Current Assessment Methods Research Articles

Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities.

Background and Objectives: Foot deformities are the basis of numerous disorders of the locomotor system. An optimized method of classification of foot deformities would enable an objective identification of the type of deformity since the current assessment methods do not show an optimal level of objectivity and reliability. The acquired results would enable an individual approach to the treatment of patients with foot deformities. Thus, the goal of this research study was the development of a new, objective model for recognizing and classifying foot deformities with the application of machine learning, by labeling baropodometric analysis data using computer vision methods. Materials and Methods: In this work, data from 91 students of the Faculty of Medicine and the Faculty of Sports and Physical Education, University of Novi Sad were used. Measurements were determined by using a baropodometric platform, and the labelling process was carried out in the Python programming language, using functions from the OpenCV library. Segmentation techniques, geometric transformations, contour detection and morphological image processing were performed on the images, in order to calculate the arch index, a parameter that gives information about the type of the foot deformity. Discussion: The foot over which the entire labeling method was applied had an arch index value of 0.27, which indicates the accuracy of the method and is in accordance with the literature. On the other hand, the method presented in our study needs further improvement and optimization, since the results of the segmentation techniques can vary when the images are not consistent. Conclusions: The labeling method presented in this work provides the basis for further optimization and development of a foot deformity classification system.

An integrated FMEA framework considering expert reliability for classification and its application in aircraft power supply system

The risk assessment of the integrated drive generator (IDG) is a significant problem in flight safety that directly affects the operation of the aircraft power system However, the current IDG risk assessment methods still have many limitations, leading to risk identification and classification that is not accurate and scientific. Therefore, this paper constructs a failure mode and effect analysis (FMEA) framework for IDG risk identification and classification considering expert reliability in unbalanced hesitant fuzzy linguistic term sets (UHFLTSs) environment. First, since the UHFLTSs can effectively describe uncertain and imprecise linguistic information of decision makers (DMs) and adapt it to practical applications, it is specially chosen to depict the evaluation of DMs. Second, the importance of experts is fully assessed by combining expert reliability and expert weight since the variation in expert reliabilities may have a substantial impact on the final sorting outcomes. In addition, the group best–worst method (GBWM) is combined with the entropy method to determine the risk factor weight. Third, to build up the accuracy and stability of classification, a new integrated TOPSIS-ELECTRE TRI decision framework is proposed to classify failure modes more rationally. The sensitivity analysis of parameters demonstrates the robustness of the proposed method. Meanwhile, the effectiveness and superiority of the method are verified both qualitatively and quantitatively. By comparing with several typical sorting methods, the proposed method has more realistic and stable classification results.

Using dynamic modelling to enhance the assessment of the beef water footprint

Current water footprint assessment methods make a meaningful assessment of livestock water consumption difficult as they are mainly static, thus poorly adaptable to understanding future water consumption and requirements. They lack the integration of fundamental ruminant nutrition and growth equations within a dynamic context that accounts for short- and long-term behaviour and time delays associated with economically significant beef-producing areas. The current study utilised the System Dynamics methodology to conceptualise a water footprint for beef cattle within a dynamic and mechanistic modelling framework. The problem of assessing the water footprint of beef cattle was articulated, and a dynamic hypothesis was formed to represent the Texas livestock water use system as the initial step in developing the Dynamic Beef Water Footprint model (DWFB). The dynamic hypothesis development resulted in three causal loop diagrams (CLD): cattle population, growth and nutrition, and the livestock water footprint, that captured the daily water footprint of beef (WFB). Simulations and sensitivity analysis from the hypothesised CLD structures indicated that the framework was able to capture the dynamic behaviour of the WFB system. These behaviours included key reinforcing and balancing feedback processes that drive the WFB. It is extremely difficult to identify policy interventions (i.e., management strategies) for complex systems, like the U.S. beef cattle system, because there are many actors (i.e., cow-calf, stocker, feedlot) and interrelated variables that have delayed effects within and across the supply chain. Identification and understanding of feedback processes driving water use over time will help to overcome policy resistance for more sustainable beef production. Thus, the causal loops identified in the current study provide a system-level insight for the drivers of the WFB within and across each major segment of the beef supply chain to address freshwater concerns more adequately. Further, the nutrient scenarios and sensitivity analysis revealed that the high versus low nutrient composition of pasture, hay, and concentrates resulted in a significant difference in the WFB (2 669 L/kg boneless beef, P < 0.05). The WFB was sensitive to changes in nutrient composition and specific water demand (m3/t) for each production phase, not only phases with high levels of concentrate feed use. As models evolve, there is potential for the DWFB to integrate precision livestock data, further improving quantification of the WFB, precision water-efficient strategies, and selection of water-efficient livestock.

Perspectives on ankle-foot technology for improving gait performance of children with Cerebral Palsy in daily-life: requirements, needs and wishes

BackgroundAnkle-foot orthoses (AFOs) are extensively used as a primary management method to assist ambulation of children with Cerebral Palsy (CP). However, there are certain barriers that hinder their prescription as well as their use as a mobility device in all kinds of daily-life activities. This exploratory research attempts to further understand the existing limitations of current AFOs to promote a better personalization of new design solutions.MethodsStakeholders’ (professionals in CP and end-users with CP) perspectives on AFO technology were collected by two online surveys. Respondents evaluated the limitations of current assistive solutions and assessment methods, provided their expectations for a new AFO design, and analyzed the importance of different design features and metrics to enrich the gait performance of these patients in daily-life. Quantitative responses were rated and compared with respect to their perceived importance. Qualitative responses were classified into themes by using content analysis.Results130 survey responses from ten countries were analyzed, 94 from professionals and 36 from end-users with CP. The most highly rated design features by both stakeholder groups were the comfort and the ease of putting on and taking off the assistive device. In general, professionals preferred new features to enrich the independence of the patient by improving gait at functional levels. End-users also considered their social acceptance and participation. Health care professionals reported a lack of confidence concerning decision-making about AFO prescription. To some degree, this may be due to the reported inconsistent understanding of the type of assistance required for each pathological gait. Thus, they indicated that more information about patients’ day-to-day walking performance would be beneficial to assess patients’ capabilities.ConclusionThis study emphasizes the importance of developing new approaches to assess and treat CP gait in daily-life situations. The stakeholders’ needs and criteria reported here may serve as insights for the design of future assistive devices and for the follow-up monitoring of these patients.

Automatic non-invasive blood pressure measurement in left ventricular assist device patients with a photoplethysmography assisted device

Non-invasive measurement of blood pressure in patients with a left ventricular assist device (LVAD) is challenging due to the mechanical properties of these circulatory support devices. Keeping blood pressure in a target range is crucial to reduce the risk of LVAD complications. As current assessment methods require trained personnel resulting in infrequent monitoring, devices for home-based measurements are sorely needed. In this paper, we report a measurement method and a custom-made monitoring device based on photoplethysmography (PPG) enabling automated measurement of the mean arterial pressure (MAP) of patients with LVAD. The method and the device were tested on 21 adult patients with LVAD, and the estimated MAP values were compared to MAP values measured simultaneously by a human expert applying the commonly used Doppler-based method. Results of the proposed method showed highly significant correlation with Doppler-based MAP values (R = 0.85 for inflation, p < 0.001; R = 0.96 for deflation, p<0.001). The mean difference of the proposed method to the Doppler-based MAP values was 1.48 ± 5.43 mmHg for inflation and -0.19 ± 2.71 mmHg for deflation. The results demonstrate that the proposed method is a promising direction to achieve accurate, automated, non-invasive BP measurement, applicable in home health monitoring for patients with LVAD.

Food anaphylaxis diagnostic marker compilation in machine learning design and validation.

Traditional food allergy assessment of anaphylaxis remains limited in accuracy and accessibility. Current methods of anaphylaxis risk assessment are costly with low predictive accuracy. The Tolerance Induction Program (TIP) for anaphylactic patients undergoing TIP immunotherapy produced large-scale diagnostic data across biosimilar proteins, which was used to develop a machine learning model for patient-specific and allergen-specific anaphylaxis assessment. In explanation of construct, this work describes the algorithm design for assignment of peanut allergen score as a quantitative measure of anaphylaxis risk. Secondarily, it confirms the accuracy of the machine learning model for a specific cohort of food anaphylactic children. Machine learning model design for allergen score prediction utilized 241 individual allergy assays per patient. Accumulation of data across total IgE subdivision served as the basis of data organization. Two regression based Generalized Linear Models (GLM) were utilized to position allergy assessment on a linear scale. The initial model was further tested with sequential patient data over time. A Bayesian method was then used to improve outcomes by calculating the adaptive weights for the results of the two GLMs of peanut allergy score prediction. A linear combination of both provided the final hybrid machine learning prediction algorithm. Specific analysis of peanut anaphylaxis within one endotype model is estimated to predict the severity of possible anaphylactic reaction to peanut with a recall of 95.2% on a dataset of 530 juvenile patients with various food allergies, including but not limited to peanut allergy. Receiver Operating Characteristic analysis yielded over 99% AUC (area under curve) results within peanut allergy prediction. Machine learning algorithm design established from comprehensive molecular allergy data produces high accuracy and recall in anaphylaxis risk assessment. Subsequent design of additional food protein anaphylaxis algorithms is needed to improve the precision and efficiency of clinical food allergy assessment and immunotherapy treatment.

Atrial cardiomyopathy: Current and future imaging methods for assessment of atrial structure and function.

Changes in atrial size and function have historically been considered a surrogate marker of ventricular dysfunction. However, it is now recognized that atrial cardiomyopathy (ACM) may also occur as a primary myocardial disorder. Emerging evidence that ACM is a major risk factor for atrial fibrillation, heart failure, and thromboembolic stroke, has highlighted the significance of this disorder and the need for better assessment of atrial metrics in clinical practice. Key barriers in this regard include a lack of standardized criteria or hierarchy for the diagnosis of ACM and lack of consensus for the most accurate phenotyping methods. In this article we review existing literature on ACM, with a focus on current and future non-invasive imaging methods for detecting abnormalities of atrial structure and function. We discuss the relative advantages and disadvantages of transthoracic echocardiography and cardiac magnetic resonance imaging for assessing a range of parameters, including atrial size and contractile function, strain, tissue characteristics, and epicardial adipose tissue. We will also present the potential application of novel imaging methods such as sphericity index and four- or five-dimensional flow.

An Innovative Possibilistic Fingerprint Quality Assessment (PFQA) Filter to Improve the Recognition Rate of a Level-2 AFIS.

In this paper, we propose an innovative approach to improve the performance of an Automatic Fingerprint Identification System (AFIS). The method is based on the design of a Possibilistic Fingerprint Quality Assessment (PFQA) filter where ground truths of fingerprint images of effective and ineffective quality are built by learning. The first approach, QS_I, is based on the AFIS decision for the image without considering its paired image to decide its effectiveness or ineffectiveness. The second approach, QS_PI, is based on the AFIS decision when considering the pair (effective image, ineffective image). The two ground truths (effective/ineffective) are used to design the PFQA filter. PFQA discards the images for which the AFIS does not generate a correct decision. The proposed intervention does not affect how the AFIS works but ensures a selection of the input images, recognizing the most suitable ones to reach the AFIS's highest recognition rate (RR). The performance of PFQA is evaluated on two experimental databases using two conventional AFIS, and a comparison is made with four current fingerprint image quality assessment (IQA) methods. The results show that an AFIS using PFQA can improve its RR by roughly 10% over an AFIS not using an IQA method. However, compared to other fingerprint IQA methods using the same AFIS, the RR improvement is more modest, in a 5-6% range.

Global Prevalence of Diabetic Retinopathy in Pediatric Type 2 Diabetes

Type 2 diabetes (T2D) is increasing globally. Diabetic retinopathy (DR) is a leading cause of blindness in adults with T2D; however, the global burden of DR in pediatric T2D is unknown. This knowledge can inform retinopathy screening and treatments to preserve vision in this population. To estimate the global prevalence of DR in pediatric T2D. MEDLINE, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, the Web of Science, and the gray literature (ie, literature containing information that is not available through traditional publishing and distribution channels) were searched for relevant records from the date of database inception to April 4, 2021, with updated searches conducted on May 17, 2022. Searches were limited to human studies. No language restrictions were applied. Search terms included diabetic retinopathy; diabetes mellitus, type 2; prevalence studies; and child, adolescent, teenage, youth, and pediatric. Three teams, each with 2 reviewers, independently screened for observational studies with 10 or more participants that reported the prevalence of DR. Among 1989 screened articles, 27 studies met the inclusion criteria for the pooled analysis. This systematic review and meta-analysis followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines for systematic reviews and meta-analyses. Two independent reviewers performed the risk of bias and level of evidence analyses. The results were pooled using a random-effects model, and heterogeneity was reported using χ2 and I2 statistics. The main outcome was the estimated pooled global prevalence of DR in pediatric T2D. Other outcomes included DR severity and current DR assessment methods. The association of diabetes duration, sex, race, age, and obesity with DR prevalence was also assessed. Among the 27 studies included in the pooled analysis (5924 unique patients; age range at T2D diagnosis, 6.5-21.0 years), the global prevalence of DR in pediatric T2D was 6.99% (95% CI, 3.75%-11.00%; I2 = 95%; 615 patients). Fundoscopy was less sensitive than 7-field stereoscopic fundus photography in detecting retinopathy (0.47% [95% CI, 0%-3.30%; I2 = 0%] vs 13.55% [95% CI, 5.43%-24.29%; I2 = 92%]). The prevalence of DR increased over time and was 1.11% (95% CI, 0.04%-3.06%; I2 = 5%) at less than 2.5 years after T2D diagnosis, 9.04% (95% CI, 2.24%-19.55%; I2 = 88%) at 2.5 to 5.0 years after T2D diagnosis, and 28.14% (95% CI, 12.84%-46.45%; I2 = 96%) at more than 5 years after T2D diagnosis. The prevalence of DR increased with age, and no differences were noted based on sex, race, or obesity. Heterogeneity was high among studies. In this study, DR prevalence in pediatric T2D increased significantly at more than 5 years after diagnosis. These findings suggest that retinal microvasculature is an early target of T2D in children and adolescents, and annual screening with fundus photography beginning at diagnosis offers the best assessment method for early detection of DR in pediatric patients.

Regulatory requirements and optimization of multiple criteria decision analysis to quantify the benefit-risk assessment of medical devices

ABSTRACT Introduction Worldwide medical device regulatory authorities increasingly rely on the benefit-risk ratio for decision-making. However, current benefit-risk assessment (BRA) methods are mostly descriptive, not quantitative. Areas Covered We aimed to summarize the regulatory requirements of BRA, discuss the feasibility of adopting multiple criteria decision analysis (MCDA), and explore factors for optimizing the MCDA for quantitative BRA of devices. Expert Opinion Regulatory organizations emphasize BRA in their guidance, and some recommend user-friendly worksheets to conduct qualitative/descriptive BRA. The MCDA is considered one of the most useful and relevant quantitative BRA methods by pharmaceutical regulatory agencies and the industry; the International Society for Pharmacoeconomics and Outcomes Research summarized the principles and good practice guidance of MCDA. We recommend optimizing the MCDA by considering the following unique characteristics of the device BRA: using data from state of the art as a control and clinical data from post-market surveillance and literature; considering the device’s diverse characteristics when selecting controls; assigning weight according to type, magnitude/severity, and duration of benefits and risks; and including physician and patient opinions in the MCDA. This article is the first to explore using MCDA for device BRA and might lead to a novel quantitative BRA method for devices.

The concept for innovative Comprehensive Assessment of Lowland Rivers.

Current river assessment methods focus on evaluating a single aspect (e.g. the physical and chemical quality of the water or its hydromorphological state) and usually do not integrate various factors. The lack of an interdisciplinary method makes it difficult to correctly assess the condition of a river as a complex ecosystem significantly influenced by humans. This study aimed to develop a novel Comprehensive Assessment of Lowland Rivers (CALR) method. It is designed to integrate and evaluate all-natural and anthropopressure-related elements that influence a river. The CALR method was developed using the Analytic Hierarchy Process (AHP). The application of the AHP allowed the assessment factors to be determined and given weights to define the importance of each assessment element. As a result of AHP analyses, the following ranks were determined for the six main parts of the CALR method: hydrodynamic assessment (0.212), hydromorphological assessment (0.194), macrophyte assessment (0.192), water quality assessment (0.171), hydrological assessment (0.152) hydrotechnical structures assessment (0.081). In the comprehensive assessment of lowland rivers, each of the six elements listed above is rated on a scale of 1-5 (where 5 means very good and 1 bad) and multiplied by an appropriate weighting. After summing up the obtained results, a final value is obtained, classifying the river. CALR can be successfully applied to all lowland rivers thanks to its relatively simple methodology. The widespread use of the CALR method may facilitate the assessment process and enable the comparison of the condition of lowland rivers worldwide. The research conducted in this article is one of the first attempts to develop a comprehensive method for evaluating rivers that considers all aspects.

Safety of Automated Agricultural Machineries: A Systematic Literature Review

Automated agricultural machinery has advanced significantly in the previous ten years; however, the ability of such robots to operate safely will be critical to their commercialization. This study provides a holistic evaluation of the work carried out so far in the field of automated agricultural machines’ safety, as well as a framework for future research considerations. Previous automated agricultural machines’ safety-related studies are analyzed and grouped into three categories: (1) environmental perception, (2) risk assessment as well as risk mitigation, and (3) human factors as well as ergonomics. The key findings are as follows: (1) The usage of single perception, multiple perception sensors, developing datasets of agricultural environments, different algorithms, and external solutions to improve sensor performance were all explored as options to improve automated agricultural machines’ safety. (2) Current risk assessment methods cannot be efficient when dealing with new technology, such as automated agricultural machines, due to a lack of pre-existing knowledge. Full compliance with the guidelines provided by the current International Organization for Standardization (ISO 18497) cannot ensure automated agricultural machines’ safety. A regulatory framework and being able to test the functionalities of automated agricultural machines within a reliable software environment are efficient ways to mitigate risks. (3) Knowing foreseeable human activity is critical to ensure safe human–robot interaction.

A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber–Physical Attacks

Transportation networks are fundamental to the efficient and safe functioning of modern societies. In the past, physical and cyber space were treated as isolated environments, resulting in transportation network being considered vulnerable only to threats from the physical space (e.g., natural hazards). The integration of Internet of Things-based wireless sensor networks into the sensing layer of critical transportation infrastructure has resulted in transportation networks becoming susceptible to cyber–physical attacks due to the inherent vulnerabilities of IoT devices. However, current vulnerability assessment methods lack details related to the integration of the cyber and physical space in transportation networks. In this paper, we propose a new vulnerability assessment approach for transportation networks subjected to cyber–physical attacks at the sensing layer. The novelty of the approach used relies on the combination of the physical and cyber space, using a Bayesian network attack graph that enables the probabilistic modelling of vulnerability states in both spaces. A new probability indicator is proposed to enable the assignment of probability scores to vulnerability states, considering different attacker profile characteristics and control barriers. A probability-based ranking table is developed that details the most vulnerable nodes of the graph. The vulnerability of the transportation network is measured as a drop in network efficiency after the removal of the highest probability-based ranked nodes. We demonstrate the application of the approach by studying the vulnerability of a transportation network case study to a cyber–physical attack at the sensing layer. Monte Carlo simulations and sensitivity analysis are performed as methods to evaluate the results. The results indicate that the vulnerability of the transportation network depends to a large extent on the successful exploitation of vulnerabilities, both in the cyber and physical space. Additionally, we demonstrate the usefulness of the proposed approach by comparing the results with other currently available methods. The approach is of interest to stakeholders who are attempting to incorporate the cyber domain into the vulnerability assessment procedures of their system.

A probabilistic framework of creep life assessment of structural components at elevated temperatures

Current creep life assessment methods of components at elevated temperatures are mainly based on deterministic analysis strategies, which could not achieve the goal of probabilistic evaluation on creep failure. Based on this, a probabilistic framework of creep life assessment for components at elevated temperatures was provided. A method of determining distribution characteristics of material parameters was provided by randomly selecting a group of results at each stress level. Monte Carlo simulation was combined with finite element analysis technology to capture the distribution characteristic of creep rupture life of one typical structural component. Effect of standard derivation of material parameters on creep reliability assessment was discussed. Comparisons between probabilistic and deterministic creep design methods were made. Results indicated that the probabilistic analysis strategy can calculate the specific value of failure probability at various loading conditions, not two values of failure probability (i.e. 100% and 0%) by deterministic analysis strategy. The effect of standard derivation on mean values of effective stress and creep rupture life of the component is dependent on distribution characteristics of material parameters and related variables. A small standard derivation reduces the data scatter of effective stress and creep rupture life of the component.

Seismic resilience assessment of buildings considering the effects of mainshock and multiple aftershocks

Seismic resilience assessment is a hot topic for highlighting the functional recovery of structures in post-earthquake environments. The current resilience assessment method generally ignores the effects of aftershocks, even though more than 85% of mainshocks are followed by multiple aftershocks that further increase functionality loss and recovery time. This study proposes a method to quantify the resilience of structures under mainshocks and multiple aftershocks. The concept of aftershock affecting time window is proposed to assess the effects of aftershocks on the recovery time of structures. Additionally, the effects of infill walls on the state-dependent fragility results and resilience of the structure are considered in this method. The proposed method is applied to two reinforced concrete (RC) frame structures, and the effects of multiple aftershocks and infill walls on the resilience are quantitatively studied. The results indicate that multiple aftershocks increase the economic loss by 20%–30% on average, and the aftershock affecting time can account for more than 20% of the total recovery time. The exclusion of infill walls in the finite element model of the structure induces a significant bias in the resilience assessment results at a minimum level of 25%.

Genomic health is dependent on long-term population demographic history.

Current genetic methods of population assessment in conservation biology have been challenged by genome-scale analyses due to their quantitatively novel insights. These analyses include assessments of runs-of-homozygosity (ROH), genomic evolutionary rate profiling (GERP), and mutational load. Here, we aim to elucidate the relationships between these measures using three divergent ungulates: white-tailed deer, caribou, and mountain goat. The white-tailed deer is currently expanding, while caribou are in the midst of a significant decline. Mountain goats remain stable, having suffered a large historical bottleneck. We assessed genome-wide signatures of inbreeding using the inbreeding coefficient F and %ROH (FROH ) and identified evolutionarily constrained regions with GERP. Mutational load was estimated by identifying mutations in highly constrained elements (CEs) and sorting intolerant from tolerant (SIFT) mutations. Our results showed that F and FROH are higher in mountain goats than in caribou and white-tailed deer. Given the extended bottleneck and low Ne of the mountain goat, this supports the idea that the genome-wide effects of demographic change take time to accrue. Similarly, we found that mountain goats possess more highly constrained CEs and the lowest dN/dS values, both of which are indicative of greater purifying selection; this is also reflected by fewer mutations in CEs and deleterious mutations identified by SIFT. In contrast, white-tailed deer presented the highest mutational load with both metrics, in addition to dN/dS, while caribou were intermediate. Our results demonstrate that extended bottlenecks may lead to reduced diversity and increased FROH in ungulates, but not necessarily an increase in mutational load, probably due to the purging of deleterious alleles in small populations.

Assessment of Volume Status in Hospitalized Patients With Chronic Heart Failure.

Assessment of volume status in hospitalized patients with heart failure is a critically important diagnostic skill that clinicians utilize frequently. However, accurate assessment is challenging and there is often significant inter-provider disagreement. This review serves as an appraisal of current methods of volume assessment amongst different categories of evaluation including patient history, physical exam, laboratory analysis, imaging, and invasive procedures. Within each category, this review highlights methods that are particularly sensitive or specific, or those that carry impactful positive or negative likelihood ratios. Utilization of the information that this review provides will allow clinicians to determine volume status of hospitalized heart failure patients more accurately and more precisely in order to provide appropriate and effective therapies.

Urban sustainability assessment for vernacular and traditional built environments

Despite the growing literature on sustainability assessment in the urban context, the resulting approaches and methods utilise several differing theoretical frameworks and lack a unifying vision and modus operandi. Innumerable tools and instruments have been developed for particular purposes and rather narrow goals. Curiously, these tools and instruments remain unable to trace and assess sustainability in vernacular forms and traditional built environments, even though environments such as these demonstrably possess sustainable principles beside their aesthetic values and spatial qualities.This paper reviews the theoretical background underpinning the current sustainability assessment methods in the urban context to identify their general limitations and their specific applicability to vernacular and traditional built environments. Also, this paper discusses some of the cultural and spatial qualities of traditional built form to identify its embedded sustainable strategies and practices. The paper concludes with an outline conceptual framework intended to develop general sustainability principles for traditional built forms in response to their natural and cultural contexts.A literature review of the concepts of sustainability assessment in the urban context and embedded sustainable principles in vernacular and traditional built form is followed by a thematic analysis of its limitations, which feeds into this conceptualisation of a new, principle-based framework. A total of 10 principles of sustainability are proposed to assess sustainability in traditional built environments, taking into account the variation of locality and site-specific context.

Noise quality estimation on portraits in realistic controlled scenarios

The wide use of cameras by the public has raised the interest of image quality evaluation and ranking. Current cameras embed complex processing pipelines that adapt strongly to the scene content by implementing, for instance, advanced noise reduction or local adjustment on faces. However, current methods of Image Quality assessment are based on static geometric charts which are not representative of the common camera usage that targets mostly portraits. Moreover, on non-synthetic content most relevant features such as detail preservation or noisiness are often un-tractable. To overcome this situation, we propose to mix classical measurements and Machine learning based methods: we reproduce realistic content triggering this complex processing pipelines in controlled conditions in the lab which allows for rigorous quality assessment. Then, ML based methods can reproduce perceptual quality annotated previously. In this paper, we focus on noise quality evaluation and test on two different set ups: closeup and distant portraits. These setups provide scene capture conditions flexibility, but most of all, allow the evaluation of all quality camera ranges from high quality DSLR to poor quality video conference. Our numerical results show the relevance of our solution compared to geometric charts and the importance of adapting to realistic content.

Abstract B023: Enhanced survival and adherence of melphalan-induced senescent-like dormant multiple myeloma cells co-cultured with bone marrow stromal cells

Abstract Multiple myeloma (MM) is an incurable plasma cell cancer. Therapeutic advances have greatly improved patient survival; however, all patients are expected to relapse. New treatments are critically needed to overcome relapse and provide durable response. There is evidence that dormant MM cells exist in the bone marrow, where they exhibit reduced proliferative capacity and resistance to treatment. These cells are present over extended periods of time and may regain proliferative capacity to drive relapse. Tumor dormancy shares features with senescence, a state of stress-induced growth arrest. Chemotherapies, including those used in the treatment of MM, have been shown to cause therapy-induced senescence in a variety of tissues. Dormant MM cells have been shown to be maintained in their non-proliferative state by interactions with bone lining cells. Interestingly, matrix signaling has also been shown to protect against chemotherapy-induced apoptosis in an in vitro ovarian cancer organoid model. Taken together, this suggests that interaction of MM cells with the bone niche maintains growth arrest and survival. We hypothesize that therapy-induced senescence drives dormancy in MM cells that depends on interactions with the mesenchymal bone niche. GFP-expressing 5TGM1 mouse MM cells were treated with vehicle or melphalan for 48 hours and then cultured in the presence or absence of ex vivo mouse bone marrow stromal cells (BMSCs). Cells were maintained for 12 additional days to allow for the development of senescence after the initial stress response. Cells were imaged and quantified during media replacements to assess adherence/survival. Cells were then fixed and stained for nuclei, phospho-γH2AX, and telomeres by immunofluorescence/fluorescence in-situ hybridization (IF/FISH). Melphalan-treated 5TGM1 cells cultured with BMSCs were significantly growth arrested over 2 weeks compared to vehicle-treated cells. Co-culture with BMSCs significantly enhanced melphalan-treated 5TGM1 adherence compared to culture without BMSCs during media replacement. Of note, melphalan-treated 5TGM1 did not survive in the absence of BMSCs after a week in culture. Melphalan-treated 5TGM1 also exhibited significantly greater adherence with BMSCs than vehicle-treated cells. Melphalan-treated 5TGM1 were significantly larger than vehicle-treated cells and exhibited both heterochromatin foci and persistent DNA damage foci associated with telomeres, which are all markers of cellular senescence. Our findings suggest that interactions with BMSCs promote the survival of melphalan-treated MM tumor cells. The enhanced adherence of these cells also suggests that current methods for minimal residual disease assessment may be inadequate, as rare populations of MM cells adhered to the marrow stroma may not be captured by routine bone marrow aspiration. These cells exhibit features of therapy-induced senescence which may be key to MM dormancy. Thus, targeting senescence survival pathways via senolytic therapy may be a novel approach to eliminate dormant MM cells and prevent disease relapse. Citation Format: Angelo J. Guilatco, Gabriel Alvares Borges, Tamar Tchkonia, James L. Kirkland, Taxiarchis Kourelis, Matthew T. Drake, Megan Weivoda. Enhanced survival and adherence of melphalan-induced senescent-like dormant multiple myeloma cells co-cultured with bone marrow stromal cells [abstract]. In: Proceedings of the AACR Special Conference: Aging and Cancer; 2022 Nov 17-20; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_1):Abstract nr B023.