Sex-dependent effects of chronic intermittent hypoxia: implication for obstructive sleep apnea.

BACKGROUND: Obstructive sleep apnea (OSA) affects 10-26% of adults in the United States with known sex differences in prevalence and severity. OSA is characterized by elevated inflammation, oxidative stress (OS), and cognitive dysfunction. However, there is a paucity of data regarding the role of sex in the OSA phenotype. Prior findings suggest women exhibit different OSA phenotypes than men, which could result in under-reported OSA prevalence in women. To examine the relationship between OSA and sex, we used chronic intermittent hypoxia (CIH) to model OSA in rats. We hypothesized that CIH would produce sex-dependent phenotypes of inflammation, OS, and cognitive dysfunction, and these sex differences would be dependent on mitochondrial oxidative stress (mtOS). METHODS: Adult male and female Sprague Dawley rats were exposed to CIH or normoxia for 14 days to examine the impact of sex on CIH-associated circulating inflammation (IL-1ß, IL-6, IL-10, TNF-α), circulating steroid hormones, circulating OS, and behavior (recollective and spatial memory; gross and fine motor function; anxiety-like behaviors; and compulsive behaviors). Rats were implanted with osmotic minipumps containing either a mitochondria-targeting antioxidant (MitoTEMPOL) or saline vehicle 1 week prior to CIH initiation to examine how inhibiting mtOS would affect the CIH phenotype. RESULTS: Sex-specific differences in CIH-induced inflammation, OS, motor function, and compulsive behavior were observed. In female rats, CIH increased inflammation (plasma IL-6 and IL-6/IL-10 ratio) and impaired fine motor function. Conversely, CIH elevated circulating OS and compulsivity in males. These sex-dependent effects of CIH were blocked by inhibiting mtOS. Interestingly, CIH impaired recollective memory in both sexes but these effects were not mediated by mtOS. No effects of CIH were observed on spatial memory, gross motor function, or anxiety-like behavior, regardless of sex. CONCLUSIONS: Our results indicate that the impact of CIH is dependent on sex, such as an inflammatory response and OS response in females and males, respectively, that are mediated by mtOS. Interestingly, there was no effect of sex or mtOS in CIH-induced impairment of recollective memory. These results indicate that mtOS is involved in the sex differences observed in CIH, but a different mechanism underlies CIH-induced memory impairments.

Sleep apnea is a common sleeping condition in adults with a wide range of symptoms that include inflammation, oxidative stress, memory problems, anxiety, and compulsivity. Men are diagnosed with sleep apnea more often than women. Although there is limited information on how sleep apnea affects men and women differently, previous studies suggest that women may exhibit different sleep apnea symptoms than men. To examine the impact of male and female sex on common sleep apnea symptoms, we exposed adult male and female rats to a model of sleep apnea called chronic intermittent hypoxia (CIH). We found that many effects of CIH were different in males and females. CIH females had increased inflammation and motor problems, whereas CIH males had increased oxidative stress and compulsivity. To investigate the reason for these CIH sex differences, we blocked mitochondrial oxidative stress. Blocking mitochondrial oxidative stress decreased CIH associated sex differences. However, blocking mitochondrial oxidative stress had no impact on CIH-induced memory impairment that was observed in male and female rats. Our findings support previous reports that suggest that women exhibit different sleep apnea symptoms than men. Further, we extend these findings by showing that mitochondrial oxidative stress is involved in these sex differences. Clinically, patients diagnosed with sleep apnea are typically treated with continuous positive airway pressure (CPAP) machines, which have high rates of non-compliance (15­40%). Therefore, understanding why sleep apnea is causing these symptoms will be important in developing therapeutics.

Removal of an Atypical Foreign Body Using Flexible Bronchoscopy Under Local Anesthesia.

While uncommon among adults, the act of inhaling a foreign object is a grave incident that might potentially endanger one's life or result in substantial repercussions. A 43-year-old patient with a history of asthma and epilepsy from infancy appeared with worsening respiratory distress and the presence of purulent secretions one week following an epileptic seizure. The chest X-rays and abdominal ultrasound revealed no anomalies. A bronchoscopy performed with local anesthesia enabled clear vision of the foreign object, and its removal was successfully executed, eliminating the need for a more invasive procedure. Bronchoscopy is crucial for both diagnosis and treatment, particularly in cases where there is a suspicion of inhalation of a foreign object that cannot be seen on X-rays. However, X-rays can still be useful for detecting radiopaque foreign objects or for identifying indirect symptoms of their existence.

Deep learning-assisted concentration gradient generation for the study of 3D cell cultures in hydrogel beads of varying stiffness.

The study of dose-response relationships underpins analytical biosciences. Droplet microfluidics platforms can automate the generation of microreactors encapsulating varying concentrations of an assay component, providing datasets across a large chemical space in a single experiment. A classical method consists in varying the flow rate of multiple solutions co-flowing into a single microchannel (producing different volume fractions) before encapsulating the contents into water-in-oil droplets. This process can be automated through controlling the pumping elements but lacks the ability to adapt to unpredictable experimental scenarios, often requiring constant human supervision. In this paper, we introduce an image-based, closed-loop control system for assessing and adjusting volume fractions, thereby generating unsupervised, uniform concentration gradients. We trained a shallow convolutional neural network to assess the position of the laminar flow interface between two co-flowing fluids and used this model to adjust flow rates in real-time. We apply the method to generate alginate microbeads in which HEK293FT cells could grow in three dimensions. The stiffnesses ranged from 50 Pa to close to 1 kPa in Young modulus and were encoded with a fluorescent marker. We trained deep learning models based on the YOLOv4 object detector to efficiently detect both microbeads and multicellular spheroids from high-content screening images. This allowed us to map relationships between hydrogel stiffness and multicellular spheroid growth.

A pixel-wise labelled dataset of Moroccan aircraft emergency landing sites for semantic segmentation applications.

Detecting emergency aircraft landing sites is crucial for ensuring passenger and crew safety during unexpected forced landings caused by factors like engine malfunctions, adverse weather, or other aviation emergencies. In this article, we present a dataset consisting of Google Maps images with their corresponding masks, specifically crafted with manual annotations of emergency aircraft landing sites, distinguishing between safe areas with suitable conditions for emergency landings and unsafe areas presenting hazardous conditions. Drawing on detailed guidelines from the Federal Aviation Administration, the annotations focus on key features such as slope, surface type, and obstacle presence, with the goal of pinpointing appropriate landing areas. The proposed dataset has 4180 images, with 2090 raw images accompanied by their corresponding annotation instances. This dataset employs a semantic segmentation approach, categorizing the image pixels into two "Safe" and "Unsafe" classes based on authenticated terrain-specific attributes, thereby offering a nuanced understanding of the viability of various landing sites in emergency scenarios.

Beyond visual integration: sensitivity of the temporal-parietal junction for objects, places, and faces.

One important role of the TPJ is the contribution to perception of the global gist in hierarchically organized stimuli where individual elements create a global visual percept. However, the link between clinical findings in simultanagnosia and neuroimaging in healthy subjects is missing for real-world global stimuli, like visual scenes. It is well-known that hierarchical, global stimuli activate TPJ regions and that simultanagnosia patients show deficits during the recognition of hierarchical stimuli and real-world visual scenes. However, the role of the TPJ in real-world scene processing is entirely unexplored. In the present study, we first localized TPJ regions significantly responding to the global gist of hierarchical stimuli and then investigated the responses to visual scenes, as well as single objects and faces as control stimuli. All three stimulus classes evoked significantly positive univariate responses in the previously localized TPJ regions. In a multivariate analysis, we were able to demonstrate that voxel patterns of the TPJ were classified significantly above chance level for all three stimulus classes. These results demonstrate a significant involvement of the TPJ in processing of complex visual stimuli that is not restricted to visual scenes and that the TPJ is sensitive to different classes of visual stimuli with a specific signature of neuronal activations.

MBAN: multi-branch attention network for small object detection.

Recent years small object detection has seen remarkable advancement. However, small objects are difficult to accurately detect in complex scenes due to their low resolution. The downsampling operation inevitably leads to the loss of information for small objects. In order to solve these issues, this article proposes a novel Multi-branch Attention Network (MBAN) to improve the detection performance of small objects. Firstly, an innovative Multi-branch Attention Module (MBAM) is proposed, which consists of two parts, i.e. Multi-branch structure consisting of convolution and maxpooling, and the parameter-free SimAM attention mechanism. By combining these two parts, the number of network parameters is reduced, the information loss of small objects is reduced, and the representation of small object features is enhanced. Furthermore, to systematically solve the problem of small object localization, a pre-processing method called Adaptive Clustering Relocation (ACR) is proposed. To validate our network, we conducted extensive experiments on two benchmark datasets, i.e. NWPU VHR-10 and PASCAL VOC. The findings from the experiment demonstrates the significant performance gains of MBAN over most existing algorithms, the mAP of MBAN achieved 96.55% and 84.96% on NWPU VHR-10 and PASCAL VOC datasets, respectively, which proves that MBAN has significant performance in small object detection.

Towards a GDPR-compliant cloud architecture with data privacy controlled through sticky policies.

Data privacy is one of the biggest challenges facing system architects at the system design stage. Especially when certain laws, such as the General Data Protection Regulation (GDPR), have to be complied with by cloud environments. In this article, we want to help cloud providers comply with the GDPR by proposing a GDPR-compliant cloud architecture. To do this, we use model-driven engineering techniques to design cloud architecture and analyze cloud interactions. In particular, we develop a complete framework, called MDCT, which includes a Unified Modeling Language profile that allows us to define specific cloud scenarios and profile validation to ensure that certain required properties are met. The validation process is implemented through the Object Constraint Language (OCL) rules, which allow us to describe the constraints in these models. To comply with many GDPR articles, the proposed cloud architecture considers data privacy and data tracking, enabling safe and secure data management and tracking in the context of the cloud. For this purpose, sticky policies associated with the data are incorporated to define permission for third parties to access the data and track instances of data access. As a result, a cloud architecture designed with MDCT contains a set of OCL rules to validate it as a GDPR-compliant cloud architecture. Our tool models key GDPR points such as user consent/withdrawal, the purpose of access, and data transparency and auditing, and considers data privacy and data tracking with the help of sticky policies.

[Identifying Novel Coronavirus Pneumonia With CT Images: A Deep Learning Approach With Detail Upsampling and Attention Guidance]. / 从CTå›¾åƒä¸­æ£€æµ‹æ–°åž‹å† çŠ¶ç— æ¯’æ„ŸæŸ“å¯¼è‡´çš„è‚ºç‚Žï¼šä¸€ç§ç»†èŠ‚ä¸Šé‡‡æ ·å’Œæ³¨æ„åŠ›å¼•å¯¼çš„æ·±åº¦å­¦ä¹ æ–¹æ³•.

Objective: To construct a deep learning-based target detection method to help radiologists perform rapid diagnosis of lesions in the CT images of patients with novel coronavirus pneumonia (NCP) by restoring detailed information and mining local information. Methods: We present a deep learning approach that integrates detail upsampling and attention guidance. A linear upsampling algorithm based on bicubic interpolation algorithm was adopted to improve the restoration of detailed information within feature maps during the upsampling phase. Additionally, a visual attention mechanism based on vertical and horizontal spatial dimensions embedded in the feature extraction module to enhance the capability of the object detection algorithm to represent key information related to NCP lesions. Results: Experimental results on the NCP dataset showed that the detection method based on the detail upsampling algorithm improved the recall rate by 1.07% compared with the baseline model, with the AP50 reaching 85.14%. After embedding the attention mechanism in the feature extraction module, 86.13% AP50, 73.92% recall, and 90.37% accuracy were achieved, which were better than those of the popular object detection models. Conclusion: The feature information mining of CT images based on deep learning can further improve the lesion detection ability. The proposed approach helps radiologists rapidly identify NCP lesions on CT images and provides an important clinical basis for early intervention and high-intensity monitoring of NCP patients.

How Sharp Objects Injuries Impact our Healthcare Workers: Unveiling Perspective and Preventive Imperatives.

Introduction Sharp object injuries in the medical field present a considerable occupational hazard for healthcare workers (HCWs), encompassing a spectrum of consequences from immediate discomfort to enduring health consequences. These injuries may expose HCWs to potential infections. Despite efforts to control sharp object injuries in healthcare environments, they are present at every stage involving using or disposing of medical sharp instruments. In Jordan, limited research has focused on sharp object injuries, with most data included from studies concentrating on practicing nurses or nursing students. Consequently, further research is necessary to comprehend the causes behind the high sharp object injury rate and the insufficient knowledge of safety practices and preventive guidelines. Objectives This study was conducted to investigate the impact of sharp object injuries on HCWs, underlying causes, and potential consequences causes of needlestick injuries. To highlight perspective and preventive imperatives. Methods and patients This retrospective institutional-based cross-sectional chart analysis was conducted by reviewing all sharp object injuries report sheets and extracting data directly from these reports for analysis. The study encompassed all reported cases occurring between 2018 and 2023. All the participants' data handling was accomplished according to the Declaration of Helsinki (2013) and the Health Insurance Portability and Accountability (HIPAA) Acts.  Results A total of 146 self-reported hospital workers were included in the study. Within the final cohort, 52.73% of the participants were male (77/146), with an average age at diagnosis of 38.6±7.87 years (ranging from 20 to 52 years). Conversely, females comprised 47.27% of the cohort population (69/146) and had an average age at diagnosis of 34.73±6.73 years (ranging from 19 to 47 years). The age group 20-29 years was the most prominent age group, statistical analysis of age and gender data revealed significant differences. The overall prevalence of sharp object injuries was 11.83%, indicating that a sizable portion of HCWs is at risk of exposure to bloodborne pathogens. Among the different professional categories, Physicians constituted the majority of sharp object injuries reported victims in 41 cases (28.08%), followed by nurses in 38 cases (26.02%). Statistical analysis of the profession's data revealed significant differences (P<0.001). Notably, sharp object injuries were most reported in wards. The leading procedures that caused sharp object injuries were identified as during needle recapping in 53 instances (36.30%), then followed by medical waste treatment in 32 cases (21.92%). The left hand was the most affected body part, reported in 83 cases (56.84%). All injured individuals reported the incident promptly. No seroconversions were documented within the reviewed cases during the study period. Conclusion Injuries caused by sharp objects persist as a significant danger for hospital employees, posing immediate harm and long-term health risks linked to bloodborne pathogens. The findings stress the continuous responsibility of healthcare institutions to prioritize staff safety by addressing the root causes of sharp object injuries and fostering reporting and prevention cultures. Underreporting reasons are diverse, encompassing factors like time constraints, fear of consequences, and the misconception of injury insignificance.

ESF-YOLO: an accurate and universal object detector based on neural networks.

As an excellent single-stage object detector based on neural networks, YOLOv5 has found extensive applications in the industrial domain; however, it still exhibits certain design limitations. To address these issues, this paper proposes Efficient Scale Fusion YOLO (ESF-YOLO). Firstly, the Multi-Sampling Conv Module (MSCM) is designed, which enhances the backbone network's learning capability for low-level features through multi-scale receptive fields and cross-scale feature fusion. Secondly, to tackle occlusion issues, a new Block-wise Channel Attention Module (BCAM) is designed, assigning greater weights to channels corresponding to critical information. Next, a lightweight Decoupled Head (LD-Head) is devised. Additionally, the loss function is redesigned to address asynchrony between labels and confidences, alleviating the imbalance between positive and negative samples during the neural network training. Finally, an adaptive scale factor for Intersection over Union (IoU) calculation is innovatively proposed, adjusting bounding box sizes adaptively to accommodate targets of different sizes in the dataset. Experimental results on the SODA10M and CBIA8K datasets demonstrate that ESF-YOLO increases Average Precision at 0.50 IoU (AP50) by 3.93 and 2.24%, Average Precision at 0.75 IoU (AP75) by 4.77 and 4.85%, and mean Average Precision (mAP) by 4 and 5.39%, respectively, validating the model's broad applicability.

Adaptive under-sampling strategy for fast imaging in compressive sensing-based atomic force microscopy.

Compressive sensing (CS) can reconstruct the rest information almost without distortion by advanced computational algorithm, which significantly simplifies the process of atomic force microscope (AFM) scanning with high imaging quality. In common CS-AFM, the partial measurements randomly come from the whole region to be measured, which easily leads to detail loss and poor image quality in regions of interest (ROIs). Consequently, important microscopic phenomena are missed probably. In this paper, we developed an adaptive under-sampling strategy for CS-AFM to optimize the process of sampling. Under a certain under-sampling ratio, the weight coefficient of ROIs and regions of base (ROBs) were set to control the distribution of under-sampling points and corresponding measurement matrix. A series of simulations were completed to demonstrate the relationship between the weight coefficient of ROIs and image quality. After that, we verified the effectiveness of the method on our homemade AFM. Through a lot of simulations and experiments, we demonstrated how the proposed method optimized the sampling process of CS-AFM, which speeded up the process of AFM imaging with high quality.

A neurocomputational model of decision and confidence in object recognition task.

How does the brain process natural visual stimuli to make a decision? Imagine driving through fog. An object looms ahead. What do you do? This decision requires not only identifying the object but also choosing an action based on your decision confidence. In this circumstance, confidence is making a bridge between seeing and believing. Our study unveils how the brain processes visual information to make such decisions with an assessment of confidence, using a model inspired by the visual cortex. To computationally model the process, this study uses a spiking neural network inspired by the hierarchy of the visual cortex in mammals to investigate the dynamics of feedforward object recognition and decision-making in the brain. The model consists of two modules: a temporal dynamic object representation module and an attractor neural network-based decision-making module. Unlike traditional models, ours captures the evolution of evidence within the visual cortex, mimicking how confidence forms in the brain. This offers a more biologically plausible approach to decision-making when encountering real-world stimuli. We conducted experiments using natural stimuli and measured accuracy, reaction time, and confidence. The model's estimated confidence aligns remarkably well with human-reported confidence. Furthermore, the model can simulate the human change-of-mind phenomenon, reflecting the ongoing evaluation of evidence in the brain. Also, this finding offers decision-making and confidence encoding share the same neural circuit.

Vaginal foreign body in the pediatric patient: A systematic review.

BACKGROUND: Vaginal foreign bodies represent a clinical and diagnostical challenge in pediatric gynecology. Several case reports, case series and retrospective studies have been published, highlighting rare or complex cases. A comprehensive systematic review is lacking. METHODS: Published English-language articles on vaginal foreign objects in patients aged 16 years and younger, with full-text availability were included. Articles on adult patients and patients with an object migrating from the abdominal cavity into the vagina were excluded. RESULTS: Out of the 215 screened articles 75 were included, comprising a total of 522 patients. The age ranged from 6 months to 16 years, with an average of 6 years and 3 months. The presenting symptoms were documented in 340 patients, with the two most common being vaginal bleeding (n = 172) and vaginal discharge (n = 134). Toilet paper or tissue was the most common object, in 155 out of 447 patients. Ultrasonography was the most utilized diagnostic method, with a sensitivity of 79.9 %. Radiography showed more false-negative than true-positive results, with a sensitivity of 33.3 %. Complications were reported in 35 patients. Evidence of sexual abuse was found in a small group of 16 patients. Vaginoscopy under sedation was the most frequently used therapeutic approach. CONCLUSION: A swift and accurate diagnosis is crucial, with clinical examination and ultrasonography playing pivotal roles. Vaginoscopy is the gold standard for definitive diagnosis and therapy. Attention should be given to a potential context of sexual abuse.

How small changes to one eye's retinal image can transform the perceived shape of a very familiar object.

Vision can provide useful cues about the geometric properties of an object, like its size, distance, pose, and shape. But how the brain merges these properties into a complete sensory representation of a three-dimensional object is poorly understood. To address this gap, we investigated a visual illusion in which humans misperceive the shape of an object due to a small change in one eye's retinal image. We first show that this illusion affects percepts of a highly familiar object under completely natural viewing conditions. Specifically, people perceived their own rectangular mobile phone to have a trapezoidal shape. We then investigate the perceptual underpinnings of this illusion by asking people to report both the perceived shape and pose of controlled stimuli. Our results suggest that the shape illusion results from distorted cues to object pose. In addition to yielding insights into object perception, this work informs our understanding of how the brain combines information from multiple visual cues in natural settings. The shape illusion can occur when people wear everyday prescription spectacles; thus, these findings also provide insight into the cue combination challenges that some spectacle wearers experience on a regular basis.

Deep learning techniques to detect rail indications from ultrasonic data for automated rail monitoring and maintenance.

The increasing number of passengers and services using railways and the corresponding increase in rail use has caused the acceleration of rail wear and surface defects which makes rail defect identification an important issue for rail maintenance and monitoring to ensure safe and efficient operation. Traditional visual inspection methods for identifying rail defects are time-consuming, less accurate, and associated with human errors. Deep learning has been used to improve railway maintenance and monitoring tasks. This study aims to develop a structured model for detecting railway artifacts and defects by comparing different deep-learning models using ultrasonic image data. This research showed whether it is practical to identify rail indications using image classification and object detection techniques from ultrasonic data and which model performs better among the above-mentioned methods. The methodology includes data processing, labeling, and using different conventional neural networks to develop the model for both image classification and object detection. The results of CNNs for image classification, and YOLOv5 for object detection show 98%, and 99% accuracy respectively. These models can identify rail artifacts efficiently and accurately in real-life scenarios, which can improve automated railway infrastructure monitoring and maintenance.

Detection and Recognition of the Invasive species, Hylurgus ligniperda, in Traps, Based on a Cascaded Convolution Neural Network.

BACKGROUND: Hylurgus ligniperda, an invasive species originating from Eurasia, is now a major forestry quarantine pest worldwide. In recent years, it has caused significant damage in China. While traps have been effective in monitoring and controlling pests, manual inspections are labor-intensive and require expertise in insect classification. To address this, we applied a two-stage cascade convolutional neural network, YOLOX-MobileNetV2 (YOLOX-Mnet), for identifying H. ligniperda and other pests captured in traps. This method streamlines target and non-target insect detection from trap images, offering a more efficient alternative to manual inspections. RESULTS: Two cascade convolutional neural network models were employed in two stages to detect both target and non-target insects from images captured in the same forest. Initially, You Only Look Once X (YOLOX) served as the target detection model, identifying insects and non-insects from the collected images, with non-insect targets subsequently filtered out. In the second stage, MobileNetV2, a classification network, classified the captured insects. This approach effectively reduced false positives from non-insect objects, enabled the inclusion of additional classification terms for multi-class insect classification models, and utilized sample control strategies to enhance classification performance. CONCLUSION: Application of the cascade convolutional neural network model accurately identified H. ligniperda, and Mean F1-score of all kinds of insects in the trap was 0.98. Compared to traditional insect classification, this method offers great improvement in the identification and early warning of forest pests, as well as provide technical support for the early prevention and control of forest pests. This article is protected by copyright. All rights reserved.

Vision matters for shape representation: Evidence from sculpturing and drawing in the blind.

Shape is a property that could be perceived by vision and touch, and is classically considered to be supramodal. While there is mounting evidence for the shared cognitive and neural representation space between visual and tactile shape, previous research tended to rely on dissimilarity structures between objects and had not examined the detailed properties of shape representation in the absence of vision. To address this gap, we conducted three explicit object shape knowledge production experiments with congenitally blind and sighted participants, who were asked to produce verbal features, 3D clay models, and 2D drawings of familiar objects with varying levels of tactile exposure, including tools, large nonmanipulable objects, and animals. We found that the absence of visual experience (i.e., in the blind group) led to stronger differences in animals than in tools and large objects, suggesting that direct tactile experience of objects is essential for shape representation when vision is unavailable. For tools with rich tactile/manipulation experiences, the blind produced overall good shapes comparable to the sighted, yet also showed intriguing differences. The blind group had more variations and a systematic bias in the geometric property of tools (making them stubbier than the sighted), indicating that visual experience contributes to aligning internal representations and calibrating overall object configurations, at least for tools. Taken together, the object shape representation reflects the intricate orchestration of vision, touch and language.

Crebinostat facilitates memory formation.

Protein modifications importantly contribute to memory formation. Protein acetylation is a post-translational modification of proteins that regulates memory formation. Acetylation level is determined by the relative activities of acetylases and deacetylases. Crebinostat is a histone deacetylase inhibitor. Here we show that in an object recognition task, crebinostat facilitates memory formation by a weak training. Further, this compound enhances acetylation of α-tubulin, and reduces the level of histone deacetylase 6, an α-tubulin deacetylase. The results suggest that enhanced acetylation of α-tubulin by crebinostat contributes to its facilitatory effect on memory formation.

Age-appropriate adaptation of creativity tasks for infants aged 12-24 months.

Creativity is an important skill that relates to innovation, problem-solving and artistic achievement. However, relatively little is known about the early development of creative potential in very young children, in part due to a paucity of tasks suitable for use during infancy. Current measures of creativity in early childhood include the Unusual Box Test, Torrance's Thinking Creatively in Action and Movement (TCAM) task and the Toca Kitchen Monsters task. These tasks are designed for children aged above 12, 36 and 18 months respectively, but very few measures of creativity can be used for infants aged below 2. Accordingly, here we report age-appropriate adaptations of TCAM and Toca Kitchen Monsters tasks for infants as young as 12 to 24 months. Considerations taken into account include (1) infants' cognitive capacities (i.e., attention span, language comprehension skills, motor skills, and approach to play), and (2) practicality of the stimuli, including suitability for use amid the COVID-19 pandemic. The modified creativity battery for infants includes three tasks: Music Play, Object Play and Exploratory Play tasks. The task protocols elaborated in this paper are intended to facilitate studies on the early development of creativity in infants aged between 12 and 24 months. Primary highlights include:•Age-appropriate adaptation of creativity tasks for use with infants aged between 12 and 24 months.•Consideration of infants' cognitive capacities and stimulus practicality.•Innovative use of movement as expression of infants' creative behaviour.

The Motion-Silencing Illusion Depends on Object-Centered Representation.

Motion silencing is a striking and unexplained visual illusion wherein changes that are otherwise salient become difficult to perceive when the changing elements also move. We develop a new method for quantifying illusion strength (Experiments 1a and 1b), and we demonstrate a privileged role for rotational motion on illusion strength compared with highly controlled stimuli that lack rotation (Experiments 2a to 3b). These contrasts make it difficult to explain the illusion in terms of lower-level detection limits. Instead, we explain the illusion as a failure to attribute changes to locations. Rotation exacerbates the illusion because its perception relies upon structured object representations. This aggravates the difficulty of attributing changes by demanding that locations are referenced relative to both an object-internal frame and an external frame. Two final experiments (4a and 4b) add support to this account by employing a synchronously rotating external frame of reference that diminishes otherwise strong motion silencing. All participants were Johns Hopkins University undergraduates.