVP sheath as a sacrificial layer generates uniform and the strongest adsorption ability on the (110) phase among different LLTO crystal planes, which induces the crystal to preferentially grow along the c-axis (the fastest Li+ transfer direction) during the nucleation and growth processes. As a result, the prepared LLTO-caNW displays an ultrahigh bulk ionic conductivity of 3.13 × 10-3 S cm-1, exceeding most LLTO crystals and approaching the theoretical conductivity. Meanwhile, the oriented crystal growth imparts to LLTO-caNW significantly reduced grain boundary resistance, and the grain-boundary conductivity reaches up to 1.09 × 10-3 S cm-1. This endows the composite solid electrolyte with high ionic conduction performance and superior cycle stability in the assembled all-solid-state lithium battery.

The Effect of Hyperconjugation and Hydrogen Bonding on the Conformers of Methylated Monosaccharides.

The conformational landscapes of four 1-O-methylated monosaccharides-methyl a-glucose, methyl b-glucose, methyl a-galactose, and methyl b-galactose-were characterized using jet-cooled broadband rotational spectroscopy, supported by density functional theory calculations. A newly designed, simple pulsed nozzle assembly was used to introduced the sugar samples into a jet expansion without thermal degradation, eliminating the need for a complex and expensive laser ablation system. Ten conformers were experimentally identified by assigning their rotational spectra, and the intricate methyl internal rotation splittings were analysed. Notably, methylation alters the directionality of intramolecular hydrogen bonding of a-galactose highlighting its impact on structural preference. Natural bond orbital, intrinsic bond strength, and non-covalent interaction analyses were conducted to explore the interplay between hydrogen bonding and hyperconjugation. A set of σ to σ* neutral hyperconjugative interactions were found to override a strong hydrogen bond, driving a preference for the gauche conformers.

Structure Derivatization of IgG-Binding Peptides and Analysis of Their Secondary Structure by Circular Dichroism Spectroscopy.

Mid-sized cyclic peptides are a promising modality for modern drug discovery. Their larger interaction area coupled with an appropriate secondary structure is more suitable than small molecules for binding to the target protein. In this study, we conducted a structure derivatization of an immunoglobulin G (IgG)-binding peptide (15-IgBP), a β-hairpin-like cyclic peptide with a twisted β-strand and assessed the effect of the secondary structure on IgG-binding activity using circular dichroism (CD) spectra analysis. As a result, derivatization at the Ala5 and Gly9 positions affected the secondary structure of 15-IgBP, in particular the appearance of a small positive peak in the 220-240 nm region characteristic of 15-IgBP in the CD spectrum. Maintaining this peak at a moderate level may be important for the expression of IgG binding activity. We found the small methyl group at Ala5 to be crucial for retaining the preferred secondary structure; we also found Gly9 could be replaced by D-amino acids. By integrating these findings with previous results of the structure-activity relationship, we obtained four potent affinity peptides for IgG binding (Kd = 4.24-5.85 nM). Furthermore, we found the Gly9 position can be substituted for D-Lys. This is a new potential site for attaching functional units for conjugation with IgG for the preparation of homogeneous antibody-drug conjugates.

Programmable Chemical Evolution with Natural/Non‐Natural Building Blocks

AbstractNon‐natural building blocks (BBs) present a vast reservoir of chemical diversity for molecular recognition and drug discovery. However, leveraging evolutionary principles to efficiently generate bioactive molecules with a larger number of diverse BBs poses challenges within current laboratory evolution systems. Here, we introduce programmable chemical evolution (PCEvo) by integrating chemoinformatic classification and high‐throughput array synthesis/screening. PCEvo initiates evolution by constructing a diversely combinatorial library to create ancestral molecules, streamlines the molecular evolution process and identifies high‐affinity binders within 2–4 cycles. By employing PCEvo with 108 BBs and exploring >1017 chemical space, we identify bicyclic peptidomimetic binders against targets SAR‐CoV‐2 RBD and Claudin18.2, achieving nanomolar affinity. Remarkably, Claudin18.2 binders selectively stain gastric adenocarcinoma cell lines and patient samples. PCEvo achieves expedited evolution in a few rounds, marking a significant advance in utilizing non‐natural building blocks for rapid chemical evolution applicable to targets with or without prior structural information and ligand preference.

Chemochromism and Tunable Acoustic Phonons in Intercalated MoO3: Ag-, Bi-, In-, Mo-, Os-, Pd-, Pt-, Rh-, Ru-, Sb-, and W-MoO3.

Intercalation of several elements (Ag, Bi, In, Mo, Os, Pd, Pt, Rh, Ru, Sb, and W) is used to chemically alter a wide range of properties of two-dimensional layered α-MoO3. Intercalation modifies acoustic phonons and elastic constants, as measured with Brillouin scattering. Intercalation alters electronic bandgaps, color, structure, Raman shifts, and electron binding energies. Optical chemochromism is demonstrated with intercalants changing the color of MoO3 from transparent to brilliant blue (In, Mo, Os, and Ru) and orange (Ag). Correlations are investigated among material properties. There is evidence that in-plane longitudinal stiffness c11 correlates with changes in the bandgap, while various Raman modes appear to be connected to a variety of properties, including shear modulus c55, Mo binding energies, lattice constants, and the preferred crystal structure of the intercalant. The results indicate a surprising degree of complexity, suggesting competition among multiple distinct mechanisms and interactions involving specific intercalant species.

The impact of demographic change on value set validity and obsolescence.

To investigate the contribution of demographic trends in countries' age and gender composition to value set validity and obsolescence. Time-trade off (TTO) valuation data from 3 EQ-5D-3L value sets of 20 years or older from the United Kingdom, Japan, and the United States were re-analyzed using Bayesian heteroskedastic Tobit models with sex and age group-specific scale parameters. Original value sets were obtained by weighting the original preference structures with the countries' original demographic composition at the time of the data collection. Updated value sets were created using the original preference structure weighted using the countries' most recent demographic composition. The differences between the original and updated value sets were monitored and compared based on 95% credible intervals. The gender and age composition of the investigated countries changed in all 3 countries over time. The modelled health state preferences also depended on the respondents' gender and age. However, the overall impact of this demographic change on the investigated value sets was negligeable in all 3 countries and this finding was robust to accounting for the impact of ethnicity trends in the United States. Value sets may become redundant and obsolete for various reasons, but demographic change was not identified as a contributing factor.

Equilibrium analysis of the seller’s fulfillment channels and sales channels

Sellers’ products can be sold either through platform channels or through their own channels. Also, sellers’ orders can either be fulfilled by platforms (FBP) or be fulfilled by third-party merchants (FBM). We consider a platform and a representative seller that compete in selling two substitutable products. We develop an analytical framework for identifying each firm’s structure preference by comparing four modes: own channel with FBP (Mode OP), own channel with FBM (Mode OM), platform channel with FBP (Mode PP), and platform channel with FBM (Mode PM). Our research establishes several insights. When the seller’s products are sold through either his own channel or the platform channel with a low commission rate, the platform always prefers to provide fulfillment services for the seller – who may be unwilling to accept that service. In contrast, when the seller’s products are sold through the platform channel with a high commission rate, the platform has more incentive to “protect” the seller’s profit by offering (resp. not offering) fulfillment services to him when he prefers FBP (resp. FBM). These outcomes indicate that the likelihood of platform and seller agreeing on a fulfillment channel for the seller’s orders is strongly affected by the commission rate and the seller’s sales channel. Besides, even though the platform is always willing to provide sales channels for the seller, a seller’s acceptance of that offer is influenced by his own fulfillment channels. Finally, our analysis reveals that the only possible win–win equilibrium structures for the platform and the seller are Mode PP and Mode PM.

Ionic species representations for materials informatics

High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials. However, the characteristics of elements change when they adopt a given oxidation state, with distinct structural preferences and physical properties. We explore several methods for developing embedding vectors of elements decorated with oxidation states. Graphs generated from 110 160 crystals are used to train representations of 84 elements that form 336 species. Clustering these learned representations of ionic species in low-dimensional space reproduces expected chemical heuristics, particularly the separation of cations from anions. We show that these representations have enhanced expressive power for property prediction tasks involving inorganic compounds. We expect that ionic representations, necessary for the description of mixed valence and complex magnetic systems, will support more powerful machine learning models for materials.