Key Data Research Articles

Maximum likelihood estimation of log-concave densities on tree space

Phylogenetic trees are key data objects in biology, and the method of phylogenetic reconstruction has been highly developed. The space of phylogenetic trees is a nonpositively curved metric space. Recently, statistical methods to analyze samples of trees on this space are being developed utilizing this property. Meanwhile, in Euclidean space, the log-concave maximum likelihood method has emerged as a new nonparametric method for probability density estimation. In this paper, we derive a sufficient condition for the existence and uniqueness of the log-concave maximum likelihood estimator on tree space. We also propose an estimation algorithm for one and two dimensions. Since various factors affect the inferred trees, it is difficult to specify the distribution of a sample of trees. The class of log-concave densities is nonparametric, and yet the estimation can be conducted by the maximum likelihood method without selecting hyperparameters. We compare the estimation performance with a previously developed kernel density estimator numerically. In our examples where the true density is log-concave, we demonstrate that our estimator has a smaller integrated squared error when the sample size is large. We also conduct numerical experiments of clustering using the Expectation-Maximization algorithm and compare the results with k-means++ clustering using Fréchet mean.

Safety of the novel oral poliovirus vaccine type 2 (nOPV2) in infants and young children aged 1 to

Novel oral poliovirus vaccine type 2 (nOPV2) has been engineered to improve the genetic stability of Sabin oral poliovirus vaccine (OPV) and reduce the emergence of circulating vaccine-derived polioviruses. This trial aimed to provide key safety and immunogenicity data required for nOPV2 licensure and WHO prequalification. This phase 3 trial recruited infants aged 18 to <52 weeks and young children aged 1 to <5 years in The Gambia. Infants randomly assigned to receive one or two doses of one of three lots of nOPV2 or one lot of bivalent OPV (bOPV). Young children were randomised to receive two doses of nOPV2 lot 1 or bOPV. The primary immunogenicity objective was to assess lot-to-lot equivalence of the three nOPV2 lots based on one-dose type 2 poliovirus neutralising antibody seroconversion rates in infants. Equivalence was declared if the 95% CI for the three pairwise rate differences was within the -10% to 10% equivalence margin. Tolerability and safety were assessed based on the rates of solicited adverse events to 7 days, unsolicited adverse events to 28 days, and serious adverse events to 3 months post-dose. Stool poliovirus excretion was examined. The trial was registered as PACTR202010705577776 and is completed. Between February and October, 2021, 2345 infants and 600 young children were vaccinated. 2272 (96·9%) were eligible for inclusion in the post-dose one per-protocol population. Seroconversion rates ranged from 48·9% to 49·2% across the three lots. The minimum lower bound of the 95% CIs for the pairwise differences in seroconversion rates between lots was -5·8%. The maximum upper bound was 5·4%. Equivalence was therefore shown. Of those seronegative at baseline, 143 (85·6%) of 167 (95% CI 79·4-90·6) infants and 54 (83·1%) of 65 (71·7-91·2) young children seroconverted over the two-dose nOPV2 schedule. The post-two-dose seroprotection rates, including participants who were both seronegative and seropositive at baseline, were 604 (92·9%) of 650 (95% CI 90·7-94·8) in infants and 276 (95·5%) of 289 (92·4-97·6) in young children. No safety concerns were identified. 7 days post-dose one, 78 (41·7%) of 187 (95% CI 34·6-49·1) infants were excreting the type 2 poliovirus. nOPV2 was immunogenic and safe in infants and young children in The Gambia. The data support the licensure and WHO prequalification of nOPV2. Bill & Melinda Gates Foundation.

Citizen science data on urban forageable plants: a case study inBrazil.

This paper presents two key data sets derived from the Pomar Urbano project. The first data set is a comprehensive catalog of edible fruit-bearing plant species, native or introduced to Brazil. The second data set, sourced from the iNaturalist platform, tracks the distribution and monitoring of these plants within urban landscapes across Brazil. The study includes data from the capitals of all 27 federative units of Brazil, focusing on the ten cities that contributed the most observations as of August 2023. The research emphasizes the significance of citizen science in urban biodiversity monitoring and its potential to contribute to various fields, including food and nutrition, creative industry, study of plant phenology, and machine learning applications. We expect the data sets presented in this paper to serve as resources for further studies in urban foraging, food security, cultural ecosystem services, and environmental sustainability.

Three-Dimensional Double Random-Phase Encryption for Simultaneous Two-Primary Data

In this paper, we propose a three-dimensional (3D) optical encryption technique for simultaneous two-primary data using double random-phase encryption (DRPE). In conventional DRPE, the primary data can be encrypted through two different random phase masks optically. Thus, its speed is the same as the speed of light. However, in this method, each primary dataset can be decrypted by the individual key data. For simultaneous two primary dataset such as stereo images or multi-view images, a new encryption technique is required. Therefore, in this paper, we encrypt the simultaneous two different primary datasets by DRPE. In our method, the first and second primary data are regarded as the amplitude and phase with single key data for encryption. To verify the feasibility of our method, we implement the simulation and measure the performance metrics such as thw peak signal to noise ratio (PSNR) and the peak sidelobe ratio (PSR). As a result, PSNR values of two-dimensional decryption results for the first (“LENA” text) and second (lena image) primary data by our proposed method with the correct and incorrect key data are 311.0139, 41.9609, 12.0166, and 7.4626, respectively, since the first primary data are lossless, and the second primary data are lossy. For 3D reconstruction, PSR values of the first and second primary data are 914.2644 and 774.1400, respectively.

A Review of the Application of Spatial Transcriptomics in Neuroscience.

Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.

Automatic Extension of a Semi-Detailed Synthetic Fuel Reaction Mechanism

To identify promising sustainable fuels, e.g., to select novel synthetic fuels with the greatest impact on minimizing global warming, new methods for rapid and economical technical fuel assessment are urgently needed. Here, numerical models that are capable of predicting technical key data quickly and without experimental setup are necessary. One method is the use of chemical kinetic models, which are able to predict the technical key parameters related to combustion behavior. For a rapid technical fuel assessment, these chemical kinetic models need to be validated for new fuel components and for different temperature and pressure ranges. This work presents a new approach to extend the existing semi-detailed chemical kinetic models. For the application of the approach, the semi-detailed reaction mechanism DLR Concise was selected and extended for the low temperature combustion modeling of n-heptane and isooctane. The open-source software reaction mechanism generator (RMG) was used for this extension. Furthermore, an optimization of the merged chemical kinetic model with the linear transformation model (linTM) was conducted in order to improve the reproducibility of ignition delay times. The improvement of the predictive performance of ignition delay times at low temperatures for both species was successfully demonstrated. Therefore, this approach can be used to quickly add new species or reaction pathways to an existing semi-detailed reaction mechanism to enable a model-based technical fuel assessment for the early identification of promising fuels.

Community infant safe sleep and breastfeeding promotion and population level-outcomes: A mixed methods study

ProblemIn the U.S., sudden unexpected infant deaths due to accidental suffocation and strangulation in bed are increasing. Though breastfeeding is a protective factor against sudden unexpected infant death, motivations to breastfeed often couple with unsafe infant sleep practices. Racial/ethnic disparities are present in sudden unexpected infant death, accidental suffocation and strangulation in bed, and breastfeeding. BackgroundPromoting infant safe sleep and breastfeeding through community-level initiatives could address disparities in related outcomes. AimInvestigate the relationship between community-level strategies and associated state-level outcomes for infant safe sleep and breastfeeding. MethodsWe employed an intervention mixed methods framework and exploratory sequential design. The qualitative component entailed a hermeneutical phenomenological framework to analyze key informant interview data from seven U.S. community-level providers participating in a practice improvement initiative. The quantitative component entailed descriptively analyzing infant safe sleep and breastfeeding indicators from the 2019 Pregnancy Risk Assessment Monitoring System and Ohio Pregnancy Assessment Survey. Qualitative and quantitative data were linked through embedded integration. FindingsWe identified two mixed insights: gaps in promotion and outcomes, and persistent disparities between infant safe sleep and breastfeeding promotion and outcomes. DiscussionOur findings indicate conversational approaches could improve infant safe sleep and breastfeeding promotion, outcomes, and relative disparities. We find that community collaboration is needed to address organizational capacity limitations in promoting infant safe sleep and breastfeeding. ConclusionCommunity-level organizations and providers should consider tailoring program offerings and care delivery to include conversational approaches and community collaboration to promote infant safe sleep and breastfeeding and decrease relative disparities in outcomes.

Machine learning-enhanced hybrid cryptography and image steganography algorithm for securing cloud data

Ensuring data security in cloud computing is crucial due to the growing reliance on cloud-based services. Hybrid cryptography and image steganography have emerged as robust techniques to enhance data confidentiality in the cloud. In this research paper, we propose a novel algorithm, “Machine Learning-Enhanced Hybrid Cryptography and Image Steganography,” integrating these methods to provide comprehensive data protection. The algorithm employs key generation, encryption, steganography, cloud storage, data retrieval, and machine learning-based attack detection to defend against advanced cyber threats. Our experimentation demonstrates the algorithm’s effectiveness in detecting DoS attacks, data breaches, and data leakage attempts using SVM, Neural Network, Isolation Forest, and Random Forest models. The proposed approach offers broad applicability, fortifying data security and fostering further advancements in cloud security research.

Structural and biophysical analysis of a Haemophilus influenzae tripartite ATP-independent periplasmic (TRAP) transporter

Tripartite ATP-independent periplasmic (TRAP) transporters are secondary-active transporters that receive their substrates via a soluble-binding protein to move bioorganic acids across bacterial or archaeal cell membranes. Recent cryo-electron microscopy (cryo-EM) structures of TRAP transporters provide a broad framework to understand how they work, but the mechanistic details of transport are not yet defined. Here we report the cryo-EM structure of the Haemophilus influenzae N-acetylneuraminate TRAP transporter (HiSiaQM) at 2.99 Å resolution (extending to 2.2 Å at the core), revealing new features. The improved resolution (the previous HiSiaQM structure is 4.7 Å resolution) permits accurate assignment of two Na+ sites and the architecture of the substrate-binding site, consistent with mutagenic and functional data. Moreover, rather than a monomer, the HiSiaQM structure is a homodimer. We observe lipids at the dimer interface, as well as a lipid trapped within the fusion that links the SiaQ and SiaM subunits. We show that the affinity (KD) for the complex between the soluble HiSiaP protein and HiSiaQM is in the micromolar range and that a related SiaP can bind HiSiaQM. This work provides key data that enhances our understanding of the ‘elevator-with-an-operator’ mechanism of TRAP transporters.

Structural and biophysical analysis of a Haemophilus influenzae tripartite ATP-independent periplasmic (TRAP) transporter.

Tripartite ATP-independent periplasmic (TRAP) transporters are secondary-active transporters that receive their substrates via a soluble-binding protein to move bioorganic acids across bacterial or archaeal cell membranes. Recent cryo-electron microscopy (cryo-EM) structures of TRAP transporters provide a broad framework to understand how they work, but the mechanistic details of transport are not yet defined. Here we report the cryo-EM structure of the Haemophilus influenzae N-acetylneuraminate TRAP transporter (HiSiaQM) at 2.99 Å resolution (extending to 2.2 Å at the core), revealing new features. The improved resolution (the previous HiSiaQM structure is 4.7 Å resolution) permits accurate assignment of two Na+ sites and the architecture of the substrate-binding site, consistent with mutagenic and functional data. Moreover, rather than a monomer, the HiSiaQM structure is a homodimer. We observe lipids at the dimer interface, as well as a lipid trapped within the fusion that links the SiaQ and SiaM subunits. We show that the affinity (KD) for the complex between the soluble HiSiaP protein and HiSiaQM is in the micromolar range and that a related SiaP can bind HiSiaQM. This work provides key data that enhances our understanding of the 'elevator-with-an-operator' mechanism of TRAP transporters.

Which factors of corporate governance influence the financial sustainability of non-bank financial institutions in Bangladesh? Panel data analysis by the PCSE estimator

In Bangladesh's financial industry, non-bank financial institutions (NBFIs) are essential players in addition to banks. To guarantee the sustainability of this industry, proper corporate governance is required. Corporate governance consists of various factors. The study provides new evidence on which factors of corporate governance influence the financial sustainability of non-bank financial institutions in Bangladesh. The researcher compiled the panel data for the period 2011-2022 from financial disclosures of sampled non-bank organizations. The researcher adopted key panel data techniques. Specifically, the panel corrected standard error (PCSE) estimator was employed to determine the factors influencing the corporate governance of non-bank financial institutions (NBFIs). The researcher assessed the data using the panel least squares, Hausman test, Wooldridge test, as well as the Breusch-Pagan/Cook-Weisberg test. The findings revealed that board independence, board activity intensity, audit committee size, and audit committee activity intensity play a significant role in reshaping corporate governance and achieving the financial sustainability of non-bank financial organizations. On the other hand, board size, board composition, board independence, audit committee independence, and gender variety are not found to be significant factors. Since there isn't much research in this area, the study seems to be highly valuable from an academic standpoint. The study's findings will assist the regulatory bodies in implementing suitable corporate governance in Bangladesh's non-banking financial sector.

Assessing a Digital Scorecard on Global Immunization Progress: Stakeholder Views and Implications for Enhancing Performance and Accountability.

Global health agencies and regional and national stakeholders collaborated to develop the Immunization Agenda 2030 Scorecard, a digital data visualization platform displaying global, regional, and country-level immunization progress. The scorecard serves to focus attention and enable strategic actions around the measures visualized. To assess the scorecard's usability, appropriateness, and context for use, we interviewed 15 immunization officers working across five global regions. To further understand the implementation context, we also reviewed the characteristics of 15 public platforms visualizing population health data. We integrated thematic findings across both methods. Many platforms highlight service gaps and enable comparisons between geographies to foster political pressure for service improvements. We observed heterogeneity regarding the platforms' focus areas and participants' leading concerns, which were management capacity and resourcing. Furthermore, one-third of platforms were out of date. Results yielded recommendations for the scorecard, which participants felt was well suited to focus the attention of decision makers on key immunization data. A simpler design coupled with implementation strategies that more actively engage policymakers would better align the scorecard with other public platforms engaging intended users. For population health platforms to serve as effective accountability mechanisms, studying implementation determinants, including usability testing, is vital to meet stakeholder needs.

Strategic Impact: Ross Store Commercial and Sales Strategies in Quevedo 2023

The research set out to analyze the commercial strategies and their influence on Ross Store's sales in Quevedo during the year 2023. To achieve this objective, detailed procedures were carried out, including the collection of data on the commercial strategies implemented by the company, such as promotions, advertising, and inventory management. Interviews were conducted with key personnel and relevant financial data was collected. The analysis focused on the correlation between the strategies employed and sales performance, assessing their impact on profitability and market share. The results revealed significant insights into the most effective tactics, identifying areas for improvement and providing strategic recommendations to optimize Ross Store's business performance in the future. This study not only provides an in-depth understanding of the specific business dynamics of Ross Store in Quevedo, but also provides valuable insights for the retail sector in general, highlighting successful business practices that could be replicated in other similar companies.&#x0D;

A Planning Support System for Monitoring Aging Neighborhoods in Germany

Many single-family homes built in Germany in the first decades following the Second World War are now occupied by elderly residents. If local conditions are unfavorable, a large number of these buildings may enter the real estate market in a short period of time and put pressure on the local housing market. Planners and decision-makers therefore need detailed spatiotemporal information about these neighborhoods to effectively address and counteract such developments. We present the design and implementation of a planning support system that can generate the required information. The architecture of this newly developed software consists of a composite, multitier framework to perform the complex tasks of data importation, data processing, and visualization. Legally mandated municipal population registers provide the key data for the calculation of indicators as a base for spatiotemporal analyses and visualizations. These registers offer high data quality in terms of completeness, logical consistency, spatial, and temporal and thematic accuracy. We demonstrate the implemented method using population data from a local government in a rural area in southwestern Germany. The results show that the new tool, which relies on open software components, is capable to identify and prioritize areas with particularly high levels of problem pressure. The tool can be used not only for analyses in a local context, but also at a regional level.

Choice of refractive surgery types for myopia assisted by machine learning based on doctors’ surgical selection data

In recent years, corneal refractive surgery has been widely used in clinics as an effective means to restore vision and improve the quality of life. When choosing myopia-refractive surgery, it is necessary to comprehensively consider the differences in equipment and technology as well as the specificity of individual patients, which heavily depend on the experience of ophthalmologists. In our study, we took advantage of machine learning to learn about the experience of ophthalmologists in decision-making and assist them in the choice of corneal refractive surgery in a new case. Our study was based on the clinical data of 7,081 patients who underwent corneal refractive surgery between 2000 and 2017 at the Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences. Due to the long data period, there were data losses and errors in this dataset. First, we cleaned the data and deleted the samples of key data loss. Then, patients were divided into three groups according to the type of surgery, after which we used SMOTE technology to eliminate imbalance between groups. Six statistical machine learning models, including NBM, RF, AdaBoost, XGBoost, BP neural network, and DBN were selected, and a ten-fold cross-validation and grid search were used to determine the optimal hyperparameters for better performance. When tested on the dataset, the multi-class RF model showed the best performance, with agreement with ophthalmologist decisions as high as 0.8775 and Macro F1 as high as 0.8019. Furthermore, the results of the feature importance analysis based on the SHAP technique were consistent with an ophthalmologist’s practical experience. Our research will assist ophthalmologists in choosing appropriate types of refractive surgery and will have beneficial clinical effects.

Enabling coastal blue carbon in Aotearoa New Zealand: opportunities and challenges

Blue carbon is the carbon sequestered by coastal and marine habitats such as mangroves, saltmarsh, and seagrasses. The carbon sequestration service provided by these habitats could help to mitigate climate change by reducing greenhouse gas (GHG) emissions, as well as providing other important ecosystem services. Restoration of coastal habitats for the purpose of sequestering blue carbon can generate carbon credits, potentially offsetting the costs of restoration and any lost revenue for landowners. Coastal blue carbon projects have been successfully implemented overseas, but a blue carbon market has not yet been established in Aotearoa New Zealand (ANZ). Here we identify key data gaps that will be necessary to fill to develop a blue carbon market in ANZ. Calculation of carbon abatement through development of a standardised method is the first step and will allow economic assessment of potential restoration sites. Economic assessment will determine if the carbon credits generated will cover restoration costs and lost revenue from restored lands. Once economically feasible potential restoration sites have been identified, prioritisation of sites could be determined by the value of co-benefits produced (i.e., biodiversity). There are also legal uncertainties in ANZ and ownership of the foreshore has been a contentious topic. Current legislation provides that neither the Crown nor any other person owns or can own the common marine and coastal area, although Māori may apply for recognition of customary rights, interests, and title in the area. The legal status of property rights will have significant implications for privately owned land, as it is unclear whether land will be considered foreshore when inundated in future with sea level rise. Here, we discuss further policy enablers including the role of government and the insurance industry that could encourage uptake of carbon projects by private landowners. Filling these gaps in market assessments and recognising the key role of Indigenous owners and customary rights holders to coastal land can facilitate operationalising of coastal blue carbon opportunities in Aotearoa New Zealand.

Study within a trial 119: The effectiveness of a thank you card to improve trial follow up; a randomised study within a trial

Background With attrition common in randomised trials, strategies are needed to minimise this. Many retention strategies include ‘thanks’ elements however there is currently no evidence of the effectiveness of a ‘thank you’ intervention separate to other trial activity or information. This Study Within A Trial (SWAT) sought to assess if a thank you card increases completion of the host trial primary outcome. Methods A two arm SWAT, using a 1:1 (intervention:control) allocation ratio, embedded within the DISC trial. The primary outcome was the difference in retention rate at 1 year post-treatment. Secondary outcomes were outcome data completeness, cost, and retention at 2 years post-treatment. Analyses were conducted using logistic regression adjusting for SWAT and host trial allocation. Results A total of 358 participants were randomised and included in the SWAT analyses. Completion of the 1-year outcome visit was 89.7% ( n = 157) in the intervention group and 90.2% (165) in the control group (adjusted odds ratio (OR) 0.95, 95% CI 0.48 to 1.90, p = .89). There was no evidence of a difference in completeness of key outcome data (adjusted OR 1.84, 95% CI 0.71 to 4.73, p = .20) or retention at 2 years post treatment (adjusted OR 1.13, 95% CI 0.59 to 2.17, p = .72). Conclusion It remains unclear if thank you cards increased the rate of primary outcome follow-up completion within the DISC trial. However, as the first evaluation of a distinct ‘thank you’ intervention for improving retention rates, further replications are required to determine effectiveness, ideally in populations other than older, male, Caucasians.

Abstract WP91: Stroke Tracking: Improving Core Measure Compliance Tracking &amp; Efficiency

Background: Stroke program development and expansion requires excellent patient care and continued quality improvement via ongoing data collection. Substantial time and effort are required to identify patient records and manually assess metrics within charts. While real-time assessment of this data is ideal, achieving this is challenging. Purpose: We sought to create a real-time, electronic medical record (EMR) derived patient tracking list to improve accuracy, efficiency, and timeliness of stroke patient data collection. Methods: Ischemic stroke patient identification was optimized utilizing current patient lists, which over capture, and applying filters based on EMR entries applicable to stroke patients. Patient capture was reviewed to ensure adequate sensitivity and specificity. Subsequently, key stroke quality data points were identified. Custom columns were created to pull data from across the EMR into a central list. Column data was validated to ensure accuracy and confirm improved efficiency via timed chart reviews. A Mann-Whitney test was used to compare means pre and post-review. Of note, different reviewers performed pre and post reviews. Results: A stroke tracking list was successfully implemented. Chart review in the previous process averaged 173.2 seconds (STD 40.6) vs. 121.3 seconds (STD 17.8) in the new process (p&lt;0.05). Patient identification times were not compared, though identifying charts in the old workflow ranged from 12 to 15 minutes per chart. Conclusions: Creation of a central tracking list within the electronic medical record is feasible and can improve patient identification, coordination of critical data sharing, and data collection efficiency. Utilization decreased data collection times and prompted creation of similar tracking lists for other core measure data. While sample sizes were small and pre and post reviewers were different, this work demonstrates feasibility and early foundational work.

Development of an Assessment Model for Measuring Mechanical Engineering Companies’ Circularity and Maturity Levels

The circular economy has gained significance in recent years due to its potential to achieve economic benefits and address ecological challenges. Mechanical engineering companies face the challenge of transitioning to a circular economy, which offers advantages like improved resource utilisation, reduced dependence on external suppliers, and enhanced production and supply chain efficiency. However, implementing circular economy principles proves difficult for manufacturing firms. Measuring circularity poses significant challenges, necessitating assessing methods' development, application, and validation. Industry-specific key indicators and data quality assurance are crucial in this regard. Existing models struggle to adapt to diverse contexts and industries. The outcome of this paper is the C-METRIC (Circular Manufacturing Evaluation and Rating for Industrial Circularity), an industry-specific method for assessing mechanical engineering companies' circularity and maturity level, developed using the Design-Science-Research Methodology. Based on the circular value chain, the method evaluates the maturity and circularity levels of the focused sector through 66 specific questions in 33 different areas of the company. The results are divided into the maturity and circularity levels of value-adding and strategic processes and are visualised using spider diagrams. The purpose of this model is to survey the circularity and circular economy maturity of mechanical engineering companies of specific regions and sectors.

Application solutions of highway freight information systems based on quantum communication.

To improve the security of data transmission in the highway freight information system, this study is an application plan for the highway freight information system based on quantum communication. This solution is based on quantum communication technology to encrypt and transmit key sensitive data[1]; it realizes unified management of quantum keys through the quantum key cloud terminal and provides key services for the highway freight information system; it realizes access to the system through the quantum key cloud service platform. The secure use of mobile terminal quantum keys improves the overall security of the road freight information system. This scheme uses the quantum encryption key generated only once, effectively protecting the entire system's security. The quantum key management server and quantum key cloud platform defined in this plan manage terminals and quantum keys respectively, and jointly produce and distribute quantum keys with the help of other hardware facilities and software to provide secure transmission of key information.