Monitoring Of Progression Research Articles

Effect of Human Interaction Modalities on the Usability and Performance of a Robotic Cyber-Physical System for Construction Progress Monitoring

The construction industry has been slow to adopt advanced technologies like Cyber-Physical Systems (CPS), underscoring the necessity for a human-centered approach to their development. This study aims to understand the significance and impact of human-CPS interaction modalities on human perceptions of CPS, specifically in the use case of construction progress monitoring. A mixed-method approach comprising quantitative observations and qualitative interviews was employed to evaluate the system’s usability and performance with different human-CPS interaction modalities. The findings reveal variable levels of usability and performance across different human-CPS interaction modalities. Users expressed a preference for the three-dimensional immersive environment compared to a two-dimensional setting due to a better sense of presence. However, they experienced heightened cognitive load when utilizing novel interaction methods such as hand gestures and voice commands. Interestingly, despite this increased cognitive demand, users exhibited comparable performance in the assigned task, irrespective of their familiarity with the interaction modality. The results underscore the importance of human-centered design in CPS development. It contributes empirical evidence crucial for understanding and improving human-CPS interactions, thereby advancing the implementation of CPS in construction progress monitoring.

Automatic recognition of excavator working cycles using supervised learning and motion data obtained from inertial measurement units (IMUs)

This paper proposes an automatic method for excavator working cycle recognition using supervised classification methods and motion information obtained from four inertial measurement units (IMUs) attached to moving parts of an excavator. Monitoring and analyzing tasks that have been performed by heavy-duty mobile machines (HDMMs) are significantly required to assist management teams in productivity and progress monitoring, efficient resource allocation, and scheduling. Nevertheless, traditional methods depend on human observations, which are costly, time-consuming, and error-prone. There is a lack of a method to automatically detect excavator major activities. In this paper, a data-driven method is presented to identify excavator activities, including loading, trenching, grading, and idling, using motion information, such as angular velocities and joint angles, obtained from moving parts, including swing body, boom, arm, and bucket. Firstly, a dataset lasting 3 h is collected using a medium-rated excavator. One experienced and one inexperienced operator performed tasks under different working conditions, such as different types of material, swing angle, digging depth, and weather conditions. Four classification methods, including support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and naive Bayes, are off-line trained. The results show that the proposed method can effectively identify excavator working cycles with a high accuracy of 99%. Finally, the impacts of parameters, such as time window, overlapping configuration, and feature selection methods, on the classification accuracy are comprehensively analyzed.

Data-Driven Net-Zero Carbon Monitoring: Applications of Geographic Information Systems, Building Information Modelling, Remote Sensing, and Artificial Intelligence for Sustainable and Resilient Cities

NZCCs aim to minimise urban carbon emissions for healthier cities in line with national and international low-carbon targets and Sustainable Development Goals (SDGs). Many countries have recently adopted Net-Zero Carbon City (NZCC) policies and strategies. While there are many studies available on NZCC cities’ definitions and policymaking, currently, research is rare on understanding the role of urban data-driven technologies such as Building Information Modelling (BIM) and Geographic Information Systems (GIS), as well as AI, for achieving the goals of NZCCs in relation to sustainable development goals (SDGs), e.g., SDGs 3, 7,11, 13, and 17. This paper aims to fill this gap by establishing a systematic review and ascertaining the opportunities and barriers of data-driven approaches, analytics, digital technologies, and AI for supporting decision-making and monitoring progress toward achieving NZCC development and policy/strategy development. Two scholarly databases, i.e., Web of Science and Scopus databases, were used to find papers based on our selected relevant keywords. We also conducted a desktop review to explore policies, strategies, and visualisation technologies that are already being used. Our inclusion/exclusion criteria refined our selection to 55 papers, focusing on conceptual and theoretical research. While digital technologies and data analytics are improving and can help in the move from net-zero carbon concepts and theories to practical analysis and the evaluation of cities’ emission levels and in monitoring progress toward reducing carbon, our research shows that these capabilities of digital technologies are not used thoroughly yet to bridge theory and practice. These studies ignore advanced tools like city digital twins and GIS-based spatial analyses. No data, technologies, or platforms are available to track progress towards a NZCC. Artificial Intelligence, big data collection, and analytics are required to predict and monitor the time it takes for each city to achieve net-zero carbon emissions. GIS and BIM can be used to estimate embodied carbon and predict urban development emissions. We found that smart city initiatives and data-driven decision-making approaches are crucial for achieving NZCCs.

Barriers and Enablers to Maintaining Behaviour Changes Following a Lifestyle Intervention for Adults With Type 2 Diabetes.

To explore short-term barriers and enablers to maintaining behaviour changes in adults with type 2 diabetes who had completed a lifestyle behaviour change program. Focus groups with 44 participants identified barriers and enablers at the end-of-program (n = 8 groups: anticipated); and, 1-month post-program (n = 6 groups: experienced). Thematic inductive analysis was undertaken independently by two authors, discussed, then deductively grouped according to the Capability (physical, psychological), Opportunity (physical, social), Motivation (automatic, reflective) - Behaviour (COM-B) model. Nine barriers were identified: two anticipated, one experienced, and six common to both timepoints. Key barriers related to physical capability (health ability), physical opportunity (difficulty accessing and using resources), social opportunity (unwillingness to invest in social networks), and reflective motivation (lack of internal drive). Eleven enablers were identified: all across both timepoints. Key enablers related to psychological capability (knowledge), physical opportunity (access and use of program resources), social opportunity (sense of belonging and safety within the program), automatic motivation (beliefs and awareness around perceived risk; monitoring of progress), and reflective motivation (committed to change). Findings suggest diabetes management programs should enable ongoing access to their resources. Investing in mechanisms to scaffold program graduates into suitable community-based activities may also be beneficial.

Diffusion tensor imaging biomarkers and clinical assessments in amyotrophic lateral sclerosis (ALS) patients: an exploratory study.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive loss of upper and lower motor neurons. Biomarkers are needed to improve diagnosis, gauge progression, and evaluate treatment. Diffusion tensor imaging (DTI) is a promising biomarker for detecting microstructural alterations in the white matter tracts. This study aimed to assess DTI metrics as biomarkers and to examine their relationship with clinical assessments in patients with ALS. Eleven patients with ALS and 21 healthy controls (HCs) underwent 3T MRI with DTI. DTI metrics, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD), were compared between key motor and extra-motor tract groups. Group comparisons and correlations between DTI metrics also correlated with clinical scores of disability (ALSFRS-R), muscle strength (dynamometry), and motor unit loss (MUNIX). Widespread differences were found between patients with ALS and HCs in DTI metrics, including decreased FA and increased diffusivity metrics. However, MD and RD are more sensitive metrics for detecting white matter changes in patients with ALS. Significant interhemispheric correlations between the tract DTI metrics were also observed. DTI metrics showed symmetry between the hemispheres and correlated with the clinical assessments. MD, RD, and AD increases significantly correlated with lower ALSFRS-R and MUNIX scores and weaker dynamometry results. DTI reveals microstructural damage along the motor and extra-motor regions in ALS patients. DTI metrics can serve as quantitative neuroimaging biomarkers for diagnosis, prognosis, monitoring of progression, and treatment. Combined analysis of imaging, electrodiagnostic, and functional biomarkers shows potential for characterizing disease pathophysiology and progression.

A novel digital tool for detection and monitoring of amyotrophic lateral sclerosis motor impairment and progression via keystroke dynamics

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative condition leading to progressive muscle weakness, atrophy, and ultimately death. Traditional ALS clinical evaluations often depend on subjective metrics, making accurate disease detection and monitoring disease trajectory challenging. To address these limitations, we developed the nQiALS toolkit, a machine learning-powered system that leverages smartphone typing dynamics to detect and track motor impairment in people with ALS. The study included 63 ALS patients and 30 age- and sex-matched healthy controls. We introduce the three core components of this toolkit: the nQiALS-Detection, which differentiated ALS from healthy typing patterns with an AUC of 0.89; the nQiALS-Progression, which separated slow and fast progression at specific thresholds with AUCs ranging between 0.65 and 0.8; and the nQiALS-Fine Motor, which identified subtle progression in fine motor dysfunction, suggesting earlier prediction than the state-of-the-art assessment. Together, these tools represent an innovative approach to ALS assessment, offering a complementary, objective metric to traditional clinical methods and which may reshape our understanding and monitoring of ALS progression.

An AprilTags-Based Approach for Progress Monitoring and Quality Control in Modular Construction

Traditional approaches to modular construction progress monitoring and quality control with stringent and tight tolerances for on-site and off-site assembly processes are usually based on 3D laser scanning, but the high equipment costs associated with acquiring point clouds have economic ramifications. This paper provides the details of a new and inexpensive method through the integration of AprilTags and an ordinary phone. By using AprilTags instead of QR codes to label modules, progress management is achieved through the rapid identification and association of multiple modules based on a single image. Moreover, a virtual multi-view vision algorithm based on AprilTags is proposed to generate 3D reverse models of the construction site; the quality result can be acquired by comparing the offset and rotation values of the reverse model and the BIM model. Finally, all the algorithms are validated through comparing the reverse models with the reference models made with 3D printing and 3D laser scanning, which verifies the accuracy and efficiency of the proposed method.

DeMambaNet: Deformable Convolution and Mamba Integration Network for High-Precision Segmentation of Ambiguously Defined Dental Radicular Boundaries.

The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression monitoring. Nonetheless, conventional segmentation frameworks often encounter significant setbacks attributable to the intrinsic limitations of X-ray imaging, including compromised image fidelity, obscured delineation of structural boundaries, and the intricate anatomical structures of dental constituents such as pulp, enamel, and dentin. To surmount these impediments, we propose the Deformable Convolution and Mamba Integration Network, an innovative 2D dental X-ray image segmentation architecture, which amalgamates a Coalescent Structural Deformable Encoder, a Cognitively-Optimized Semantic Enhance Module, and a Hierarchical Convergence Decoder. Collectively, these components bolster the management of multi-scale global features, fortify the stability of feature representation, and refine the amalgamation of feature vectors. A comparative assessment against 14 baselines underscores its efficacy, registering a 0.95% enhancement in the Dice Coefficient and a diminution of the 95th percentile Hausdorff Distance to 7.494.

Process evaluation of the implementation of the assessment of burden of chronic conditions tool in Dutch primary care – lessons from a qualitative implementation study

BackgroundThe Assessment of Burden of Chronic Conditions (ABCC-)tool is developed to facilitate a personalized approach to care in the patient-healthcare provider (HCP) conversation based on shared decision-making and individualized care plans. An effectiveness study highlighted its effect on the perceived quality of care and patient activation. Successful implementation of novel interventions necessitates an understanding of the user’s actual application, user experiences and an evaluation of implementation outcomes. This study aims to evaluate the implementation of the ABCC-tool by HCPs in Dutch primary care.MethodsThis study is the process evaluation of a larger type 1 effectiveness-implementation hybrid trial. Semi-structured interviews with HCPs, who were interventionists in the hybrid trial, were held at three and twelve months after they started using the ABCC-tool. The Reach-Effectiveness-Adoption-Implementation-Maintenance (RE-AIM) framework was used to evaluate implementation outcomes. The Implementation domain was further strengthened with an evaluation of implementation fidelity using Carroll’s framework. Inductive coding and thematic analysis were applied to identify relevant participant experiences and implementation outcomes within the RE-AIM framework.ResultsSeventeen HCPs (1 general practitioner, 16 practice nurses) participated in the study, representing 39% of potentially eligible participants. Most HCPs applied the tool after finishing their own routines instead of how it is intended to be used, namely from the beginning of the consultation. HCPs reached 2–6 patients. The ABCC-tool was initially adopted, but twelve HCPs stopped using the tool due to COVID-19 related cancellation of consultations. High fidelity was found for applying the questionnaire and visualization. Low fidelity was present for applying shared decision-making, formulating care goals and monitoring progress. HCPs indicated that maintaning the ABCC-tool depended on accompanying training and implementation support.ConclusionsHCPs applied the ABCC-tool critically different from intended, potentially diminishing its benefits and ease of use. This evaluation stresses the need for a tailored implementation plan that includes more detailed training and guidance on how and when to use the ABCC-tool.

Evaluating Food Procurement against the EAT-Lancet Planetary Health Diet in a Sample of U.S. Universities.

Aligning institutional food procurement with planetary health targets offers opportunities to improve nutrition and reduce food-related greenhouse gas (GHG) emissions. This study compared foods procured by 19 university dining programs in the U.S. in 2022 with the EAT-Lancet planetary health diet. Each university's procurement was then modeled to align with the EAT-Lancet planetary health diet, and changes to Healthy Eating Index (HEI) scores and GHG emissions were evaluated. For a subset of universities that provided cost data, changes in annual total food costs were also estimated. Universities in this study exceeded EAT-Lancet planetary health targets for beef (x- = 657% of target), pork (x- = 587%), poultry (x- = 379%), and eggs (x- = 293%). All universities failed to achieve planetary health targets for legumes and nuts (x- = 39% of the target) and vegetables (x- = 68%). Aligning food procurement with the planetary health diet would result in an estimated average 46.1% reduction in GHG emissions and a 19.7 point increase in HEI scores. Universities that provided cost data saw an average 9.7% reduction in food costs in the EAT-Lancet-aligned scenario. The procurement metrics assessed in this study can help university dining programs and other institutional food service organizations set goals and monitor progress toward planetary health targets.

Contrast-enhanced photon-counting micro-CT of tumor xenograft models

Objective. Photon-counting micro-computed tomography (micro-CT) is a major advance in small animal preclinical imaging. Small molecule- and nanoparticle-based contrast agents have been widely used to enable the differentiation of liver tumors from surrounding tissues using photon-counting micro-CT. However, there is a notable gap in the application of these market-available agents to the imaging of breast and ovarian tumors using photon-counting micro-CT. Herein, we have used photon-counting micro-CT to determine the effectiveness of these contrast agents in differentiating ovarian and breast tumor xenografts in live, intact mice. Approach. Nude mice carrying different types of breast and ovarian tumor xenografts (AU565, MDA-MB-231 and SKOV-3 human cancer cells) were injected with ISOVUE-370 (a small molecule-based agent) or Exitron Nano 12000 (a nanoparticle-based agent) and subjected to photon-counting micro-CT. To improve tumor visualization using photon-counting micro-CT, we developed a novel color visualization method, which changes color tones to highlight contrast media distribution, offering a robust alternative to traditional material decomposition methods with less computational demand. Main results. Our in vivo experiments confirm the effectiveness of this color visualization approach, showing distinct enhancement characteristics for each contrast agent. Qualitative and quantitative analyses suggest that Exitron Nano 12000 provides superior vasculature enhancement and better quantitative consistency across scans, while ISOVUE-370 delivers a more comprehensive tumor enhancement but with significant variance between scans due to its short blood half-time. Further, a paired t-test on mean and standard deviation values within tumor volumes showed significant differences between the AU565 and SKOV-3 tumor models with the nanoparticle-based contrast agent (p-values < 0.02), attributable to their distinct vascularity, as confirmed by immunohistochemical analysis. Significance. These findings underscore the utility of photon-counting micro-CT in non-invasively assessing the morphology and anatomy of different tumor xenografts, which is crucial for tumor characterization and longitudinal monitoring of tumor progression and response to treatments.

Gold Nanoparticles: An Emerging Novel Technology for Targeted Delivery System for Site-Specific Diseases

Abstract: Gold Nanoparticles (GNPs) have emerged as a novel technology in the field of targeted delivery systems, offering promising solutions for site-specific disease treatment. These nanoparticles possess unique physicochemical properties, such as controlled size, shape, and surface chemistry, which enable precise manipulation for enhanced therapeutic efficacy. The biocompatibility and ease of functionalization of GNPs facilitate the conjugation with various biomolecules, including drugs, peptides, and nucleic acids, thereby improving their targeted delivery capabilities. Recent advancements in nanotechnology have leveraged GNPs for the treatment of a range of diseases, particularly in oncology, cardiology, and neurology. In cancer therapy, GNPs can be engineered to target tumor cells selectively, minimizing damage to healthy tissues and reducing side effects. This is achieved through the conjugation of GNPs with tumor-specific ligands, antibodies, or aptamers, which direct the nanoparticles to malignant cells, allowing for localized drug release and improved therapeutic outcomes. Moreover, GNPs exhibit remarkable potential in diagnostic imaging and photothermal therapy. Their unique optical properties, such as surface plasmon resonance, enable their use as contrast agents in imaging techniques, providing high-resolution and real-time monitoring of disease progression. In photothermal therapy, GNPs convert light energy into heat, effectively destroying targeted cells with minimal invasiveness. The development of GNP-based delivery systems also addresses significant challenges in drug resistance and bioavailability. By overcoming biological barriers and enhancing cellular uptake, GNPs improve the pharmacokinetics and pharmacodynamics of therapeutic agents. However, despite these advancements, the clinical translation of GNPs faces challenges such as potential toxicity, long-term stability, and regulatory hurdles. In conclusion, gold nanoparticles represent a cutting-edge approach in targeted delivery systems, offering significant potential for site-specific disease treatment. Continued research and innovation are essential to overcome existing challenges and fully realize the clinical applications of GNPs, ultimately revolutionizing precision medicine. Gold nanoparticles exhibit unique physicochemical and optical properties. The gold nanoparticles have advanced techniques to cure different chronic diseases. Today, gold nanoparticles are aided by photodynamic therapy and radiation therapy as drug carriers. Due to this, researchers now focus on medical sciences to treat various diseases and therapeutic applications. This review provides all the aspects of gold-based nanoparticles, methods, and their pharmacological benefits in different fields of medical sciences. We also discuss various preparation methods and their advantages in pharmaceutical formulations.

Recent progress in Nanomaterial based biosensors for the detection of cancer biomarkers in human fluids

Cancer is a global health challenge, and early detection is crucial for effective treatment to improve patient outcomes. In recent years, nanomaterial-based biosensors have emerged as powerful tools for the detection of cancer biomarkers in human fluids. This article highlights the recent progress in biosensor technology for the detection of cancer biomarkers, focusing on advancements in sensitivity, selectivity, multiplexed detection, liquid biopsies, point-of-care testing, wearable biosensors, and integration with artificial intelligence (AI). Recent advancements have significantly improved the sensitivity and selectivity of biosensors, allowing for the detection of low concentrations of cancer biomarkers in complex biological samples. Novel sensing technologies, such as nanomaterial-based sensors and aptamer-based sensors, have played a crucial role in enhancing biosensor performance. Multiplexed biosensors have the ability to simultaneously detect multiple cancer biomarkers, providing comprehensive diagnostic information. This capability is particularly valuable for accurate cancer diagnosis and prognosis. Liquid biopsies, which involve the detection of cancer biomarkers in circulating tumor cells, cell-free DNA, or exosomes present in body fluids, have gained considerable attention. Biosensors have played a pivotal role in the development of liquid biopsy technologies, offering non-invasive and real-time monitoring of cancer progression, treatment response, and the emergence of drug resistance. The integration of biosensors with AI algorithms has shown great potential. AI can analyze and interpret biosensor data, identifying patterns, correlations, and biomarker signatures that may be difficult to detect with traditional methods.

Investigating algorithmic bias in student progress monitoring

This research investigates bias in AI algorithms used for monitoring student progress, specifically focusing on bias related to age, disability, and gender. The study is motivated by incidents such as the UK A-level grading controversy, which demonstrated the real-world implications of biased algorithms. Using the Open University Learning Analytics Dataset, the research evaluates fairness with metrics like ABROCA, Average Odds Difference, and Equality of Opportunity Difference. The analysis is structured into three experiments. The first experiment examines fairness as an attribute of the data sources and reveals that institutional data is the primary contributor to model discrimination, followed by Virtual Learning Environment data, while assessment data is the least biased. In the second experiment, the research introduces the Optimal Time Index, which pinpoints Day 60 of an average 255-day course as the optimal time for predicting student outcomes, balancing timely interventions, model accuracy, and efficient resource allocation. The third experiment implements bias mitigation strategies throughout the model's life cycle, achieving fairness without compromising accuracy. Finally, this study introduces the Student Progress Card, designed to provide actionable personalized feedback for each student.

Assessment of Motivations, Treatment Risks, and Oral Health in Adults with Fixed Orthodontic Care: A Cross-Sectional Study.

Aim: This cross-sectional study aims to investigate the reasons why adults seek orthodontic treatment, their perceived treatment risks, oral hygiene habits, and awareness of oral health. Materials and Methods: This study, which involved 246 adults, used a self-report questionnaire to collect sociodemographic data and examine the participants' understanding of oral health, self-assessment of oral health status, adherence to oral hygiene routines, and use of oral hygiene products. In addition, the reasons for opting for orthodontic treatment, negative associations, and expected outcomes were examined. The collected data were subjected to statistical analysis, including descriptive and regression methods. Results: The results showed that factors such as previous oral health education and regular use of specialized toothbrushes, including rotary toothbrushes, were significantly associated with higher knowledge levels (p < 0.05). However, daily oral hygiene practices were suboptimal, with the low utilization of aids: only 58.9% reported using interdental brushes, and 36.6% used dental floss regularly. Commonly reported dental problems included food debris between teeth (46.3%), tartar (35.4%), and tooth sensitivity to cold (26.4%). The primary motivations for orthodontic treatment were aesthetic improvement (63.8%), enhancements in oral function (49.6%), and addressing psychosocial concerns (42.7%). In contrast, the most frequently mentioned negative aspects associated with fixed orthodontic treatment were aesthetic concerns (30.9%), treatment duration (27.6%), and mouth ulcers (24.0%). Conclusions: Based on the research findings, regular oral hygiene education is recommended for adult patients considering fixed orthodontic therapy, including the use of additional tools such as interdental brushes and floss to improve oral health and reduce the risk of complications during treatment. The importance of chemical compounds in dentifrices and mouthwashes cannot be overstated, as they play a crucial role in addressing various dental issues. In addition, it is crucial to carefully weigh the pros and cons of therapy and to visit a dentist regularly to maintain oral health and monitor progress during treatment.

Advanced Techniques for Liver Fibrosis Detection: Spectral Photoacoustic Imaging and Superpixel Photoacoustic Unmixing Analysis for Collagen Tracking.

Liver fibrosis, a major global health issue, is marked by excessive collagen deposition that impairs liver function. Noninvasive methods for the direct visualization of collagen content are crucial for the early detection and monitoring of fibrosis progression. This study investigates the potential of spectral photoacoustic imaging (sPAI) to monitor collagen development in liver fibrosis. Utilizing a novel data-driven superpixel photoacoustic unmixing (SPAX) framework, we aimed to distinguish collagen presence and evaluate its correlation with fibrosis progression. We employed an established diethylnitrosamine (DEN) model in rats to study liver fibrosis over various time points. Our results revealed a significant correlation between increased collagen photoacoustic signal intensity and advanced fibrosis stages. Collagen abundance maps displayed dynamic changes throughout fibrosis progression. These findings underscore the potential of sPAI for the noninvasive monitoring of collagen dynamics and fibrosis severity assessment. This research advances the development of noninvasive diagnostic tools and personalized management strategies for liver fibrosis.

Neurofibroma detection using joint imaging genomics and ensemble learning

Accurate detection of neurofibromas is crucial for quantitative monitoring of tumor progression and surgical assessment. This paper proposes a method for neurofibroma detection in whole-body magnetic resonance imaging (WBMRI) using joint imaging genomics and ensemble learning. Firstly, we enhance texture features through a combination of image sharpening, filtering, brightness adjustment, and contrast enhancement. Then, we employ a weighted boxes fusion (WBF) technique based on test-time augmentation (TTA) under a single model and further integrate multiple models using the dual fusion approach of TTA and WBF. For segmentation, we utilize minimum bounding boxes based on segmentation masks for position calibration. Finally, false positive tumor regions are further eliminated through imaging genomics features. The experimental MRI data is obtained from collaboration between Harvard Medical School and domestic tertiary hospitals, comprising 158 cases with a total of 1380 tumors. Five-fold cross-validation is conducted with segmentation annotations completed by domain experts. Compared to the best results of single models, our proposed method achieves a 10.1% increase in average precision (AP), 7.8% increase in sensitivity, reduction of average false positives to 3.58, a decrease of 17.68, and an 8.5% improvement in competitive performance metric (CPM). This method effectively enhances the accuracy of neurofibroma detection and is applicable to detecting tumors and lesions in other medical imaging applications.

Neurofibroma detection using joint imaging genomics and ensemble learning

Accurate detection of neurofibromas is crucial for quantitative monitoring of tumor progression and surgical assessment. This paper proposes a method for neurofibroma detection in whole-body magnetic resonance imaging (WBMRI) using joint imaging genomics and ensemble learning. Firstly, we enhance texture features through a combination of image sharpening, filtering, brightness adjustment, and contrast enhancement. Then, we employ a weighted boxes fusion (WBF) technique based on test-time augmentation (TTA) under a single model and further integrate multiple models using the dual fusion approach of TTA and WBF. For segmentation, we utilize minimum bounding boxes based on segmentation masks for position calibration. Finally, false positive tumor regions are further eliminated through imaging genomics features. The experimental MRI data is obtained from collaboration between Harvard Medical School and domestic tertiary hospitals, comprising 158 cases with a total of 1380 tumors. Five-fold cross-validation is conducted with segmentation annotations completed by domain experts. Compared to the best results of single models, our proposed method achieves a 10.1% increase in average precision (AP), 7.8% increase in sensitivity, reduction of average false positives to 3.58, a decrease of 17.68, and an 8.5% improvement in competitive performance metric (CPM). This method effectively enhances the accuracy of neurofibroma detection and is applicable to detecting tumors and lesions in other medical imaging applications.

A streamlined and adaptive approach for accrediting a French education system program by ABET-EAC

IntroductionIn recent times, there has been a notable increase in the demand for accreditations, driven by their essential role in recognizing diplomas, elevating the quality of higher education, facilitating student mobility, enabling the pursuit of multiple degrees, and influencing university rankings. This surge in demand reflects the evolving landscape of higher education in a world that is becoming progressively interconnected and competitive.MethodsThis paper presents a comprehensive methodology for accrediting study programs in French higher education institutions worldwide that are seeking the Accreditation Board for Engineering and Technology (ABET) accreditation. ABET accreditation is a globally recognized organism of quality for education programs in engineering, computer science, and related fields. The methodology proposed in this paper focuses on aligning ABET’s requirements and standards with the specific context of the French Education System (FES). It provides clear guidelines and practical steps for higher education institutions to effectively navigate the accreditation process. The methodology covers the verification of existing study programs, identification of areas for improvement, implementation of corrective actions, and continuous progress monitoring. It emphasizes the importance of continuous improvement and aligning programs with the Program Educational Objectives (POEs). Concrete examples and best practices are shared to illustrate successful implementation of the methodology developed based on the example of the engineering college programs at our university.Results and discussionThe results obtained in this case study demonstrate the applicability and effectiveness of this approach within a specific educational context. The approach presented in this contribution can serve as a model for other countries seeking to adapt ABET accreditation requirements to their own higher education systems, further promoting diversity and openness. This reinforces the credibility of the methodology and paves the way for its use in other institutions and programs that operate within similar systems.

The Brain-Gut Axis, an Important Player in Alzheimer and Parkinson Disease: A Narrative Review.

Neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), are severe age-related disorders with complex and multifactorial causes. Recent research suggests a critical link between neurodegeneration and the gut microbiome, via the gut-brain communication pathway. This review examines the role of trimethylamine N-oxide (TMAO), a gut microbiota-derived metabolite, in the development of AD and PD, and investigates its interaction with microRNAs (miRNAs) along this bidirectional pathway. TMAO, which is produced from dietary metabolites like choline and carnitine, has been linked to increased neuroinflammation, protein misfolding, and cognitive decline. In AD, elevated TMAO levels are associated with amyloid-beta and tau pathologies, blood-brain barrier disruption, and neuronal death. TMAO can cross the blood-brain barrier and promote the aggregation of amyloid and tau proteins. Similarly, TMAO affects alpha-synuclein conformation and aggregation, a hallmark of PD. TMAO also activates pro-inflammatory pathways such as NF-kB signaling, exacerbating neuroinflammation further. Moreover, TMAO modulates the expression of various miRNAs that are involved in neurodegenerative processes. Thus, the gut microbiome-miRNA-brain axis represents a newly discovered mechanistic link between gut dysbiosis and neurodegeneration. MiRNAs regulate the key pathways involved in neuroinflammation, oxidative stress, and neuronal death, contributing to disease progression. As a direct consequence, specific miRNA signatures may serve as potential biomarkers for the early detection and monitoring of AD and PD progression. This review aims to elucidate the complex interrelationships between the gut microbiota, trimethylamine-N-oxide (TMAO), microRNAs (miRNAs), and the central nervous system, and the implications of these connections in neurodegenerative diseases. In this context, an overview of the current neuroradiology techniques available for studying neuroinflammation and of the animal models used to investigate these intricate pathologies will also be provided. In summary, a bulk of evidence supports the concept that modulating the gut-brain communication pathway through dietary changes, the manipulation of the microbiome, and/or miRNA-based therapies may offer novel approaches for implementing the treatment of debilitating neurological disorders.