Progression free survival of myeloma patients who become IFE-negative correlates with the detection of residual monoclonal free light chain (FLC) by mass spectrometry.

Deeper responses are associated with improved survival in patients being treated for myeloma. However, the sensitivity of the current blood-based assays is limited. Historical studies suggested that normalisation of the serum free light chain (FLC) ratio in patients who were negative by immunofixation electrophoresis (IFE) was associated with improved outcomes. However, recently this has been called into question. Mass spectrometry (MS)-based FLC assessments may offer a superior methodology for the detection of monoclonal FLC due to greater sensitivity. To test this hypothesis, all available samples from patients who were IFE negative after treatment with carfilzomib and lenalidomide-based induction and autologous stem cell transplantation (ASCT) in the Myeloma XI trial underwent FLC-MS testing. FLC-MS response assessments from post-induction, day+100 post-ASCT and six months post-maintenance randomisation were compared to serum FLC assay results. Almost 40% of patients had discordant results and 28.7% of patients with a normal FLC ratio had residual monoclonal FLC detectable by FLC-MS. FLC-MS positivity was associated with reduced progression-free survival (PFS) but an abnormal FLC ratio was not. This study demonstrates that FLC-MS provides a superior methodology for the detection of residual monoclonal FLC with FLC-MS positivity identifying IFE-negative patients who are at higher risk of early progression.

Characteristics of papers that affect citations in the Journal of Fish Biology.

Identifying the factors that influence the citation of articles helps authors improve the impact and reach of their research. Analysis of publications in the Journal of Fish Biology between 2008 and 2021 revealed that variables such as the number of keywords, abstract length, number of authors, and page length were associated with higher impact papers. These trends applied to both review and regular papers. These findings suggest that papers that are more informative, have higher numbers of authors, and have more keywords are more likely to be cited. Adoption of some simple "best-practice" behaviors can improve the likelihood that a paper is cited.

Learning Meta-Learning (LML) dataset: Survey data of meta-learning parameters.

The 'Learning Meta-Learning' dataset presented in this paper contains both categorical and continuous data of adult learners for 7 meta-learning parameters: age, gender, degree of illusion of competence, sleep duration, chronotype, experience of the imposter phenomenon, and multiple intelligences. Convenience sampling and Simple Random Sampling methods are used to structure the anonymous online survey data collection voluntarily for LML dataset creation. The responses from the 54 survey questionnaires contain raw data from 1021 current students from 11 universities in Bangladesh. The entire dataset is stored in an excel file and the entire questionnaire is accessible at (10.5281/zenodo.8112213) In this article mean and standard deviation for the participant's baseline attributes are given for scale parameters, and frequency and percentage are calculated for categorical parameters. Academic curriculum, courses as well as professional training materials can be reviewed and redesigned with a focus on the diversity of learners. How the designed courses will be learned by learners along with how they will be taught is a significant point for education in any discipline. As the survey questionnaires are set for adult learners and only current university students have participated in this survey, this dataset is appropriate for study andragogy and heutagogy but not pedagogy.

Explainable AI-based Alzheimer's prediction and management using multimodal data.

BACKGROUND: According to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world's elderly people. Day by day the number of Alzheimer's patients is rising. Considering the increasing rate and the dangers, Alzheimer's disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer's diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data. OBJECTIVE: To solve these issues, this paper proposes a novel explainable Alzheimer's disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, MRI segmentation data, and psychological data. However, currently, there is very little understanding of multimodal five-class classification of Alzheimer's disease. METHOD: For predicting five class classifications, 9 most popular Machine Learning models are used. These models are Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaB), Support Vector Machine (SVM), and Naive Bayes (NB). Among these models RF has scored the highest value. Besides for explainability, SHapley Additive exPlanation (SHAP) is used in this research work. RESULTS AND CONCLUSIONS: The performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer's disease, cognitively normal, non-Alzheimer's dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer's disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer's patient management architecture is also proposed in this work.

Generation of simulated data for Bengali text localization in natural images.

In the domain of vision-based applications, the importance of text cannot be underestimated due to its natural capacity to provide accurate and comprehensive information. The application of scene text editing systems enables the modification and enhancement of textual material included in natural images while maintaining the integrity of the overall visual layout. The complexity of keeping the original background context and font styles when altering, however, is an extremely difficult challenge considering the changed image must perfectly blend with the original without being altered. This article contains significant simulated data on the dynamic features of digital image editing, advertising, content development, and related fields. The system comprises key components such as 2D simulated text on the styled image (is), text image (it), masking of text (maskt), real background image (tb), real sample image (tf), text skeleton (tsk), and text styled image (tt). The source dataset contains diverse components such as background images, color variations, fonts, and text content, while the synthetic dataset consists of 49,000 randomly generated images. The dataset provides both researchers and practitioners with a rich resource for identifying and evaluating these dynamic features. The dataset is publicly accessible via the link: https://data.mendeley.com/datasets/h9kry9y46s/3.

A machine learning pipeline to classify foetal heart rate deceleration with optimal feature set.

Deceleration is considered a commonly practised means to assess Foetal Heart Rate (FHR) through visual inspection and interpretation of patterns in Cardiotocography (CTG). The precision of deceleration classification relies on the accurate estimation of corresponding event points (EP) from the FHR and the Uterine Contraction Pressure (UCP). This work proposes a deceleration classification pipeline by comparing four machine learning (ML) models, namely, Multilayer Perceptron (MLP), Random Forest (RF), Naïve Bayes (NB), and Simple Logistics Regression. Towards an automated classification of deceleration from EP using the pipeline, it systematically compares three approaches to create feature sets from the detected EP: (1) a novel fuzzy logic (FL)-based approach, (2) expert annotation by clinicians, and (3) calculated using National Institute of Child Health and Human Development guidelines. The classification results were validated using different popular statistical metrics, including receiver operating characteristic curve, intra-class correlation coefficient, Deming regression, and Bland-Altman Plot. The highest classification accuracy (97.94%) was obtained with MLP when the EP was annotated with the proposed FL approach compared to RF, which obtained 63.92% with the clinician-annotated EP. The results indicate that the FL annotated feature set is the optimal one for classifying deceleration from FHR.

TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles.

Connected and autonomous vehicles (CAVs) have witnessed significant attention from industries, and academia for research and developments towards the on-road realisation of the technology. State-of-the-art CAVs utilise existing navigation systems for mobility and travel path planning. However, reliable connectivity to navigation systems is not guaranteed, particularly in urban road traffic environments with high-rise buildings, nearby roads and multi-level flyovers. In this connection, this paper presents TAKEN-Traffic Knowledge-based Navigation for enabling CAVs in urban road traffic environments. A traffic analysis model is proposed for mining the sensor-oriented traffic data to generate a precise navigation path for the vehicle. A knowledge-sharing method is developed for collecting and generating new traffic knowledge from on-road vehicles. CAVs navigation is executed using the information enabled by traffic knowledge and analysis. The experimental performance evaluation results attest to the benefits of TAKEN in the precise navigation of CAVs in urban traffic environments.

Closed-loop optogenetic control of the dynamics of neural activity in non-human primates.

Electrical neurostimulation is effective in the treatment of neurological disorders, but associated recording artefacts generally limit its applications to open-loop stimuli. Real-time and continuous closed-loop control of brain activity can, however, be achieved by pairing concurrent electrical recordings and optogenetics. Here we show that closed-loop optogenetic stimulation with excitatory opsins enables the precise manipulation of neural dynamics in brain slices from transgenic mice and in anaesthetized non-human primates. The approach generates oscillations in quiescent tissue, enhances or suppresses endogenous patterns in active tissue and modulates seizure-like bursts elicited by the convulsant 4-aminopyridine. A nonlinear model of the phase-dependent effects of optical stimulation reproduced the modulation of cycles of local-field potentials associated with seizure oscillations, as evidenced by the systematic changes in the variability and entropy of the phase-space trajectories of seizures, which correlated with changes in their duration and intensity. We also show that closed-loop optogenetic neurostimulation could be delivered using intracortical optrodes incorporating light-emitting diodes. Closed-loop optogenetic approaches may be translatable to therapeutic applications in humans.

ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals.

Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML's popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.

RoseNet: Rose leave dataset for the development of an automation system to recognize the diseases of rose.

For the welfare of self-development and the country's economic evolution, people invest their youth and money in different cultivation and sustainable production business sectors. The crops or fruits get all the attention for this purpose, but currently, the commercial cultivation of flowers is becoming a numerous beneficial investment. As a consequence, the rose(Genus Rosa) is one of the most beautiful and commercially demanding flowers among different flowers. However, insecticide resistance is considered one of the lion's share issues facing agricultural production of roses by decreasing plants' growth and the quality as well as the quantity of healthy-looking flowers. Apart from this, due to different natural and environmental issues, rose's quality and production level are losing their fame. Additionally, the cultivators of this sector are not educated enough to identify the initial affection of different diseases of leaves with beard eyes. Besides, the lack of communication skills to consult with an agriculturist timely turns the situation worst more than the estimation of the production. With this concern, early detection of diseases that affected different parts of roses, such as leaves, is crucial. Recently, image processing techniques and machine learning classifiers have been primarily applied to recognize multiple diseases. This article presents an extensive dataset of rose leaves images, both diseases affected and diseases free are classified into three classes (Blackspot, Downy Mildew, and Fresh Leaf). The dataset is composed of the collected images which were captured during the seasonal time of diseases affection with the consultation of a domain expert and the dataset is accessible at https://data.mendeley.com/datasets/7z67nyc57w/2.

Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays.

Novel Coronavirus 2019 disease or COVID-19 is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The use of chest X-rays (CXRs) has become an important practice to assist in the diagnosis of COVID-19 as they can be used to detect the abnormalities developed in the infected patients' lungs. With the fast spread of the disease, many researchers across the world are striving to use several deep learning-based systems to identify the COVID-19 from such CXR images. To this end, we propose an inverted bell-curve-based ensemble of deep learning models for the detection of COVID-19 from CXR images. We first use a selection of models pretrained on ImageNet dataset and use the concept of transfer learning to retrain them with CXR datasets. Then the trained models are combined with the proposed inverted bell curve weighted ensemble method, where the output of each classifier is assigned a weight, and the final prediction is done by performing a weighted average of those outputs. We evaluate the proposed method on two publicly available datasets: the COVID-19 Radiography Database and the IEEE COVID Chest X-ray Dataset. The accuracy, F1 score and the AUC ROC achieved by the proposed method are 99.66%, 99.75% and 99.99%, respectively, in the first dataset, and, 99.84%, 99.81% and 99.99%, respectively, in the other dataset. Experimental results ensure that the use of transfer learning-based models and their combination using the proposed ensemble method result in improved predictions of COVID-19 in CXRs.

[Effect of a multimodal pain management protocol and age on wound healing after thermal disbudding of female German Holstein calves]. / Einfluss eines multimodalen Schmerz­managements sowie des Alters zum Zeitpunkt des Eingriffes auf die Wundheilung nach thermischer Zerstörung der Hornanlage bei weiblichen Deutsch-Holstein-Kälbern.

INTRODUCTION: Hot-iron disbudding of calves is a stressful and painful procedure and leaves a burn wound. Pain management procedures and the effects of hot-iron disbudding on biochemical markers of pain perception and stress response have been widely investigated in recent years. The aim of this study was to investigate the potential effects of pain management and age of the calf on the healing of burn wounds caused by disbudding. 327 healthy female German Holstein calves were included in this randomised, triple-blinded, prospective study. Calves were either disbudded at the age of four to 10 or 15 to 28 days using a gas-powered hot iron. Each calf was randomly allocated to one of nine possible treatment groups (BG). All calves received either the active ingredients to be tested (xylazine hydrochloride with 0.2 or 0.05 mg / kg body mass (BM) intramuscular for sedation, procaine hydrochloride (2 %) each 8 ml locally on both sides subcutaneously (SC) to the cornual nerves, meloxicam with 0,5 mg / kg BM SC for anti-inflammatory purposes) or an identical amount of saline solution (placebo). Calves in the group `thermE` and `ScheinE` received only placebo. In group `ScheinE` disbudding was simulated and in `thermE` it was carried out. The calves were clinically monitored starting one day before and ending 28 days after the procedure and the burn wounds were assessed. Both the rectal temperature and parameters of wound healing changed significantly during the study period and had characteristic profiles over time. Wound healing was not influenced by the different analgesic protocols, indicating that a multimodal analgesia does not pose a risk for wound healing after thermal disbudding. There were no observed differences between the age groups. The results of this study show, that disbudding of young calves and a multimodal pain management protocol does not affect wound healing in calves.

INTRODUCTION: L'ébourgeonnage thermique des veaux est une procédure stressante et douloureuse qui laisse une brûlure. Les procédures de gestion de la douleur et les effets de l'ébourgeonnage thermique sur les marqueurs biochimiques de la perception de la douleur et de la réponse au stress ont été largement étudiés ces dernières années. Le but de cette étude était d'étudier les effets potentiels de la gestion de la douleur et de l'âge du veau sur la cicatrisation des brûlures causées par l'ébourgeonnage. 327 veaux Holstein allemands femelles en bonne santé ont été inclus dans cette étude prospective randomisée en triple aveugle. Les veaux ont été soit ébourgeonnés à l'âge de 4 à 10 jours ou de 15 à 28 jours à l'aide d'un thermocautère à gaz. Chaque veau a été réparti au hasard dans l'un des neuf groupes de traitement possibles (BG). Tous les veaux ont reçu soit les principes actifs à tester (chlorhydrate de xylazine à 0,2 ou 0,05 mg/kg de masse corporelle (BM) par voie intramusculaire pour sédation, chlorhydrate de procaïne (2 %) 8 ml localement des deux côtés par voie sous-cutanée (SC) jusqu'aux nerfs cornuaux , méloxicam à 0,5 mg/kg de masse corporelle SC à visée anti-inflammatoire) ou une quantité identique de solution saline (placebo). Les veaux du groupe « thermE ¼ et « ScheinE ¼ ont reçu uniquement un placebo. Dans le groupe

ACCU3RATE: A mobile health application rating scale based on user reviews.

BACKGROUND: Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being. OBJECTIVE: This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU3RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings. METHOD: Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users' sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer's statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. RESULTS AND CONCLUSIONS: ACCU3RATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCU3RATE, matches more closely to the rating performed by experts.

[Cortisol concentrations in sheep before, during and after sham foot trimming on a tilt table - the suitability of different matrices]. / Kortisolkonzentrationen bei Schafen vor, während und nach der simulierten Klauenbehandlung im Kippstand ­ Eignung verschiedener Matrices.

INTRODUCTION: Matrices that can be collected non-invasively for quantification of a stress response in sheep have received little attention in the veterinary literature. This study examines the suitability of blood, tears and saliva for determining a stress response in sheep undergoing sham foot trimming on a tilt table. The cortisol concentration of blood, tears and saliva and the concentration of cortisol metabolites in faeces were measured in 13 healthy Meat Merino ewes once a day for six days. Sham foot trimming on a tilt table was used as the stressor and was done during a one-hour period on day 4; cortisol concentrations of blood and tears were measured at 0, 10, 20, 30, 40 and 60 minutes. Cortisol concentrations of blood (maximum at 30 minutes) and tears (maximum at 40 minutes) increased during the procedure and then decreased. There were significant correlations between cortisol concentrations of blood and tears (p = 0,04) during sham foot trimming (area under the curve, 0 to 60 minutes). Over the entire 6-day study period, significant correlations were seen between the cortisol concentrations of blood and tears (r = 0,55; p.

INTRODUCTION: Les marqueurs qui peuvent être collectés de manière non invasive pour quantifier une réponse au stress chez le mouton ont fait l'objet de peu d'attention dans la littérature vétérinaire. Cette étude examine la pertinence du sang, des larmes et de la salive pour déterminer une réponse au stress chez des moutons subissant un parage fictif des pieds sur une table basculante. La concentration de cortisol dans le sang, les larmes et la salive ainsi que la concentration de métabolites de cortisol dans les fèces ont été mesurées chez 13 brebis Meat Merino saines une fois par jour pendant six jours. Le parage fictif des pieds sur une table inclinable a été utilisé comme facteur de stress et a été effectué pendant une période d'une heure le jour 4; les concentrations de cortisol dans le sang et les larmes ont été mesurées à 0, 10, 20, 30, 40 et 60 minutes. Les concentrations de cortisol dans le sang (maximum à 30 minutes) et les larmes (maximum à 40 minutes) ont augmenté au cours de la procédure puis ont diminué. Il y avait des corrélations significatives entre les concentrations de cortisol dans le sang et les larmes (p = 0,04) lors du parage fictif des onglons (aire sous la courbe, 0 à 60 minutes). Sur l'ensemble de la période d'étude de 6 jours, des corrélations significatives ont été observées entre les concentrations de cortisol dans le sang et les larmes (r = 0,55 ; p < 0,001), le sang et la salive (r = 0,53 ; p < 0,001) et les larmes et la salive (r = 0,78 ; p < 0,001). La concentration fécale de métabolites de cortisol était significativement augmentée au jour 5 (p 0,05), mais la concentration de cortisol des autres supports n'a pas changé de manière significative au cours de la période d'étude de 6 jours. Le parage fictif des pieds sur une table basculante a été considéré comme un facteur de stress aigu chez les moutons en raison de l'augmentation des concentrations de cortisol dans le sang, des larmes et de l'augmentation des concentrations de métabolites de cortisol dans les selles. La concentration de cortisol dans les larmes était similaire à celle du sang et, par conséquent, la collecte de larmes représente une alternative viable et non invasive au sang pour les tests de cortisol. Le délai des pics entre la concentration maximale de cortisol dans les larmes et le sang doit être pris en compte lors de l'interprétation des résultats.

Silicene Quantum Capacitance Dependent Frequency Readout to a Label-Free Detection of DNA Hybridization- A Simulation Analysis.

The use of deoxyribonucleic acid (DNA) hybridization to detect disease-related gene expression is a valuable diagnostic tool. An ion-sensitive field-effect transistor (ISFET) with a graphene layer has been utilized for detecting DNA hybridization. Silicene is a two-dimensional silicon allotrope with structural properties similar to graphene. Thus, it has recently experienced intensive scientific research interest due to its unique electrical, mechanical, and sensing characteristics. In this paper, we proposed an ISFET structure with silicene and electrolyte layers for the label-free detection of DNA hybridization. When DNA hybridization occurs, it changes the ion concentration in the surface layer of the silicene and the pH level of the electrolyte solution. The process also changes the quantum capacitance of the silicene layer and the electrical properties of the ISFET device. The quantum capacitance and the corresponding resonant frequency readout of the silicene and graphene are compared. The performance evaluation found that the changes in quantum capacitance, resonant frequency, and tuning ratio indicate that the sensitivity of silicene is much more effective than graphene.

TCERG1L allelic variation is associated with cisplatin-induced hearing loss in childhood cancer, a PanCareLIFE study.

In children with cancer, the heterogeneity in ototoxicity occurrence after similar treatment suggests a role for genetic susceptibility. Using a genome-wide association study (GWAS) approach, we identified a genetic variant in TCERG1L (rs893507) to be associated with hearing loss in 390 non-cranial irradiated, cisplatin-treated children with cancer. These results were replicated in two independent, similarly treated cohorts (n = 192 and 188, respectively) (combined cohort: P = 5.3 × 10-10, OR 3.11, 95% CI 2.2-4.5). Modulating TCERG1L expression in cultured human cells revealed significantly altered cellular responses to cisplatin-induced cytokine secretion and toxicity. These results contribute to insights into the genetic and pathophysiological basis of cisplatin-induced ototoxicity.

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals.

Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.

Cross-cultural validation of the Arabic version of the Developmental Coordination Disorder Questionnaire DCDQ'07, in a Lebanese sample of children.

BACKGROUND: The revised version of the Developmental Coordination Disorder Questionnaire (DCDQ'07) is a parent questionnaire designed to identify Developmental Coordination Disorder in 5-15-year-old children. AIM: The aim of this study was to carry out the translation and cross-cultural adaptation of the DCDQ'07, to examine psychometric properties, and to define the cut-off scores of the Arabic-Lebanese version of the questionnaire (DCDQ-AL). METHOD: 38 parents of children with and without motor difficulties participated in the translation and cross-cultural phase. As for the validation phase and the study of the psychometric properties, a total of one hundred and twenty-four typically developing children (N=124) aged between 5 and 15 years were recruited through schools in different districts across Lebanon, whereas the clinical sample (N = 56) of children with motor difficulties was recruited via psychomotor rehabilitation centers in Beirut and psychomotor therapists working in private clinics across the country. This study used the Movement Assessment Battery for Children - second edition (MABC-2) motor test developed to classify children according to their degree of motor impairment. RESULTS: For test-retest reliability and inter-rater reliability, excellent Intraclass Correlation Coefficients (ICC) were shown with values of 0.94 and 0.9, respectively. The internal consistency value for the DCDQ-AL was high (Cronbach's alpha = 0.947). Correlations between the DCDQ-AL scores and Movement Assessment Battery for Children (MABC-2) show adequate convergent validity (ρ = 0.65, p < .001). Differences in DCDQ-AL scores between children with and without motor difficulties (p < .001) provide clear evidence of discriminative validity. The Lebanese cut-offs are very similar to the Canadian version, except for the 5-7 age band. The DCDQ-AL shows a sensitivity of 0.91 and specificity of 0.77. The adapted questionnaire showed solid psychometric properties, allowing us to conclude that the DCDQ-AL can be used to support a diagnosis of DCD. CONCLUSION: The results provide evidence that the DCDQ-AL is a valid clinical screening tool for DCD that can assist Arabic speaking professionals in screening children aged 5-15 years old who are at risk of having DCD.