Suboptimal Data Research Articles

Efficient and Accurate Semi-Automatic Neuron Tracing with Extended Reality.

Neuron tracing, alternately referred to as neuron reconstruction, is the procedure for extracting the digital representation of the three-dimensional neuronal morphology from stacks of microscopic images. Achieving accurate neuron tracing is critical for profiling the neuroanatomical structure at single-cell level and analyzing the neuronal circuits and projections at whole-brain scale. However, the process often demands substantial human involvement and represents a nontrivial task. Conventional solutions towards neuron tracing often contend with challenges such as non-intuitive user interactions, suboptimal data generation throughput, and ambiguous visualization. In this paper, we introduce a novel method that leverages the power of extended reality (XR) for intuitive and progressive semi-automatic neuron tracing in real time. In our method, we have defined a set of interactors for controllable and efficient interactions for neuron tracing in an immersive environment. We have also developed a GPU-accelerated automatic tracing algorithm that can generate updated neuron reconstruction in real time. In addition, we have built a visualizer for fast and improved visual experience, particularly when working with both volumetric images and 3D objects. Our method has been successfully implemented with one virtual reality (VR) headset and one augmented reality (AR) headset with satisfying results achieved. We also conducted two user studies and proved the effectiveness of the interactors and the efficiency of our method in comparison with other approaches for neuron tracing.

Advancing immunisation information systems: a proposal for the “unvaccinated registry”

Abstract Through a systematic review of 253 studies, we identified significant advancements in IIS functionalities including real-time data access, interoperability, and life-course vaccination records, crucial for robust public health responses. Our review reveals that vaccinations are vital for public health and societal welfare; however, vaccine hesitancy is a significant barrier, driven by complex factors. Most IIS implementations predominantly utilise web-based platforms focused on children’s vaccinations with features that enhance data interoperability and public health surveillance. Despite these advancements, gaps in vaccine coverage and hesitancy mapping persist due to suboptimal data integration and system interoperability. Parallelly, we propose a comprehensive registry that includes unvaccinated individuals, aiming to address vaccine hesitancy by integrating demographic and socio-economic data. This initiative promises to enhance health equity by providing actionable insights into vaccination gaps and facilitating targeted interventions. Ethical considerations, particularly data privacy, are paramount, as well as adhering to regulations such as GDPR to ensure the protection of personal health information. The integration of IIS with a registry that includes unvaccinated populations represents a transformative approach to public health, potentially increasing vaccine coverage and effectively informing policy decisions. This dual strategy leverages not only technological advancements but also incorporates essential behavioural insights, offering a path forward in the ongoing battle against vaccine-preventable diseases. This model aligns with the Italian National Vaccination Prevention Plan and underscores proactive, evidence-based strategies to enhance vaccination uptake, serving as a cornerstone for broader application in other national contexts.

Effectiveness and Safety of Dupuytren Contracture Treatments: A Systematic Review and Meta-Analysis Using the GRADE Approach.

There is currently no consensus on the optimal treatment for Dupuytren contracture. Prior meta-analyses have been limited by suboptimal data synthesis methodologies. We conducted an updated evidence review comparing the effectiveness and safety of percutaneous needle fasciotomy (PNF), collagenase clostridium histolyticum (CCH), and limited fasciectomy (LF) using the GRADE approach. CENTRAL, MEDLINE, and Embase were searched for randomized controlled trials comparing outcomes following PNF, CCH, and LF for Dupuytren contracture treatment. Outcomes of interest included residual contracture, recurrence rate, hand function, pain, global satisfaction, and adverse events. Time points included 3-months, 1-year, and 2-3 years. Seventeen publications (1,010 patients) were included. High to moderate certainty evidence showed no clinically important difference in long-term contracture reduction (PNF vs. LF (mean difference (MD): 7.6°; 95% CI: 1.8°-13.4°), CCH vs. LF (MD: 4.8°; 95% CI: -1.3°-10.9°)). Moderate certainty evidence indicated that LF provides the lowest risk of long-term recurrence (PNF vs. LF (relative risk (RR): 12.3; 95% CI: 1.6-92.4), CCH vs. LF (RR: 9.5; 95% CI 1.2-73.4)), LF has a higher risk of serious adverse events than PNF (RR: 0.5; 95% CI 0.3-0.9), and CCH has a higher risk of overall adverse events than PNF (RR: 4.8; 95% CI 2.9-7.0). CCH, PNF, and LF are equally effective in long-term contracture reduction. However, LF yields more durable results at a higher risk of rare but serious adverse events. Current evidence suggests the use of PNF over CCH. However, ultimate treatment decisions should be tailored to individual patient preferences.

The Power of Reagent Titration in Flow Cytometry.

Flow cytometry facilitates the detection of multiple cell parameters simultaneously with a high level of resolution and throughput, enabling in-depth immunological evaluations. High data resolution in flow cytometry depends on multiple factors, including the concentration of reagents used in the staining protocol, and reagent validation and titration should be the first step in any assay optimization. Titration is the process of finding the concentration of the reagent that best resolves a positive signal from the background, with the saturation of all binding sites, and minimal antibody excess. The titration process involves the evaluation of serial reagent dilutions in cells expressing the antigen target for the tested antibody. The concentration of antibody that provides the highest signal to noise ratio is calculated by plotting the percentage of positive cells and the intensity of the fluorescence of the stained cells with respect to the negative events, in a concentration-response curve. The determination of the optimal antibody concentration is necessary to ensure reliable and reproducible results and is required for each sample type, reagent clone and lot, as well as the methods used for cell collection, staining, and storage conditions. If the antibody dilution is too low, the signal will be too weak to be accurately determined, leading to suboptimal data resolution, high variability across measurements, and the underestimation of the frequency of cells expressing a specific marker. The use of excess antibodies could lead to non-specific binding, reagent misuse, and detector overloading with the signal off scale and higher spillover spreading. In this publication, we summarized the titration fundamentals and best practices, and evaluated the impact of using a different instrument, sample, staining, acquisition, and analysis conditions in the selection of the optimal titer and population resolution.

Artificial intelligence and informatics in neonatal resuscitation

Neonatal intensive care unit resuscitative care continually evolves and increasingly relies on data. Data driven precision resuscitation care can be enabled by leveraging informatics tools and artificial intelligence. Despite technological advancements, these data are often underutilized due to suboptimal data capture, aggregation, and low adoption of artificial intelligence and analytic tools. This review describes the fundamentals and explores the evidence behind informatics and artificial intelligence tools supporting neonatal intensive care unit resuscitative care, training and education. Key findings include the need for effective interface design for accurate data capture followed by storage and translation to wisdom using analytics and artificial intelligence tools. This review addresses the issues of data privacy, bias, liability and ethical frameworks when adopting these tools. While these emerging technologies hold great promise to improve resuscitation, further study of these applications in neonatal population and awareness of informatics and artificial intelligence principles among clinicians is imperative.

A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes

BackgroundPrecision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs.MethodsTo address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the “borrowing of information” across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model.ResultsWe demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs.ConclusionThe study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

Critical Evaluation and Meta-Analysis of Ecotoxicological Data on Per- and Polyfluoroalkyl Substances (PFAS) in Freshwater Species.

Despite the increasing concern regarding the ecological risks posed by per- and polyfluoroalkyl substances (PFAS), a lack of comprehensive understanding of their actual ecotoxicity remains. Through a meticulous examination of 91 peer-reviewed studies investigating effects at a population level and constructing probabilistic species sensitivity distributions (PSSDs), we present a state-of-the-science hazard assessment of PFAS in freshwater species. Using data subsets containing suboptimal data led to an overestimation of the predicted no-effect concentrations (PNECs) of PFAS. We report PNECs of perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonates (PFSAs) in freshwater to be 4.8-2000 μg/L and 0.4-8.9 μg/L, respectively, derived from high-quality data. Statistical analyses revealed that both functional groups and carbon chain length significantly influenced (p < 0.05) the variations in toxicity observed among different PFAS. This study underscores the importance of obtaining high-quality PFAS ecotoxicity data to comprehend associated hazards. The PNECs of PFAS derived in this study are higher compared to those of micro/nanoplastics and persistent organic pollutants. Our research offers valuable insights into prioritizing the regulation of more toxic PFAS.

Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research

ObjectivesTo identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as...

Offline DRL for Price-Based Demand Response: Learning From Suboptimal Data and Beyond

Offline DRL for Price-Based Demand Response: Learning From Suboptimal Data and Beyond

Effective Supply Chain Strategies in Addressing Demand and Supply Uncertainty: A Case Study of Ethiopian Pharmaceutical Supply Services.

Ensuring the consistent availability of essential medicines is crucial for effective healthcare systems. However, Ethiopian public health facilities have faced frequent stockouts of crucial medications, highlighting systemic challenges such as inadequate forecasting, prolonged procurement processes, a disjointed distribution system, suboptimal data quality, and a shortage of trained professionals. This study focuses on the Ethiopian Pharmaceutical Supply Services (EPSS), known for its highly unstable and volatile supply chain, aiming to identify risks and mitigation strategies. Using a mixed-method approach involving surveys and interviews, the research investigates successful and less successful strategies, key success factors, and barriers related to pharmaceutical shortages. Proactive measures such as communication, stock assessment, supervision, and streamlined procurement are emphasized as vital in mitigating disruptions, while reactive strategies like safety stock may lack long-term efficacy. The study highlights the importance of aligning supply chain strategies with product uncertainties, fostering collaboration, and employing flexible designs for resilience. Managerial implications stress the need for responsive structures that integrate data quality, technology, and visibility. This study contributes by exploring proactive and reactive strategies, elucidating key success factors for overcoming shortages in countries with unstable supply chains, and offering actionable steps for enhancing supply chain resilience. Embracing uncertainty and implementing proactive measures can help navigate volatile environments, thereby enhancing competitiveness and sustainability.

Reduced B0/B1 + sensitivity in velocity-selective inversion arterial spin labeling using adiabatic refocusing pulses.

To mitigate the B0/B1 + sensitivity of velocity-selective inversion (VSI) pulse trains for velocity-selective arterial spin labeling (VSASL) by implementing adiabatic refocusing. This approach aims to achieve artifact-free VSI-based perfusion imaging through single-pair label-control subtractions, reducing the need for the currently required four-pair dynamic phase-cycling (DPC) technique when using a velocity-insensitive control. We introduce a Fourier-transform VSI (FT-VSI) train that incorporates sinc-modulated hard excitation pulses with MLEV-8-modulated adiabatic hyperbolic secant refocusing pairs. We compare performance between this train and the standard composite refocusing train, including with and without DPC, for dual-module VSI VSASL. We evaluate (1) simulated velocity-selective profiles and subtraction fidelity across a broad B0/B1 + range, (2) subtraction fidelity in phantoms, and (3) image quality, artifact presence, and gray-matter perfusion heterogeneity (as measured by the spatial coefficient of variation) in healthy human subjects. Adiabatic refocusing significantly improves FT-VSI robustness to B0/B1 + inhomogeneity for a single label-control subtraction. Subtraction fidelity is dramatically improved in both simulation and phantoms compared with composite refocusing without DPC, and is similar compared with DPC methods. In humans, marked artifacts seen with the non-DPC composite refocusing approach are eliminated, corroborated by significantly reduced gray-matter heterogeneity (via lower spatial coefficient of variation values). A novel VSASL labeling train using adiabatic refocusing pulses for VSI was found to reduce artifacts related to B0/B1 + inhomogeneity, thereby providing an alternative to DPC and its associated limitations, which include increased vulnerability to physiological noise and motion, reduced functional MRI applicability, and suboptimal data censoring.

Evaluating airborne, mobile and terrestrial laser scanning for urban tree inventories: A case study in Ghent, Belgium

In urban tree inventories, structural measurements such as diameter at breast height (DBH), tree height (H), crown projection area (CPA) and crown volume (CV) are essential for diverse applications, including accurate ecosystem service estimation and management decisions. Traditionally, tree measurements are obtained using range finders and diameter tape. These measurements can be integrated into urban tree inventories through 3D laser scanning (also known as LiDAR). Multiple platforms for laser scanning exist, each with its own advantages and disadvantages, which have not yet been compared explicitly for urban tree inventories. We collected terrestrial laser scanning (TLS), mobile laser scanning (MLS) and airborne laser scanning (ALS) in leaf-on (TLS, MLS, ALS) and leaf-off conditions (TLS, MLS) in Ghent, Belgium. We evaluated the DBH, H, CPA and CV of 95 individual trees acquired from each acquisition platform, benchmarking against TLS. Our results show accurate DBH derivation from both TLS and MLS (bias < 2 cm, concordance correlation coefficient (CCC) ≈ 1). However, during leaf-on conditions, occlusion from shrubs and ivy is observed. For leaf-on MLS, point clouds of large trees exhibited occlusion in the top canopy, impacting crown volume (CV MLS leaf-on: bias = −116 m², CCC=0.85) and, to a lesser extent, tree height (H MLS leaf-on: bias = −0.38 m, CCC=0.99). Crown projected area was less affected (bias = 0.49 m², CCC=0.99), with differences more attributed to varying point precision among sensors. The difference between the metric and benchmark increased with tree size and the structural complexity of the surroundings (e.g. buildings), especially for MLS, for which limited GNSS coverage, traffic, and suboptimal walking patterns impeded ideal data collection. Our results will help city councils and tree managers choose the most optimal LiDAR platform for urban tree inventories, accounting for their purpose, site complexity and budget.

Understanding random resampling techniques for class imbalance correction and their consequences on calibration and discrimination of clinical risk prediction models

ObjectiveClass imbalance is sometimes considered a problem when developing clinical prediction models and assessing their performance. To address it, correction strategies involving manipulations of the training dataset, such as random undersampling or oversampling, are frequently used. The aim of this article is to illustrate the consequences of these class imbalance correction strategies on clinical prediction models’ internal validity in terms of calibration and discrimination performances. MethodsWe used both heuristic intuition and formal mathematical reasoning to characterize the relations between conditional probabilities of interest and probabilities targeted when using random undersampling or oversampling. We propose a plug-in estimator that represents a natural correction for predictions obtained from models that have been trained on artificially balanced datasets (“naïve” models). We conducted a Monte Carlo simulation with two different data generation processes and present a real-world example using data from the International Stroke Trial database to empirically demonstrate the consequences of applying random resampling techniques for class imbalance correction on calibration and discrimination (in terms of Area Under the ROC, AUC) for logistic regression and tree-based prediction models. ResultsAcross our simulations and in the real-world example, calibration of the naïve models was very poor. The models using the plug-in estimator generally outperformed the models relying on class imbalance correction in terms of calibration while achieving the same discrimination performance. ConclusionRandom resampling techniques for class imbalance correction do not generally improve discrimination performance (i.e., AUC), and their use is hard to justify when aiming at providing calibrated predictions. Improper use of such class imbalance correction techniques can lead to suboptimal data usage and less valid risk prediction models.

Rancangan Aplikasi Sistem Pengelolaan dan Pelaporan Aset di SMPN 218 dengan Metode Sequential Linear Waterfall menggunakan PHP dan MySQL Berbasis Web

Effective asset management is crucial for ensuring that assets are easily monitored and tracked. Currently, SMP Negeri 218 Jakarta manages assets manually by recording them in books before inputting them into spreadsheet software. This manual process results in suboptimal data management and asset reporting, leading to inefficiencies and increased risk of asset loss. The proposed solution is to design an asset management information system in the form of a web application to improve data handling and reporting efficiency. This study employs the PHP programming language within the Laravel framework and a MySQL database on the Xampp web server, utilizing the Sequential Linear (Waterfall) method. Functional testing of the system by web experts yielded a feature completeness score of 1, indicating its suitability and reliability for use in managing assets at SMP Negeri 218 Jakarta. The developed system aims to streamline asset management processes, enhance report accuracy, and provide timely access to asset data, ultimately supporting better decision-making and resource management within the school.

Successive model-agnostic meta-learning for few-shot fault time series prognosis

Meta learning is a promising technique for solving few-shot fault prediction problems, which have attracted the attention of many researchers in recent years. Existing meta-learning methods for time series prediction, which predominantly rely on random and similarity matching-based task partitioning, facing three major limitations: (1) feature exploitation inefficiency; (2) suboptimal task data allocation; and (3) limited robustness with small samples. To this end, we introduce a novel‘meta-task’ partitioning scheme, underpinned by a differential autoregressive algorithm, that treats a continuous time period of a time series as a meta-task, composed of multiple successive short time periods. This approach allows us to extract more comprehensive features and relationships from the data, resulting in more accurate predictions. Furthermore, our findings indicate that the differential autoregressive approach significantly bolsters robustness across diverse datasets. Extensive experiments on several fault and time series prediction datasets demonstrate that our approach substantially enhances prediction performance and generalization capability under both few-shot and general conditions.

Pharmacokinetic models for first-in-human dose selection of immune-activating products in oncology

Pharmacokinetic (PK) models are increasingly submitted to the FDA to support first-in-human (FIH) dose selection of immune-oncology products. To examine whether a simple PK modeling (SPM) using clearance for scaling was acceptable for dose estimation, FIH(SPM) doses were computed and compared to doses that were safely administered to patients. We concluded that the SPM approach is acceptable in FIH dose estimation, but the variables should be carefully selected for CD3 constructs. For CD3 constructs, use of 60 kg BWh, a clearance exponent of 0.75, and a targeted plasma concentration based on relevant and/or sensitive activity assays was an acceptable approach for FIH dose selection; use of 0.85 as the scaling factor is questionable at this time as it resulted in a FIH dose that was too close to the AHD for one product (7%). Immune activating mAbs were not sensitive to changes in the clearance exponent (0.75–0.85) or body weight (60–70 kg). For PD-1/PD-L1 mAbs, using products’ in vitro EC50 in the model resulted in suboptimal FIH doses and clinical data of closely related products informed FIH dose selection. PK models submitted by sponsors were diverse in methods, assumptions, and variables, and the resulting FIH doses were not always optimal.

Inverse Reinforcement Learning with Agents’ Biased Exploration Based on Sub-Optimal Sequential Action Data

Inverse reinforcement learning (IRL) estimates a reward function for an agent to behave along with expert data, e.g., as human operation data. However, expert data usually have redundant parts, which decrease the agent’s performance. This study extends the IRL to sub-optimal action data, including lack and detour. The proposed method searches for new actions to determine optimal expert action data. This study adopted maze problems with sub-optimal expert action data to investigate the performance of the proposed method. The experimental results show that the proposed method finds optimal expert data better than the conventional method, and the proposed search mechanisms perform better than random search.

Implementation of INLISLITE 3.1 in Processing Printed Book Materials at SMAN 1 Kalanganyar Library

This research aims to provide an overview of the implementation of the INLISLite in processing printed book materials at the library of SMAN 1 Kalanganyar. The INLISLite 3.1 is a library automation system application designed and officially developed by the National Library of the Republic of Indonesia, used to assist in the process of managing library materials. The method used in this research is a descriptive qualitative research method. Utilizing data collection techniques through observations and interviews with two informants consisting of the head librarian and library staff of SMAN 1 Kalanganyar. The implementation of the INLISLite serves as a solution for the SMAN 1 Kalanganyar library in transitioning from conventional to automated processing of printed book materials. Equipped with comprehensive program features, it aids the staff in their library activities. However, the use of the INLISLite 3.1 at SMAN 1 Kalanganyar has not been fully optimized and is still hindered by human resources, resulting in suboptimal data input due to a lack of cataloging expertise.

A decade of technology advancement in seismic processing: A case study from reprocessing legacy sparse OBC data in Kashagan Field

Ceaseless advancements in seismic data processing technology and workflow enable the extraction of increasing amounts of subsurface information essential for derisking and optimizing natural resource development. The Kashagan oil field, one of the world's largest carbonate reservoirs, is faced with significant development optimization challenges due to a combination of complex geology and suboptimal seismic data coverage. The latest processing technologies were applied to the 2001–2002 vintage sparse ocean-bottom cable data and produced step-change improvements over the 2010–2011 legacy processing and imaging results. Such significant image uplift is representative of technology advancements over the span of a decade fully leveraging the newly preprocessed input data, including (1) an integrated model building workflow, (2) adaptive application of full-waveform inversion, (3) geologically constrained reflection tomography, and (4) least-squares imaging. The new seismic results improved the structural and stratigraphic imaging of multiple layers of shallow carbonates, mitigated fault shadow and other complex overburden effects, increased the resolution of subsalt carbonate reservoir features such as karst and internal fractures, improved well marker depth misfit, and ultimately influenced the placement of upcoming wells and reservoir development plans. Further improvements in seismic imaging would be feasible with a modern acquisition of more densely sampled seismic data, which would allow the full potential of the latest seismic processing technologies and workflows to be unlocked.

Simulation of Filter-Cake Formations on Vertical and Inclined Wells Under Elevated Temperature and Pressure

Summary Drilling fluids consist of colloidal suspensions that are made up of solid particles suspended in liquid. The aqueous component of the drilling mud invades a permeable formation due to differential pressure, leaving solid deposits on the drilled formation walls, forming a filter cake. Typical drilling fluids should create a thin, slick filter cake and fluid loss control to seal permeable zones as swiftly as possible. Tight holes, elevated levels of rotational resistance and friction, wellbore obstruction, fluid loss into formations, suboptimal well-log data, and reservoir formation impairment result from thick filter cakes and excessive filtration. Controlling infiltration of fluids to establish a minimal, low-porosity filter cake is frequently required to avoid drilling and production issues. In this study, we used computational fluid dynamics (CFD) to model the development of filter cakes induced by pipe rotation in vertical and inclined wellbore walls under elevated temperature and pressure during deep drilling. Multiple tests validate the basic model for application in both shallow and deep drilling operations. By use of an Eulerian-Eulerian approach to describe drilling fluids as a binary-phase system, including particles in suspension in the non-Newtonian fluid, our research investigates the power-law and Herschel-Bulkley rheological models. These models successfully predict the flow pattern of drilling fluid at the bottom region of deep drilling operations. The study further explores filter-cake formation by examining the influence of overbalanced pressure on filter-cake thickness at various pressures. Drilling simulations, incorporating pipe rotation and eccentricity effects under realistic operating parameters, accurately project filter-cake thickness, aligning closely with experimental measurements and existing CFD literature in this domain.