Selection of an alternate cementitious mortar using ceramic tile dust waste: a hybrid MCDM approach.

Selection of a suitable alternative material from a pool of alternatives with many conflicting criteria becomes a Multi-Criteria Decision Making (MCDM) problem. In the present study, ternary blended mortars were prepared using ceramic tile dust waste (CTD), fly ash (FA), and ground granulated blast furnace slag (GGBFS) as binder components. Crusher dust (CD) was used as a fine aggregate component. Binder to aggregate ratios of 1:3 and 1:1 were prepared considering suitable flow. A total of 16 mortar mixes were cast. These mortars were tested for various conflicting criteria compressive strength, flexural strength, porosity, water absorption, bulk density, thermal conductivity, specific heat, thermal diffusivity, and thermal effusivity whose weightages obtained were 29.09%, 20.08%, 12.77%, 10.60%, 8.74%, 6.74%, 5.54%, 4.47%, and 1.97%, respectively, as per AHP analysis. Later, considering these different criteria and alternate mortars, it was observed that a 1:1 mortar with 20% CTD, 30% FA, and 50% GGBFS (RC20F30G50) is found to be the suitable mortar with the highest relative closeness coefficient of 0.861 and the highest net outranking flow of 0.316 with respect to MCDM techniques: technique for order of preference by similarity to ideal solution (TOPSIS) and preference ranking organization method for enrichment of evaluations (PROMETHEE-II), respectively. The ranking of the mortar in both methods complies with the relative weightages of the criteria and the performance of the mortars with respect to the above criteria.

Effects of pulse shape on pitch sensitivity of cochlear implant users.

Contemporary cochlear implants (CIs) use cathodic-leading symmetric biphasic (C-BP) pulses for electrical stimulation. It remains unclear whether asymmetric pulses emphasizing the anodic or cathodic phase may improve spectral and temporal coding with CIs. This study tested place- and temporal-pitch sensitivity with C-BP, anodic-centered triphasic (A-TP), and cathodic-centered triphasic (C-TP) pulse trains on apical, middle, and basal electrodes in 10 implanted ears. Virtual channel ranking (VCR) thresholds (for place-pitch sensitivity) were measured at both a low and a high pulse rate of 99 (Experiment 1) and 1000 (Experiment 2) pulses per second (pps), and amplitude modulation frequency ranking (AMFR) thresholds (for temporal-pitch sensitivity) were measured at a 1000-pps pulse rate in Experiment 3. All stimuli were presented in monopolar mode. Results of all experiments showed that detection thresholds, most comfortable levels (MCLs), VCR thresholds, and AMFR thresholds were higher on more basal electrodes. C-BP pulses had longer active phase duration and thus lower detection thresholds and MCLs than A-TP and C-TP pulses. Compared to C-TP pulses, A-TP pulses had lower detection thresholds at the 99-pps but not the 1000-pps pulse rate, and had lower MCLs at both pulse rates. A-TP pulses led to lower VCR thresholds than C-BP pulses, and in turn than C-TP pulses, at the 1000-pps pulse rate. However, pulse shape did not affect VCR thresholds at the 99-pps pulse rate (possibly due to the fixed temporal pitch) or AMFR thresholds at the 1000-pps pulse rate (where the overall high performance may have reduced the changes with different pulse shapes). Notably, stronger polarity effect on VCR thresholds (or more improvement in VCR with A-TP than with C-TP pulses) at the 1000-pps pulse rate was associated with stronger polarity effect on detection thresholds at the 99-pps pulse rate (consistent with more degeneration of auditory nerve peripheral processes). The results suggest that A-TP pulses may improve place-pitch sensitivity or spectral coding for CI users, especially in situations with peripheral process degeneration.

Risk assessment of multiple pesticide residues in Agrocybe aegerita: Based on a 3-year monitoring survey.

The presence of pesticide residues in Agrocybe aegerita has raised an extensive concern. In this paper, based on a 3-year monitoring survey, the dietary exposure risks through A. aegerita consumption for different population subgroups were assessed using both deterministic and semi-probabilistic approaches under the best-case and the worst-case scenarios. Among the 52 targeted pesticides, 28 different compounds were identified in the concentration range of 0.005-3.610 mg/kg, and 87.4 % of samples contained one or more pesticide residues. The most frequently detected pesticide was chlormequat, followed by chlorfenapyr and cyhalothrin. The overall risk assessment results indicated extremely low chronic, acute, and cumulative dietary exposure risks for consumers. Using the ranking matrix, intake risks of pesticides were ranked, revealing endsoluran, chlorpyrifos, and methamidophos to be in the high-risk group. Finally, considering various factors such as the toxicity and risk assessment outcomes of each positive pesticide, use suggestions were proposed for A. aegerita cultivation.

Dataset of measurable indicators of the Slovak state institutions' websites.

The dataset contains 11 measurable indicators for the website evaluation from different points of view. These indicators were collected for 60 websites of the Slovak state institutions. It provides information about the directly measurable variables, which may affect or reflect the usability, popularity and visibility of the website. Most variables were measured by online tools. The dataset is a mixture of binary, ordinal, discrete numeric and continuous numeric variables, which gives many opportunities to analyze the relations between the measurable websites' indicators. It can be used to find the structure consisting of latent variables, which cannot be directly measured (such as usability or popularity of the website). Another use is to find subgroups of state institutions, which have similar websites from some point of view.

Spatiotemporal distribution of oxidative potential in PM2.5 and its key components across six Chinese cities.

Increasing evidence has supported that oxidative potential (OP) serves as a crucial indicator of health risk of exposure to PM2.5 over mass concentration. However, there is a lack of comparative studies across multiple cities, particularly on a fine temporal scale. In this study, we aim to investigate daily variation of ambient PM2.5 OP through simultaneous samplings in six Chinese cities for one year. Results showed that more than 60 % of the sampling days exhibited non-zero ranking difference between volume-normalized oxidative potential (OPv) and mass concentration among the six cities. Key components contributing to OPv inculde Mn, NO3-, and K+, followed by Ca2+, Al, SO42-, Cl-, Fe, and NH4+. Based on these chemical components, we developed a stepwise multivariable linear regression model (R2: 0.71) for OPv prediction. The performance of the model is comparable to both species- and sources-based ones in the literature. These findings suggest that a relatively lower daily-averaged mass concentration of PM2.5 does not necessarily indicate a lower oxidative risk. Future studies and policy developments on health benefits should also consider OPv rather than mass concentration alone. Priority could be given to sources/species that contribute significantly to oxidative potential of ambient PM2.5. SYNOPSIS: This study highlights inclusion of oxidative potential as a complementary metric for air pollution assessment and control.

Into the Jungle of Biological Agents of Foodborne Diseases: Time to Put Some Order for the French Risk Manager.

Consumers can be exposed to many foodborne biological hazards that cause diseases with varying outcomes and incidence and, therefore, represent different levels of public health burden. To help the French risk managers to rank these hazards and to prioritize food safety actions, we have developed a three-step approach. The first step was to develop a list of foodborne hazards of health concern in mainland France. From an initial list of 335 human pathogenic biological agents, the final list of "retained hazards" consists of 24 hazards, including 12 bacteria (including bacterial toxins and metabolites), 3 viruses and 9 parasites. The second step was to collect data to estimate the disease burden (incidence, Disability Adjusted Life Years) associated with these hazards through food during two time periods: 2008-2013 and 2014-2019. The ranks of the different hazards changed slightly according to the considered period. The third step was the ranking of hazards according to a multicriteria decision support model using the ELECTRE III method. Three ranking criteria were used, where two reflect the severity of the effects (Years of life lost and Years lost due to disability) and one reflects the likelihood (incidence) of the disease. The multicriteria decision analysis approach takes into account the preferences of the risk managers through different sets of weights and the uncertainties associated with the data. The method and the data collected allowed to estimate the health burden of foodborne biological hazards in mainland France and to define a prioritization list for the health authorities.

Advanced machine learning approaches for predicting permeability in reservoir pay zones based on core analyses.

Permeability is the most important petrophysical characteristic for determining how fluids pass through reservoir rocks. This study aims to develop and assess intelligent computer-based models for predicting permeability. The research focuses on three novel models-Decision Tree, Bagging Tree, and Extra Trees-while also investigating previously applied techniques such as random forest, support vector regressor (SVR), and multiple variable regression (MVR). The primary dataset consists of 197 data points from a heterogeneous petroleum reservoir in the Jeanne d'Arc Basin, including laboratory-derived permeability (K), oil saturation ( S O ), water saturation ( S W ), grain density ( ρ g r ), porosity (φ), and depth. The most effective machine learning models are identified by a thorough analysis that makes use of a variety of statistical metrics, such as the coefficient of the determinant (R2), mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), maximum error (maxE), and minimum error (minE). Additionally, core features are ranked based on their importance in permeability modeling. This study deviates from conventional approaches by proposing an efficient means of forecasting permeability, reducing reliance on labor-intensive and time-consuming laboratory work. The findings reveal that MVR is unsuitable for permeability prediction, with all developed models outperforming it. Extra Trees emerges as the most accurate model, with an R2 of 0.976, while random forest and bagging tree exhibit slightly lower R2 values of 0.961 and 0.964, respectively. The ranking of these algorithms based on performance criteria is as follows: extra trees, bagging tree, random forest, SVR, decision tree, and MVR. The study also presents a detailed analysis of the impact of input parameters, highlighting porosity (φ) and water saturation ( S W ) as the most influential, while grain density ( ρ g r ), oil saturation ( S O ), and depth are considered less important. This study contributes to the petroleum industry's knowledge by showcasing the inadequacy of MVR and highlighting the superior performance of machine learning models, particularly Extra Trees. The proposed models employed in this study can help engineers and researchers determine reservoir permeability quickly and accurately by using a few core attributes, reducing the dependency on resource-intensive and time-consuming laboratory work.

Sentiment analysis of U.S. News & World Report Best Children's Hospital urology rankings: A difference in positivity between the public and academic worlds.

INTRODUCTION: Initiated in 2009, the U.S. News & World Report (USNWR) pediatric urology rankings aim to guide patients and families towards high-quality urologic care. Despite this, the pediatric urology community remains divided, with significant debate over the rankings' accuracy, utility, and potential for misleading information. While some professionals argue for a collective opt-out from these rankings, citing these concerns, others highlight their positive impact on patient care, hospital benchmarking, and financial support. OBJECTIVE: Recognizing the lack of formal evaluation on how these rankings are viewed beyond the pediatric urology community, this research endeavors to fill the gap through sentiment analysis of public news articles and academic publications. STUDY DESIGN: We captured news articles from Google News and academic papers from Ovid Medline and Embase, focusing specifically on content related to the USNWR pediatric urology rankings from 2009 to 2023. Sentiment analysis was conducted using the Valence Aware Dictionary and Sentiment Reasoner (VADER) package on both news and academic texts, aiming to capture the overall sentiment through a compound score derived from the presence of sentiment-laden words. Sensitivity analysis was performed using TextBlob Pattern Analyzer tool. RESULTS: The analysis revealed a significant divergence in sentiment between news articles and academic literature. News articles exhibited a predominantly positive sentiment, with an average compound score of 0.681, suggesting a general approval or celebration of the rankings in the public sphere. Conversely, academic literature showed a more moderate sentiment, with an average score of 0.534, indicating a nuanced perspective that includes both positive views and critical reflections on the rankings. Sensitivity analysis confirmed this observation (Figure). DISCUSSION: This difference may reflect the distinct nature of news media and academic discourse. While news outlets may prioritize celebratory narratives that align with public interest and institutional pride, academic discussions tend to offer a balanced view that critically assesses both the merits and limitations of the rankings. This discrepancy underscores the complexity of interpreting and acting upon the rankings within the pediatric urology community. CONCLUSION: While the USNWR pediatric urology rankings are generally received positively by the public, as reflected in news media, the academic community presents a more reserved sentiment. These findings suggest the need for ongoing dialogue and research to understand the implications of these rankings fully. It also calls for a strategic approach to address the concerns and perceptions of healthcare professionals, aiming to leverage the rankings in a way that truly benefits patient care and informed decision-making.

Bayesian mixture modelling with ranked set samples.

We consider the Bayesian estimation of the parameters of a finite mixture model from independent order statistics arising from imperfect ranked set sampling designs. As a cost-effective method, ranked set sampling enables us to incorporate easily attainable characteristics, as ranking information, into data collection and Bayesian estimation. To handle the special structure of the ranked set samples, we develop a Bayesian estimation approach exploiting the Expectation-Maximization (EM) algorithm in estimating the ranking parameters and Metropolis within Gibbs Sampling to estimate the parameters of the underlying mixture model. Our findings show that the proposed RSS-based Bayesian estimation method outperforms the commonly used Bayesian counterpart using simple random sampling. The developed method is finally applied to estimate the bone disorder status of women aged 50 and older.

A time-robust group recommender for featured comments on news platforms.

Introduction: Recently, content moderators on news platforms face the challenging task to select high-quality comments to feature on the webpage, a manual and time-consuming task exacerbated by platform growth. This paper introduces a group recommender system based on classifiers to aid moderators in this selection process. Methods: Utilizing data from a Dutch news platform, we demonstrate that integrating comment data with user history and contextual relevance yields high ranking scores. To evaluate our models, we created realistic evaluation scenarios based on unseen online discussions from both 2020 and 2023, replicating changing news cycles and platform growth. Results: We demonstrate that our best-performing models maintain their ranking performance even when article topics change, achieving an optimum mean NDCG@5 of 0.89. Discussion: The expert evaluation by platform-employed moderators underscores the subjectivity inherent in moderation practices, emphasizing the value of recommending comments over classification. Our research contributes to the advancement of (semi-)automated content moderation and the understanding of deliberation quality assessment in online discourse.

Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling.

BACKGROUND: Passive mobile sensing provides opportunities for measuring and monitoring health status in the wild and outside of clinics. However, longitudinal, multimodal mobile sensor data can be small, noisy, and incomplete. This makes processing, modeling, and prediction of these data challenging. The small size of the data set restricts it from being modeled using complex deep learning networks. The current state of the art (SOTA) tackles small sensor data sets following a singular modeling paradigm based on traditional machine learning (ML) algorithms. These opt for either a user-agnostic modeling approach, making the model susceptible to a larger degree of noise, or a personalized approach, where training on individual data alludes to a more limited data set, giving rise to overfitting, therefore, ultimately, having to seek a trade-off by choosing 1 of the 2 modeling approaches to reach predictions. OBJECTIVE: The objective of this study was to filter, rank, and output the best predictions for small, multimodal, longitudinal sensor data using a framework that is designed to tackle data sets that are limited in size (particularly targeting health studies that use passive multimodal sensors) and that combines both user agnostic and personalized approaches, along with a combination of ranking strategies to filter predictions. METHODS: In this paper, we introduced a novel ranking framework for longitudinal multimodal sensors (FLMS) to address challenges encountered in health studies involving passive multimodal sensors. Using the FLMS, we (1) built a tensor-based aggregation and ranking strategy for final interpretation, (2) processed various combinations of sensor fusions, and (3) balanced user-agnostic and personalized modeling approaches with appropriate cross-validation strategies. The performance of the FLMS was validated with the help of a real data set of adolescents diagnosed with major depressive disorder for the prediction of change in depression in the adolescent participants. RESULTS: Predictions output by the proposed FLMS achieved a 7% increase in accuracy and a 13% increase in recall for the real data set. Experiments with existing SOTA ML algorithms showed an 11% increase in accuracy for the depression data set and how overfitting and sparsity were handled. CONCLUSIONS: The FLMS aims to fill the gap that currently exists when modeling passive sensor data with a small number of data points. It achieves this through leveraging both user-agnostic and personalized modeling techniques in tandem with an effective ranking strategy to filter predictions.

A Ranking Tool for "Category Killer" Microbial Biobanks.

Microbial biobanks preserve and provide microbial bioresources for research, training, and quality control purposes. They ensure the conservation of biodiversity, contribute to taxonomical research, and support scientific advancements. Microbial biobanks can cover a wide range of phylogenetic and metabolic diversity ("category killers") or focus on specific taxonomic, thematic, or disease areas. The strategic decisions about strain selection for certain applications or for the biobank culling necessitate a method to support prioritization and selection. Here, we propose an unbiased scoring approach based on objective parameters to assess, categorize, and assign priorities among samples in stock in a microbial biobank. We describe the concept of this ranking tool and its application to identify high-priority strains for whole genome sequencing with two main goals: (i) genomic characterization of quality control, reference, and type strains; (ii) genome mining for the discovery of natural products, bioactive and antimicrobial molecules, with focus on human diseases. The general concept of the tool can be useful to any biobank and for any ranking or culling needs.

Pre-Operative Rank of Expectations for Shoulder Surgery (PRESS): A Novel Survey.

BACKGROUND: Patient expectations for orthopedic surgeries, and elective shoulder surgery in particular, have been shown to be important for patient outcomes and satisfaction. Current surveys assessing patient expectations lack clinical applicability and allow patients to list multiple expectations at the highest level of importance. The purpose of this study was to develop and evaluate the use of a novel, rank-based survey assessing the relative importance of patient expectations for shoulder surgery. METHODS: The Preoperative Rank of Expectations for Shoulder Surgery (PRESS) survey was developed by polling 100 patients regarding their expectations for surgery. The PRESS survey consisted of eight common expectations for elective shoulder surgery by importance and a 0-100% scale of expected pain relief and range of motion improvement. After initial development of the PRESS survey, it was administered preoperatively to 316 patients undergoing surgery for shoulder arthritis, rotator cuff tear, subacromial pain syndrome, or glenohumeral instability between August 2020 and April 2021. Patients also completed preoperative outcome measures such as ASES, PROMIS PF, and PROMIS PI surveys. PROM surveys were administered six months postoperatively. RESULTS: Improvement in range of motion was the expectation most often ranked first for the entire study group (18%), arthritis subgroup (23%), and rotator cuff tear subgroup (19%). Subacromial pain syndrome patients most often ranked improving ability to complete ADL's and relieving daytime pain first (19%). Shoulder instability patients most often ranked improving ability to participate in sports first (31%). Patients that ranked improving range of motion or sports highly had better PROMs. Those who ranked relieving pain highly had worse PROMs. Patients with high (>90%) expectations of pain relief had better PROMIS PI scores. Patients with high pain relief expectations in the arthritis and subacromial pain syndrome groups had better PROMs, while patients with instability were less satisfied. CONCLUSION: The novel PRESS survey assesses patient expectations for shoulder surgery in a new, more clinically applicable rank-based format. The responses provided by patients provide actionable information to clinicians and are related to postoperative outcomes. Therefore the PRESS survey represents a useful tool for guiding discussions between patients and surgeons, as well as aiding in overall patient-centered clinical decision making.

Use of artificial intelligence embryo selection based on static images to predict first-trimester pregnancy loss.

RESEARCH QUESTION: Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos? DESIGN: In a blind, retrospective study, a cohort of 172 blastocysts from IVF cases with single embryo transfer and a positive biochemical pregnancy test was ranked retrospectively by the artificial intelligence morphometric algorithm ERICA. Making use of static embryo images from a light microscope, each blastocyst was assigned to one of four possible groups (optimal, good, fair or poor), and linear regression was used to correlate the results with the presence or absence of a normal fetal heart beat as an indicator of ongoing pregnancy or spontaneous abortion, respectively. Additional analyses included modelling for recipient age and chromosomal status established by preimplantation genetic testing for aneuploidy (PGT-A). RESULTS: Embryos classified as optimal/good had a lower incidence of spontaneous abortion (16.1%) compared with embryos classified as fair/poor (25%; OR = 0.46, P = 0.005). The incidence of spontaneous abortion in chromosomally normal embryos (determined by PGT-A) was 13.3% for optimal/good embryos and 20.0% for fair/poor embryos, although the difference was not significant (P = 0.531). There was a significant association between embryo rank and recipient age (P = 0.018), in that the incidence of spontaneous abortion was unexpectedly lower in older recipients (21.3% for age ≤35 years, 17.9% for age 36-38 years, 16.4% for age ≥39 years; OR = 0.354, P = 0.0181). Overall, these results support correlation between risk of spontaneous abortion and embryo rank as determined by artificial intelligence; classification accuracy was calculated to be 67.4%. CONCLUSIONS: This preliminary study suggests that artificial intelligence (ERICA), which was designed as a ranking system to assist with embryo transfer decisions and ploidy prediction, may also be useful to provide information for couples on the risk of spontaneous abortion. Future work will include a larger sample size and karyotyping of miscarried pregnancy tissue.

Clustering on heterogeneous IoT information network based on meta path.

As the Internet and Internet of Things (IoT) continue to develop, Heterogeneous Information Networks (HIN) have formed complex interaction relationships among data objects. These relationships are represented by various types of edges (meta-paths) that contain rich semantic information. In the context of IoT data applications, the widespread adoption of Trigger-Action Patterns makes the management and analysis of heterogeneous data particularly important. This study proposes a meta-path-based clustering method for heterogeneous IoT data called I-RankClus, which aims to improve the modeling and analysis efficiency of IoT data. By combining ranking with clustering algorithms, the PageRank algorithm was used to calculate the intraclass influence of objects in the network. The HITS algorithm then transfers the influence to the core objects, thereby optimizing the classification of objects during the clustering process. The I-RankClus algorithm does not process each meta-path individually, but instead integrates multiple meta-paths to enhance the interpretability and clustering performance of the model. The experimental results show that the I-RankClus algorithm can process complex IoT datasets more effectively than traditional clustering methods and provide more accurate clustering outcomes. Furthermore, through a detailed analysis of meta-paths, this study explored the influence and importance of different meta-paths, thereby validating the effectiveness of the algorithm. Overall, the research presented in this paper not only improves the application effects of HINs in IoT data analysis but also provides valuable methods and insights for future network data processing.

Heterogeneous Thresholds, Social Ranking, and the Emergence of Vague Categories.

Threshold models in which an individual's response to a particular state of the world depends on whether an associated measured value exceeds a given threshold are common in a variety of social learning and collective decision-making scenarios in both natural and artificial systems. If thresholds are heterogeneous across a population of agents, then graded population level responses can emerge in a context in which individual responses are discrete and limited. In this article, I propose a threshold-based model for social learning of shared quality categories. This is then combined with the voting model of fuzzy categories to allow individuals to learn membership functions from their peers, which can then be used for decision-making, including ranking a set of available options. I use agent-based simulation experiments to investigate variants of this model and compare them to an individual learning benchmark when applied to the ranking problem. These results show that a threshold-based approach combined with category-based voting across a social network provides an effective social mechanism for ranking that exploits emergent vagueness.

Current and potential future impacts of food- and water-borne parasites in a changing world: A Norwegian perspective.

In 2021, the Norwegian Scientific Committee for Food and Environment published a multi-criteria risk ranking of 20 potentially food-borne pathogens in Norway. The pathogens ranked included five parasite taxa (3 species, one genus, one family): Toxoplasma gondii, Echinococcus multilocularis, Giardia duodenalis, Cryptosporidium spp., and Anisakidae. Two of these, T. gondii and E. multilocularis, scored very highly (1st and 3rd place, respectively), Cryptosporidium was about midway (9th place), and G. duodenalis and Anisakidae ranked relatively low (15th and 20th place, respectively). Parasites were found, on average, more likely to present an increasing food-borne disease burden in the future than the other pathogens. Here, we review the current impact of these five potentially food-borne parasites in Norway, and factors of potential importance in increasing their future food-borne disease burden. Climate change may affect the contamination of water and fresh produce with transmission stages of the first four parasites, potentially leading to increased infection risk. Alterations in host distribution (potentially due to climate change, but also other factors) may affect the occurrence and distribution of Toxoplasma, Echinococcus, and Anisakidae, and these, coupled with changes in food consumption patterns, could also affect infection likelihood. Transmission of food-borne pathogens is complex, and the relative importance of different pathogens is affected by many factors and will not remain static. Further investigation in, for example, ten-years' time, could provide a different picture of the relative importance of different pathogens. Nevertheless, there is clearly the potential for parasites to exert a greater risk to public health in Norway than currently occurs.

AVBAE-MODFR: A novel deep learning framework of embedding and feature selection on multi-omics data for pan-cancer classification.

Integration analysis of cancer multi-omics data for pan-cancer classification has the potential for clinical applications in various aspects such as tumor diagnosis, analyzing clinically significant features, and providing precision medicine. In these applications, the embedding and feature selection on high-dimensional multi-omics data is clinically necessary. Recently, deep learning algorithms become the most promising cancer multi-omic integration analysis methods, due to the powerful capability of capturing nonlinear relationships. Developing effective deep learning architectures for cancer multi-omics embedding and feature selection remains a challenge for researchers in view of high dimensionality and heterogeneity. In this paper, we propose a novel two-phase deep learning model named AVBAE-MODFR for pan-cancer classification. AVBAE-MODFR achieves embedding by a multi2multi autoencoder based on the adversarial variational Bayes method and further performs feature selection utilizing a dual-net-based feature ranking method. AVBAE-MODFR utilizes AVBAE to pre-train the network parameters, which improves the classification performance and enhances feature ranking stability in MODFR. Firstly, AVBAE learns high-quality representation among multiple omics features for unsupervised pan-cancer classification. We design an efficient discriminator architecture to distinguish the latent distributions for updating forward variational parameters. Secondly, we propose MODFR to simultaneously evaluate multi-omics feature importance for feature selection by training a designed multi2one selector network, where the efficient evaluation approach based on the average gradient of random mask subsets can avoid bias caused by input feature drift. We conduct experiments on the TCGA pan-cancer dataset and compare it with four state-of-the-art methods for each phase. The results show the superiority of AVBAE-MODFR over SOTA methods.

Misinformation does not reduce trust in accurate search results, but warning banners may backfire.

People rely on search engines for information in critical contexts, such as public health emergencies-but what makes people trust some search results more than others? Can search engines influence people's levels of trust by controlling how information is presented? And, how does the presence of misinformation influence people's trust? Research has identified both rank and the presence of misinformation as factors impacting people's search behavior. Here, we extend these findings by measuring the effects of these factors, as well as misinformation warning banners, on the perceived trustworthiness of individual search results. We conducted three online experiments (N = 3196) using Covid-19-related queries, and found that although higher-ranked results are clicked more often, they are not more trusted. We also showed that misinformation does not damage trust in accurate results displayed below it. In contrast, while a warning about unreliable sources might decrease trust in misinformation, it significantly decreases trust in accurate information. This research alleviates some concerns about how people evaluate the credibility of information they find online, while revealing a potential backfire effect of one misinformation-prevention approach; namely, that banner warnings about source unreliability could lead to unexpected and nonoptimal outcomes in which people trust accurate information less.

Leveraging artificial intelligence and software engineering methods in epidemiology for the co-creation of decision-support tools based on mechanistic models.

Epidemiological modeling is a key lever for infectious disease control and prevention on farms. It makes it possible to understand the spread of pathogens, but also to compare intervention scenarios even in counterfactual situations. However, the actual capability of decision makers to use mechanistic models to support timely interventions is limited. This study demonstrates how artificial intelligence (AI) techniques can make mechanistic epidemiological models more accessible to farmers and veterinarians, and how to transform such models into user-friendly decision-support tools (DST). By leveraging knowledge representation methods, such as the textual formalization of model components through a domain-specific language (DSL), the co-design of mechanistic models and DST becomes more efficient and collaborative. This facilitates the integration of explicit expert knowledge and practical insights into the modeling process. Furthermore, the utilization of AI and software engineering enables the automation of web application generation based on existing mechanistic models. This automation simplifies the development of DST, as tool designers can focus on identifying users' needs and specifying expected features and meaningful presentations of outcomes, instead of wasting time in writing code to wrap models into web apps. To illustrate the practical application of this approach, we consider the example of Bovine Respiratory Disease (BRD), a tough challenge in fattening farms where young beef bulls often develop BRD shortly after being allocated into pens. BRD is a multi-factorial, multi-pathogen disease that is difficult to anticipate and control, often resulting in the massive use of antimicrobials to mitigate its impact on animal health, welfare, and economic losses. The DST developed from an existing mechanistic BRD model empowers users, including farmers and veterinarians, to customize scenarios based on their specific farm conditions. It enables them to anticipate the effects of various pathogens, compare the epidemiological and economic outcomes associated with different farming practices, and decide how to balance the reduction of disease impact and the reduction of antimicrobial usage (AMU). The generic method presented in this article illustrates the potential of artificial intelligence (AI) and software engineering methods to enhance the co-creation of DST based on mechanistic models in veterinary epidemiology. The corresponding pipeline is distributed as an open-source software. By leveraging these advancements, this research aims to bridge the gap between theoretical models and the practical usage of their outcomes on the field.