Scarcity Of Data Research Articles

Ecotoxicological risk assessment of active pharmaceutical ingredients (APIs) against different aquatic species leveraging intelligent consensus prediction and i-QSTTR modeling

The increasing presence of active pharmaceutical ingredients (APIs) in aquatic ecosystems, driven by widespread human use, poses significant risks, including acute and chronic toxicity to aquatic species. However, the scarcity of experimental toxicity data on APIs and related compounds due to the high costs, time requirements, and ethical concerns associated with animal testing hinders comprehensive risk assessment. In response, we developed quantitative structure-toxicity relationship (QSTR) and interspecies quantitative structure toxicity-toxicity relationship (i-QSTTR) models for three key aquatic species: zebrafish, water fleas, and green algae, using NOEC as an endpoint, following OECD guidelines. Algae, daphnia, and fish, recognized as standard organisms in toxicity testing, are crucial bio-indicators due to their size, transparency, adaptability, and regulatory acceptance. We used partial least squares (PLS) and multiple linear regression (MLR) methods for model development alongside machine learning techniques such as Random Forest (RF), Support Vector Machines (SVM), K-nearest Neighbor (kNN), and Neural Networks (NN) to enhance the predictivity. Lipophilicity, electronegativity, unsaturation, a molecular cyclized degree in molecular structure, large fragments, aliphatic secondary C(sp2), and R–CR–R groups were identified as critical biomarkers for API toxicity. Screening of the PPDB (pesticide properties databases) and DrugBank validated the practical application of these models, offering valuable tools for regulatory decisions, safer API design, and the preservation of aquatic biodiversity.

A Federated Transfer Fault Diagnosis Method for Cross-Domain and Incomplete Data

In fact, multiple clients often use similar devices and collect fault data separately, so joint multi-client collaborative fault diagnosis modeling can solve the problem of data scarcity, but this poses great challenges to data privacy protection. In this paper, we propose a federated transfer fault diagnosis method based on federated learning for cross-domain incomplete data. The proposed method only exchanges the parameters of the local training model, which achieves the privacy protection of the client’s local data. We construct a multi-client collaborative learning framework to address the problem of weak generalization ability caused by the lack of terms in single client training samples. We also propose a targeted semi-supervised fine-tuning strategy based on relative distance to reduce the probability of negative fine-tuning of out-of-distribution samples and improve the accuracy of diagnostic models. The results of cross-condition and cross-equipment experiments demonstrate that the proposed method has obvious advantages over the existing fault diagnosis methods.

Dynamic Maintenance of Underground Pipelines via a Systematic Approach for Conservative Estimation of Pipeline Defect Probability Density Under Data Scarcity

Dynamic Maintenance of Underground Pipelines via a Systematic Approach for Conservative Estimation of Pipeline Defect Probability Density Under Data Scarcity

Interpretable and Physicochemical-Intuitive Deep Learning Approach for the Design of Thermal Resistance of Energetic Compounds.

Thermal resistance of energetic materials is critical due to its impact on safety and sustainability. However, developing predictive models remains challenging because of data scarcity and limited insights into quantitative structure-property relationships. In this work, a deep learning framework, named EM-thermo, was proposed to address these challenges. A data set comprising 5029 CHNO compounds, including 976 energetic compounds, was constructed to facilitate this study. EM-thermo employs molecular graphs and direct message-passing neural networks to capture structural features and predict thermal resistance. Using transfer learning, the model achieves an accuracy of approximately 97% for predicting the thermal-resistance property (decomposition temperatures above 573.15 K) in energetic compounds. The involvement of molecular descriptors improved model prediction. These findings suggest that EM-thermo is effective for correlating thermal resistance from the atom and covalent bond level, offering a promising tool for advancing molecular design and discovery in the field of energetic compounds.

Anthropogenic and climate change-driven coastline dynamics will erode future nesting habitats of the kentish plover on the central adriatic coast

Shorebirds play a crucial role in assessing the health of coastal ecosystems due to their life history, behaviour, migratory patterns, and feeding preferences. Many shorebird species are experiencing population declines worldwide, driven by habitat loss and climate change. Understanding these challenges requires extensive data on their distribution and breeding ecology. The Kentish Plover (Anarhynchus alexandrinus) is a small shorebird that constructs its nests on sandy beaches. However, there is a scarcity of data regarding the reproductive ecology of this species in many areas, including the Molise region of Southern Italy. This study aimed at assessing the potential role of environmental variables derived from remote sensing to model current and future Kentish Plover nesting habitat suitability accounting for future scenarios of coastal dynamics in 2040. Data on Kentish Plover nests were collected along the Molise coast over several years and used to calibrate ecological niche models (ENMs) relying on environmental variables derived from coastal images. Kentish Plover nesting habitat suitability was mostly affected by distance from open sand, artificial surfaces, shoreline, and herbaceous vegetation and forested areas. The study predicts an average decline of more than 22% in suitable nesting environments for the Kentish Plover within the next two decades. Results indicated that our modelling approach can offer valuable insights for the conservation strategy of Kentish Plover in the region. In conclusion, we assert that preserving the nesting habitat of the Kentish Plover will not only safeguard this species, but also other species that inhabit similar environments, along with providing protection for coastal landscapes and associated ecosystem services.

An Improved YOLOv8-Based Foreign Detection Algorithm for Transmission Lines

This research aims to overcome three major challenges in foreign object detection on power transmission lines: data scarcity, background noise, and high computational costs. In the improved YOLOv8 algorithm, the newly introduced lightweight GSCDown (Ghost Shuffle Channel Downsampling) module effectively captures subtle image features by combining 1 × 1 convolution and GSConv technology, thereby enhancing detection accuracy. CSPBlock (Cross-Stage Partial Block) fusion enhances the model’s accuracy and stability by strengthening feature expression and spatial perception while maintaining the algorithm’s lightweight nature and effectively mitigating the issue of vanishing gradients, making it suitable for efficient foreign object detection in complex power line environments. Additionally, PAM (pooling attention mechanism) effectively distinguishes between background and target without adding extra parameters, maintaining high accuracy even in the presence of background noise. Furthermore, AIGC (AI-generated content) technology is leveraged to produce high-quality images for training data augmentation, and lossless feature distillation ensures higher detection accuracy and reduces false positives. In conclusion, the improved architecture reduces the parameter count by 18% while improving the mAP@0.5 metric by a margin of 5.5 points when compared to YOLOv8n. Compared to state-of-the-art real-time object detection frameworks, our research demonstrates significant advantages in both model accuracy and parameter size.

Long-term modifications of the peripheral immune repertoire after switching from sequestering disease-modifying treatments in multiple sclerosis.

Scarce data are available on the long-term immunological effects of multiple sclerosis (MS) disease-modifying treatments (DMTs). This study aimed to investigate the long-term modifications of the peripheral immune repertoire on interruption of a sequestering DMT (natalizumab, fingolimod) and switch to another high-efficacy DMT. Lymphocyte subpopulations were assessed, every 6 months up to 48 months, in patients switched from fingolimod or natalizumab to ocrelizumab, and in patients switched from fingolimod to natalizumab, compared to patients switched to ocrelizumab or natalizumab from a moderate-efficacy DMT and to naive patients. We included 389 MS patients (200 ocrelizumab and 189 natalizumab). After adjusting for baseline variables, patients switched from fingolimod to ocrelizumab showed lower CD3 + and CD4 + lymphocytes up to 48 months after switch (with lower percentage of naive CD4 +), and increased odds of total, CD3+, CD4+ lymphopenia. Patients switched from natalizumab to ocrelizumab showed higher CD3 + lymphocytes up to 36 months after switch, and higher CD4+, CD8+ lymphocytes up to 24 months. The frequency of infections was not influenced by previous treatment. A long-term persistence of the residual effects of the exposure to sequestering DMTs (fingolimod and less natalizumab) on the peripheral immune repertoire was observed after switching to another high-efficacy DMT.

A novel reinforcement learning agent for rotating machinery fault diagnosis with data augmentation

The problem of data imbalance and limited sample poses a prominent challenge to fault diagnosis in industry, especially in rotating machinery fault diagnosis due to the constraints of sampling and labeling. Reinforcement learning is known for its auto-optimized learning ability but it is severely limited by the quality and quantity of data it is able to collect. This paper applies the Equilibrium Deep Q-Network (Equilibrium-DQN) based agent with the Variational Autoencoder with Wasserstein Generative Adversarial Network and Gradient Penalty (VAE-WGAN-GP) for rotating machinery fault diagnosis. The Equilibrium-DQN possesses the property of leveraging multiple target networks to improve training stability and accuracy. The VAE-WGAN-GP excels in resolving data scarcity problems in fault diagnosis, especially in enriching labeled data with more typical samples while keeping the original data features. Herein, a novel framework is presented that integrates both data augmentation techniques with reinforcement learning agent for fault diagnosis. The proposed framework improves the weakness of insufficient samples in fault diagnosis and reveals its potential in complex industrial applications. The experiments, both on the test bench dataset and on the real locomotive dataset, confirm the advantage of the proposed framework.

7404 Evaluation Of Cortisol Reserve And Adrenal Imaging In Patients With Graves’ Disease Before And Six Months After Treatment

Abstract Disclosure: P. Sharma: None. S. Mir: None. S. Mohd Patto: None. A.H. Bhat: None. B. Dar: None. M. Bhat: None. Introduction: There is scarcity of data regarding the effect of thyrotoxicosis on cortisol metabolism and adrenal gland size. An increase in cortisol metabolism can deplete the functional reserve of adrenal cortex and lead to adrenocortical insufficiency even when there is no pre-existing Addison's disease. In developing countries patients with Graves’ disease present in advanced states of thyrotoxicosis. Besides they are also exposed to a variety of environmental stresses such as recurrent febrile illnesses, salt, and water loss due to manual labour in hot and humid climates etc., which can precipitate an adrenal crisis in the presence of compromised adrenal reserve. So present study was undertaken to assess cortisol reserve and adrenal gland size in patients with treatment naive Graves' disease. Objectives- The present study, was undertaken to assess cortisol reserve and adrenal imaging in patients with treatment naive Graves' disease. Specific information in this regard would help alert physicians dealing with clinical thyrotoxicosis in developing countries of tropics to the possibility of co-existing adrenal decompensation and adopt appropriate life saving measures promptly. Material & Methods: We examined 38 treatment-naive Graves' Disease patients at GMC, SSH, Srinagar, Kashmir, INDIA. Basal cortisol and cortisol response to ACTH were measured before and after six months of treatment, alongside adrenal CT imaging. Results: In hyperthyroid state, Graves' patients exhibited significantly lower basal cortisol compared to euthyroid and healthy controls (306 vs. 410 vs. 421 nmol/l, p < 0.05). 23.6% patients had subnormal cortisol response to ACTH(250ug). Those with T3 ≥ 400 ng/dl had lower cortisol response (560±133 nmol/l) and delta cortisol response than T3 < 400 ng/dl (724 ± 294 nmol/l, 213+/-73 vs. 428+/-265 nmol/l). After achieving euthyroid state, cortisol levels improved significantly (410±213 nmol/l vs. 306 ± 103 nmol/l, p < 0.05). Adrenal CT scans in 34 patients was done out of which 19 showed statistically significant increases in gland thickness, width, and AP diameter compared to controls (p < 0.01, p < 0.05. Repeat CT in 26 patients demonstrated resolution of hyperplasia (p < 0.05). Conclusion: Treatment-naive Graves' disease patients have compromised adrenocortical reserve and adrenal gland enlargement. Adrenal changes are reversible after achieving euthyroid state. These findings support steroid supplementation in impending thyrotoxic crisis and highlight a unique feature of reversible adrenal gland enlargement in this context. Keywords: Graves’ Disease, Adrenal hyperplasia, Cortisol reserve. Presentation: 6/3/2024

8180 Rare Variants Of Adrenocortical Carcinoma: A Retrospective Cohort Study

Abstract Disclosure: C.A. Villavicencio Torres: None. I. Bancos: None. Background: Adrenocortical carcinoma (ACC) is a rare malignancy with an estimated annual incidence of 0.7-2 cases per million per year. Scarce data are available on rare histological variants of ACC. Objective: To describe the clinical presentation and outcomes of patients diagnosed with oncocytic, sarcomatoid and myxoid ACC variants. Methods: Retrospective single center cohort study, 1992-2021 of patients with rare histological ACC variant. Results: A total of 20 patients were diagnosed with oncocytic (17, 85%), sarcomatoid (2, 10%), or myxoid (1, 5%) ACC. Median age of diagnosis was 47 years (range 17-80), and 14 (70%) were women. All patients had unilateral ACC (12, 60% left) with a median tumor size of 11 cm (range 4-29). Adrenal hormone excess was documented in 10 (50%) patients, mostly with either glucocorticoid or androgen excess. ENSAT stage I, II, III, and IV was documented in 2 (10%), 10 (50%), 6 (30%), and 2 (10%) patients, respectively. All patients were treated with adrenalectomy (laparoscopic in 5 and open in 15). In addition, patients were treated with mitotane (10, 50%), radiation (7, 35%), and chemotherapy (5,25%). Mitotane was administered for a median of 12 months (range 4-31) and was discontinued mainly due to side effects in 9 (90%) patients. Patients were followed for a median follow up of 6.5 years (range 0.4-12). In addition to 2 patients who presented with ENSAT stage IV metastatic disease, metastatic disease developed in additional 6 (30%) patients who initially presented with localized ACC (3 stage 2, 3 stage 3, 5 oncocytic, 1 sarcomatoid). Metastatic sites included lung (n=2), omentum (n=1), duodenum (n=1), liver (n=4), and bones (n=1). At the end of follow up, 11 (55%) patients remained disease free (53% oncocytic, 50% sarcomatoid, 100% myxoid), 5 (25%) patients were alive with metastatic ACC (23% oncocytic, 50% sarcomatoid), and 4 patients died of ACC (median time to death 21 months (range 10-50) post-adrenalectomy). Conclusions: Patients with oncocytic, sarcomatoid, and myxoid ACC may have a better prognosis than in reported literature. Larger multicenter studies are needed to identify patient and tumor-specific factors that guide management to assure improved outcomes in these very rare patients. Presentation: 6/2/2024

A cross-sectional pilot study to estimate the frequency of minor blood group alleles and phenotypes in RhD-negative North Indian blood donors by DNA microarray analysis

Abstract: INTRODUCTION: There are scarce data on Indian blood donors with respect to blood group phenotypes using molecular diagnostic modalities. Hence, we planned to estimate frequencies of blood group alleles/phenotypes using DNA microarray analysis in the north Indian RhD-negative blood donor population. With this initial pilot study, we plan to expand it to our entire donor population. METHODOLOGY: The cross-sectional prospective study was conducted on 50 Indian blood donors (O RhD negative), to study the blood group genotype frequency. Genotyping for the most relevant red blood cell antigens (Rh, Kell, Duffy, Kidd, MNS, Lutheran, and Dombrock) was done using Bioarray Precise TypeHM Human Erythrocyte Antigen BeadChip kit containing probes directed to polymorphic sites. RESULTS: In the Rh system, the most common alleles were RHCE*e/RHCE*e (98%) and RHCE*c/RHCE*c (80%). Phenotype K-k+ (genotype- KEL*02/KEL*02) was seen in 98% of samples, Js(a-b+) (KEL*02.07/KEL*02.07) was detected in 98% (49/50) of the samples tested. Jk(a + b+) (JK*01/JK*02) was the most common phenotype (48%) in the Kidd blood group system. In MNSs system, M+N+ (GYPA*01/GYPA*02) 44% and S+s+U+ (GYPB*03/GYPB*04) 34% were the most common phenotypes detected. CONCLUSION: This pilot study shows the feasibility of genotyping a Northern Indian donor population. To the best of our knowledge, it is the first study on molecular blood grouping in Indian blood donors using the Bioarray platform.

Machine Learning Valuation in Dual Market Dynamics: A Case Study of the Formal and Informal Real Estate Market in Dar es Salaam

The housing market in Dar es Salaam, Tanzania, is expanding and with it a need for increased market transparency to guide investors and other stakeholders. The objective of this paper is to evaluate machine learning (ML) methods to appraise real estate in formal and informal housing markets in this nascent market sector. Various advanced ML models are applied with the aim of improving property value estimates in a market with limited access to information. The dataset used included detailed property characteristics and transaction data from both market types. Regression, decision trees, neural networks, and ensemble methods were employed to refine property appraisals across these settings. The findings indicate significant differences between formal and informal market valuations, demonstrating ML’s effectiveness in handling limited data and complex market dynamics. These results emphasise the potential of ML techniques in emerging markets where traditional valuation methods often fail due to the scarcity of transaction data.

12406 Epidemiology Of Multiple Endocrine Neoplasia Type 1 In South Korea: A Nationwide Cohort Study

Abstract Disclosure: K. Kim: None. Y. Hwang: None. M. Yu: None. S. Moon: None. N. Hong: None. Y. Rhee: None. Multiple Endocrine Neoplasia Type 1 (MEN1) is one of the rarest endocrine disorders, with a scarcity of comprehensive epidemiological data globally. This deficiency in data is even more pronounced in Asian populations, where MEN1 remains an under-researched area. Therefore, our aim is to identity the prevalence, demographic characteristics, and patterns of comorbidities associated with MEN1.This nationwide cohort study assessed likely MEN1 cases from the Korean National Health Insurance Service database (2003-2020), selected via two operational definitions: one requiring an ICD-10 code for MEN1 (D44.8) and the other mandating at least two related procedures for MEN1-associated conditions (Primary Hyperparathyroidism, Pituitary Adenomas, and Duodenopancreatic Neuroendocrine Tumors). Cases were 1:10 matched by age, sex, and index year with controls who did not have any MEN-1 associated tumors. Our study evaluated 412 patients with likely MEN1 (mean age 43.6 ± 15.8 years; 35.4% male) against an age-sex matched control group of 4,120. Baseline characteristics showed a higher prevalence of comorbidities in MEN1 patients, including diabetes mellitus (22.6% vs 4.8%), hypertension (38.1% vs 16.4%), and dyslipidemia (20.6% vs 11.2%), all with p<0.001. Cancer prevalence was markedly elevated in MEN1 patients across most types, compared to controls. Adrenal involvements were observed in 34.7% of MEN1 patients, with adrenal adenoma being the most common (31.6%). Parathyroid, pituitary, and duodenopancreatic involvements were identified in 58.6%, 22.3%, and 19.9% of MEN1 patients, respectively. Mortality analysis revealed MEN1 patients had a higher mortality rate (16.0 per 1000 person-years) and a younger mean age at death (60.1 years) compared to controls (68.0 years), with an adjusted mortality hazard ratio of 3.1. Additionally, mortality risk varied with organ involvement; parathyroid involvement showed an adjusted HR of 2.7, pituitary involvement 4.7, and duodenopancreas involvement 4.0. The risk increased significantly with multi-organ involvement, where patients affected in more than two organs had HRs from 2.8 to 7.3. Our findings indicate that MEN1 significantly impacts patient morbidity and mortality, necessitating improved surveillance and tailored management to enhance early detection and optimize care for this vulnerable patient group. These insights underscore the pressing need for further research into MEN1 and other rare diseases to advance patient care and outcomes. Presentation: 6/2/2024

Seasonal Impact on Wound Healing and Surgical Site Infections after Reduction Mammoplasty.

Background: The incidence of reduction mammoplasty has been steadily increasing over recent decades. Surgical site infections (SSIs) represent a common yet preventable complication across surgical disciplines. Studies across various surgical specialties have indicated a seasonal influence on SSIs, primarily correlated with higher temperatures and humidity. However, there remains a scarcity of clear data regarding the seasonal effects on complications specifically in breast surgery. Methods: We conducted a retrospective review encompassing all patients who underwent primary bilateral reduction mammoplasties at our institution between 1 June 2016, and 1 September 2019. The data collected included patient demographics, surgical details, and postoperative complications. The rates of SSIs and wound healing disturbances (WHDs) were correlated with local meteorological data at the time of surgery. Results: A total of 808 patients (1616 breasts) met the inclusion criteria. The mean age was 41 ± 14.8 years, with a mean BMI of 28.9 ± 5.2 kg/m2 and a mean follow-up duration of 8.9 ± 9.8 months. Nineteen cases (2.35%) of surgical site infections and 77 cases (9.52%) of wound healing disturbances were reported. No statistically significant increase in the risk of SSIs (p = 0.928) or WHDs (p = 0.078) was observed during the warmer months of the year. Although no specific risk factors were identified for surgical site infections, both resection weight (p < 0.001) and diabetes mellitus (p = 0.001) demonstrated increased risks for wound healing disturbances. Conclusions: While seasonal temperature variations have been shown to impact SSIs and WHDs in body contouring procedures, our findings suggest that breast reduction surgery may not be similarly affected.

Time-evolving traffic resilience performance forecasting during hazardous weather toward proactive intervention

Transportation systems experience significant disruptions and loss during hazardous weather events, exhibiting great needs of timely intervention to effectively improve the resilience of the affected traffic systems. An informed and science-based proactive intervention strategy depends on accurate forecasting of the resilience performance of traffic systems with essential lead time during hazards. A new resilience performance forecasting methodology at both global and local scales is proposed for traffic networks under natural hazards by addressing unique challenges such as scarcity and time-evolving nature of hazard-specific data. The proposed methodology consists of two modules: the local traffic resilience performance short-term forecasting module based on the modified diffusion convolutional recurrent neural network (DCRNN) and transfer learning techniques, and the global traffic resilience performance forecasting module integrating percolation-based robustness assessment and SIR-based congestion propagation modeling. A case study of an urban traffic network during a major snowstorm hazard is conducted as a demonstration, followed by the feasibility investigation to guide proactive intervention during hazards. It is found the proposed methodology can forecast the time-evolving traffic resilience performance with good accuracy at both global and local scales. With sufficient lead time for the forecast, it bears promising potential to assist the stakeholders to make informed and timely decision about possible proactive intervention by providing key information to help identify the optimal moments and individual strategic links for possible intervention.

Fault diagnosis of planetary gearboxes under small and imbalanced samples based on enhanced Siamese network with improved downsampling module

Due to the limitations of the operating conditions and equipment status, in practice, the normal data for planetary gearboxes are often sufficient, but the fault data are scarce and difficult to obtain, causing the fault diagnosis to face not only an imbalance problem but also a small sample problem. It leads to serious misdiagnosis and poor generalizability. To overcome above challenges, an enhanced Siamese network with improved downsampling module is proposed to reduce the negative impact of small and imbalanced datasets on fault diagnosis. Firstly, the Siamese network is combined with data enhancement to adaptively increase the fault training data with different small sample degrees. It can make the number of training data of each class get rid of small sample, while avoiding the excessive use of scarce fault data causing additional overfitting, so as to improve the generalization of the model. Among them, the data enhancement adopts the extreme noise expansion method proposed in this work, which can quickly generate samples that meet the requirements and help to improve the performance of the model in noisy environments. Secondly, adding the improved downsampling module to the Siamese network. The improved downsampling module introduces density-based spatial clustering of applications with noise (DBSCAN) and distribution ratio calculation, which can consider the distribution range and density of data when selecting. It can not only balance the training data, but also avoid serious information loss, and then reduce the probability of misdiagnosis. Finally, using six imbalanced planetary gearbox datasets with different small sample degrees for verification. The experimental results show that the proposed method can efficiently increase and balance the training data, which greatly improves the accuracy of fault diagnosis under the condition of small and imbalanced samples, especially when the small sample degree is very serious, the accuracy is improved by 57.76% on average.

7038 Continuous Subcutaneous Hydrocortisone Infusion Is Practical And Effective In Select Patients With Adrenal Insufficiency

Abstract Disclosure: J.E. Lee: None. O. Hamidi: None. S. Mirfakhraee: None. Continuous subcutaneous hydrocortisone infusion (CSHI) via insulin pump is an alternative therapy for treating patients with adrenal insufficiency (AI) and allows for adjustable hydrocortisone delivery to provide more physiologic glucocorticoid replacement. Retrospective case series of 9 patients on CSHI showed a reduction in total daily dose of glucocorticoid by 34% and a decrease in hospitalization admissions due to adrenal crisis by 50%. In a double-blind placebo-controlled study, CSHI in patients with primary AI did not demonstrate benefit in subjective health status metrics compared to oral hydrocortisone; however, patients had good baseline subjective health scores. Due to a scarcity of data, CSHI is likely underutilized in patients who may benefit from this individualized therapy. Our research objectives were to analyze the change in total daily glucocorticoid dose, number of adrenal crises events and hospitalization days, glucocorticoid-related comorbidities, and quality of life of patients transitioned to CSHI. We performed a single-center, retrospective longitudinal follow-up study in 23 consecutive patients (87% women, median age 40 years at time of CSHI initiation) treated with CSHI between 2015 and 2023. Types of adrenal insufficiency were 48% secondary AI, 35% glucocorticoid induced AI, and 17% primary AI. CSHI delivery settings were generated by a formula that we derived using each patient’s daily oral glucocorticoid dose and preferred waking time. Median time from AI diagnosis to CSHI implementation was 41 months, and median duration of CSHI was 25 months. Total daily dose of glucocorticoid (in hydrocortisone equivalent) before CSHI vs during CSHI decreased from 30.0mg (15.0mg to 80.0mg) to 26.6mg (14.4mg to 200.5mg) (p= 0.45). Median number of adrenal crisis events decreased from 1 (0 to 13) to 0 (0 to 38) (p=0.25), and hospitalization days due to adrenal crisis decreased from 2 (0 to 46) to 0 (0 to 58) (p=0.53). No significant differences were noted for change in weight, blood pressure, diabetes, cardiovascular and cerebrovascular events, total cholesterol, LDL, and triglyceride levels. Median HDL decreased from 59 to 51 (p=0.01). In terms of subjective health status, 10 patients completed SF-36 survey while on CSHI. Significant impairments were noted in physical health, vitality, and general health with median score less than 40. At the conclusion of the study, 20 patients (87%) preferred CSHI therapy. Only 3 patients stopped CSHI for reasons of inconvenience, skin irritation at pump site, and better symptom control on oral therapy. No significant CSHI-related safety concerns were noted. In conclusion, CSHI is a safe and effective way to deliver individualized therapy to patients with difficult to control AI. CSHI led to reduction in glucocorticoid exposure and fewer adrenal crisis events and hospitalization days due to adrenal crisis. Presentation: 6/1/2024

An Image-Based Sensor System for Low-Cost Airborne Particle Detection in Citizen Science Air Quality Monitoring.

Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people's exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of PM10 particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for PM10 particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.

MIMO system identification and uncertainty calibration with a limited amount of data using transfer learning

Multiple-input multiple-output (MIMO) systems are fundamental in numerous advanced engineering applications, from aerospace to telecommunications, where precise system identification is critical for optimal performance. However, the identification of such systems often faces significant hurdles due to data scarcity, with existing approaches typically requiring substantial amounts of data for effective training. Addressing this challenge, this paper introduces a novel transfer learning framework designed specifically for MIMO system identification under conditions of limited data and inherent uncertainties. The proposed framework is applied to two case studies: the first in metal additive manufacturing, specifically the laser-blown powder-directed energy deposition as the source domain and the laser hot wire-directed energy deposition as the target domain, and the second involving a nonlinear case study of a continuous stirred-tank reactor (CSTR) with a temperature-dependent reaction. The results underscore the framework's effectiveness in capturing the dynamics of the target systems, including the ability to effectively model nonlinear dynamics. Comparative analyses highlight the benefits of employing dimensionless numbers in dynamic system modelling, offering reduced dimensionality, more physical meaning, and increased model accuracy. Overall, the proposed framework presents a promising approach to enhance system identification in MIMO systems with limited data and uncertainties, with potential applications across diverse domains.

Performance assessment of the Surpass Evolve flow diverter for the treatment of intracranial aneurysms: A systematic review and meta-analysis.

The Surpass Evolve (SE) has emerged as a promising alternative treatment from the flow diverter series. The utilization of the SE has gradually increased, however, there is a scarcity of comprehensive data on the solidity of this technology in the endovascular treatment of intracranial aneurysms (IAs). This meta-analysis aimed to evaluate the safety and effectiveness of the SE flow diverter. A systematic literature search from inception to April 2024 was conducted across five databases for studies involving IAs treated with the SE. The primary effectiveness outcome was the proportion of complete aneurysm occlusion at the final follow-up, and the primary safety outcome comprised a composite of early and delayed complications. Subgroup analyses based on aneurysm size, anatomical location, and rupture status were also conducted. Our analysis included nine studies with 645 patients and 722 IAs. Effectiveness outcomes revealed an overall complete aneurysm occlusion rate of 69% (95% confidence interval (CI) = 58%-78%; I2 = 72%) and a favorable aneurysm occlusion rate of 91% (95% CI = 82%-96%; I2 = 49%). Safety outcomes demonstrated an overall complications rate of 6% (95% CI = 3%-12%; I2 = 66%), with an early complications rate of 6% (95% CI = 4%-11%; I2 = 0%), and a delayed complications rate of 0% (95% CI = 0%-7%; I2 = 0%). Our findings suggest a favorable outcome with a high rate of complete aneurysm occlusion at the last follow-up, with acceptable rates of neurological complications. Future research efforts should focus on larger, prospective studies with standardized outcome measures to further elucidate the clinical utility of the SE flow diverter in the management of IAs.