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INTRODUCTION: Prescribing fenbendazole medicated feed for pheasants in the USA is considered extra-label drug use under CPG Sec 615.115, and a safe estimated withdrawal interval (WDI) must be applied following administration to this minor food-producing species. This study sought to determine the pharmacokinetic and residue depletion profile for fenbendazole and its major metabolites to estimate a WDI for pheasants following fenbendazole administration as an oral medicated feed. METHOD: Pheasants (n = 32) were administered fenbendazole as an oral medicated feed (100 ppm) for 7 days. Fenbendazole, fenbendazole sulfoxide, and fenbendazole sulfone (FBZ-SO2) in liver and muscle samples were analyzed using HPLC-UV. Tissue WDIs were estimated using FDA, European Medicines Agency (EMA), and half-life multiplication methods for US poultry tolerances, EMA maximum residue limits, and the analytical limit of detection (LOD; 0.004 ppm). Terminal tissue elimination half-lives (T1/2) were estimated by non-compartmental analysis using a naïve pooled data approach. RESULTS: The tissue T1/2 was 14.4 h for liver, 13.2 h for thigh muscle, and 14.1 h for pectoral muscle. The maximum estimated withdrawal interval was 153 h (7 days) for FBZ-SO2 in pectoral muscle using the FDA tolerance method (95% confidence interval for the 99th percentile of the population), and the LOD as the residue limit. DISCUSSION: The results from this study support the use of FBZ-SO2 as the marker residue in the liver of pheasants and the provision of evidence based WDIs following the extra-label administration of fenbendazole medicated feed (100 ppm) for 7 days.

BACKGROUND: Substance Use Disorder (SUD) persists as a significant public health challenge worldwide, with an estimated prevalence of approximately 10-15% across the global populace. This condition is characterized by a notably high risk of lapses and relapses, even subsequent to treatment interventions. Mobile health interventions, owing to their widespread accessibility, emerge as a promising approach to diminish the risk of relapse post-treatment and to broaden the scope of care, especially in regions with a scarcity of trained medical professionals. METHOD: This study is designed to assess the effectiveness of mobile interventions in mitigating cravings and preventing lapses among individuals diagnosed with SUD. Employing a two-armed, randomized controlled trial framework, the study will evaluate a self-administered psychological intervention delivered through a mobile application, Nałogometr 2.0. Over a period of three months, participants will engage with intervention modules that primarily incorporate mindfulness techniques and Cognitive Behavioral Therapy (CBT) principles. Ecological Momentary Assessment (EMA) will be utilized to gather longitudinal data on a range of variables that are indicative of craving intensity and the risk of lapse. In addition to this, a monthly-administered battery of questionnaires will be employed to gauge the severity of substance dependence, as well as to measure levels of anxiety, depression, and overall life satisfaction. RESULTS: Results will be submitted for publication in peer-reviewed journals. CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/, identifier [NCT05730504].

Introduction: The European Medicines Agency Innovation Task Force (ITF) acts as early point of contact for medicine and technology developers to enable innovation during early drug development stages through ITF briefing meetings. Aim: To reflect on the current pace of innovation and to assess the potential of ITF stakeholder interactions, a comprehensive analysis of the ITF briefing meetings held between 2021 and 2022 was conducted with a focus on individual questions raised by the developers and the related feedback provided by the European regulators. Methods: Questions raised during ITF briefing meetings were extracted and categorised into main and sub-categories, revealing different themes across the whole medicine development process such as manufacturing technologies, pre-clinical developments, and clinically relevant questions. Results: There was positive feedback from regulators who gave initial guidance in 85% of the answers, provided concrete examples in 20% of the answers and recommended to continue discussions through additional regulatory procedures in 22% of the answers. Conclusion: This analysis frames the content and the type of topics discussed during ITF briefing meetings. Moreover, it describes the type of regulatory feedback provided to medicine developers and identified potential for improvement of these early interactions. Therefore, this analysis emphasises the role of ITF briefing meetings in fostering innovation in medicine. [ABSTRACT FROM AUTHOR]

ABSTRACT Mobile health studies often collect multiple within‐day self‐reported assessments of participants' behavior and well‐being on different scales such as physical activity (continuous scale), pain levels (truncated scale), mood states (ordinal scale), and the occurrence of daily life events (binary scale). These assessments, when indexed by time of day, can be treated and analyzed as functional data corresponding to their respective types: continuous, truncated, ordinal, and binary. Motivated by these examples, we develop a functional principal component analysis that deals with all four types of functional data in a unified manner. It employs a semiparametric Gaussian copula model, assuming a generalized latent non‐paranormal process as the underlying generating mechanism for these four types of functional data. We specify latent temporal dependence using a covariance estimated through Kendall's τ$$ \tau $$ bridging method, incorporating smoothness in the bridging process. The approach is then extended with methods for handling both dense and sparse sampling designs, calculating subject‐specific latent representations of observed data, latent principal components and principal component scores. Simulation studies demonstrate the method's competitive performance under both dense and sparse sampling designs. The method is applied to data from 497 participants in the National Institute of Mental Health Family Study of Mood Spectrum Disorders to characterize differences in within‐day temporal patterns of mood in individuals with the major mood disorder subtypes, including Major Depressive Disorder and Type 1 and 2 Bipolar Disorder. Software implementation of the proposed method is provided in an R‐package. [ABSTRACT FROM AUTHOR]

Ecological momentary assessment (EMA), a structured diary assessment technique, has shown feasibility to capture psychotic(-like) symptoms across different study groups. We investigated whether EMA combined with unsupervised machine learning can distinguish groups on the continuum of genetic risk toward psychotic illness and identify individuals with need for extended healthcare. Individuals with psychotic disorder (PD, N = 55), healthy individuals (HC, N = 25) and HC with first-degree relatives with psychosis (RE, N = 20) were assessed at two sites over 7 days using EMA. Cluster analysis determined subgroups based on similarities in longitudinal trajectories of psychotic symptom ratings in EMA, agnostic of study group assignment. Psychotic symptom ratings were calculated as average of items related to hallucinations and paranoid ideas. Prior to EMA we assessed symptoms using the Positive and Negative Syndrome Scale (PANSS) and the Community Assessment of Psychic Experience (CAPE) to characterize the EMA subgroups. We identified two clusters with distinct longitudinal EMA characteristics. Cluster 1 (NPD = 12, NRE = 1, NHC = 2) showed higher mean EMA symptom ratings as compared to cluster 2 (NPD = 43, NRE = 19, NHC = 23) (p < 0.001). Cluster 1 showed a higher burden on negative (p < 0.05) and positive (p < 0.05) psychotic symptoms in cross-sectional PANSS and CAPE ratings than cluster 2. Findings indicate a separation of PD with high symptom burden (cluster 1) from PD with healthy-like rating patterns grouping together with HC and RE (cluster 2). Individuals in cluster 1 might particularly profit from exchange with a clinician underlining the idea of EMA as clinical monitoring tool. [ABSTRACT FROM AUTHOR]

In the realm of underwater object detection, conventional methodologies often encounter challenges in accurately identifying and detecting small targets. These difficulties stem primarily from the intricate nature of underwater environments, suboptimal lighting conditions, and the diminutive scale of the targets themselves. To address this persistent challenge, the MarineYOLO network is introduced. This approach involves refining the conventional C2f module into the EC2f module, alongside the integration of the Efficient Multi-scale Attention (EMA) module into YOLOv8. Additionally, the Convolutional Block Attention Module (CBAM) is introduced to further refine the Feature Pyramid Network (FPN), facilitating enhanced feature extraction pertinent to small targets. Furthermore, the conventional CIoU is replaced with Wise-IoU to augment the precision and stability of target localization. Experimental findings demonstrate that MarineYOLO achieves an average precision (AP) of 78.5% on the RUOD dataset and 88.1% on the URPC dataset, marking improvements of 12.2% and 16.8%, respectively, compared to YOLOv8n. As an emerging paradigm in underwater object detection, MarineYOLO harbors significant potential in both practical applications and scholarly endeavors, furnishing an efficacious remedy to the challenges associated with detecting small targets in underwater settings. [ABSTRACT FROM AUTHOR]

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The latest country‐specific regulatory guidance for assessing effectiveness of risk minimization measures (RMM) strategies was identified across five continents—Africa (Egypt, South Africa), Asia (Australia, China, Japan, South Korea, Singapore), Europe (EU‐27, United Kingdom), North America (Unites States, Canada) and South America (Brazil)—and compared to the Reporting recommendation Intended for pharmaceutical Risk Minimization Evaluation Studies (RIMES) checklist, developed to assess the quality of effectiveness evaluations and endorsed by the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). RIMES checklist items address study hypothesis, participants, measures, statistical analysis and results. European Medical Agency (EMA) and Food and Drug Administration (FDA) guidance only partially aligned with RIMES, primarily for measures and results. In the absence of country‐specific guidance, most countries recommended following EMA or FDA guidelines; Japan and South Africa mentioned the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH E2E) guideline; Brazil and China had no guidance/recommendations. Worldwide, there was a lack of RMM‐specific guidance and, when guidance existed, they were not harmonized, and alignment with the RIMES checklist was limited. [ABSTRACT FROM AUTHOR]

Deep learning technology has recently become increasingly prevalent in the field of education due to the rapid growth of artificial intelligence. Teachers' teaching behavior is a crucial component of classroom teaching activities, and identifying and examining teachers' classroom teaching behavior is an important way to assess teaching. However, the traditional teaching evaluation method involves evaluating by either listening to the class on-site or playing back the teaching video afterward, which is a time-consuming and inefficient manual method. Therefore, this paper obtained teaching behavior data from a real smart classroom scenario and observed and analyzed the teacher behavior characteristics in this scenario. Aiming at the problems of complex classroom environments and the high similarity between teaching behavior classes, a method to improve YOLOv7 for large target classroom behavior recognition in smart classroom scenarios is proposed. First, we constructed the Teacher Classroom Behavior Data Set (TCBDS), which contains 6660 images covering six types of teaching behaviors: facing the board (to_blackboard, tb), facing the students (to_student, ts), writing on the board (writing, w), teaching while facing the board (black_teach, bt), teaching while facing the students (student_teach, st), and interactive (interact, i). This research adds a large target detection layer to the backbone network so that teachers' instructional behaviors can be efficiently identified in complex classroom circumstances. Second, the original model's backbone was extended with an effective multiscale attention module (EMA) to construct cross-scale feature dependencies under various branches. Finally, the bounding box loss function of the original model was replaced with MPDIoU, and a bounding box scaling factor was introduced to propose the Inner_MPDIoU loss function. Experiments were conducted using the TCBDS dataset. The method proposed in this study achieved mAP@.50, mAP@.50:.95, and recall values of 96.2%, 82.5%, and 92.9%, respectively—improvements of 1.1%, 2.0%, and 2.3% over the original model. This method outperformed other mainstream models compared to the current state of the art. The experimental results demonstrate the method's excellent performance, its ability to identify various classroom behaviors of teachers in realistic scenarios, and its potential to facilitate the analysis and visualization of teacher classroom behaviors. [ABSTRACT FROM AUTHOR]

The solid electrolyte (SE) membrane plays a crucial role in sulfide‐based all‐solid‐state batteries (ASSBs). However, the challenge of finding appropriate polymer binders with excellent (electro‐)chemical compatibility and adhesive properties, remains a significant obstacle for wet slurry processing of sulfide SE membranes. Herein, a novel “polar‐nonpolar synergistic” finely‐tuned strategy is employed to design an ethylene‐methyl acrylate (EMA) copolymer binder to facilitate wet‐slurry‐based fabrication of sulfide SE membranes. Significantly, by adjusting the ratio of polar and nonpolar groups, this methodology enables the binder to dissolve effectively in a toluene‐based slurry and also ensures good adhesion between the EMA binder and SE particles. The SE membrane prepared with EMA binder exhibits an ultra‐thin thickness (36 µm), flexibility, and excellent ionic conductivity (1.43 mS cm−1). The ASSB assembled with the SE membrane shows an excellent capacity retention rate of 92.9% after 120 cycles at 0.5 C. This work on the “polar‐nonpolar synergistic” finely‐tuned effect of binder provides insight for manufacturing high‐quality sulfide SE membranes. [ABSTRACT FROM AUTHOR]

Background: Scholars have suggested negative self-perceptions are central to understanding risk for non-suicidal (NSSI) and suicidal self-injury. Body attitudes are a core aspect of the self, and research has found that negative body attitudes relate to both NSSI and suicide, but it remains unclear if the risk is more distal or proximal. Method: The current study utilized a 21-day EMA protocol to examine how momentary changes in body appreciation (valuing the body, a facet of positive body image) corresponded to concurrent and next-day NSSI and suicide urges. Participants included 25 adult outpatients (Mage = 35.6, SD = 14.3) who received notifications three times daily, randomized within 4-hour time blocks, across the 21 days (1,301 total responses). At each notification, participants indicated their current level of body appreciation, and both NSSI and suicide urges. Results: Both state (within-subject) and trait (between-subject) body appreciation were negatively associated with concurrent NSSI and suicide urges. Only trait body appreciation was prospectively associated with NSSI urges; no other significant prospective relationships were observed. Conclusions: These findings provide evidence that body appreciation has a momentary protective effect on NSSI and suicide urges, as well as may reduce prospective risk for NSSI. The results are consistent with theoretical arguments emphasizing the importance of body attitudes in conceptualizing risk and could open innovative avenues for intervention and prevention. Highlights: Cross-sectional studies suggest body attitudes may impact risk for self-injurious and suicidal behaviors. Using EMA, we examined momentary associations of body appreciation, NSSI urges, and suicide urges. Both state and trait body appreciation were negatively related to concurrent NSSI and suicide urges. Trait body appreciation was prospectively associated with NSSI urges but not suicide urges. Body appreciation may help to reduce risk for NSSI and suicide. [ABSTRACT FROM AUTHOR]

Achieving a perfectly spherical nanostructures might not be feasible in experiments due to factors like growth kinetics, solvent's nature and surface effects. This numerical investigation examines how the shape of a surrounding shell (prolate, spherical or oblate) in core/shell quantum dots (CSQDs) buried into two commonly used dielectric oxides (SiO2 or HfO2), microscopically influences their electro-optical characteristics. In the context of the effective mass approach (EMA) and the density matrix formalism (DMF), we have reached the stationary eigenstates and their matching wave functions by solving the Schrödinger equation. Our computations revealed that the discrete electronic states can fluctuate with ellipticity parameter as a consequence of different quantum confinement origins along the major and minor axes. The shell anisotropy provided an effective opportunity to finely adjust resonant frequencies and calibrate the magnitude order of the Quadratic electro-optic effects (QEOEs), electro-absorption (EA) process, optical absorption characteristics (OACs) and refractive index changes (RICs) within QD/oxide interfacs. Computed coefficients have experienced red/blue shift contingent upon variations in the inner core radius, ellipticity parameter and the types of capping oxides. [ABSTRACT FROM AUTHOR]

Many concerns exist about potential long-term psychosocial impacts of the COVID-19 pandemic on young people. While the school has been identified as having a vital role in psychological recovery post-disaster more generally, it is unclear as yet how young people have adapted to the return to in-person education. This paper reports on the preliminary findings from an intensive Ecological Momentary Analysis exploration of the affective wellbeing, experiences and coping of 82 Irish second-level students. The participants were found to experience more positive than negative events, and to have moderate-high levels of positive affect and perceived coping during the 7-day period of monitoring. However, the findings also suggest that some students, particularly those with pre-existing psychological difficulties, may be in need of additional targeted support. Accordingly, it is recommended that in the short to medium-term, second-level schools should strive to prioritise the psychological recovery and resilience of students such as through an emphasis on re-establishing and consolidating a sense of student connectedness. [ABSTRACT FROM AUTHOR]

Background: The new EU Medical Device Regulation (MDR) places greater importance on the role of clinical evidence to establish safety and performance. Article 54 of the MDR calls for expert committees to independently review the scientific, technical, and clinical evidence supporting the market authorization of certain novel devices independently from the established process of Notified Body reviews. These experts provide a review and opinion that ultimately is taken into consideration alongside the information reviewed by the Notified Body during the review process. Four expert committees (General and Plastic Surgery and Dentistry; Orthopaedics, Traumatology, Rehabilitation, Rheumatology; Circulatory System; and Neurology) have published at least one Scientific Opinion (SO) under the Clinical Evaluation Consultation Procedure (CECP) in 2021–2022. Methods: The four expert committees with published CECP opinions were reviewed to assess the academic backgrounds and professional expertise of each member with respect to clinical, technical, and biological domains on a 0–2 scale for each domain. A content review was conducted on the 10 CECP opinions published by these committees to assess their consistency with the goals and outcome expectations set by the MDR. The extent of content related to each of the clinical, technical, and biological domains was also assessed on a 0–2 scale. Results: All committees were composed primarily by members with strong clinical expertise, but only a few had strong technical and biological expertise. Across committees, the average scores of members related to academic background and professional expertise both ranged from 1.64 to 2.00 in the clinical domain, but only 0–0.15 and 0.15–0.69, respectively, in the biological domain, and 0.12–0.55 and 0.23–0.73, respectively, in the technical domain. A content review for the 10 SOs showed that all opinions focused exclusively or primarily on the clinical evidence. Three contained a modest amount of additional text directed at technical/engineering issues and five at biological issues. Conclusion: Expert committees are composed predominantly of expert clinical reviewers but have many fewer members with significant technical or biological expertise. This may limit the ability of the committees to evaluate the significant technical and biological risks that are often best understood by preclinical testing. Broadening the expertise across the committees may improve the depth of their benefit/risk critiques. [ABSTRACT FROM AUTHOR]

Electric vehicle accidents on the road occur frequently, and head injuries are often the cause of serious casualties. However, most electric vehicle riders seldom wear helmets. Therefore, combining target detection algorithms with road cameras to intelligently monitor helmet-wearing has extremely important research significance. Therefore, a helmet-wearing detection algorithm based on the improved YOLOv8n model, PRE-YOLO, is proposed. First, we add small target detection layers and prune large target detection layers. The sophisticated algorithm considerably boosts the effectiveness of data manipulation while significantly reducing model parameters and size. Secondly, we introduce a convolutional module that integrates receptive field attention convolution and CA mechanisms into the backbone network, enhancing feature extraction capabilities by enhancing attention weights within both channel and spatial aspects. Lastly, we incorporate an EMA mechanism into the C2f module, which strengthens feature perception and captures more characteristic information while maintaining the same model parameter size. The experimental outcomes indicate that in comparison to the original model, the proposed PRE-YOLO model in this paper has improved by 1.3%, 1.7%, 2.2%, and 2.6% in terms of precision P, recall R, mAP@0.5, and mAP@0.5:0.95, respectively. At the same time, the number of model parameters has been reduced by 33.3%, and the model size has been reduced by 1.8 MB. Generalization experiments are conducted on the TWHD and EBHD datasets to further verify the versatility of the model. The research findings provide solutions for further improving the accuracy and efficiency of helmet-wearing detection on complex traffic roads, offering references for enhancing safety and intelligence in traffic. [ABSTRACT FROM AUTHOR]

Biosimilars are a new category of medicines that have revolutionized the treatment of patients with life-threatening conditions, such as cancer and autoimmune diseases. A biosimilar is a biological product that is very similar to an already approved biological medicine that is used as its reference. These medicines go through less clinical studies than their reference product and therefore the cost of their development process is significantly lower, giving patients access to them more quickly and at a more affordable price. However, due to the structural complexity and inherent degree of variability of these products, it is very difficult to develop biosimilar medicines that are exactly the same as the reference product. Thus, it is extremely important to define strict controls to guarantee that these minor differences are not clinically significant in terms of safety and efficacy. Like any other medicine, biosimilars have to go through a complex approval process, which involves a thorough assessment by regulatory authorities to ensure these products meet the necessary standards of quality, safety, and efficacy before being placed on the market. Due to their nature and complexity, the approval process of biosimilar medicines contains some unique and specific considerations. This review aims to address the regulatory framework of biosimilar medicines, their development process and the approval requirements by the European Medicines Agency (EMA) and the Food and Drug Administration (FDA). [ABSTRACT FROM AUTHOR]

BACKGROUND: The prevalence of substance and behavioral addiction is estimated between 10 and 15% of the global population and remains a severe public health concern. Moreover, addiction treatment has several barriers, such as a lack of access to professional treatment or stigmatization. Mobile health interventions emerge as a promising solution. METHODS: This two-armed randomized controlled trial (RCT) aims to assess the efficacy of a mobile app-based self-guided psychological intervention delivered via a smartphone app (Nałogometr) in reducing craving and lapse risk in problematic behaviors and substance use compared to a control condition. Participant recruitment and data collection will start in June 2022 and end in September 2022. Due to the nature of the study, i.e., a nationwide study of problematic substance use and behaviors, we will aim to recruit all individuals willing to participate. The four-week intervention condition includes short-term and long-term modules based mainly on mindfulness and cognitive behavioral therapy. Longitudinal data on several variables related to craving and lapse risk are collected daily using ecological momentary assessment (EMA). The primary outcomes of interest will be the self-reported number of lapses and craving level in daily EMA. Moreover, a questionnaire battery assessment is administered at baseline in the first week following onboarding, after five weeks, and after six months. The secondary outcome measures will include the severity of problematic substance use or behaviors, anxiety and depression, and life satisfaction. RESULTS: Results will be submitted for publication in peer-reviewed journals. CLINICAL TRIAL REGISTRATION: [https://clinicaltrials.gov/], identifier [NCT054 34,429].

In the realm of underwater object detection, conventional methodologies often encounter challenges in accurately identifying and detecting small targets. These difficulties stem primarily from the intricate nature of underwater environments, suboptimal lighting conditions, and the diminutive scale of the targets themselves. To address this persistent challenge, the MarineYOLO network is introduced. This approach involves refining the conventional C2f module into the EC2f module, alongside the integration of the Efficient Multi-scale Attention (EMA) module into YOLOv8. Additionally, the Convolutional Block Attention Module (CBAM) is introduced to further refine the Feature Pyramid Network (FPN), facilitating enhanced feature extraction pertinent to small targets. Furthermore, the conventional CIoU is replaced with Wise-IoU to augment the precision and stability of target localization. Experimental findings demonstrate that MarineYOLO achieves an average precision (AP) of 78.5% on the RUOD dataset and 88.1% on the URPC dataset, marking improvements of 12.2% and 16.8%, respectively, compared to YOLOv8n. As an emerging paradigm in underwater object detection, MarineYOLO harbors significant potential in both practical applications and scholarly endeavors, furnishing an efficacious remedy to the challenges associated with detecting small targets in underwater settings.

Bird’s nest on power transmission lines pose a threat to the safe operation of power equipment and may even affect the stability of the entire power system. To address the challenge that traditional methods face in achieving accurate real-time detection in complex environments, this paper proposes the BN-YOLO bird’s nest detection method based on the YOLOv8s baseline model, enhancing its suitability for real-time applications in intricate settings. First, we replaced the original backbone network of YOLOv8s with a lightweight FasterNet module, thereby reducing computational burden and improving network performance. Second, the feature fusion network was redesigned, incorporating the efficient multi-scale attention module (EMA) to optimize feature fusion capabilities across different scales. Subsequently, we proposed a lighter and faster C2f structure by substituting the standard convolution in the C2f structure with partial convolution (PConv). Finally, Wise-IOUv3 was utilized as the regression loss function to mitigate the effects of low-quality annotations and accelerate network convergence. Experimental results demonstrate that on our self-constructed transmission line bird’s nest dataset, the bird’s nest detection method we proposed achieved a 97.73% score. Compared to the original YOLOv8s, the mean average precision (mAP) increased by 2.19%, and the detection speed improved from 61 FPS to 83 FPS. In addition, our method outperforms other mainstream object detection algorithms, such as SSD, DETR, and RT-DETR, providing higher detection efficiency while maintaining high accuracy. These results confirm that the proposed method can effectively detect bird’s nest targets in complex environments and fulfill the requirements for real-time inspection. This research not only enhances the accuracy and efficiency of bird’s nest detection in power transmission lines but also offers a novel solution for the safe operation and maintenance of smart grids, which holds significant practical application value. [ABSTRACT FROM AUTHOR]

This study evaluates positive (topophilic) and negative (topophobic) perceptions of places using participatory mapping methods. Current research on mapping perceptions of urban environments relies heavily on retrospective self-reports from citizens. These methods are often susceptible to recall bias and do not capture granular information about urban environments. Places are dynamic, and peoples’ perceptions of them vary by time and space. To address these gaps in methods, we collected data from individuals living in two cities, Olomouc, Czech Republic and Brisbane, Australia. GIS was used to analyse a combined total of 634 momentary assessments from Olomouc, and 318 assessments from Brisbane. Our findings suggest that this approach can yield accurate and reliable data about perceptions of topophobia and topophilia in the two cities as well as enable researchers to clearly define hotspots and hot times related to individual activity spaces.

X-ray security images face significant challenges due to complex backgrounds, item overlap, and multi-scale target detection. Traditional methods often struggle to accurately identify objects, especially under cluttered conditions. This paper presents an advanced detection model, called YOLOv8n-GEMA, which incorporates several enhancements to address these issues. Firstly, the generalized efficient layer aggregation network (GELAN) module is employed to augment the feature fusion capabilities. Secondly, to tackle the problems of overlap and occlusion in X-ray images, the efficient multi-scale attention (EMA) module is utilized, effectively managing the feature capture and interdependencies among overlapping items, thereby boosting the model's detection capability in such scenarios. Lastly, addressing the diverse sizes of items in X-ray images, the Inner-CIoU loss function uses auxiliary bounding boxes at varying scale ratios for loss calculation, ensuring faster and more effective bounding box predictions. The enhanced YOLOv8 model was tested on the public datasets SIXRay, HiXray, CLCXray, and PIDray, where the improved model's mean average precision (mAP) reached 94.4%, 82.0%, 88.9%, and 85.9%, respectively, showing improvements of 3.6%, 1.6%, 0.9%, and 3.4% over the original YOLOv8. These results demonstrate the effectiveness and universality of the proposed method. Compared to current mainstream X-ray images of dangerous goods detection models, this model significantly reduces the false detection rate of dangerous goods in X-ray security images and achieves substantial improvements in the detection of overlapping and multi-scale targets, realizing higher accuracy in dangerous goods detection. [ABSTRACT FROM AUTHOR]

The intelligent detection of young peaches is the main technology of fruit-thinning robots, which is crucial for enhancing peach fruit quality and reducing labor costs. This study presents the lightweight YOLO-PEM model based on YOLOv8s to achieve high-precision and automatic detection of young "Okubo" peaches. Firstly, the C2f_P module was devised by partial convolution (PConv), replacing all C2f modules in YOLOv8s to achieve the model's lightweight. Secondly, embedding the efficient multi-scale attention (EMA) module in the lightweight C2f_P_1 module of the backbone network enhanced the feature extraction capability and accuracy for young peaches. Finally, the MPDIoU loss function was utilized to replace the original CIoU loss function, which improved the detection accuracy of the bounding box while speeding up the convergence of the model. The experimental results demonstrate that the YOLO-PEM model achieved an average precision (AP) of 90.86%, F1 score of 86.70%, and model size of 16.1 MB, which was a 1.85% improvement in the AP, 0.85% improvement in the F1 score, and 5.3 MB reduction in the model size compared with YOLOv8s. The AP was 6.26%, 6.01%, 2.05%, 2.12%, and 1.87% higher compared with the other lightweight detection models YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv6s, and YOLOv7-tiny, respectively. Furthermore, the FPS of YOLO-PEM was 196.2 f·s-1, which can fulfill the demand for the real-time detection of young peaches. YOLO-PEM effectively detects young peaches in complex orchard environments and can offer a basis for the theoretical design of the vision system of the "Okubo" peach fruit-thinning robot and scientific management of orchards. [ABSTRACT FROM AUTHOR]

Background: Individuals with a substance use disorder complete ecological momentary assessments (EMA) at lower rates than community samples. Previous research in tobacco users indicates that early log‐in counts to smoking cessation websites predicted subsequent smoking cessation website usage. We extended this line of research to examine individuals who are seeking to change their drinking behaviors through mutual support groups. We examined whether adherence in the first 7 days (1487 observations) of an intensive longitudinal study design could predict subsequent EMA protocol adherence (50% and 80% adherence separately) at 30 (5700 observations) and 60 days (10,750 observations). Methods: Participants (n = 132) attending mutual‐help groups for alcohol use completed two assessments per day for 6 months. We trained four classification models (logistic regression, recursive partitioning, support vector machines, and neural networks) using a training dataset (80% of the data) with each of the first 7 days' cumulative EMA assessment completion. We then tested these models to predict the remaining 20% of the data and evaluated model classification accuracy. We also used univariate receiver operating characteristic curves to examine the minimal combination of days and completion percentage to best predict subsequent adherence. Results: Different modeling techniques can be used with early assessment completion as predictors to accurately classify individuals that will meet minimal and optimal adherence rates later in the study. Models ranged in their performance from poor to outstanding classification, with no single model clearly outperforming other models. Conclusions: Traditional and machine learning approaches can be used concurrently to examine several methods of predicting EMA adherence based on early assessment completion. Future studies could investigate the use of several algorithms in real time to help improve participant adherence rates by monitoring early adherence and using early assessment completion as features in predictive modeling. [ABSTRACT FROM AUTHOR]

In response to the problems of complex model networks, low detection accuracy, and the detection of small targets prone to false detections and omissions in pedestrian detection, this paper proposes FedsNet, a pedestrian detection network based on RT-DETR. By constructing a new lightweight backbone network, ResFastNet, the number of parameters and computation of the model are reduced to accelerate the detection speed of pedestrian detection. Integrating the Efficient Multi-scale Attention(EMA) mechanism with the backbone network creates a new ResBlock module for improved detection of small targets. The more effective DySample has been adopted as the upsampling operator to improve the accuracy and robustness of pedestrian detection. SIoU is used as the loss function to improve the accuracy of pedestrian recognition and speed up model convergence. Experimental evaluations conducted on a self-built pedestrian detection dataset demonstrate that the average accuracy value of the FedsNet model is 91 % , which is a 1.7 % improvement over the RT-DETR model. The parameters and model volume are reduced by 15.1 % and 14.5 % , respectively. When tested on the public dataset WiderPerson, FedsNet achieved the average accuracy value of 71.3 % , an improvement of 1.1 % over the original model. In addition, the detection speed of the FedsNet network reaches 109.5 FPS and 100.3 FPS, respectively, meeting the real-time requirements of pedestrian detection. [ABSTRACT FROM AUTHOR]

Background: Despite continuing advancements in treatments for opioid use disorder (OUD), continued high rates of relapse indicate the need for more effective approaches, including novel pharmacological interventions. Glucagon-like peptide 1 receptor agonists (GLP-1RA) provide a promising avenue as a non-opioid medication for the treatment of OUD. Whereas GLP-1RAs have shown promise as a treatment for alcohol and nicotine use disorders, to date, no controlled clinical trials have been conducted to determine if a GLP-1RA can reduce craving in individuals with OUD. The purpose of the current protocol was to evaluate the potential for a GLP-1RA, liraglutide, to safely and effectively reduce craving in an OUD population in residential treatment. Method: This preliminary study was a randomized, double-blinded, placebo-controlled clinical trial designed to test the safety and efficacy of the GLP-1RA, liraglutide, in 40 participants in residential treatment for OUD. Along with taking a range of safety measures, efficacy for cue-induced craving was evaluated prior to (Day 1) and following (Day 19) treatment using a Visual Analogue Scale (VAS) in response to a cue reactivity task during functional near-infrared spectroscopy (fNIRS) and for craving. Efficacy of treatment for ambient craving was assessed using Ecological Momentary Assessment (EMA) prior to (Study Day 1), across (Study Days 2–19), and following (Study Days 20–21) residential treatment. Discussion: This manuscript describes a protocol to collect clinical data on the safety and efficacy of a GLP-1RA, liraglutide, during residential treatment of persons with OUD, laying the groundwork for further evaluation in a larger, outpatient OUD population. Improved understanding of innovative, non-opioid based treatments for OUD will have the potential to inform community-based interventions and health policy, assist physicians and health care professionals in the treatment of persons with OUD, and to support individuals with OUD in their effort to live a healthy life. Trial registration: ClinicalTrials.gov: NCT04199728. Registered 16 December 2019, https://clinicaltrials.gov/study/NCT04199728?term=NCT04199728. Protocol Version: 10 May 2023 [ABSTRACT FROM AUTHOR]

Infrared images are susceptible to interference from a variety of factors during acquisition and transmission, resulting in the inclusion of mixed noise, which seriously affects the accuracy of subsequent vision tasks. To solve this problem, we designed a mixed noise removal algorithm for infrared images based on improved CycleGAN. First, we proposed a ResNet-E Block that incorporates the EMA (Efficient Multi-Scale Attention Module) and build a generator based on it using the skip-connection structure to improve the network's ability to remove mixed noise of different strengths. Second, we added the PSNR (Peak Signal-to-Noise Ratio) as an extra calculation item of cycle consistency loss, so that the network can effectively retain the detailed information of infrared images while denoising. Finally, we conducted experimental validation on both synthetic noisy images and real noisy images, which proved that our algorithm can effectively remove the mixed noise in infrared images and the denoising effect is better than other similar methods. [ABSTRACT FROM AUTHOR]

Background: Depression is characterized by abnormalities in emotional processing, but the specific drivers of such emotional abnormalities are unknown. Computational work indicates that both surprising outcomes (prediction errors; PEs) and outcomes (values) themselves drive emotional responses, but neither has been consistently linked to affective disturbances in depression. As a result, the computational mechanisms driving emotional abnormalities in depression remain unknown. Methods: Here, in 687 individuals, one-third of whom qualify as depressed via a standard self-report measure (the PHQ-9), we use high-stakes, naturalistic events – the reveal of midterm exam grades – to test whether individuals with heightened depression display a specific reduction in emotional response to positive PEs. Results: Using Bayesian mixed effects models, we find that individuals with heightened depression do not affectively benefit from surprising, good outcomes – that is, they display reduced affective responses to positive PEs. These results were highly specific: effects were not observed to negative PEs, value signals (grades), and were not related to generalized anxiety. This suggests that the computational drivers of abnormalities in emotion in depression may be specifically due to positive PE-based emotional responding. Conclusions: Affective abnormalities are core depression symptoms, but the computational mechanisms underlying such differences are unknown. This work suggests that blunted affective reactions to positive PEs are likely mechanistic drivers of emotional dysregulation in depression. [ABSTRACT FROM AUTHOR]

This research is based on three fundamental aspects of successful biosimilar development in the challenging biopharmaceutical market. First, biosimilar regulations in eight selected countries: Japan, South Korea, the United States, Canada, Brazil, Argentina, Australia, and South Africa, represent the four continents. The regulatory aspects of the countries studied are analyzed, highlighting the challenges facing biosimilars, including their complex approval processes and the need for standardized regulatory guidelines. There is an inconsistency depending on whether the biosimilar is used in a developed or developing country. In the countries observed, biosimilars are considered excellent alternatives to patent-protected biological products for the treatment of chronic diseases. In the second aspect addressed, various analytical AI modeling methods (such as machine learning tools, reinforcement learning, supervised, unsupervised, and deep learning tools) were analyzed to observe patterns that lead to the prevalence of biosimilars used in cancer to model the behaviors of the most prominent active compounds with spectroscopy. Finally, an analysis of the use of active compounds of biosimilars used in cancer and approved by the FDA and EMA was proposed. [ABSTRACT FROM AUTHOR]

Cell and gene therapy poses evolving challenges. The current article summarizes the discussions held by European Regional Committee of the International Society for Cell & Gene Therapy and the European Society for Blood and Marrow Transplantation (EBMT) on the current challenges in this field, focusing on the European setting. This article emphasizes the imperative assessment of real-world cell and gene therapy activity, advocating for expanded registries beyond hematopoietic transplantation and chimeric antigen receptor–T-cell therapy. Accreditation's role in ensuring standardized procedures, as exemplified by JACIE (The Joint Accreditation Committee of ISCT-Europe and EBMT), is crucial for safety. Access to commercial products and reimbursement variations among countries underscore the need for uniform access to advanced therapy medical products (ATMPs). Academic product development and point-of-care manufacturing face barriers to patient access. Hospital Exemption's potential, demonstrated by some initial experiences, may increase patient accessibility in individual situations. Regulatory challenges, including the ongoing European ATMPs legislation review, necessitate standardized criteria for Hospital Exemption and mandatory reporting within registries. Efforts to combat unproven therapies and fraud involve collaboration between scientific societies, regulatory bodies and patient groups. Finally, is important to highlight the vital role of education and workforce development in meeting the escalating demand for specialized professionals in the ATMP field. Collaboration among scientific societies, academic institutions, industry, regulatory bodies and patient groups is crucial for overcoming all these challenges to increase gene and cell therapy activity in Europe. [ABSTRACT FROM AUTHOR]

Experience sampling (ES)—also referred to as ecological momentary assessment (EMA)—is a data collection method that involves asking study participants to report on their thoughts, feelings, behaviors, activities, and environments in (or near) real time. ES/EMA is typically administered using an intensive longitudinal design (repeated assessments within and across days). Although use of ES/EMA is widespread in psychology and health sciences, uptake of the method among sociologists has been limited. We argue that ES/EMA offers key advantages for the investigation of sociologically relevant phenomena, particularly in light of recent disciplinary emphasis on investigating the everyday mechanisms through which social structures and micro (individual and relational) processes are mutually constitutive. We describe extant and potential research applications illustrating the advantages of ES/EMA regarding enhanced validity, illuminating micro-temporal processes, and the potential for linkage with spatially and temporally referenced data sources. We also consider methodological challenges facing sociological research using ES/EMA. [ABSTRACT FROM AUTHOR]

In the infrared imaging scene where the target is at a long distance and the background is cluttered, due to the interference of noise and background texture information, the infrared image is prone to problems such as low contrast between the target and the background, and feature confusion, which makes it difficult to accurately extract and detect the target. To solve this problem, firstly, the infrared image is enhanced by combining DDE and MSR algorithm to improve the contrast and detail visibility of the image. For the RT-DETR network structure, the EMA attention mechanism is introduced into the backbone to enhance the feature extraction ability of the model by extracting context information. The CAMixing convolutional attention module is introduced into CCFM, and the multi-scale convolutional self-attention mechanism is introduced to focus on local information and enhance the detection ability of small targets. The filtering rules of the prediction box are improved, combined with Shape-IoU, and the convergence speed of the loss function in the detection and the detection accuracy of small targets are improved by paying attention to the influence of the intrinsic properties of the bounding box itself on the regression. In the experiment, the infrared weak target image dataset of the National University of Defense Technology was selected, labeled and trained. Experimental results show that compared with the original DETR algorithm, the average precision of the improved algorithm (mAP) is increased by 3.2%, and it can effectively detect infrared weak and small targets in different complex backgrounds, which reflects good robustness and adaptability, and can be effectively applied to infrared weak and small target detection in complex backgrounds. [ABSTRACT FROM AUTHOR]

The perception system in autonomous driving mainly uses object detection algorithms to obtain the distribution of obstacles for recognition and analysis. Current object detection algorithms have rapidly developed, but it is challenging to balance the requirements of real-time detection and high detection accuracy in actual application scenarios. To solve the above problems, this paper uses YOLOv8n as the baseline model and proposes an object detection network named SES-YOLOv8n. Firstly, the SPPF module in the network was replaced by the SPPCSPC module to enhance further the model's fusion ability under feature maps of different scales. The efficient multi-scale attention module EMA is introduced into the C2F module of the backbone network, which improves the perception ability in critical areas and the efficiency of feature extraction. Finally, the SPD-Conv module is used to replace part of the convolution modules in the backbone network to replace the downsampling operation, which can more effectively retain the feature information and improve the network's accuracy and learning ability. Experimental results on the KITTI dataset and BDD100K dataset show that the average accuracy of the improved network model reaches 92.7% and 41.9%, which is 3.4% and 5.0% higher than that of the baseline model and is significantly better than the baseline model. This model can realize real-time image processing in general scenes based on ensuring high detection accuracy. [ABSTRACT FROM AUTHOR]

The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network's attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate whether focusing on positive listening experiences improves hearing aid outcomes in experienced hearing aid users. The participants were randomised into a control or positive focus (PF) group. At the first laboratory visit, the Client-Oriented Scale of Improvement (COSI) questionnaire was administered followed by hearing aid fitting. The participants wore the hearing aids for three weeks. The PF group was asked to report their positive listening experiences via an app. During the third week, all the participants answered questionnaires related to hearing aid benefit and satisfaction. This was followed by the second laboratory visit where the COSI follow-up questionnaire was administered. Ten participants were included in the control and eleven in the PF group. Hearing aid outcome ratings were significantly better in the PF group in comparison to the control group. Further, COSI degree of change and the number of positive reports were positively correlated. These results point to the importance of asking hearing aid users to focus on positive listening experiences and talk about them. The potential outcome is increased hearing aid benefit and satisfaction which could lead to more consistent use of the devices. [ABSTRACT FROM AUTHOR]

In 2023, seventy novel drugs received market authorization for the first time in either Europe (by the EMA and the MHRA) or in the United States (by the FDA). Confirming a steady recent trend, more than half of these drugs target rare diseases or intractable forms of cancer. Thirty drugs are categorized as "first‐in‐class" (FIC), illustrating the quality of research and innovation that drives new chemical entity discovery and development. We succinctly describe the mechanism of action of most of these FIC drugs and discuss the therapeutic areas covered, as well as the chemical category to which these drugs belong. The 2023 novel drug list also demonstrates an unabated emphasis on polypeptides (recombinant proteins and antibodies), Advanced Therapy Medicinal Products (gene and cell therapies) and RNA therapeutics, including the first‐ever approval of a CRISPR‐Cas9‐based gene‐editing cell therapy. [ABSTRACT FROM AUTHOR]

In the last decades, electromechanical actuators started to be introduced in transport aircraft for primary and secondary flight control surfaces. Some innovative architectures have been proposed in the literature to use these actuators for high-lift devices (flaps and slats). The state-of-the-art architecture is built with a central mechanical shaft powered by a power distribution unit connected to ballscrew actuators that actuate the flap and slat surfaces. New innovative concepts have the potential to improve the state-of-the-art architectures. However, there is a lack of quantitative results for such innovative architectures. A new methodology is proposed to preliminarily estimate performance and reliability aspects of conventional and innovative architectures. This allows quantitative comparisons to finally be obtained. The methodology is applied to a new architecture that uses electromechanical actuators for flaps and slats, providing results in terms of performance and reliability and comparing them to the current state-of-the-art high-lift devices. Results show that the new architecture is lighter than the reference one and can be more reliable. This is achieved thanks to the removal of the mechanical links among components, which allows each control surface to be deployed independently from the others. This highly increases the operational reliability of the system. Two cases are analyzed, with and without actuator jamming. This provides more realistic results since this failure mode is currently the main reason why electromechanical actuators are not being used for more applications. The innovative architecture outperforms the conventional one in the case where the electromechanical actuators are not affected by the jamming failure mode. [ABSTRACT FROM AUTHOR]

Structural damage in the steel bridge anchorage, if not diagnosed early, could pose a severe risk of structural collapse. Previous studies have mainly focused on diagnosing prestress loss as a specific type of damage. This study is among the first for the automated identification of multiple types of anchorage damage, including strand damage and bearing plate damage, using deep learning combined with the EMA (electromechanical admittance) technique. The proposed approach employs the 1D CNN (one-dimensional convolutional neural network) algorithm to autonomously learn optimal features from the raw EMA data without complex transformations. The proposed approach is validated using the raw EMA response of a steel bridge anchorage specimen, which contains substantial nonlinearities in damage characteristics. A K-fold cross-validation approach is used to secure a rigorous performance evaluation and generalization across different scenarios. The method demonstrates superior performance compared to established 1D CNN models in assessing multiple damage types in the anchorage specimen, offering a potential alternative paradigm for data-driven damage identification in steel bridge anchorages. [ABSTRACT FROM AUTHOR]

Body checking is common among individuals with anorexia nervosa (AN) and increases risk for dietary restriction. However, no study has examined whether body checking increases the immediate risk for engaging in other harmful weight loss behaviors, or whether this relationship is moderated by person-level traits. The current study utilized ecological momentary assessment (EMA) to examine whether (a) body checking predicted rapid use of weight loss behaviors, and (b) whether eating-related obsessionality/compulsivity moderated this relationship. Women with full or subthreshold anorexia nervosa (N = 118) completed a measure of eating-related obsessionality/compulsivity at baseline, followed by a 14-day EMA protocol during which they reported on body checking and weight loss behaviors (i.e., exercise, self-induced vomiting, laxative use, skipping meals, and increasing fluid intake). In a series of generalized linear mixed models, within-person effects indicated that momentary body checking significantly predicted subsequent meal skipping and using fluids to curb appetite. Between-person effects indicated that individuals who engage in more frequent body checking also engage in a higher frequency of self-induced vomiting, meal skipping, and use of fluids to curb appetite. An individuals degree of eating-related obsessionality/compulsivity did not moderate any of these relationships. Findings highlight body checking as an immediate precursor of dangerous weight loss behaviors among individuals with AN, and underscore the need for clinicians to address body checking during treatment.

The purpose of this study was to examine real-time associations between reflective (i.e., state motivation), regulatory (i.e., self-control), and reflexive (i.e., habit) constructs from the Multi-Process Action Control (M-PAC) model and real-time moderate-to-vigorous physical activity (MVPA) behaviour among adolescents using ecological momentary assessments. One hundred and ninety adolescents (Mage = 15.76 ± .47 years; n = 101 males) wore an accelerometer for seven consecutive days and responded to digital survey prompts up to four times daily during the after-school periods. MVPA in the 60-minute time window following each survey prompt was recorded. Multilevel mixed-effects linear and logistic models were computed with disaggregated between- and within-person effects to analyze the data. Results from both linear and logistic multilevel models revealed adolescents with higher state motivation in general and experiencing higher state motivation than one’s typical levels were associated with engaging in more MVPA and higher likelihood of engaging in ≥10 minutes of MVPA. Engaging in activities less consistent with habitual behaviours were associated with more MVPA and higher likelihood of engaging in ≥10 minutes of MVPA. By contrast, self-control was not associated with MVPA. Results from this study extend previous work demonstrating the importance of conscious and non-conscious processes on MVPA behaviour by examining associations in real-time using intensive longitudinal methods. Collectively, this study provides partial support for use of the M-PAC framework to explain acute MVPA among adolescents in real-time and natural contexts.

In today’s complex financial markets, “Algorithmic Trading” has become very important. The study delves into the amalgamation of four pivotal indicators - Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) to create and develop a potent trading strategy. Through intensive backtesting and parameter tuning, our study demonstrates 60.63 % profitable trades on the National Stock Exchange (NSE), India, surpassing the standalone indicators. The Weapon Candle Strategy created using the four indicators presents its efficiency as it was able to achieve a profit factor of 1.882. This suggests that when these four technical indicators combined to make a strategy, it can provide significantly more accurate and reliable trading signals compared to using a combination of two or three indicators. Algorithmic traders should use a multi-indicator approach to achieve a more comprehensive understanding of the market and make informed trading decisions.

Background Withdrawals of drug indications may reveal potential inadequacies in the regulatory approval processes of new drugs. Understanding potential weaknesses of the regulatory approval process is paramount given the increasing use of expedited pathways. In this paper, we focus on three poly-ADP-ribose polymerase inhibitors (olaparib, rucaparib and niraparib) for the treatment of women with heavily pretreated, recurrent ovarian cancer, which were eventually withdrawn.Methods We use a comparative case study approach to evaluate the regulatory histories of these drug indications in the US and Europe.Results Two drug indications benefited from the FDA’s accelerated approval pathway, which explicitly lowers the bar for evidence of efficacy at the time of approval. Following accelerated approval, manufacturers are mandated to conduct post-marketing studies to confirm clinical benefit. The FDA granted accelerated approval to olaparib and rucaparib based on data on surrogate endpoints and converted the approval to regular approval after the submission of additional data on surrogate endpoints from one of two required confirmatory trials, that is, without data on clinical benefit. Niraparib directly received regular approval based only on data on a surrogate endpoint. By contrast, the EMA granted conditional marketing authorisation to rucaparib and was quicker to restrict usage than the FDA.Conclusion The regulatory histories of these drug indications highlight the need to reform the accelerated approval pathway by ensuring that post-marketing requirements are followed, and that regular approval is only based on evidence of clinical benefit.