Copula based prediction models: an application to an aortic regurgitation study

<p>Abstract</p> <p>Background:</p> <p>An important issue in prediction modeling of multivariate data is the measure of dependence structure. The use of Pearson's correlation as a dependence measure has several pitfalls and hence application of regression prediction models based on this correlation may not be an appropriate methodology. As an alternative, a copula based methodology for prediction modeling and an algorithm to simulate data are proposed.</p> <p>Methods:</p> <p>The method consists of introducing copulas as an alternative to the correlation coefficient commonly used as a measure of dependence. An algorithm based on the marginal distributions of random variables is applied to construct the <it>Archimedean </it>copulas. Monte Carlo simulations are carried out to replicate datasets, estimate prediction model parameters and validate them using Lin's concordance measure.</p> <p>Results:</p> <p>We have carried out a correlation-based regression analysis on data from 20 patients aged 17–82 years on pre-operative and post-operative ejection fractions after surgery and estimated the prediction model: Post-operative ejection fraction = - 0.0658 + 0.8403 (Pre-operative ejection fraction); p = 0.0008; 95% confidence interval of the slope coefficient (0.3998, 1.2808). From the exploratory data analysis, it is noted that both the pre-operative and post-operative ejection fractions measurements have slight departures from symmetry and are skewed to the left. It is also noted that the measurements tend to be widely spread and have shorter tails compared to normal distribution. Therefore predictions made from the correlation-based model corresponding to the pre-operative ejection fraction measurements in the lower range may not be accurate. Further it is found that the best approximated marginal distributions of pre-operative and post-operative ejection fractions (using q-q plots) are gamma distributions. The copula based prediction model is estimated as: Post -operative ejection fraction = - 0.0933 + 0.8907 × (Pre-operative ejection fraction); p = 0.00008 ; 95% confidence interval for slope coefficient (0.4810, 1.3003). For both models differences in the predicted post-operative ejection fractions in the lower range of pre-operative ejection measurements are considerably different and prediction errors due to copula model are smaller. To validate the copula methodology we have re-sampled with replacement fifty independent bootstrap samples and have estimated concordance statistics 0.7722 (p = 0.0224) for the copula model and 0.7237 (p = 0.0604) for the correlation model. The predicted and observed measurements are concordant for both models. The estimates of accuracy components are 0.9233 and 0.8654 for copula and correlation models respectively.</p> <p>Conclusion:</p> <p>Copula-based prediction modeling is demonstrated to be an appropriate alternative to the conventional correlation-based prediction modeling since the correlation-based prediction models are not appropriate to model the dependence in populations with asymmetrical tails. Proposed copula-based prediction model has been validated using the independent bootstrap samples.</p

Hybrid mini-grid power system for electrification of remote and rural locations in Fiji

Hybrid mini-grids appear to be one of the most promising technological options for electrifying remote and rural areas. However, there are still important questions, technical and non-technical, regarding their capabilities and appropriate application. This thesis focuses on two key opportunities to improve hybrid system; advanced load modelling with the concept of load prioritization, and system design to permit relatively graceful degradation of hybrid mini-grid performance when some of the component technologies fail and cannot be immediately repaired. More broadly, the thesis has also sought to identify some wider challenges of successful project implementation in the particular context of Fiji. This thesis then presents a detailed study of load modelling carried out through consultation with authorities in Fiji and some small scale load surveys at the village level. It proposes establishing a hierarchy of load priority to ensure that the hybrid system continues to supply to most important loads should its capabilities decline due to failure or unexpected events. A widely used software design tool is then applied to design appropriate hybrid systems to meet such loads. However, standard performance metrics from this tool are extended through the use of failure mode analysis to better understand the potential resilience of different designs. A detailed literature review and targeted consultations with a range of Fijian stakeholders were undertaken to better understand options for addressing the non-technical challenges of project implementation. The research suggests some key findings: different hybrid systems can have different resilience to technical failures and other unexpected events and simple least cost performance optimisation may not always be appropriate; load prioritization can help secure delivery of the most essential energy services at times of partial system failure; and successful stakeholder engagement, system design, implementation, operation and maintenance all have a key role in achieving sustainable outcomes. In conclusion, this research argues for the use of multi-objective design criteria for the design of hybrid mini-grids. As such, the study recommends that research and development should become an integral part of the evolution of hybrid mini-grids in remote and rural applications

Quantifying nematic order in evaporation-driven self-assembly of Halloysite nanotubes: Nematic islands and critical aspect ratio

Halloysite nanotubes (HNTs) are naturally occurring clay minerals found in Earth's crust that typically exist in the form of high aspect-ratio nanometers-long rods. Here, we investigate the evaporation-driven self-assembly process of HNTs and show that a highly polydisperse collection of HNTs self-sort into a spatially inhomogeneous structure, displaying a systematic variation in the resulting nematic order. Through detailed quantification using nematic order parameter $S$ and nematic correlation functions, we show the existence of well-defined isotropic-nematic transitions in the emerging structures. We also show that the onset of these transitions gives rise to the formation of nematic islands - phase coexisting ordered nematic domains surrounded by isotropic phase - which grow in size with $S$. Detailed image analysis indicates a strong correlation between local $S$ and the local aspect ratio, $L/D$, with nematic order possible only for rods with $L/D \ge 6.5 \pm 1$. Finally, we conclude that observed phenomena directly result from aspect ratio-based sorting in our system. Altogether, our results provide a unique method of tuning the local microscopic structure in self-assembled HNTs using $L/D$ as an external parameter.Comment: 9 pages, 4 figure

AI-Powered CT Scan Enhancement: Turning CTs into MRI Quality Images for Faster and Safer Diagnoses

The use of deep learning (DL) architectures like U-Net and GANs ensures secure, distributed model training across hospitals. The proposed work uses a privacy-preserving federated learning framework for emergency neuroimaging, enabling AI models to convert Computed Therapy (CT) scans into Magnetic Resonance Imaging (MRI) equivalent images as MRI images gives more accurate soft tissue details without compromising patient data. The proposed model integrates DL with saliency maps and Grad-CAM which are the Explainable AI (XAI) tools. The idea is to offer the transparency and build trust in diagnosis of disease. The image quality is measured using the metrics Structural Similarity Index (SSIM) and Paek Signal to Noise Ratio (PSNR) which ensures high-quality image synthesis. The proposed solution enhances the diagnostic accessability in resourse limited hospitals and rural hospitals by preserving patient data with standards. The enhanced model strengthens the framework, privacy techniques and secure aggregation techniques are used to prevent model data leakage during model training or updates. The study provides additional layer of protection to ensures using Federated Learning that even gradient-level information shared between hospitals cannot be traced back to individual patient data. The proposed system is scalable and enables integration with diverse hospital infractures and imaging modalities. The model provides the accessability by turning CT to MRI through secure XAI. The model accuracy ranges to 95% remaining validation accuracy close to train accuracy. The proposed idea provides emergency diagonistics with easy accesibility by preserving privacuy

DETERMINATION OF 5H-BENZO[2,3][1,4]OXAZEPINO[5,6-B]INDOLES IN RAT PLASMA BY REVERSED-PHASE HIGH-PERFORMANCE LIQUID CHROMATOGRAPHIC-ULTRAVIOLET METHOD: APPLICATION TO PHARMACOKINETIC STUDIES

Objective: Recently, we reported newly synthesized 5H-benzo[2,3][1,4]oxazepino[5,6-b]indole) derivatives and proved their cytotoxicity against hepatocellular carcinoma specific Hep-G2 cell lines. We attempted herein to describe a reversed-phase high-performance liquid chromatographic method for the determination of three most active compounds 6a, 10a, and 15a in rat plasma to predict their pharmacokinetics parameters before in vivo study.Methods: A rapid and sensitive reversed-phase high-performance liquid chromatographic was employed for the determination of 6a, 10a, and 15a in rat plasma. Each compound was separated by a gradient elution of acetonitrile and water with 1 mL/min flow rate. The detector was set at 270, 285, and 275 nm for 6a, 10a, and 15a and the recorded elution times were 2.00, 2.87, and 1.88 min, respectively.Results: The calibration curve was linear with R2 of 0.938, 0.875, and 0.923 over the concentration range of 0.1–50 μg/mL. The inter- and intra-day variations of the assay were lower than 12.26%; the average recovery of 6a, 10a, and 15a was 97.31, 92.56, and 95.23 % with relative standard deviation of 2.12%, 3.25%, and 2.28%, respectively. The Cmax and Tmax were ~ 46.34, 18.56, and 25.65 μg/mL and 2.0, 4.0, and 4.0 h for 6a, 10a, and 15a, respectively, which indicate a robust method of detection in the present experiment.Conclusion: The study suggests that all of the three compounds have a lower rate of absorption, higher volume of distribution, and lower clearance rate, indicating good therapeutic response for in vivo activity.Â

ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent

Answering complex natural language questions often necessitates multi-step reasoning and integrating external information. Several systems have combined knowledge retrieval with a large language model (LLM) to answer such questions. These systems, however, suffer from various failure cases, and we cannot directly train them end-to-end to fix such failures, as interaction with external knowledge is non-differentiable. To address these deficiencies, we define a ReAct-style LLM agent with the ability to reason and act upon external knowledge. We further refine the agent through a ReST-like method that iteratively trains on previous trajectories, employing growing-batch reinforcement learning with AI feedback for continuous self-improvement and self-distillation. Starting from a prompted large model and after just two iterations of the algorithm, we can produce a fine-tuned small model that achieves comparable performance on challenging compositional question-answering benchmarks with two orders of magnitude fewer parameters.Comment: 19 pages, 4 figures, 4 tables, 8 listing

A consensus on the utility of the anti-müllerian hormone assay in the assessment of ovarian reserve and gynecological conditions among Indian gynecologists

Infertility is a global issue that causes distress. Serum anti-Müllerian hormone (AMH) and antral follicle count are reliable ovarian reserve markers. The stability of serum AMH levels throughout the menstrual cycle makes monitoring ovarian function decline convenient. This consensus aimed to develop recommendations for the application of the AMH assay in assessing ovarian reserve and broader clinical decision-making among gynecologists in India. A modified Delphi method was used, with a panel of 10 expert gynecologists and 2 lab experts from India, to establish an expert consensus. A questionnaire consisting of 29 consensus statements was administered, covering topics related to ovarian reserve, AMH markers, assay reliability, performance, and specific conditions such as ovarian tumors and endometriosis. Through two rounds of the modified Delphi method, 21 consensus statements were ultimately formulated. The consensus was determined using an 80% cutoff. The panel reached a consensus on 19 statements and a moderate consensus on two, emphasizing the significance of AMH testing in evaluating ovarian reserve and reproductive aging. The panel agreed that AMH assays were valuable in predicting ovarian response to fertility treatments, diagnosing polycystic ovary syndrome and endometriosis, and guiding fertility preservation. It was concluded that AMH testing is crucial for infertility management in India, offering insights into ovarian reserve and reproductive aging. Standardized automated assays ensure speed and precision, aiding in diagnosing fertility conditions, predicting treatment responses, and preserving fertility during therapy. International standards for accurate interpretation are imperative. Overall, AMH testing plays a pivotal role in personalized fertility care in India