MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides.

Inflammatory responses may lead to tissue or organ damage, and proinflammatory peptides (PIPs) are signaling peptides that can induce such responses. Many diseases have been redefined as inflammatory diseases. To identify PIPs more efficiently, we expanded the dataset and designed an ensemble learning model with manually encoded features. Specifically, we adopted a more comprehensive feature encoding method and considered the actual impact of certain features to filter them. Identification and prediction of PIPs were performed using an ensemble learning model based on five different classifiers. The results show that the model's sensitivity, specificity, accuracy, and Matthews correlation coefficient are all higher than those of the state-of-the-art models. We named this model MultiFeatVotPIP, and both the model and the data can be accessed publicly at https://github.com/ChaoruiYan019/MultiFeatVotPIP. Additionally, we have developed a user-friendly web interface for users, which can be accessed at http://www.bioai-lab.com/MultiFeatVotPIP.

Cancer diagnosis based on laser-induced breakdown spectroscopy with bagging-voting fusion model.

Advances in cancer diagnostics play a pivotal role in increasing early detection of cancer. Integrating laser-induced breakdown spectroscopy (LIBS) with machine learning algorithms has attracted wide interest in cancer diagnosis. However, using a single model`s efficacy is limited by algorithm principles, making it challenging to meet the comprehensive needs of cancer diagnosis. Here, we demonstrate a bagging-voting fusion (BVF) algorithm for the detection and identification of multiple types of cancer. In the BVF model of this paper, support vector machine (SVM), artificial neural network (ANN), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), and random forest (RF) models, which have relatively small homogeneity to obtain more comprehensive decision boundaries, are fused at both the training and decision levels. LIBS spectral data was collected from four types of serum samples, including liver cancer, lung cancer, esophageal cancer, and healthy control. LIBS detection was conducted on the samples, which were directly dropped onto ordered microarray silicon substrates and dried. The results showed that the BVF model achieved an accuracy of 92.53 % and a recall of 92.92 % across the four types of serum, outperforming the best single machine-learning model (SVM: accuracy 75.86 %, recall 77.50 %). Moreover, the BVF model with manual line selection feature extraction required only 140 s for a single detection and identification. In conclusion, the aforementioned results demonstrated that LIBS with BVF has excellent performance in detecting a multitude of cancers, and is expected to provide a new method for efficient and accurate cancer diagnosis.

Why Voting and Voter Disenfranchisement Matter to Health.

Nurses can play a crucial role as trusted advocates.

Voter Support for Policies Associated With Child Health as National Campaign Priorities.

Importance: Policies that are associated with child health are rarely included in platforms of candidates for national political office. Candidates may underrecognize voter support for such priorities or perceive that such policy issues are not sufficiently divisive to appeal to partisan voters. Key policy questions associated with child health may be considered by the next Congress, including the consistency of Medicaid coverage across states and restoring the recently lapsed refundable child tax credit. Objective: To examine voter support for candidates regarding policies that are associated with child health. Design, Setting, and Participants: This nationally representative survey of registered US voters 18 years or older was conducted from March to April 2024 and included a survey-based randomized experiment to evaluate the association of message framing with voter support. Exposures: Messages conveying distinct rationales for Medicaid reform and refundable child tax credit. Main Outcomes and Measures: Likely or definite support for candidates. Results: In this sample (unweighted N = 2014; 1015 women [51.0%]), most respondents indicated they would likely or definitely vote for candidates who expressed strong support for all tested policies: extreme risk protection order (79.5%), school threat assessment (73.1%), expanded childcare (69.6%), refundable child tax credit (66.6%), federalization of Medicaid (66.0%), paid parental leave (65.5%), free school meals (65.6%), safe firearm storage and enforcement (62.9%), preventing Medicaid disenrollment for children younger than 6 years (61.9%), universal free preschool (61.6%), and summer nutrition programs (57.9%). More women than men expressed support for all tested policies. Strong majorities of Democrat and Independent voters would support candidates who endorsed child-focused policies; fewer than 50% of Republican voters expressed such support, except for the extreme risk protection order and school threat assessment. Variations in framing language regarding consistent Medicaid coverage across states were not associated with amplified or diminished voter support. Framing the refundable child tax credit as benefiting "hard-working" vs "low-income" families garnered significantly more support among men (67.0% vs 59.0%), privately insured individuals (72.0% vs 64.4%), and Republicans (54.6% vs 43.0%; all P < .05). Conclusions and Relevance: The study results suggest that most voters favor candidates who strongly support policies that are associated with child health. Voter support differs substantively by gender and political party affiliation and may be associated with language choices in messaging about policy change.

Late feature fusion using neural network with voting classifier for Parkinson's disease detection.

Parkinson's disease (PD) is classified as a neurological, progressive illness brought on by cell death in the posterior midbrain. Early PD detection will assist doctors in reducing the disease's consequences. A collection of skilled models that may be applied to regression as well as classification is known as artificial intelligence (AI). PD can be detected using a variety of dataset formats, including text, speech, and picture datasets. For the purpose of classifying Parkinson's disease, this study suggests merging deep with machine learning recognition approaches. The three primary components of the suggested approach are designed to enhance the accuracy of Parkinson's disease early diagnosis. These sections cover the topics of categorising, combining, and separating. Convolutional Neural Networks (CNN) as well as attention procedures are used to create feature extractors. The related motion signals are fed to a combination of convolutional neural network and long-short-memory model for feature extraction. Besides, for the classification of patients from non-suffers of Parkinson's disease, Random Forest, Logistic Regression, Support Vector Machine, Extreme Boot Classifier, and voting classifier were used. Our result shows that for the PD handwriting and related motion datasets, using the proposed CNN with an attention and voting classifier yields 99.95% accuracy, 99.99% precision, 99.98% sensitivity, and 99.95% F1-score. Based on these results, it is warranted to conclude that the proposed methodology of feature extraction from photos of handwriting and relating motor symptoms, fusing of those features, and following it with a voting classifier yields excellent results for PD classification.

Isolation forest-voting fusion-multioutput: A stroke risk classification method based on the multidimensional output of abnormal sample detection.

BACKGROUND AND OBJECTIVE: Stroke has become a major disease threatening the health of people around the world. It has the characteristics of high incidence, high fatality, and a high recurrence rate. At this stage, problems such as poor recognition accuracy of stroke screening based on electronic medical records and insufficient recognition of stroke risk levels exist. These problems occur because of the systematic errors of medical equipment and the characteristics of the collectors during the process of electronic medical record collection. Errors can also occur due to misreporting or underreporting by the collection personnel and the strong subjectivity of the evaluation indicators. METHODS: This paper proposes an isolation forest-voting fusion-multioutput algorithm model. First, the screening data are collected for numerical processing and normalization. The composite feature score index of this paper is used to analyze the importance of risk factors, and then, the isolation forest is used. The algorithm detects abnormal samples, uses the voting fusion algorithm proposed in this article to perform decision fusion prediction classification, and outputs multidimensional (risk factor importance score, abnormal sample label, risk level classification, and stroke prediction) results that can be used as auxiliary decision information by doctors and medical staff. RESULTS: The isolation forest-voting fusion-multioutput algorithm proposed in this article has five categories (zero risk, low risk, high risk, ischemic stroke (TIA), and hemorrhagic stroke (HE)). The average accuracy rate of stroke prediction reached 79.59 %. CONCLUSIONS: The isolation forest-voting fusion-multioutput algorithm model proposed in this paper can not only accurately identify the various categories of stroke risk levels and stroke prediction but can also output multidimensional auxiliary decision-making information to help medical staff make decisions, thereby greatly improving the screening efficiency.

Increasing Voter Participation Through Health Care-Based Voter Registration.

Importance: Young people and historically marginalized racial and ethnic groups are poorly represented in the democratic process. Addressing voting inequities can make policy more responsive to the needs of these communities. Objective: To assess whether leveraging health care settings as venues for voter registration and mobilization is useful, particularly for historically underrepresented populations in elections. Design, Setting, and Participants: In 2020, nonpartisan nonprofit Vot-ER partnered with health care professionals and institutions to register people to vote. This cross-sectional study analyzed the demographics and voting behavior of people mobilized to register to vote in health care settings, including hospitals, community health centers, and medical schools across the US. The age and racial and ethnic identity data of individuals engaged through Vot-ER were compared to 2 national surveys of US adults, including the 2020 Cooperative Election Study (CES) and the 2020 American National Election Study (ANES). Exposure: Health care-based voter registration. Main Outcomes and Measures: The main outcomes were age composition, racial and ethnic composition, and voting history. Results: Of the 12 441 voters contacted in health care settings, 41.9% were aged 18 to 29 years, 15.9% were identified as African American, 9.6% as Asian, 12.7% as Hispanic, and 60.4% as White. This distribution was significantly more diverse than the racial and ethnic distribution of the ANES (N = 5447) and CES (N = 39 014) samples, of which 72.5% and 71.19% self-identified as White, respectively. Voter turnout among health care-based contacts increased from 61.0% in 2016 to 79.8% in 2020, a turnout gain (18.8-percentage point gain) that was 7.7 percentage points higher than that of the ANES sample (11.1-percentage point gain). Demographically, the age distribution of voters contacted in health care settings was significantly different from the ANES and CES samples, with approximately double the proportion of young voters aged 18 to 29 years. Conclusion and Relevance: This cross-sectional study suggests that health care-based voter mobilization reaches a distinctly younger and more racially and ethnically diverse population relative to those who reported contact from political campaigns. This analysis of the largest health care-based voter mobilization effort points to the unique impact that medical professionals may have on voter registration and turnout in the 2024 US elections. In the long term, health equity initiatives should prioritize expanding voting access to address the upstream determinants of health in historically marginalized communities.

Establishment and evaluation of Voting algorithm-based internal quality control (ViQC), a patient-based real-time quality control.

BACKGROUND: Patient-Based Real-Time Quality Control (PBRTQC) has emerged as a supplementary programme to traditional internal quality control (iQC) mechanisms. Despite its growing popularity, practical applications in clinical settings reveal several challenges. The primary objective of this research is to introduce and develop an Artificial Intelligence (AI)-based method, named Voting algorithm based iQC (ViQC), designed to enhance the precision and reliability of existing PBRTQC systems. METHODS: In this study, we conducted a retrospective analysis of 111,925 inpatient serum glucose test results from Nanjing Drum Tower Hospital, Nanjing, China, to provide an unbiased data set. The Voting iQC (ViQC) algorithm, established by the principles of the Voting algorithm, was then developed. Its analytical performance was evaluated through the calculation of random errors (RE). Subsequently, its clinical efficacy was assessed by comparison with five statistical algorithms: Moving Average (MA), Exponentially Weighted Moving Average (EWMA), Moving Median (movMed, MM), Moving Quartile (MQ), and Moving Standard Deviation (MovSD). RESULTS: The ViQC model incorporates a variety of machine learning models, including logistic regression, Bayesian methods, K-Nearest Neighbor, decision trees, random forests, and gradient boosting decision trees, to establish a robust predictive framework. This model consistently maintains a false positive rate below 0.002 across all six evaluated error factors, showcasing exceptional precision. Notably, its performance further excels with an error factor of 3.0, where the false positive rate drops below 0.001, and achieves an accuracy rate as high as 0.965 at an error factor of 2.0. The classification effectiveness of ViQC model is evaluated by an area under the curve (AUC) exceeding 0.97 for all error factors. In comparison to five conventional PBRTQC statistical methods, ViQC significantly enhances error detection efficiency, maximum reducing the trimmed average number of patient samples required for detecting errors from 724 to 168, thereby affirming its superior error detection capability. CONCLUSION: The new established PBRTQC using artificial intelligence yielded satisfactory performance compared to the traditional PBBTQC in real world setting.

Transformation from e-voting to e-cheque.

Although e-voting scheme and e-cheque scheme are two different applications, they have similarities in the scheme definitions and security properties. This inspires us to establish a relationship between the two schemes by formalising a generic transformation from e-voting to e-cheque scheme. Firstly, we define the scheme definitions and security models for both e-voting scheme and e-cheque scheme. Subsequently, we demonstrate a generic transformation framework from e-voting to e-cheque with asymptotic complexity of [Formula: see text] and design a formal proof to show that a secure e-voting scheme can be transformed into a secure e-cheque scheme. As a proof of concept, we apply our newly proposed transformation technique to the e-voting scheme proposed by Li et al. and obtain a concrete e-cheque scheme.

Political voting in the United Kingdom 2019 general election and risk of living with obesity in a nationally representative sample.

BACKGROUND: Limited evidence from the United States suggests that county/state rates of people with obesity are positively associated with voting for the Republican Party presidential candidate, although this question has not yet been studied at the individual level, and/or outside of the United States, where the health and political systems are very different in other countries. OBJECTIVES: Using individual level data, assess differences in rates of people with obesity according to political voting in the United Kingdom 2019 general election, and examine whether people living in constituencies won by Members of Parliament (MPs) from the Conservative Party were more likely to be living with obesity than those living in constituencies won by MPs from other parties. METHODS: Data was obtained by the Ipsos KnowledgePanel where panellists are recruited via a random probability unclustered address-based sampling method. 4000/14,016 panellists were randomly invited to provide data on socio-demographics, health outcomes, voting behaviour and height/weight. RESULTS: 2668/4000 (67%) of invitees provided data, 95/2668 (3.5%) were not eligible to vote, with the remaining 2573 (96.5%) included. Conservative Party voters were more likely to be living with obesity than those who voted Labour (OR:1.42 95% CI (1.01-1.99)) or Liberal Democrats (1.54 95% CI (1.00-2.37)). Conservative Party voters on average had significantly higher BMI scores than those voting Labour and Liberal Democrats; BMI mean difference 0.88 points (95% CI: 0.16-1.61) between Conservative and Labour voters, and 1.04 points (95% CI: 0.07-2.02) between Conservatives and Liberal Democrats voters. There was no evidence participants living in constituencies won by Conservative MPs were more likely to be living with obesity than constituencies won by other party MPs. CONCLUSION: Governments and public health agencies may need to focus on the political affiliation of the public when developing strategies to reduce the number of people with obesity.

Inflation in 2022 did not affect congressional voting, but abortion did.

This study examines voting in the 2022 United States congressional elections, contests that were widely expected to produce a sizable defeat for Democratic candidates for largely economic reasons. Based on a representative national probability sample of voters interviewed in both 2020 and 2022, individuals who changed their vote from one party's congressional candidate to another party's candidate did not do so in response to the salience of inflation or declining economic conditions. Instead, we find strong evidence that views on abortion were central to shifting votes in the midterm elections. Americans who favored (opposed) legal abortions were more likely to shift from voting for Republican (Democratic) candidates in 2020 to Democratic (Republican) candidates in 2022. Since a larger number of Americans supported than opposed legal abortions, the combination of these shifts ultimately improved the electoral prospects of Democratic candidates. New voters were especially likely to weigh abortion views heavily in their vote-shifting calculus. Likewise, those respondents whose confidence in the US Supreme Court declined from 2020 to 2022 were more likely to shift from voting for Republican to Democratic congressional candidates. We provide direct empirical evidence that changes in support for the Supreme Court, a nonpartisan branch of the federal government, are implicated in partisan voting behavior in another branch of government. We explore the implications of these findings for prevalent assumptions about how economic conditions influence voting, as well as for the relationship between the judiciary and electoral politics.

Identification of factors associated with sarcopenic obesity development: Literature review and expert panel voting.

Sarcopenic obesity (SO) is defined as the combination of excess fat mass (obesity) and low skeletal muscle mass and function (sarcopenia). The identification and classification of factors related to SO would favor better prevention and diagnosis. The present article aimed to (i) define a list of factors related with SO based on literature analysis, (ii) identify clinical conditions linked with SO development from literature search and (iii) evaluate their relevance and the potential research gaps by consulting an expert panel. From 4746 articles screened, 240 articles were selected for extraction of the factors associated with SO. Factors were classified according to their frequency in the literature. Clinical conditions were also recorded. Then, they were evaluated by a panel of expert for evaluation of their relevance in SO development. Experts also suggested additional factors. Thirty-nine unique factors were extracted from the papers and additional eleven factors suggested by a panel of experts in the SO field. The frequency in the literature showed insulin resistance, dyslipidemia, lack of exercise training, inflammation and hypertension as the most frequent factors associated with SO whereas experts ranked low spontaneous physical activity, protein and energy intakes, low exercise training and aging as the most important. Although literature and expert panel presented some differences, this first list of associated factors could help to identify patients at risk of SO. Further work is needed to confirm the contribution of factors associated with SO among the population overtime or in randomized controlled trials to demonstrate causality.

Residential mobility and persistently depressed voting among disadvantaged adults in a large housing experiment.

This study examines the impact of residential mobility on electoral participation among the poor by matching data from Moving to Opportunity, a US-based multicity housing-mobility experiment, with nationwide individual voter data. Nearly all participants in the experiment were Black and Hispanic families who originally lived in high-poverty public housing developments. Notably, the study finds that receiving a housing voucher to move to a low-poverty neighborhood decreased adult participants' voter participation for nearly two decades-a negative impact equal to or outpacing that of the most effective get-out-the-vote campaigns in absolute magnitude. This finding has important implications for understanding residential mobility as a long-run depressant of voter turnout among extremely low-income adults.