Preferred Characteristics Research Articles

UDGAN: A new urban design inspiration approach driven by using generative adversarial networks

Abstract The morphological design of urban space affects the quality of the environment. The traditional experience-based design approach was greatly improved by introducing computational design tools. However, the existing urban design tools are mostly developed on pre-set rules or given targets, which have few contributions to enhance creativity or generate inspiring schemes. Therefore, this paper proposes a new computational urban design approach named UDGAN, integrating generative adversarial networks (GANs) and multi-objective optimization algorithms. This model utilizes urban design scheme plans over the past 20 years from a particular designer as training datasets. Four preference models were trained to autonomously generate stylized urban design schemes. Eight morphological parameters were used to analyze the model performance by comparing generated results with the ground truth. This GAN-based surrogate approach is combined with a morphological indicator alignment process using multi-objective optimization model to obtain better results. The result shows that the R2 predicted by the improved Pix2Pix model reaches 0.798, and the similarity of the generated results can be stably distributed between 0.7 and 0.8, so the design scheme of this preferred style can be effectively learned. At the same time, the pre-trained model greatly reduces the time consumption of the design scheme generation, taking 5 min approximately to complete a generation process. This approach quickly generated the design scheme with preferred features, supporting the designer with creativity and greatly saving the time of design creation, transforming computational design into an inspiration-driven process.

MFM: A Multiple-Features Model for Leisure Event Recommendation in Geotagged Social Networks

Event-based social networks (EBSNs) are rich in information about users and leisure events. The willingness of users to participate in leisure events is influenced by many factors such as event time, location, content, organizer, and social relationship factors of users. Event recommendation systems in EBSNs can help leisure event organizers to accurately find users who want to participate in events. However, to address the existing cold-start problems and improve the accuracy of event recommendations, we propose a multiple-feature-based leisure event recommendation model (MFM). We introduce the user’s social contacts into the user preference features and construct a user feature space by integrating the features of the user preferences for events and organizers and preferences of the user’s closest friends. Moreover, considering the behavioral differences between active and inactive users, we extracted the respective features and trained the feature weight models. Finally, the experimental results showed that in comparison with the baseline models, the precision of the MFM is higher by at least 7.9%.

Financial Characteristics of Outcomes-Based Agreements: What Do Canadian Public Payers and Pharmaceutical Manufacturers Prefer?

ObjectivesThis study sought to gain insight into the financial characteristics of outcomes-based agreements (OBAs) considered most suitable to Canadian public payers and pharmaceutical manufacturers, and the rationale for their preferences. MethodsA total of 17 public payers and pharmaceutical manufacturers participated in semistructured qualitative interviews, which assessed their knowledge of OBAs and their preferred financial characteristics. ResultsPayers identified 5 OBA financial models that they considered both acceptable and feasible, in no preferential order: (1) discontinuation of therapy, (2) rebates for nonresponders, (3) free trial period, (4) adjustable pricing, and (5) blended rebate. Payers had a clear preference for short-term OBAs (<1 year), whereas both payers and manufacturers agreed OBAs with longer durations (up to 5 years) would be manageable if appropriately designed. Six key success factors to design suitable and acceptable OBA financial models were identified, including the areas of interim reporting, easily measurable health outcomes, trusted data sources, engaging unbiased third-party data experts, harmonizing OBA billing methods, and the inclusion of budget caps. ConclusionsManufacturers and payers showed high level of interest in OBAs and a robust understanding of their potential role in supporting timely market access for patients in need, with the caveat that they need to be carefully designed to provide value. Further opportunities for discussion and engagement between public payers and manufacturers are needed to establish how to implement OBAs at a pan-Canadian level and how individual provinces and territories can incorporate them within their existing governance infrastructures.

EPAN-SERec: Expertise preference-aware networks for software expert recommendations with knowledge graph

The software knowledge community provides software developers with valuable knowledge of technologies, activities, tools and project management related to software development. However, a large number of unresolved questions and the lack of expert participation have become evident and critical challenges for the software knowledge community. To address the problems of label dependence, interactive data’s sparsity and unassociated knowledge in community-based software expert recommendation, we propose an Expertise Preference-Aware Network model for Software Expert Recommendation (EPAN-SERec) with knowledge graph. Firstly, the software knowledge graph is utilized as an auxiliary resource to provide domain knowledge representation. Secondly, we devise an expertise preference-learning framework by means of deep reinforcement learning that models the historical interactive information of experts and generate the expertise preference weight graph. To better learn expertise preference features, a graph convolutional network (GCN) model with integrated graph self-supervised learning is proposed to optimize the features representation. Finally, software knowledge entity embeddings with semantic information are obtained by exploiting the graph-embedding model, and the final features of question to be answered are obtained by fusing the expertise preference of experts. Extensive experiments on the dataset based on StackOverflow demonstrate that our approach achieves a better outcome than baseline models.

Novel host plant use by a specialist insect depends on geographic variation in both the host and herbivore species

Understanding the circumstances under which insect herbivores will adopt a novel host plant is a longstanding question in basic and applied ecology. While geographic variation in host use can arise through differences in both herbivore preference and plant characteristics, there is a tendency to attribute geographic variation in host use to regional differences in herbivore preference alone. This is especially true for herbivores specialized to one or a few plant species. We compared how geographic variation in herbivore preference and host plant origin shape regional differences in host plant use by the specialized herbivore, Euphydryas phaeton. In parts of its range, E. phaeton uses only a native host, Chelone glabra, while in others, it also uses an introduced host, Plantago lanceolata. We offered female butterflies from each region the non-native host plant sourced from both regions and compared their oviposition behavior. The non-native host was almost universally rejected by butterflies in the region where only the native plant is used. In the region where butterflies use both hosts, females accepted non-native plants from their natal region twice as often as non-native plants from the other region where they are not used. Acceptance differed substantially among individual butterflies within regions but not among plants within regions. Thus, both individual preference and regional differences in both the insect and non-native host contributed to the geographic variation in different ways. These results highlight that, in addition to herbivore preference, regional differences in perceived plant suitability may be an important driver of diet breadth.

Factors influencing patients' adoption and use of mobile health applications for diabetes self-management: a systematic review

Mobile health (mHealth) apps can facilitate diabetes self-management (DSM) and assist in reducing the risk of complications, enhancing diabetes control and improving patient outcomes. The objectives of this systematic review were to (1) determine the adoption level of DSM apps among diabetes patients, (2) identify the factors associated with the adoption and use of DSM apps, and (3) explore patients’ perspectives of DSM apps and the predominant preferred features. A comprehensive literature search was performed in four electronic databases: PubMed, Scopus, PsychNet, and IEEE Xplore digital library using the PRISMA guidelines. Relevant data and information were collected from studies published between 2016 and 2023, fulfilling the inclusion criteria (n = 26), and thematic analysis was performed. The adoption level of mHealth apps for DSM among diabetic patients ranged from 7.0% to 47.0%, and diverse factors relating to patients’ demographics, preferences and experiences were identified. Overall, older, male and less educated patients were less likely to adopt DSM apps, while the intention to use these apps was influenced by patients’ perceived benefits, recommendations by patients and healthcare professionals, and ease of use. Given that most of the reviewed studies were conducted in developed countries, the present patients’ adoption level of mHealth apps for DSM is relatively low, thereby highlighting the need for improvement. The factors identified in this study may be considered when attempting to encourage patients to use these apps. More research is needed to elucidate how mobile health apps can be effectively integrated into diabetes care and management pathways.

Unveiling Consumer Preferences and Intentions for Cocreated Features of a Combined Diet and Physical Activity App: Cross-Sectional Study in 4 European Countries.

Numerous mobile health apps are marketed globally, and these have specific features including physical activity tracking, motivational feedback, and recipe provision. It is important to understand which features individuals prefer and whether these preferences differ between consumer groups. In this study, we aimed to identify consumers' most preferred features and rewards for a mobile app that targets healthy eating and physical activity and to reduce the number of individual mobile health app features to a smaller number of key categories as perceived by consumers. In addition, we investigated the impact of differences in consumers' BMI and self-efficacy on their intention to use and willingness to pay for such an app. Finally, we identified the characteristics of different target groups of consumers and their responses toward app features via cluster analysis. A total of 212 participants from France, Italy, the United Kingdom, and Germany were recruited via the web to answer questions about app features, motivation, self-efficacy, demographics, and geographic factors. It is important to note that our study included an evenly distributed sample of people in the age range of 23 to 50 years (23-35 and 35-50 years). The app features in question were generated from a 14-day cocreation session by a group of consumers from the United Kingdom and the Republic of Ireland. "Home work out suggestions," "exercise tips," and "progress charts" were the most preferred app features, whereas "gift vouchers" and "shopping discounts" were the most preferred rewards. "Connections with other communication apps" was the least preferred feature, and "charitable giving" was the least preferred reward. Importantly, consumers' positive attitude toward the "social support and connectedness and mindfulness" app feature predicted willingness to pay for such an app (β=.229; P=.004). Differences in consumers' health status, motivational factors, and basic demographics moderated these results and consumers' intention to use and willingness to pay for such an app. Notably, younger and more motivated consumers with more experience and knowledge about health apps indicated more positive attitudes and intentions to use and willingness to pay for this type of app. This study indicated that consumers tend to prefer app features that are activity based and demonstrate progress. It also suggested a potential role for monetary rewards in promoting healthy lifestyle behaviors. Moreover, the results highlighted the role of consumers' health status, motivational factors, and socioeconomic status in predicting their app use. These results provide up-to-date, practical, and pragmatic information for the future design and operation of mobile health apps.

Analysis of association of sensory and laboratory assessments for quality and consumer acceptability of steamed East Africa highland bananas.

The relevance of several characteristics for the acceptability of steamed East Africa bananas (matooke) was assessed using consumer-preferred characteristics, the overall liking scores, check-all-that-apply (CATA) and the Just About Right scale. The study was conducted in rural and urban locations in three banana growing regions of Uganda. Two landraces and two hybrids were processed into matooke. Twelve trained panellists evaluated color, taste and texture sensory characteristics. Consumers scored matooke from landraces as the most liked. The CATA test showed that the most important characteristics were: smooth mouthfeel, soft to the touch, not sticky, moldable, deep yellow color, attractive, good matooke taste and smell. Principal component analysis confirmed that most of the preferred sensory characteristics were associated with the local genotypes, whereas the less preferred characteristics were associated with hybrids. Correlation analysis revealed strong positive correlations between the consumer assessed characteristics, hardness by touch, softness to touch and yellowness, as well as quantitative laboratory characteristics (moldable, hardness by touch, softness and yellowness) of the steamed matooke. Color assessed by consumers was strongly correlated with the laboratory-assessed color indicators. The strong associations observed between laboratory-assessed and consumer-based characteristics (moldable by touch and yellowness) suggest the possibility of predicting consumer characteristics using quantitative laboratory sensory assessments. Matooke taste as assessed by consumer panel is strongly associated with smooth texture and deep yellow color, which were the characteristics associated with landraces in the laboratory sensory assessment. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Patient message preferences to promote clinical conversations about chronic obstructive pulmonary disease (COPD): A discrete choice experiment.

The purpose of this study was to identify message features that motivate patients to initiate clinical conversations about Chronic Obstructive Pulmonary Disease (COPD). A secondary aim was to determine whether preferred message features vary by socio-demographic and behavioral characteristics. A discrete choice experiment was conducted in August 2020. Participants were asked to select which messages would motivate them to speak with a clinician about COPD. This included selecting messages across 8 choice sets, or a systematic combination of messages reflecting 6 attributes (e.g., susceptibility, call-to-action, emotion-frame, efficacy, message source, organizational support). The final sample was 928, which included adults (M=62.07; SD=10.14years old) who identified as non-Hispanic, white, and with at least some college experience. Message attributes ranked from most to least important were COPD susceptibility (25.53% [95% CI=24.39, 26.66]), message source (19.32% [95% CI=18.41-20.24]), COPD organization logo (19.13%; [95% CI=18.26, 20.01]), call-to-action (14.12%; [95% CI=13.40, 14.85], emotion-frame (13.24% [95% CI=12.55-13.94]), and efficacy (8.65%; [95% CI=8.20-9.09]). Participants preferred susceptibility messages about COPD signs/symptoms rather than risk behaviors related to smoking tobacco and environmental exposures. They also preferred messages from medical authorities (i.e., clinicians, COPD organization), a call-to-action that supports their autonomy in screening decision-making, and a message that conveys hope for living a healthy life with COPD and builds their self-efficacy to get screened. Differences in message preferences were detected according to age, gender, race, ethnicity, education level, and current vs. former smoking status. This study identified message features that motivate clinical conversations about COPD, especially those from subgroups who are disproportionately at-risk for its late-stage diagnosis.

European consumers' preferences for fresh fruit and vegetables – A cross-country analysis

Although fresh fruit and vegetables are essential for a balanced diet, their per capita consumption in Europe is below the recommended minimum intake. This study investigates 14 product attributes of fresh fruit and vegetables in a European cross-country analysis. Based on an online survey conducted in France, Germany, Hungary, Italy, and Norway (n = 3093), an object-case Best-Worst Scaling approach was applied. Results revealed both international and country-specific preference characteristics. Best-Worse scores revealed that freshness and taste scored highest in all five countries, while knowledge of the producer was considered the least important attribute. The study also derived three consumer segments by applying cluster analysis, with each segment assessed according to socio-demographic profiles. The novelty of the study is to offer a cross-country analysis of fresh fruit and vegetable consumption patterns in multiple European countries based on the Best-Worst Scaling approach and international segmentation. Managerial implications are proposed.

Detection method for analyzing water spots in fabrics by near-infrared hyperspectral imaging

Water spot detection in fabrics is conventionally reliant on direct visual assessment, a practice ingrained in our daily lives and textile inspection field. While valuable, this method’s accuracy can be significantly affected by factors like fabric color and patterns resulting from additive textile dyes. In response, we present a detection method employing near-infrared (NIR) hyperspectral imaging, effectively reducing the influence of textile dyes and highlighting water spots. Initially, we acquired NIR hyperspectral images of fabric samples post-dyeing and moisture content conditioning. Remarkably, we observed that typical textile dyes minimally affected the NIR reflectance within the wavelength range of 1265–1626 nm, with a discernible decrease correlated to higher fabric moisture content. Subsequently, we captured NIR hyperspectral images of nine fabrics exhibiting varied colors and water spot characteristics. These images were then transformed into grayscale representations. Among them, the NIR images at 1459 nm emerged as preferred feature images for water spot detection, determined through analyses of contrast, entropy, and principal component images. While conducting a spray-rating evaluation of water spots in water-sprayed samples, comparing them with their respective feature images; the results affirm the efficacy of our developed feature images in facilitating visual identification and analysis of water spots in diverse fabrics, ultimately enhancing the accuracy of fabric water-repellency evaluation.

Preservice Coach Education Students’ Rankings of Coach Characteristics: A Longitudinal Study

Preservice coach education students (PCES) can provide valid insight into the most important coaching characteristics of the successful coach. Over the course of 21 academic semesters from 2002 to 2020, 1,464 PCES responded to a coaching characteristics survey while enrolled in a coach education course (CEC) at a university in the northern Rocky Mountain region of the United States. The PCES completed a survey that required the forced ranking of 10 coaching characteristics. The students’ responses were compiled and analyzed for the categorization of the most important coaching characteristics. This study was to provide the opportunity for PCES to rank preferred coach characteristics, to determine whether significant differences existed between the coach characteristic rankings, and to establish if gender differences were present in the ranking of coach characteristics. Conclusions and recommendations are offered to contribute to the advancement of coach education and insight into the most valued coaching characteristics as determined by the PCES.

Pengaruh Substitusi Tepung Beras Dan Penambahan Puree Wortel Terhadap Tingkat Kesukaan Kue Onde-Onde Ketawa

Onde-onde ketawa cookies with substitution of rice flour and addition of carrot puree is one of the innovations in making dry cakes made from wheat flour with substitution of rice flour and addition of carrot puree. The aim of this research is to find out; 1) the effect of the interaction between the substitution of rice flour and the addition of carrot puree on the level of liking for onde-onde ketawa cake; 2) the effect of rice flour substitution and the addition of carrot puree on the level of liking for onde-onde ketawa cake; 3) the effect of adding carrot puree on the level of liking for onde-onde ketawa cake; 4) the composition of the onde-onde ketawa cake with the substitution of rice flour with the addition of carrot puree is the most preferred. The research method used is an experimental type of research with the substitution of 10%, 20% and 30% rice flour and the addition of 5% and 10% carrot puree. The liking test was carried out on 50 untrained panelists using a questionnaire as a research instrument. The data obtained was then analyzed using the double ANOVA test and the Duncan test as further tests. The results of this research show that; 1) The interaction between the substitution of rice flour and the addition of carrot puree has a significant influence on the level of liking for onde-onde ketawa cakes. 2) vi Substitution of rice flour has an effect on the level of liking for onde-onde ketawa cakes; 3) The addition of carrot puree has an effect on the level of liking of onde-onde ketawa cake. 4) Substitution of 10% rice flour and addition of 10% carrot puree produces the most preferred characteristics of onde-onde ketawa cake.

Enhancing indoor environmental quality: Personalized recommendation method for demand-oriented indoor ventilation strategy

In the context of indoor environmental control, users have varying preferences with respect to their ventilation demands. Currently, the design and control of ventilation schemes do not consider user preferences adequately. To optimize the ventilation strategies based on user demands, the weight coefficients of occupant ventilation demands were extracted in this study using questionnaire surveys. The distinctive characteristics of ventilation preferences among individuals were then analyzed. Computational fluid dynamics (CFD) was used to simulate ventilation schemes with multiple air supply parameters within an office environment, and a database of ventilation performance under different conditions was generated. A multi-index evaluation was conducted to determine the recommended air supply scheme. Finally, a backpropagation neural network was employed to establish a personalized recommendation model for the ventilation scheme. The model inputs include the environmental weight coefficients assigned by the occupants and objective environmental parameters. Its outputs include the ventilation performance and air supply parameters. The results demonstrate that the recommended model can align with user demands, providing suitable ventilation performance and air supply parameters. For user-oriented indoor environment construction, this method enables the rapid and accurate determination of recommended air supply parameters and performance, facilitating the creation of a healthy built environment.

Research on wear rate of train brake pads driven by small sample data

Increased wear leads to the failure of brake pads. Both the International Union of Railways (UIC) and the China Railway Production Certification (CRCC) take wear rate as an extremely important parameter that characterizes the degree of wear, and its value can provide reference for the braking performance of the brake pads. Since the braking performance test of brake pads is time-consuming and costly, the sample data of wear rate is small. In this study, we explore the coupling relationship between the wear rate of train brake pads and its features such as initial braking speed (IBS), braking pressure (FB), braking temperature and average coefficient of friction (ACOF), and propose a Butterfly Optimization Algorithm-Back Propagation (BOA-BP) method suitable for small sample data for the wear rate prediction of brake pads. The grey relational analysis (GRA) and Pearson correlation analysis (PCA) are used to arrive at preferred features for the prediction of wear rate of brake pads, such as IBS, FB, braking distance and ACOF. The dynamic GM(1, N) and the least square method are used for sample expansion. Furthermore, the wear rate is designated as the output feature parameter and the preferred features are designated as the input feature parameters, a BOA-BP model is established for predicting the wear rate of brake pads. The results show that the wear rate obtained by dynamic GM(1, N) is consistent with the actual braking test, the maximum temperature and the braking pressure are important factors affecting wear rate. Compared to the comparison methods such as PSO-BP, GA-BP, and BP, the BOA-BP exhibits better advantages in prediction with small samples, and the average prediction accuracy with 33 sets of data and 99 sets of data are 95.70 % and 97.21 % respectively.

192 Evaluation of Textural Characteristics (Texture Profile Analysis and Shear force) on Commercial Training Pet Treats

Abstract Characteristics like scent, flavor, and texture can impact pet treat acceptability. Pet owners rely on personal organoleptic preferences of texture to judge treat quality, and subsequently determine desire to purchase. However, quantitative textural data for commercially available pet treats is currently limited and hard to find since companies that normally measure textural parameters in their products typically keep that information for their own use. This information is important for further investigation regarding product development and sensory evaluation to determine pet preferences and purchasing decisions. The objective of this experiment was to evaluate different textural characteristics of commercially available training treats that serve as a reference to manufacture new pet food products. Six different commercial brands (brand 1, brand 2, brand 3, brand 4, brand 5, and brand 6) were randomly selected from local pet stores. A TA-HDplusC texture analyzer was used to conduct a texture profile analysis (TPA) using a TA-30 probe, while the shear force (SF) test was performed using a TA-42 45° angle chisel blade probe (n = 10 samples per test per product). Data were analyzed as a 1-way ANOVA using the GLIMMIX procedure of SAS ver. 9.4 with mean separation with the PDIFF option at P ≤ 0.05. Significant differences of TPA and SF test parameters were observed among commercial brands of training treats except for chewiness which was similar among all brands. When products were subjected to the TPA test, brand 1 required the maximum force to deform the treats (P &amp;lt; 0.001). Therefore, brands 1 and 2 had the greatest adhesion to the probe (P &amp;lt; 0.001). For resilience, brands 1 and 2 had greater ability to regain the original form, but brand 2 was similar to brands 4 and 6 (P = 0.023). Brands 1 and 2 had cohesiveness values, but these were similar to brand 6 (P = 0.002). For springiness, brands 1, 2, and 6 had greater values (P = 0.0196), but brand 6 was similar to brands 4 and 5. However, brands 1, 2, and 6 had the greatest height recovered after compression among all brands (P = 0.020). Regarding the SF test, brand 2 were firmer and tougher than all other brands (P &amp;lt; 0.001). Textural differences were observed among different training treats brands; however, treats from certain brands shared similar textural characteristics. Overall, brands 1 and 2 typically had the greater values for the textural parameters evaluated. Moreover, textural data on commercially available training treats was generated, so quantitative data of commercially available training treats can be used as a reference to manufacture new pet treats. Likewise, future consumer sensory panel research into preferred textural characteristics by pet owners is warranted.

Development of a Novel L-Asparaginase with Enhanced Properties Using Ancestral Sequence Reconstruction

The overall survival of children with acute lymphoblastic leukemia (ALL) now exceeds 90% due to the precise disease risk stratification and intensification of multi-agent chemotherapy regimens. L-asparaginase (L-ASNase) is an indispensable biotherapeutic enzyme used in treatment of pediatric ALL. Discontinuation of L-ASNase treatment due to immunological or adverse reactions leads to significantly inferior disease-free survival (73% ±7% vs 92% ± 2%; P &amp;lt;0.01, Gupta et al. JCO 2020). Clinical L-ASNases are bacterial in origin, derived from either Escherichia coli or Erwinia chrysanthemi and are therefore highly immunogenic. Additionally, emerging data suggests that the concurrent glutaminase activity seen in bacterial asparaginases exacerbates its liver and pancreatic toxicity, especially in adults thereby limiting its extensive use beyond children. Therefore, finding alternative sources with preferable biochemical features remains paramount. The inferior catalytic properties of human L-ASNase restricts its functionality in ALL treatment. However, characterization has shown that guinea pig (GP) L-ASNase demonstrates significant anti-tumor properties and improved enzyme kinetics despite a 69.8% amino acid sequence identity with human L-ASNase, as compared to 30% for bacterial L-ASNases. Additionally, guinea pig L-ASNase has no glutaminase activity. Thus, to overcome the major deficiencies of bacterial L-ASNase and develop a more humanized form of L-ASNase we performed ancestral sequence reconstruction (ASR). ASR is a protein drug discovery / optimization platform that predicts ancient DNA and protein sequences from extant sequences. This approach permits higher-resolution mapping potentially highlighting functional resides. The objective therefore is to exploit functional diversity refined by natural selection using ASR to identify therapeutic variants with superior properties. Recent work on guinea pig (GP) L-ASNase (Schalk et al. J Biol Chem 2014) identified a low micromolar (μM) Michaelis constant (Km) at 57.7 ± 6.4 μM essential for hydrolyzing the extra-cellular pool of asparagine ~50 μM in human blood; thus, equipping us with the ideal mammalian L-ASNase ortholog to serve as a template to resurrect ancient ASNase sequences. Ancestral L-ASNase sequences were generated utilizing 54 extant L- asparaginase sequences, aligned using MUSCLE and an evolutionary tree inferred using MrBayes. A total of 53 ancestral nodes were identified, and the ten ancestral L-ASNase variants spanning the ancient primate and guinea pig (GP) lineage were resurrected. The sequence identity of these ancestral L-ASNase variants ranged from 81 to 98% when compared to human L-ASNase. E. coli codon optimized complementary DNA (cDNA) sequences were synthesized and subcloned intoan expression vector. Correctly cloned plasmids were transformed into E. coli BL21 (DE3) cells for protein expression. An-ASNase candidates were isolated by Ni 2+ affinity chromatography and then further purified by size exclusion chromatography. Asparaginase activity of the An-ASNase candidates were determined via a modified Nessler's reagent assay by using a continuous spectroscopic enzyme-coupled assay. Three of the ancestral L-ASNase candidates, An-88, An-104 and An-107 demonstrated excellent asparaginase activity when tested at an enzyme concentration of 0.1 mg/mL and an excess substrate concentration, with activity within a comparable range to the clinically relevant E. coli and Erwinia asparaginases. An-88 had 81% similarity whereas both An-104 and An-107 ASNases had 88% identity to human L-ASNase, which is in stark contrast to the only 30% similarity seen with bacterial L-ASNases. Detailed enzymatic characterization of these three lead drug candidates is currently being performed. Preliminary cytotoxicity data testing the ancestral L-ASNase candidates against a T-cell ALL cell line CCRF-CEM showed an expected dose response, with An-107 showing the highest cytotoxicity. Our findings demonstrate that ASR is an efficient protein drug design/optimization platform that we have now extended beyond our previous work with recombinant coagulation factors (Zakas et al. Nat Biotech 2017, Knight et al. Blood Adv 2021) to therapeutic enzymes; thereby, enabling the creation of novel recombinant L-ASNases with translational implications due to improved enzymatic specificity and reduced toxicity.

Optimal shape design of spherical squeeze film bearings using genetic algorithms

The optimal shape design of hydrodynamic axisymmetric spherical bearings under conditions of transient axisymmetric pure squeeze is investigated using genetic algorithms (GA). The optimization problem is to determine the radial clearance shape which maximizes the load impulse resulting from transient ball motion to a target minimum film thickness specification. For fully-hemispherical cups, non-dimensional analysis shows that GA-based optimal designs yield significantly larger non-dimensional impulse values when compared with those obtained either randomly or from optimal ellipsoidal geometry. A sample design application with specified load history shows that an optimal GA-inspired composite clearance specification amenable to fabrication can be constructed from preferred features obtained from the GA-based optimal shapes. The optimal composite shape design is shown to provide significantly better bearing performance, as measured by minimum film thickness, after a specified time interval than an optimal ellipsoidal design. The optimal composite shape design is also found to be less sensitive to realistic deviations in clearance shape, load impulse, and viscosity variation when compared with similar variations to an optimal ellipsoidal cup design.

Feature-Specific Salience Maps in Human Cortex.

Priority map theory is a leading framework for understanding how various aspects of stimulus displays and task demands guide visual attention. Per this theory, the visual system computes a priority map, which is a representation of visual space indexing the relative importance, or priority, of locations in the environment. Priority is computed based on both salience, defined based on image-computable properties; and relevance, defined by an individual's current goals, and is used to direct attention to the highest-priority locations for further processing. Computational theories suggest that priority maps identify salient locations based on individual feature dimensions (e.g., color, motion), which are integrated into an aggregate priority map. While widely accepted, a core assumption of this framework, the existence of independent feature dimension maps in visual cortex, remains untested. Here, we tested the hypothesis that retinotopic regions selective for specific feature dimensions (color or motion) in human cortex act as neural feature dimension maps, indexing salient locations based on their preferred feature. We used fMRI activation patterns to reconstruct spatial maps while male and female human participants viewed stimuli with salient regions defined by relative color or motion direction. Activation in reconstructed spatial maps was localized to the salient stimulus position in the display. Moreover, the strength of the stimulus representation was strongest in the ROI selective for the salience-defining feature. Together, these results suggest that feature-selective extrastriate visual regions highlight salient locations based on local feature contrast within their preferred feature dimensions, supporting their role as neural feature dimension maps.SIGNIFICANCE STATEMENT Identifying salient information is important for navigating the world. For example, it is critical to detect a quickly approaching car when crossing the street. Leading models of computer vision and visual search rely on compartmentalized salience computations based on individual features; however, there has been no direct empirical demonstration identifying neural regions as responsible for performing these dissociable operations. Here, we provide evidence of a critical double dissociation that neural activation patterns from color-selective regions prioritize the location of color-defined salience while minimally representing motion-defined salience, whereas motion-selective regions show the complementary result. These findings reveal that specialized cortical regions act as neural "feature dimension maps" that are used to index salient locations based on specific features to guide attention.

The imperative of collaboration: Lived experience perspectives on team approaches in outpatient eating disorder treatment.

Across healthcare broadly, team treatment approaches range from siloed multidisciplinary treatment to synergistic Interprofessional Collaborative Practice (IPCP), with IPCP increasingly favored. In eating disorders, clinical practice guidelines endorse team outpatient treatment, and these approaches are widely used in clinical practice. However, there is limited evidence to describe attitudes toward and experiences of team approaches, including IPCP, among individuals with a lived experience. Twenty-seven participants (aged 20-51 years) with a formal eating disorder diagnosis were recruited. Each had received outpatient eating disorder treatment from a team or teams comprising a mental health professional, dietitian, and general practitioner (GP) in the past 2 years. Qualitative data were collected via semi-structured interviews and analyzed using Braun and Clarke's reflexive thematic analysis. Four themes were derived from the qualitative analysis. Themes included: (1) working together is better; (2) the linchpin of teamwork is communication; (3) teams should foster autonomy with limit-setting; and (4) systemic failures negatively affect team treatment. Participants favored highly collaborative treatment from a team including a mental health professional, dietitian, and GP at a minimum, where the team engaged in high-quality communication and fostered autonomy with limit-setting. Systemic failures negatively affecting team treatment were reported across the care continuum. Findings endorse the application of IPCP to outpatient eating disorder treatment as a strategy to improve treatment satisfaction, engagement, and outcomes. Given the paucity of evidence exploring IPCP in this field, however, the development and evaluation of interprofessional education and treatment models is a foundational necessity. Team eating disorder treatment is widely used in clinical practice, although there is limited evidence to guide interventions. This study explores attitudes toward and experiences of team outpatient eating disorder treatment among individuals with a lived experience. Understanding preferred team treatment characteristics delivers important information to improve treatment satisfaction, engagement, and outcomes for individuals receiving outpatient eating disorder treatment.