Comparison Criteria Research Articles

Sensor concepts for gear damage detection in wind power drives

Abstract Gearbox failure in offshore wind turbines can be caused by fatigue damages like tooth fank fracture (TFF) or pitting damage. For damage detection in current applications, simple threshold values for vibration amplitudes are used to initiate a shutdown of the wind turbine. At this time significant damage to the gear is already present and an external maintenance is inevitable. The objective of this experimental study is to determine a better sensor concept for detection of gear damage in wind power drives during operation. The damage needs to be detected at a very early stage before the health condition of the gears becomes critical. Evaluation criteria for comparison of sensor concepts include the earliest possible detection of damage, robust and reliable detection under varying operating conditions, as well as practicality and cost-effectiveness. The test results show, vibration measurement in radial direction and in high frequency ranges is most suitable and most robust for pitting detection during operation. With the best sensor concept in place, it is possible to detect gear damage like pitting at a very early stage of 0.3 % pitting size.

Modeling of Some Egg Characteristics in Henna Partridges

In this study, the distributions of egg count (daily and cumulative), width, length and weight values of chukar partridges obtained in two different egg production seasons were statistically modeled. For this purpose, in modeling cumulative egg production curves; Logistic, Gompertz, Gamma, Schunute, Brody, Richard, Negative Exponential, von Bertalanffy, Cubic Piecewise and Cubic models were used, and in evaluating daily egg production curves; Gompertz, Logistic, Richard, McNally, Gamma, Cubic Piecewise, Quadratic, Quadratic Piecewise and Modified Compartmental models were used. In modeling egg width values; Gompertz, Gamma, Cubic Piecewise and Cubic models were used, in modeling length values; Logistic, Gamma, Cubic Piecewise and Cubic models were used, and in modeling weight values; McNally, Gamma, Cubic Piecewise and Cubic models were used. In all modeling studies, as comparison and evaluation criteria; Error Mean Square, Corrected Coefficient of Determination, Accuracy Factor, Deviation Factor, Durbin-Watson, Akaike Information Criterion, Corrected Akaike Information Criterion and Bayesian Information Criterion were taken into consideration. As a result of the study; Gamma was determined as the most suitable model for modeling cumulative egg yields and length values, McNally for modeling daily egg yields and weight values, and Gompertz and Gamma models for modeling egg width values.

Two unequal shots in the same hole: statistical versus deterministic deconvolution

Determination of the source time function and earth impulse response for explosive source seismic reflection data is traditionally performed using predictive deconvolution, which assumes the reflection seismogram is the convolution of a minimum-phase wavelet with a white, random, stationary sequence of impulses. We compare this statistical method with a new deterministic method that uses two unequal shots close together in the same hole and assumes a seismogram is the convolution of a source time function with a Green's function, plus noise. The seismograms from the two shots share essentially the same Green's function to any receiver, because the shots are close together. The Green's function is estimated by deconvolving the two seismograms at a given receiver for the corresponding source time functions, modelled independently; source parameters are obtained by trial-and-error by minimising the difference between the two estimates of the Green's function. We test these competing theories using seismic reflection data acquired with two charges fired separately in the same hole into a receiver spread of 1178 bunched geophone groups at 10 m intervals: a 1 kg charge at 42 m depth and 2 kg at 40 m depth. The criterion for comparison of the theories is the similarity of the recovered impulse responses at any receiver. Each method recovers a wavelet, or source time function, for each shot and two estimates of the impulse response, or Green's function, at each receiver. The Green's functions recovered from the two shots using the deterministic method are almost identical and are measurably more similar than the impulse responses recovered using predictive deconvolution. That is, the deterministic method is better. The resolution of explosive source seismic reflection data can now be significantly improved with the new method and minimal additional field effort of loading one extra charge per shot hole.

Towards the understanding of the IVPT results variability—Development, verification and validation of the PBPK model of caffeine in vitro human skin permeation

In the context of evaluating the safety and efficacy of dermal products, pharmacokinetic (PK) studies face considerable challenges, particularly concerning topically applied formulations. This underscores the necessity for alternative methods, such as in vitro permeation tests (IVPT) and physiologically based pharmacokinetic (PBPK) modelling, to better understand the dermal pharmacokinetics of a product. The purpose of this study was to modify, verify, and validate the PBPK model of caffeine permeation through human skin previously developed by Patel et al. (2022), and compare simulation results with experimental data from IVPT studies. Moreover, the study aimed to analyse the IVPT data variability and explore the potential of using the PBPK model to understand the influence of biological and drug-related factors on the IVPT results. In total, eight manuscripts describing nine experiments were included. The overall shapes of the permeation curves were considered acceptable based on visual checks for all analysed experiments. Five out of nine experiments met the predefined standard 2-fold difference criterion for comparison of the cumulative amount of caffeine in the receptor solution.. Our investigation highlights challenges in validating PBPK models for IVPT experiments, as the quality and consistency of experimental results pose significant hurdles. Despite access to data on caffeine permeation in scientific literature, reliable model validation is currently infeasible. Inter-laboratory variation suggests that alternative validation methods may be needed. Further studies should focus on issues with other compounds, especially lipophilic ones.

Real-World Persistence with Ocrelizumab in Multiple Sclerosis: a Systematic Review.

In clinical trials, the percentage of patients discontinuing treatment with ocrelizumab due to adverse events was low. However, real-world populations are often more diverse than randomized controlled trials (RCTs), therefore it is important to assess discontinuation rates in real-world studies. This systematic literature review (SLR) was conducted to identify real-world discontinuation and persistence data for ocrelizumab in studies of patients with relapsing remitting multiple sclerosis (RRMS) and primary progressive multiple sclerosis (PPMS). Searches were conducted in MEDLINE and Embase to identify relevant real-world studies that met pre-determined Population, Intervention, Comparison, Outcomes, and Study (PICOS) criteria. Only articles published in English were included, but the study country was not restricted. A total of 30 studies were included, with the majority reporting real-world persistence data that appear to be similar to or better than in the pivotal clinical trials, with only 1 study reporting higher discontinuation rates due to adverse events compared with the clinical trials. Other studies identified reported that the risk of discontinuation was higher for other disease-modifying therapies (DMTs) compared with ocrelizumab, and adherence was also higher for ocrelizumab versus other DMTs. These findings have clinical relevance, as other studies have reported improved clinical outcomes and lower care costs for patients that are persistent or adherent to other DMTs.

Comparison between estimates of beta regression model using MSE

The multiple linear regression analysis shows the effects of explanatory variables on the dependent variable. Its function is to represent data to understand the shape and nature of the relationship between explanatory variables and the response variable. One of the main problems faced by linear regression is the nature of the data, as sometimes it does not follow a normal distribution. This has led to the emergence of different types of linear regression models such as the beta regression model, where the data of the dependent variable is less than one and confined within the period (0,1), and the data is relative and decimal. Non-traditional methods will be used to estimate beta regression parameters because some traditional methods do not provide suitable and accurate results when dealing with one of the main problems encountered in regression, which is multicollinearity. To address this issue, we used some methods include The Maximum Likelihood Estimation (MLE), ridge regression, modified Liu, and Ozkale and Kaciranlar method. Shrinking parameters are proposed for each method, and a comparison between the methods is conducted using the Mean Squared Error comparison criterion through a simulation experiment with two approaches, the simulation results showed that the ridge regression method is the best for the first and second methods of simulation. Paper type Research Paper

Weakly Increasing Solutions of Equations with p-Mean Curvature Operator

Globally positive unbounded solutions, with zero derivative at infinity, are here considered for ordinary differential equations involving the generalized Euclidean mean curvature operator. When p≥2, the results highlight an analogy with an auxiliary equation with the p-Laplacian operator. The results are obtained using some comparison criteria for the principal solutions of a class of associated half-linear equations.

Shrinkage and tree-based regression methods for the prediction of the live weight of Akkaraman sheep using morphological traits.

The prediction of live weight (LW) is of critical importance to farmers in a range of applications, including breeding and monitoring animal growth. In this context, Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic Net as shrinkage methods, and Classification and Regression Trees (CART) and Random Forest (RF) as tree-based regression methods were used in this study to predict LW of Akkaraman Sheep at 6-month age using sex, birth weight (BW) and some morphological traits such as withers height (WH), chest depth (CD), body length (BL), chest width (CW), rump height (RH), and chest circumference (CC). The dataset of 100 sheep, consisting of 44 males and 56 females, was randomly divided into training and test sets with a ratio of 80% and 20%, respectively. 10-fold cross-validation method was implemented using the training set to obtain optimum regression models and avoid overfitting. A test set was used to compare the prediction performance of regression methods based on various comparison criteria. Results revealed that LW was significantly correlated with all morphological traits and BW with coefficients ranging from 0.216 to 0.757. RF outperformed the other regression models with a coefficient of determination value (R2) of 0.865, followed by Ridge (R2 = 0.761), LASSO (R2 = 0.755), Elastic Net (R2 = 0.750), and CART (R2 = 0.654). The results indicated that WH and CD contributed the most, while sex and BW contributed the least in constructing the optimum RF model. In conclusion, the use of RF is recommended for predicting the LW of Akkaraman sheep. These results can provide a data-driven approach to improve decision-making in animal breeding.

Different copula types and reliability applications for a new fisk probability model

In this research endeavor, the authors introduce a novel lifetime probability model. This distribution is meticulously examined and characterized, offering insights into its behavior and applicability in various contexts. The proposed new density function of this distribution has various heavy tail forms that are useful in the field of reliability, insurance and statistical modeling. This allows for the representation and modeling of a wide range of data sets that are diverse in their form and nature. The new distribution is characterized by having different patterns of risk or failure rates. The researchers extend the new distribution to the bivariate domain through different methods, including the Morgenstern-Farley-Gumbel distribution, the modified Morgenstern-Farley-Gumbel distribution, the famous Clayton mathematical versions, and the Rennie versions. These extensions enhance the usefulness of the proposed distribution in modeling multivariate age and reliability data and dependencies between variables. The study presents some statistical modeling experiments on reliability data and some important comparisons are presented within the framework of some statistical comparison criteria.

Dosimetric Comparison and Selection Criteria of Intensity-Modulated Proton Therapy and Intensity-Modulated Radiation Therapy for Adaptive Re-Plan in T3-4 Nasopharynx Cancer Patients.

Proton therapy requires caution when treating patients with targets near neural structures. Intuitive and quantitative guidelines are needed to support decision-making concerning the treatment modality. This study compared dosimetric profiles of intensity-modulated proton therapy (IMPT) and intensity-modulated radiation therapy (IMRT) using helical tomotherapy (HT) for adaptive re-planning in cT3-4 nasopharyngeal cancer (NPCa) patients, aiming to establish criteria for selecting appropriate treatment modalities. HT and IMPT plans were generated for 28 cT3-4 NPCa patients undergoing definitive radiotherapy. Dosimetric comparisons were performed for target coverage and high-priority organs at risk (OARs). The correlation between dosimetric parameters and RT modality selection was analyzed with the target OAR distances. Target coverages were similar, while IMPT achieved better dose spillage. HT was more favorable for brainstem D1, optic chiasm Dmax, optic nerves Dmax, and p-cord D1. IMPT showed advantages for oral cavity Dmean. Actually, 14 IMPT and 14 HT plans were selected as adaptive plans, with IMPT allocated to most cT3 patients (92.9% vs. 42.9%, p = 0.013). The shortest distances from the target to neural structures were negatively correlated with OAR doses. Receiver operating characteristic curve analyses were carried out to discover the optimal cut-off values of the shortest distances between the target and the OARs (temporal lobes and brainstem), which were 0.75 cm (AUC = 0.908, specificity = 1.00) and 0.85 cm (AUC = 0.857, specificity = 0.929), respectively. NPCa patients with cT4 tumor or with the shortest distance between the target and critical neural structures < 0.8 cm were suboptimal candidates for IMPT adaptive re-planning. These criteria may improve resource utilization and clinical outcomes.

Trait-customized sampling of core collections from a winter wheat genebank collection supports association studies.

Subsampling a reduced number of accessions from ex situ genebank collections, known as core collections, is a widely applied method for the investigation of stored genetic diversity and for an exploitation by breeding and research. Optimizing core collections for genome-wide association studies could potentially maximize opportunities to discover relevant and rare variation. In the present study, eight strategies to sample core collections were implemented separately for two traits, namely susceptibility to yellow rust and stem lodging, on about 6,300 accessions of winter wheat (Triticum aestivum L.). Each strategy maximized different parameters or emphasized another aspect of the collection; the strategies relied on genomic data, phenotypic data or a combination thereof. The resulting trait-customized core collections of eight different sizes, covering the range between 100 and 800 accession samples, were analyzed based on characteristics such as population stratification, number of duplicate genotypes and genetic diversity. Furthermore, the statistical power for an association study was investigated as a key criterion for comparisons. While sampling extreme phenotypes boosts the power especially for smaller core collections of up to 500 accession samples, maximization of genetic diversity within the core collection minimizes population stratification and avoids the accumulation of less informative duplicate genotypes when increasing the size of a core collection. Advantages and limitations of different strategies to create trait-customized core collections are discussed for different scenarios of the availability of resources and data.

A comparative study on the bonding performance of an admixed repair mortar and high-strength flowable micro-concrete with substrate: Considerations on the physico-mechanical compatibility

The physical and mechanical properties of repair mortar (RM), incorporating a multipurpose hybrid admixture and a high-strength concrete matrix phase known as micro-concrete (MiC), have been experimentally investigated. The flexural and compressive strengths, as well as the modulus of elasticity of each mixture, were tested at 7 and 28 days. The dimensional stability of prismatic bar specimens under dry and water-saturated conditions was monitored over a period of 56 days. The thermal coefficient of expansion for each repair material was determined, and the risk of thermal cracking was evaluated. Additionally, the bonding performance of admixed repair mortar and micro-concrete with substrate mortar (SM) was assessed using a modified slant shear test setup. The influence of surface roughness on slant shear strength was also studied. The compatibility of each mix design with substrate mortar was quantified and compared using a “physico-mechanical compatibility” based proximity comparison criterion. The proposed methodology can be used to monitor the compatibility and bonding potential of a repair material for a given substrate. Test results and analysis indicated that the admixed RM, with lower compressive/tensile strength ratio, performed better than MiC in terms of substrate compatibility.

Current and emerging waste-to-energy technologies: A comparative study with multi-criteria decision analysis

In response to the rise in waste crisis and the possibility of energy utilization from waste, there has been increasing interest in waste-to-energy (WtE) conversion technologies, which requires intense scientific attention. There are diverse WtE technologies that apply to different waste types and require multidisciplinary decision support. The paper applies a Multi-criteria Decision Analysis (MCDA) tool to compare their economic, technological, socio-cultural, and environmental aspects to help identify the most promising choice. The comparison used in this study concerns four widely used technologies: Incineration (INC), Anaerobic Digestion (AD), Gasification (GAS), and Pyrolysis (PYR), and one emerging WtE conversion technology, Hydro-thermal Carbonization (HTC). The Comparison criteria are divided into four main criteria and fifteen sub-criteria. The Analytical Hierarchy Process (AHP) model was implemented using ’SuperDecisions’ software to make pairwise comparisons of identified criteria and to rank the WtE technology alternatives. Thirty-two international studies were shortlisted to gather data and provide input into the AHP model. The results show that the environmental factors are prioritized with a priority vector of 0.56. Further, the study concludes that the most suitable WtE technology, based on chosen parameters, is AD, followed by HTC, INC, and PYR with the priority vectors of 0.348, 0.201, 0.162, and 0.148, respectively, provided applicability. The emerging technology, HTC, is found to be the second most suitable technology. Further, the results represent the hierarchy structure arranged so that the main components are divided into sub-components with alternatives at the structure’s base, and the ’SuperDecisions’ model based on this hierarchy can be used in the future to find suitable WtE technology for a specific city with the necessary input for identified main and sub-criteria. This research not only provides a structured comparison of WtE technologies but also offers a scalable AHP framework that can be adapted for specific municipal contexts in future studies. By addressing the diverse needs of decision-makers across different regions, our model contributes to a more nuanced understanding of WtE technology selection and lays the groundwork for incorporating local policies and regulations in subsequent research phases.

Prognostic Factors and Outcome Measures After Rituximab Therapy in Central Nervous System Vasculitis: A Systematic Review.

Given the growing popularity of Rituximab (Rmab) treatment as a potential substitute for cyclophosphamide, we conducted this review to determine aspects related to Rmab therapy in central nervous system vasculitis (CNSV) patients, aiming to establish both the beneficial and detrimental consequences of Rmab while providing clinical guidance for managing patients' conditions. This systematic review was prepared following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The PubMed, Web of Science, and Scopus databases were utilized to investigate Rmab treatment in CNSV from January 2015 to May 2024. The research question was structured using the Population, Intervention, Comparison, Outcome, and Study Design (PICOS) criteria. Case series with only three or more unique cases, prospective or retrospective non-randomized studies, and randomized controlled trials (RCTs) were addressed. The initial evaluations were performed in PubMed, Scopus, and Web of Science. After removing duplicate entries, 177 publications were obtained, and 41 were chosen for full-text assessment. The review then incorporated information gathered from 27 studies, including 4 case series, 15 non-randomized cohorts, and 8 RCTs. Rmab is generally regarded as effective for CNSV therapy. Given its success, induction Rmab therapy is now frequently employed as maintenance therapy for CNSV patients. Rmab is a viable option for the induction of remission and maintenance treatment, with a successful reduction in relapse rates.

Systematic literature review of asthma biologic self-administration enhanced by a patient perspective

BackgroundSeveral biologics for the treatment of severe asthma are available as self-administration devices. ObjectivePerform a systematic literature review (SLR) to understand the use, benefits and challenges of these self-administration devices. MethodsElectronic databases and conference proceedings were searched using terms for asthma, biologic treatment, and at-home/self-administration (GSK study 213094). Publications were scanned for relevance using pre-specified Population, Intervention, Comparison, Outcomes and Study design (PICOS) criteria. Data on efficacy, safety, patient experience and economic outcomes were extracted; study quality was assessed. A first-hand patient perspective was obtained. ResultsThirty-five of 504 records met the inclusion criteria. Across four Phase III studies, ≥95% of biologic self-administrations were successful based on pre-defined criteria. At-home self-administration was preferred over in-clinic administration by 43–96% of patients across five studies. Most patients (≥89%) in two Phase III studies reported completing self-administration easily without repeated reference to instructions; high proportions of patients (≥98%) were confident in their ability to self-administer their biologic and ≥96% rated it as ‘extremely’, ‘very’ or ‘moderately’ easy to self-administer. Across 16 studies reporting efficacy data, there was evidence of reduced blood eosinophil counts and improved asthma control with biologic self-administration, with improved health-related quality of life shown across six studies. Economic outcomes data were limited. From a patient perspective, autonomy is the major benefit of self-administration. ConclusionWhile more evidence is needed, this SLR provides consistent evidence of high injection success rates, and supported by a patient perspective, preference for self-administration of biologics among patients with severe asthma.

Modeling the Long-Term Variability in the Surfaces of Three Lakes in Morocco with Limited Remote Sensing Image Sources

Satellite imagery has become a widespread resource for modeling variability in lake surfaces. However, the extended monitoring of a lake’s perimeter faces significant challenges due to atmospheric obstacles that cannot be rectified. Due to the atmosphere’s everchanging opacity, only half of the acquired satellite images have reliable qualitative accuracy making it possible to identify a lake’s contour. Consequently, approximately 50% of the monthly lake outline values can be determined using remote sensing methods, leaving the remaining 50% unknown. This situation is applicable to three lakes in Morocco (Abakhan, Ouiouan, and Tiglmanine), the subjects of the current research for the period between 1984 and 2022. What can we do if, during a period of time in which we monitored the evolution of the surface of a lake by satellite means, we obtain only about 50% of the possible images? Shall we just settle for this and stop the analysis? Although it may be challenging to believe, the present study introduces two statistical methods for interpolating and validating the monthly values of the lake outline: the iterative ratio method based on the autocorrelation of the monthly water balance and the Kalman filter. We estimated the reconstruction errors of the missing values and validated the methodology using an inverse philosophy, reconstructing the initial data from the table of the simulation results. Given that the difference between the initial values and the reconstructed initial values resembles white noise or an AR (1) process with a low coefficient, we deemed the methodological approach acceptable. Several comparison criteria between the two interpolation methods were employed, yet determining the more appropriate one remains challenging. Based on our surface reconstruction method, Lake Abakhan, with an average area of 22 hectares, experienced significant fluctuations, ranging from a maximum of 34 hectares in 2010 to a minimum of 0.8 hectares in 2022. Lake Ouiouan, with an average area of 14 hectares, displayed much lower variation, with a maximum of 17 hectares in 2020 and a minimum of 6.5 hectares in 1988. Lake Tiglmanine showed a pattern similar to that of Lake Abakhan but with less pronounced fluctuations. With an average area of 6.1 hectares, its maximum was 9.2 hectares in 2011 and its minimum was 4.1 hectares in 1984.

Comparison of Nonlinear Functions to Define the Growth in Intensive Feedlot System with XGBoost Algorithm

The aim of this study was to define the growth by using nonlinear functions in intensive feedlot system with XGBoost algorithm. To achieve this aim, five nonlinear functions were implemented. To implementation of the study, Brown Swiss (n=41) and Simental (n=95) breed were used. Each nonlinear functions were examined for each breed. According to the results of the nonlinear functions, logistic model was the best prediction model for defining the growth of each breed. In this study, the parameters in the best prediction model were calculated individually and the relationship of these parameters with body weight was evaluated with the XGBoost algorithm. Model comparison criteria such as standard deviation ratio (SDratio), Pearson’s correlation coefficient (PC), determination of coefficient (R2) and Akaike’s information criteria (AIC) were used to evaluate the XGBoost algorithm. In conclusion, the XGBoost algorithm can be an effective and optional approach that allows breeders to estimate live weight from growth parameters. This algorithm can operate on large data sets with high accuracy and speed, leading to significant improvements in agricultural productivity and animal health management. XGBoost enables more accurate predictions by analyzing the effects of various characteristics (e.g., nutritional level, breed, age). Therefore, this method can be used to determine critical parameters such as body weight in animal breeding practices, serving as a powerful support tool for operational decisions.

Estimating the severity of obstructive sleep apnea during wakefulness using speech: A review

Obstructive sleep apnea (OSA) is a chronic breathing disorder during sleep that affects 10-30% of adults in North America. The gold standard for diagnosing OSA is polysomnography (PSG). However, PSG has several drawbacks, for example, it is a cumbersome and expensive procedure, which can be quite inconvenient for patients. Additionally, patients often have to endure long waitlists before they can undergo PSG. As a result, other alternatives for screening OSA have gained attention. Speech, as an accessible modality, is generated by variations in the pharyngeal airway, vocal tract, and soft tissues in the pharynx, which shares similar anatomical structures that contribute to OSA. Consequently, in this study, we aim to provide a comprehensive review of the existing research on the use of speech for estimating the severity of OSA. In this regard, a total of 851 papers were initially identified from the PubMed database using a specified set of keywords defined by population, intervention, comparison and outcome (PICO) criteria, along with a concatenated graph of the 5 most cited papers in the field extracted from ConnectedPapers platform. Following a rigorous filtering process that considered the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach, 32 papers were ultimately included in this review. Among these, 28 papers primarily focused on developing methodology, while the remaining 4 papers delved into the clinical perspective of the association between OSA and speech. In the next step, we investigate the physiological similarities between OSA and speech. Subsequently, we highlight the features extracted from speech, the employed feature selection techniques, and the details of the developed models to predict OSA severity. By thoroughly discussing the current findings and limitations of studies in the field, we provide valuable insights into the gaps that need to be addressed in future research directions.

Navigating a Paradigm Shift Venetoclax Treatment Redefines Landscape of Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease characterized by the accumulation of malignant myeloid progenitor hematopoietic cells in the bone marrow and peripheral blood. Recent studies have shown promising results with the use of small molecule inhibitors and targeted therapy in the treatment of patients with AML. One such molecule is venetoclax, which has been approved in AML by the FDA in combination with hypomethylating agents or low-dose cytarabine. We thoroughly searched electronic literature related to venetoclax and its role in AML, using databases such as MEDLINE, PubMed, Google Scholar, and PsychInfo, through April 2024. We applied population, intervention, comparison, and outcome criteria, specifically focusing on studies with a population using venetoclax from review articles and clinical trials. All selected studies were required to be in English, and any study that did not involve the use of venetoclax was excluded. A meticulous literature review was conducted to consolidate the current knowledge and new combination therapies on AML. In our review article, we focused on the latest advances in the treatment of patients with AML. Based on the literature, we recommend that physicians prioritize the use of venetoclax in the management of this deadly disease because it has been shown to significantly impact the course of the disease.

Standardized statistical framework for comparison of biomarkers: Techniques from ADNI.

Well-chosen biomarkers have the potential to increase the efficiency of clinical trials and drug discovery and should show good precision as well as clinical validity. We suggest measures that operationalize these criteria and describe a general approach that can be used for inference-based comparisons of biomarker performance. The methods are applied to measures obtained from structural magnetic resonance imaging (MRI) from individuals with mild dementia (n=70) or mild cognitive impairment (MCI; n=303) enrolled in the Alzheimer's Disease Neuroimaging Initiative. Ventricular volume and hippocampal volume showed the best precision in detecting change over time in both individuals with MCI and with dementia. Differences in clinical validity varied by group. The methodology presented provides a standardized framework for comparison of biomarkers across modalities and across different methods used to generate similar measures and will help in the search for the most promising biomarkers. A framework for comparison of biomarkers on pre-defined criteria is presented. Criteria for comparison include precision in capturing change and clinical validity. Ventricular volume has high precision in change for both dementia and mild cognitive impairment (MCI) trials. Imaging measures' performance in clinical validity varies more for dementia than for MCI.