Diverse Methods Research Articles

Phosphodiesterase 5 Inhibitor Treatment Is Associated With Improved Survival in Pulmonary Hypertension Associated With COPD in the Pulmonary Vascular Research Institute GoDeep Meta-Registry

BackgroundPatients with COPD frequently develop pulmonary hypertension (PH-COPD). Severe PH-COPD, identified by a pulmonary vascular resistance (PVR) >5 Wood Units (WU), is closely linked to impaired transplant-free survival. The impact of PH-targeting pharmacotherapy in this context remains unclear. Research QuestionIs PH-targeted therapy associated with improved transplant-free survival in PH-COPD? Study Design and MethodsThis study included PVRI GoDeep meta-registry patients with PH-COPD and available right heart catheterization at diagnosis. We investigated PH-targeted therapy prevalence and its association with transplant-free survival using diverse statistical methods, including Cox regression and subgroup analyses based on PH severity, comorbidities, and pulmonary function tests. Immortal time bias was addressed through a landmark approach. ResultsAs of December 2023, the GoDeep meta-registry included 26981 patients (28% PH-Group 1, 13% PH-Group 2, 12% PH-Group 3, 10% PH-Group 4, 2% PH-Group 5, 26% undefined and 9% control). Out of these, 836 patients were diagnosed as PH-COPD and included in this analysis, with median age 66 [59,73]years, FEV1 51 [34,69]%predicted, mPAP 35 [28,44]mmHg, PVR 5 [4,8]WU, cardiac index 2.5 [2.0,2.9]L/min.m2, and mostly WHO functional class III were included. 5-year transplant-free survival was 42%, significantly worse than group 1 PH. A multivariable Cox proportional hazards model identified PVR, but not FEV1 as a major predictor of outcome. 418 patients (50%) received phosphodiesterase-5 inhibitor (PDE5i) therapy, which was associated with significantly reduced mortality: hazard ratio 0.65 [0.57,0.75] for the entire PH-COPD cohort and 0.83 [0.74,0.94] when performing landmark analysis. This PDE5i effect was robustly reproduced when performing subgroup analyses for patients with moderate/severe PH, various comorbidities, and supplemental oxygen requirement, and when assessing the impact of unobserved confounders. InterpretationPH-COPD patients exhibit poor transplant-free survival, with PVR being a predictor of mortality. In this meta-registry, PDE5i therapy was associated with a significant reduction in mortality across all tested models.

Systematic review and meta-analysis of diagnostic test accuracy in chronic wound's microbiology.

This study aims to assess the diagnostic accuracy of non-culture-based methodologies for detecting microorganisms in chronic wounds. We systematically reviewed studies that evaluated the diagnostic accuracy of alternative tests in chronic wound samples, excluding studies focused on animal samples or unrelated conditions. The search encompassed PubMed, CINAHL, Scopus and Web of Science databases, employing the QUADAS-2 tool for risk of bias assessment. Our search included the PubMed, CINAHL, Scopus and Web of Science databases, and we assessed the risk of bias using the QUADAS-2 tool. A meta-analysis was conducted on polymerase chain reaction (PCR) and colorimetric methods to determine sensitivity, specificity, diagnostic odds ratio, and summary receiver-operating characteristic (sROC) curves using a random-effects model. For methods not suitable for quantitative synthesis, a narrative synthesis was performed. Nineteen studies involving various types of chronic wounds were analysed, revealing diverse diagnostic methods including fluorescence, PCR, colorimetry, voltammetry, electronic nose, biosensors, enzymatic methods, staining and microscopy. Combining fluorescence with clinical signs and symptoms (CSS) versus culture showed significant accuracy. Colorimetry demonstrated low sensitivity but high specificity, with a diagnostic odds ratio of 6.3. PCR generally exhibited good accuracy, although significant heterogeneity was noted, even in subgroup analyses. This study identified a broad spectrum of diagnostic approaches, highlighting the superior diagnostic accuracy achieved when microbiological analysis is combined with clinical assessments. However, the heterogeneity and methodological variations across studies present challenges in meta-analysis. Future research should aim for standardized and homogeneous study designs to enhance the assessment of diagnostic accuracy for alternative methods.

Enhancing competency-based assessment: Implementing oral boards in nursing education.

Competency-based education, pivotal in nursing programs, emphasizes learning methods over knowledge accumulation, integrating ongoing assessments for performance and outcomes. The American Association College of Nursing introduced competency-based education, urging educators to explore diverse assessment methods. This article explores the implementation of mock oral boards as an assessment method to synchronize performance standards with competencies in nursing education, specifically in preparing nurse practitioner students for clinical rotations.

Combating COVID-19 health disparities in Black, Indigenous, and People of Colour Communities-A call for critical systems thinking.

The Black, Indigenous, and People of Colour populations in the United States are disproportionately affected by the emerging health threat SARS-CoV-2, which causes COVID-19. This paper aims to demonstrate the usefulness of critical systems thinking by using scenario planning based on epidemiological data and tying epidemiology with soft systems methodology to investigate COVID-19 disparities among disproportionately affected Black, Indigenous, and People of Colour populations. Using a review of the COVID-19 literature and publicly available US COVID-19 data, critical systems thinking is applied in a scenario planning example and a call to link soft systems methodology with epidemiology. According to the four plausible Endgame scenarios, levels of community transmission as well as the current state transmission are based on the driving forces of the scenarios. In addition, soft systems methodology explores the effect on stakeholders and strengthens the picture of disease burden beyond sole reliance on traditional data sources. This analysis underscores employing critical systems thinking to critically assess diverse methods appropriate for the ongoing complexity of global crises. It is argued that critically engaged subjectivity should be given space alongside data-dependent objectivity. COVID-19 disparities are reliant on the social determinants of health's effects as driving forces on disease transmission in Black, Indigenous, and People of Colour populations. It is moreover argued that critical systems thinking is demonstrated by linking epidemiological evidence with scenario planning and soft systems methodology. This in turn supports a critical systems thinking approach to uncover the state of health disparities among minoritized communities under COVID-19.

Leveraging multiple data types for improved compound-kinase bioactivity prediction

Machine learning provides efficient ways to map compound-kinase interactions. However, diverse bioactivity data types, including single-dose and multi-dose-response assay results, present challenges. Traditional models utilize only multi-dose data, overlooking information contained in single-dose measurements. Here, we propose a machine learning methodology for compound-kinase activity prediction that leverages both single-dose and dose-response data. We demonstrate that our two-stage approach yields accurate activity predictions and significantly improves model performance compared to training solely on dose-response labels. This superior performance is consistent across five diverse machine learning methods. Using the best performing model, we carried out extensive experimental profiling on a total of 347 selected compound-kinase pairs, achieving a high hit rate of 40% and a negative predictive value of 78%. We show that these rates can be improved further by incorporating model uncertainty estimates into the compound selection process. By integrating multiple activity data types, we demonstrate that our approach holds promise for facilitating the development of training activity datasets in a more efficient and cost-effective way.

Detection of Intrusions using Transformer - LSTM Model

The project focuses on addressing the escalating issue of web attacks and ensuring cyberspace security in the era of advancing internet technology. The primary goal is to enhance intrusion detection systems (IDS) by moving beyond traditional keyword and rule- based methods. The project explores the integration of artificial intelligence, specifically deep learning techniques, to develop a more robust and adaptive approach to identifying network intrusion events. With the limitations of conventional detection methods in mind, there is a critical need for a more effective and versatile intrusion detection system. The rise in diverse attack methods necessitates an intelligent system capable of autonomously adapting to new attack patterns without relying on manual rule creation. This project is designed to benefit both individuals and organizations reliant on network services. Improved intrusion detection ensures a higher level of cybersecurity, safeguarding sensitive data and systems. The project's outcomes have the potential to contribute significantly to the broader field of network security. Leveraging the power of deep learning, the proposed model- Transformer (autoencoder) LSTM combines Transformer and LSTM architectures. Multiple attention mechanisms are employed to select and extract features, while the LSTM model facilitates understanding the sequential relationships within network traffic, enabling the accurate identification of intrusion events. And we are comparing the model with DNN, LSTM and Transformer(autoencoder). To enhance performance, CNN and CNN+LSTM extensions were incorporated into the project, complementing the Transformer (autoencoder) LSTM model. These models aim to further refine feature extraction and exploit spatial relationships within network traffic. Key Words:intrusion detection, transformer, LSTM, deep learning.

Online Mindfulness-Based Interventions for Healthcare Professionals: A Scoping Review.

Healthcare professionals work in challenging environments with extended working hours and excessive workloads to provide high-quality care. However, they hardly get the time to care for themselves. Online mindfulness-based interventions-which are not restricted by time or location-can be a powerful strategy to help healthcare professionals. This scoping review aims to explore the current evidence about online mindfulness-based interventions for healthcare professionals. A scoping review was conducted. A literature search was conducted to retrieve articles published between October 2016 and March 2023 in seven databases, including backward and forward citation tracking from the included articles. Among 1,278 articles retrieved, 33 met the criteria for review. Two-thirds of the studies utilized commercialized mindfulness programs (n = 22). Duration of the mindfulness interventions varied from 1 week to 4 months, and half of the studies used audio-guided programs. Online mindfulness-based intervention studies were targeted to reduce burnout (n = 26), and the majority reported reduction in burnout (n = 18). Most studies used individual audio-guided programs that warrant the use of diverse delivery methods such as video media, simulation, and virtual reality for interactions in the future. Although the literature has accumulated evidence for commercialized online mindfulness-based interventions, further studies are needed to develop and test tailored interventions for healthcare professionals.

Police officers’ perceptions of a training course designed to enhance open-ended questions with adult witnesses

This study evaluated police officers’ perceptions of a new training program aimed at improving interviewing skills for adult interviewees. Drawing from successful strategies used in interviewing child complainants of abuse, the program focused on enhancing the use of open-ended questions. The investigation aimed to identify effective aspects, limitations, the practicality of open-ended questions in real-world scenarios, and avenues for enhancing post-training interviewer performance. Through diverse qualitative data collection methods including email feedback, discussion boards, questionnaires, and follow-up interviews, officers’ feedback was thematically analysed. Four key themes emerged: the utility of open-ended questions, improved skills due to course content, variability in narrative detail needs across contexts, and opportunities for enhancing learning outcomes. These findings have implications for both research and practical applications, highlighting the effectiveness of the training program and suggesting areas for further improvement in enhancing police officers’ interviewing skills with adults.

Synthesis of Samarium Nitride Thin Films on Magnesium Oxide (001) Substrates Using Molecular Beam Epitaxy

Rare-earth nitrides are an exciting family of materials with a wide variety of properties desirable for new physics and applications in spintronics and superconducting devices. Among them, samarium nitride is an interesting compound reported to have ferromagnetic behavior coupled with the potential existence of p-wave superconductivity. Synthesis of high-quality thin films is essential in order to manifest these behaviors and understand the impact that vacancies, structural distortions, and doping can have on these properties. In this study, we report the synthesis of samarium nitride monocrystalline thin films on magnesium oxide (001) substrates with a chromium nitride capping layer using molecular beam epitaxy (MBE). We observed a high-quality monocrystalline SmN film with matching orientation to the substrate, then optimized the growth temperature. Despite the initial 2 nm of growth showing formation of a potential samarium oxide layer, the subsequent layers showed high-quality SmN, with semiconducting behavior revealed by an increase in resistivity with decreasing temperature. These promising results highlight the importance of studying diverse heteroepitaxial schemes and open the door for integration of rare-earth nitrides and transition metal nitrides for future spintronic devices.

Multimodal music datasets? Challenges and future goals in music processing

The term “multimodal music dataset” is often used to describe music-related datasets that represent music as a multimedia art form and multimodal experience. However, the term “multimodality” is often used differently in disciplines such as musicology, music psychology, and music technology. This paper proposes a definition of multimodality that works across different music disciplines. Many challenges are related to constructing, evaluating, and using multimodal music datasets. We provide a task-based categorization of multimodal datasets and suggest guidelines for their development. Diverse data pre-processing methods are illuminated, highlighting their contributions to transparent and reproducible music analysis. Additionally, evaluation metrics, methods, and benchmarks tailored for multimodal music processing tasks are scrutinized, empowering researchers to make informed decisions and facilitating cross-study comparisons.

Nearest neighbors and density-based undersampling for imbalanced data classification with class overlap

While addressing the problem of imbalanced data classification, most existing resampling methods primarily focus on balancing class distribution. However, they often overlook class overlap and fail to adequately consider the feature distributions of different classes. Consequently, when resampling is performed under such conditions, samples within areas of overlap remain susceptible to misclassification, failing to substantially improve overall performance. To address these shortcomings, we propose a novel data resampling technique, Nearest Neighbors and Density-based Undersampling (NDU). This method employs within-class k-nearest neighbors and between-class probability densities to design a weight assignment strategy. Leveraging this strategy, we establish an exclusive metric, the F_factor, to evaluate the importance of majority class samples in overlap areas. Subsequently, NDU promotes a gradient-based segmented undersampling strategy, which applies varying degrees of undersampling to majority class samples across segmented regions. Through experiments on binary imbalanced datasets with class overlap, we evaluate the efficiency of diverse resampling methods concerning classification performance. The results demonstrate that our proposed method effectively addresses class overlap challenges.

Cyclodextrin-Based Nanogels for Stabilization and Sensing of Curcumin.

Curcumin (CUR), a polyphenolic substance from turmeric, displays diverse medicinal properties. However, its instability poses challenges in detection. Cyclodextrin-based nanogels (CyDngs) offer a transformative solution, enhancing CUR's stability in aqueous solutions. Multisensing approaches involving fluorescence, electrochemistry, and NMR spectroscopy were employed, demonstrating CyDngs' pivotal role in CUR detection. Langmuir analysis revealed a binding constant of 1.4 × 104 M-1 for CyDngs, highlighting their effectiveness over native β-CyDs. The study emphasized CyDngs' superiority in stabilizing CUR and enabling reliable and sensitive detection with very diverse methods.

Exemplar-based ingroup projection: The superordinate national category is associated more strongly with ingroup than outgroup political leaders

We studied mental representations of social categories in the context of political groups nested within national identities. Extending previous works derived from the Ingroup Projection Model, which had investigated category representations based on prototypical attributes, we examined category representations based on prototypical exemplars, focusing on group leaders. We hypothesized that the mental representation of the superordinate, national, category is more strongly associated with the mental representation of ingroup political leaders than outgroup political leaders. We tested our hypothesis in three preregistered experiments, looking at two different national-political contexts and using diverse methods. In Studies 1 (N = 145) and 2 (N = 103), both conducted in Israel, we found that participants explicitly associated the national category with leaders of their own political camp more than with leaders of the rival political camp. In Study 2, we further found that participants were more likely to falsely remember that ingroup relative to outgroup political leaders were paired with the national category (versus their political wing). In Study 3 (N = 381), conducted in the USA, we found using an implicit measure of association (ST-IAT) that Democrats and Republicans sorted stimuli, representing political leaders, faster when the national category (represented by American symbols) was paired with the ingroup rather than the outgroup category. Implications for theoretical accounts of ingroup projection, as well as for the understanding of political polarization and intergroup leadership, are discussed.

Test Procedures and Mechanical Properties of Three-Dimensional Printable Concrete Enclosing Different Mix Proportions: A Review and Bibliometric Analysis

Three-dimensional printable concrete (3DPC) has become increasingly popular in the building and architecture industries due to its low cost and fast design. Currently, there is great interest in the mix design methods and mechanical properties of 3DPC, particularly in relation to yield stress analysis. The ability to extrude and build 3D-printed objects can be significantly affected by factors such as the rate of extrusion, nozzle size, and type of pumps used. It has been observed that a yield stress lower than 1.5 to 2.5 kPa is not sufficient to maintain the shape stability of concrete, while a yield stress above this range can limit the material’s extrudability. Furthermore, the strength properties of 3DPC are influenced by factors such as changes in yield stress and superplasticiser dosages. To meet the high mechanical strength and durability requirements of 3DPC in the construction industry, it is essential to analyse the material’s early-age mechanical properties. However, the development of standardised test methods for 3DPC is still deficient. To address this issue, a bibliometric analysis was conducted to comprehensively review the diverse test methods and mechanical characteristics of 3DPC with different mix proportions. To produce high-performance concrete from various additives and waste materials, it is critical to have a basic understanding of the hydration processes of 3DPC. Moreover, a detailed analysis of the environmental impact and energy efficiency of 3DPC is necessary for its widespread implementation. This review article will highlight the recent trends, upcoming challenges, and benefits of using 3DPC. It serves as a taxonomy to navigate the field of 3DPC towards sustainable development.

Leadership Behaviours and Goals Achievement in Public Senior Secondary Schools

This study aims to analyze the relationship between leadership behavior and the achievement of educational goals in public high schools in Kwara State, Nigeria. The focus of this research is on the three main elements of leadership behavior, namely the welfare package, the role of supervision, and the communication system, as well as how these elements affect educational outcomes. The research method used is a quantitative approach with a correlational descriptive design. Data were collected through adapted questionnaires and interviews with 368 teachers in various public high schools across 16 local government areas in Kwara State. Data analysis was carried out using descriptive and inferential statistics with Pearson correlation at a significance level of 0.05. The results of the study show that there is a significant relationship between welfare packages, supervisory roles, and communication systems and the achievement of educational goals. An adequate welfare package improves teacher motivation and performance, an effective supervisory role contributes to the quality of teaching, and a good communication system ensures better coordination in achieving school goals. The implications of this study emphasize the importance of holistic integration of these elements in leadership strategies to create a conducive learning environment and achieve optimal educational outcomes. This research also opens up space for further research that considers local contexts, demographic variations, and more diverse research methods to gain a more comprehensive understanding.

Internet-based enrollment of a myositis patient cohort-a national experience.

Recruitment for idiopathic inflammatory myopathies (IIM) research is a challenge due to the rarity of the disease and the scarcity of specialized myositis centers. Online recruitment may be a feasible alternative to reach rare disease patients. We evaluated various online recruitment methods in a large longitudinal IIM cohort. The "Myositis Patient Centered Tele-Research" (My Pacer) is a prospective 6-month observational study of IIM, recruited online and through traditional clinic visits. We utilized diverse recruitment methods, such as physician referrals, social media, websites, direct emails, and partnerships with patient-support organizations. Participants self-enrolled and completed pre-screening, e-consenting, and release of medical information via the study-specific app or website. We compared the effectiveness of various recruitment and enrollment methods and the characteristics of the population recruited. A total of 841 participants completed the pre-screening; 408 completed e-consent and registration. From those, 353 (86.5%) were remotely recruited. Email (201; 49.26%) and social media (77; 18.87%) were important recruitment tools. Patient-support organizations were responsible for disseminating the study to 312 (75.46%) participants. The study app was used by 232 (65.72%) individuals for enrollment, with app users being slightly younger than website users (p = 0.001). Participants were mostly female 317 (77.76%), mean age of 54.84 years, White 328 (80.42%), Black 49 (12%), Asian 13 (3.26%), and non-Hispanic 378 (92.65%). Our study reached all U.S. regions and 45 (90%) U.S. states. Social media and partnerships with patient-support organizations lead to a high rate of recruitment, with a wide reach, and a reasonably diverse population.

Synthesis of Self-Checking Circuits for Train Route Traffic Control at Intermediate Stations with Control of Calculations Based on Weight-Based Sum Codes

When synthesizing systems for railway interlocking, it is recommended to use automated models to implement the logic of railway automation and remote control units. Finite-state machines (FSMs) can be implemented on any hardware component. When using relay technology, the functional safety of electrical interlocking is achieved by using uncontrolled (safety) relays with a high coefficient of asymmetry of failures in types 1 → 0 and 0 → 1. When using programmable components, the use of backup and diverse protection methods is required. This paper presents a flexible approach to synthesizing FSMs for railway automation and remote control units that offer both individual and route-based control. Unlike existing solutions, this proposal considers the pre-failure states of railway automation and remote control units during the finite-state machine synthesis stage. This enables the implementation of self-checking and self-diagnostic modules to manage automation units. By increasing the number of states for individual devices and considering the states of interconnected objects, the transition graphs can be expanded. This expansion allows for the synthesis of the transition graph of the control subsystem and other systems. The authors used a field-programmable gate array (FPGA) to implement a finite-state machine. In this case, the proposal is to encode the states of a finite-state machine using weight-based sum codes in the residue class ring based on a given modulus. The best coverage of errors occurring at the outputs of the logic converter in the structure of the FSM can be ensured by selecting the weighting coefficients and the value of the module. This paper presents an example of synthesizing an FPGA-based FSM using state encoding through modular weight-based sum codes. The operation of the synthesized device was modeled. It was found to operate according to the same algorithm as the real devices. When synthesizing self-checking and self-controlled train control devices, it is recommended to consider the solutions proposed in this paper.

A hybrid intersection control strategy for CAVs under fluctuating traffic demands: A value approximation approach

Confronted with growing and fluctuating traffic demands, urban transportation system has been encountering mounting challenges in traffic congestion, especially at intersections. With enhanced traffic control precision enabled by the emerging Connected and Automated Vehicle (CAV) technologies, this study proposes a hybrid control strategy for connected and automated traffic at urban intersections, which enables the integration of diverse control schemes to harness their strengths and mitigate their weaknesses. With rolling horizon strategy, a nonlinear optimization model is developed to determine the optimal traffic control plans considering both current status and forthcoming vehicle arrivals. Vehicle delays are elaborately characterized without relying on any empirical assumptions. The original model is converted to a Mixed Integer Programming with Quadratic Constraints (MIP-QC) by employing appropriate linearization techniques, which could be solved by commercial solvers. For the acquisition of instant and reliable solutions, a multilayer feedforward network-based approximate algorithm is developed, referred as Value Approximation Control (VAC) algorithm. Theoretical derivation is provided to validate the capability of VAC algorithm in the precise approximations of the value function in the traffic plan optimization problem, and ultimately enabling to acquire global optimal solutions via specific network design and training techniques. Numerical experiments on both artificial and researcher-collected datasets demonstrate that our proposed VAC algorithm achieves performance nearly equivalent to the mathematical model. Significantly, it outperforms current state-of-art traffic control methods in terms of both intersection throughput and average vehicle delay. Moreover, sensitivity analysis reveals the robustness of the VAC algorithm against inaccuracy in vehicle arrival information, and the stable performance even in the presence of significant disturbances.

A novel method for the classification of 3D point clouds based on the improved PointNet++

Abstract In deep learning, point clouds are used as the primary input format for 3D data, which can provide detailed geometric information about objects in the original 3D space. PointNet++ is a deep learning network that uses point cloud data as an input format, which avoids the losses associated with the previous conversion of point cloud into 3D voxelization and a collection of 2D images. Although PointNet++ can directly process point cloud data in various ways, due to the disordered, irregular, and unevenly distributed nature of point cloud data, the effect of extracting point cloud features could be better. The large amount of point cloud data also leads to the training model falling into the local optimal solution, which affects the training results. In recent years, some effective methods and strategies have emerged to address these problems. In this thesis, three methods are proposed based on the PointNet++ network: feature similarity-based attention pooling, small kernel convolution, and diverse branch block method to improve the performance of the PointNet++ network. Experiments show that the improvement methods proposed in this paper effectively improve the feature extraction accuracy, which improves the accuracy of the PointNet++ network for classification on the ModelNet40_Normal_Resampled dataset, with an overall improvement of 1% compared with PointNet++.

RCDdb: A manually curated database and analysis platform for regulated cell death

Regulated cell death is a pivotal regulatory mechanism governing the development and homeostasis of multicellular organisms. A comprehensive understanding of RCD's regulatory mechanisms is crucial for developing novel therapeutic strategies against diseases associated with cell death, such as cancer and neurodegenerative diseases. However, existing data repositories support limited types of cell death data and lack comprehensive annotation and analytical functionalities. Thus, establishing an extensive cell death database is an urgent imperative. To address this gap, we developed the Regulated Cell Death Database (RCDdb, chenyclab.com/RCDdb), the first comprehensively manually annotated database designed to support annotations and analytical capabilities across all RCD types. We compiled 3090 marker gene annotations associated with 15 RCD types from 2180 relevant articles. The RCDdb includes annotation data on these marker genes concerning diseases, drugs, pathways, proteins, and gene expressions. Furthermore, it provides 49 diverse visualization methods to present this information. More importantly, the RCDdb features three online analysis tools for identifying and analyzing RCD-related features within user-submitted data. Furthermore, the RCDdb offers a user-friendly interface for querying, browsing, analysis, and visualization of detailed information associated with each RCD category. This resource promises to significantly aid researchers in better understanding the mechanisms of cell death, thereby accelerating progress in research and therapeutic strategies aimed at combating RCD-related diseases.