Single Click Research Articles

A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling.

Brain-computer interfaces (BCIs) can restore communication for movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command click detectors provide a basic yet highly functional capability. We sought to test the performance and long-term stability of click decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis. We trained the participant's click detector using a small amount of training data (<44 min across 4 days) collected up to 21 days prior to BCI use, and then tested it over a period of 90 days without any retraining or updating. Using a click detector to navigate a switch scanning speller interface, the study participant can maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation can interrupt usage of a fixed model, a new click detector can achieve comparable performance despite being trained with even less data (<15 min, within 1 day). These results demonstrate that a click detector can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.

Rapid monitoring of structural deformation based on unsupervised segmentation model

This paper presents a novel approach for rapid structural deformation monitoring using an enhanced segmentation model, SAM-DM (Segment Anything Model-Deformation Monitoring). The method enhances the SAM model by integrating an innovative point prompt generation technique, image fusion, and connected domain analysis, enabling structural deformation monitoring with just a single manual click on the target area in the initial video frame. Specifically, this manual click generates coordinate information for the target area, serving as a point prompt for the SAM-DM model. This process results in the creation of a high-quality binary mask, which is then fused with the original image to isolate only the target area. Subsequently, connected domain analysis is employed to automatically process the mask, extracting the pixel centroid coordinates of the target area, which are then used as prompts for segmenting subsequent frames. This approach allows continuous tracking of changes in the target area's coordinates, thereby facilitating rapid structural deformation monitoring. Experimental results demonstrate that this method can accurately monitor deformation across all target points and adapt to varying lighting conditions and noise levels. Applied to monitor the bending deformation of reinforced concrete beams and the in-plane deformation of concrete slabs, the method achieves results closely aligned with actual measurements, yielding relative errors of 0.95 % and 0.48 %, respectively. Additionally, in monitoring the vibrational deformation of steel frames, the primary frequency of the curve precisely matches measured values, confirming that the SAM-DM-based method delivers both high accuracy and strong robustness.

A-199 Use of R programming for monthly quality control (QC) data review in a university hospital laboratory

Abstract Background Our laboratory information system (LIS) provides only cursory capability for monthly review of quality control measurements. We streamlined the major portion of this process using customized programming in R, as described below. Methods Month-long QC data were obtained using the LIS archive’s data retrieval program (QLIK). Data were read and processed using our R program (QC-R.R), for each analyte and QC level, to produce both Levey-Jennings (LJ) plots (for evaluation of possible temporal trends in QC data) as well as cumulative distribution (CD) plots (to evaluate whether QC data are normally distributed). Plots were sent to graphics files whereby they could be reviewed sequentially by a single forward-arrow click. Results Our laboratory utilizes two QC measurements for each of 157 analytes. Thus, monthly review involves evaluation of 314 datasets for QC results. With the R program outputs, results can be reviewed visually in rapid fashion, to determine whether there exist discrepancies between fixed limits and current data. An example CD plot is shown in Figure. In this example, QC data (points) were, appropriately, normally distributed (theoretical curved line based on data mean and SD). The currently operative fixed mean±2SD limits (vertical dashed lines) were, however, disparate from those of data (vertical solid lines). This set of results was thus marked for updating of mean and SD, with reference to a master list of results of calculations generated by the QC-R.R program. Time for review of monthly QC results was dramatically reduced, from more than 12 man-hours to less than 4 man-hours per month. Conclusions Limitations of our LIS for monthly QC review were bypassed using R-based programming to automate a significant fraction of the process. This led to a significant savings in labor attending to this important task. The use of CD plots is helpful for rapid data interpretation.

HN-SIB-BPI: A single click, sub-site specific, dosimetric scorecard tuned RapidPlan model created from a foundation model for treating head and neck with bilateral neck

To develop a Knowledge Based Planning (KBP) model for creating quantifiably high quality VMAT treatment plans in a single click for head and neck cases treated Simultaneous Integrated Boost (SIB) with bilateral parotid involvement (BPI) where both parotids are near, abutting or partially overlapping target volume. Eclipse RapidPlan and the publicly available PlanScorecard tool were used to assess existing Head and Neck RapidPlan models on two representative cases. The best performer was used as a foundation model to assist in creating new initial training set doses from previously treated cases. Those initial 27 cases were first replanned using only the selected foundation model, then further improved based on manual replanning, informed by dosimetric scorecard assessment. A new, initial model was trained from those 27 foundation model created cases that had been manually improved. Then, that initial model was used to replan those cases again, resulting in higher scores. Additional cases were also replanned using the initial model along with some manual changes to the optimization objectives to increase the score. This resulted in a total of 66 cases from which the final, released, HN-SIB-BPI was trained. A 27 case subset of the full training set was replanned and rescored at each phase of the process with a 260 total point 3-target scorecard. The average score increased: 210.5 foundation model; 226.96 manually improved plans; 230.1 initial model; 231.7 HN-SIB-BPI. On the same 27 case subset, mean ipsilateral and contralateral parotid dose decreased by 1.05Gy and 1.58Gy respectively from the foundation model to HN-SIB-BP. Eight external cases were created from HN-SIB-BPI with dosimetric scorecard validation on Halcyon (3-PTV:221.38/260; 2-PTV:196.1/228.5) and TrueBeam (3-PTV:222.01/260; 2-PTV:202.24/228.5). A specific clinical intent (ie: max parotid sparing) can be articulated in a comprehensive and precise manner by creating a dosimetric scorecard with individual metrics points assigned to each OAR and target metric reflecting their relative importance. This process improved the KBP model (HN-SIB-BPI) in several quantifiable ways including further sparing of parotid dose. All results and tools in this work are shared publicly.

ImageGP 2 for enhanced data visualization and reproducible analysis in biomedical research.

ImageGP is an extensively utilized, open-access platform for online data visualization and analysis. Over the past 7 years, it has catered to more than 700,000 usages globally, garnering substantial user feedback. The updated version, ImageGP 2 (available at https://www.bic.ac.cn/BIC), introduces a redesigned interface leveraging cutting-edge web technologies to enhance functionality and user interaction. Key enhancements include the following: (i) Addition of modules for data format transformation, facilitating operations such as matrix merging, subsetting, and transformation between long and wide formats. (ii) Streamlined workflows with features like preparameter selection data validation and grouping of parameters with similar attributes. (iii) Expanded repertoire of visualization functions and analysis tools, including Weighted Gene Co-Expression Network Analysis, differential gene expression analysis, and FASTA sequence processing. (iv) Personalized user space for uploading large data sets, tracking analysis history, and sharing reproducible analysis data, scripts, and results. (v) Enhanced user support through a simplified error debugging feature accessible with a single click. (vi) Introduction of an R package, ImageGP, enabling local data visualization and analysis. These updates position ImageGP 2 as a versatile tool serving both wet-lab and dry-lab researchers with expanded capabilities.

Digital Technologies in the Classroom

Digital revolution across the world has influenced every aspect of our lives. Like any other field, technology has brought radical changes in the lives of teachers and students. Nowadays, it is impossible for a teacher to stay away from the impact of digitalization of education. In fact, a teacher can empower himself and his students with the use of digital technologies in the classroom. In this digital climate, technology has taken the main seat, and students have access to the best sources in the world on a single click. Digital technologies like projectors and cameras are commonly used along with virtual screen, smart devices, and digital pens in twenty first-century classrooms. This module has been designed to attain the following learning outcomes.

3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation

Despite that the segment anything model (SAM) achieved impressive results on general-purpose semantic segmentation with strong generalization ability on daily images, its demonstrated performance on medical image segmentation is less precise and unstable, especially when dealing with tumor segmentation tasks that involve objects of small sizes, irregular shapes, and low contrast. Notably, the original SAM architecture is designed for 2D natural images and, therefore would not be able to extract the 3D spatial information from volumetric medical data effectively. In this paper, we propose a novel adaptation method for transferring SAM from 2D to 3D for promptable medical image segmentation. Through a holistically designed scheme for architecture modification, we transfer the SAM to support volumetric inputs while retaining the majority of its pre-trained parameters for reuse. The fine-tuning process is conducted in a parameter-efficient manner, wherein most of the pre-trained parameters remain frozen, and only a few lightweight spatial adapters are introduced and tuned. Regardless of the domain gap between natural and medical data and the disparity in the spatial arrangement between 2D and 3D, the transformer trained on natural images can effectively capture the spatial patterns present in volumetric medical images with only lightweight adaptations. We conduct experiments on four open-source tumor segmentation datasets, and with a single click prompt, our model can outperform domain state-of-the-art medical image segmentation models and interactive segmentation models. We also compared our adaptation method with existing popular adapters and observed significant performance improvement on most datasets. Our code and models are available at: https://github.com/med-air/3DSAM-adapter

WARCreate

WARCreate is an open-source Google Chrome add-on that allows users to archive web pages as .WARC files with a single click, created by Mat Kelly and Michele Weigle at Old Dominion University. This tool addresses the limitations of other web archiving services like the Wayback Machine by enabling users to independently archive specific web pages, including those behind passwords or with unique geolocations. Its applications in Library and Information Science (LIS) include preserving digital content for research and teaching, and supporting the ethical archiving of sensitive information. Despite some initial usability challenges and a need for more comprehensive support materials, WARCreate remains a valuable tool for capturing and preserving web data.

A graphical user interface tool to teach plane curves in pandemic situations through a flipped classroom

During the pandemic and lockdown situations of the last few years, we transitioned to online, physical, and hybrid education approaches at various times. We had difficulty teaching multiple calculus and analytical geometry units online. In light of the current situation, a graphical user interface (GUI) teaching tool has been developed to teach university students many plane curve modules through a flipped classroom. With a few inputs and a single click as output, these executable spreadsheets can assist physics and mathematics students in practising several principles of analytical geometry. Spreadsheets can readily draw planes in three-dimensional geometry due to their widespread use. Some online vector GUIs present the results; however, this GUI conveniently illustrates the plot. The well-designed user interface also helps create new questions with answer keys and gives access to the teachers of physics and mathematics, which can make online teaching very easy. The effectiveness of this GUI was checked through Kolmogorov-Smirnov and Shapiro-Wilk tests, and a good agreement has been found in the results.Contribution: This article contributes to Mathematics teaching through GUIs.

CODE: Code once, deploy everywhere serverless functions in federated FaaS

Infrastructure-as-Code (IaC) frameworks empower developers to swiftly define and provision their infrastructure with a single click. However, the domain-specific languages (DSLs) utilized for coding the infrastructure often lean towards provider specificity rather than being application-centric. This results in increased developer effort, as they are compelled to duplicate data when deploying serverless functions across diverse regions and providers within federated FaaS environments. To mitigate this challenge, we introduce CODE, a framework engineered to streamline the deployment of functions in federated FaaS settings. CODE facilitates automatic deployment directly from the storage of any provider, eliminating the need for additional development effort to upload or copy deployment packages between disparate providers. Aligned with the guiding principle of “code once, deploy everywhere”, CODE adopts a three-level hierarchy: function → providers → cloud regions. This architectural approach dramatically reduces the lines of code (LoC) in IaC scripts by up to 9.23× when contrasted with prevailing IaC frameworks such as Terraform and Serverless Framework. Additionally, CODE’s unified storage interface slashes LoC by up to 81.8%, both within CODE itself and when coding functions that use storage from providers such as AWS and GCP. In our comprehensive evaluation, we assessed the correlation between deployment package size and deployment time for various functions within a real-world serverless workflow across four regions of AWS and GCP. Our findings indicate that AWS deployment packages are significantly larger, often in the tens of megabytes, compared to GCP. Despite the larger size, AWS deploys these packages up to 6× faster than GCP.

Design and implementation of an interactive networked condition monitoring strategy for plant-wide production equipment toward Industry 4.0

Equipment condition monitoring holds immense significance in modern industrial factories, facilitating sustained operation with heightened efficiency, safety, and cost-effectiveness. This paper presents an interactive networked condition monitoring strategy to enhance plant-wide production equipment condition monitoring, leveraging networked control and edge computing approaches. This strategy offers a rapid and efficient solution that comprises four key components: drag-and-drop monitoring algorithm design, executable program generation, networked algorithm implementation, and interactive monitoring process. Initially, algorithms for equipment condition monitoring are designed using pre-defined components and then seamlessly compiled into executable programs with a single click. Subsequently, these algorithms are implemented within an edge computing-based monitoring system using the networked control approach, where the generated programs are remotely deployed into edge nodes positioned adjacent to the equipment. Moreover, the monitoring process features visual displays of parameters and results using configurable widgets, enabling interactive adjustments. Real-world application cases from an aluminum manufacturing factory showcase the strategy’s effectiveness in comprehensive condition monitoring of hundreds of equipment across nine workshops, significantly reducing the equipment inspection time to tens of minutes compared to manual inspection. With extensive implementation in various industrial factories, this strategy emerges as a promising solution for equipment condition monitoring in smart factories.

Understanding the Use of Mobile E-Books among Mathematics Postgraduates

Objectives: The main objective of this study is to understand the factors that encourage the use of mobile ebooks among mathematics postgraduate students. Method: This study employed a qualitative case study approach. Eight mathematics postgraduate students from the algebra research group participated in the study. Data were collected via interview and analyzed by using constant comparative analysis. Results: Utilitarian, cognitive, affective, social norm, and content qualities were the main factors influencing the use of mobile ebooks among the participants. Conclusions: Mobile ebooks are easy to use and provide a meaningful platform to find information. In addition, the mobile ebook provides instant access to new information with a single finger click, thus enabling students to extract the needed information and make constructive comparisons on the subject matter. Theory and/or Practice Implications: Results expand understanding of the factors that motivate the use of mobile ebooks for learning activities among mathematics postgraduate students. In addition, the study also provides support for institutions planning to incorporate mobile ebooks as an important reference material for postgraduate learning and research activities.

The Art of Dance in a Digital World / Arta dansului într-o eră digitală

In the last decades, the influence of digital technology has affected almost all aspects of our lives, including the enchanting world of dance. Nowadays, due to Internet resources, dance is broadcast faster than ever. As for instance, more rapidly than in the late 16th century, when Italian ballet masters gained access to the French Court, or when the Russian ballet was dispersed after Diaghilev’s death in 1929. Moreover, with a single click, everyone has the possibility to access more than 900.000 dance videos, being able to view a performance or dance tutorial whenever it is desired. Art renews itself when it manages to integrate in the present, and digital technology has embraced the art of dance, giving birth to new interdisciplinary approaches. This paper surveys the use of technology regarding dance education and performance on the screen and analyses in what manner the development of digital machineries is supporting innovation in dace.

FitTrack BloodLink

FitTrack BloodLink is an innovative Android application designed to enhance users fitness tracking experience by seamlessly integrating it with life-saving blood donation and request functionalities. This comprehensive app automatically determines the user's starting point as their current location and sets the destination point upon finishing the run. Utilizing GPS tracking, FitTrack BloodLink monitors users routes throughout their runs, capturing essential fitness metrics like distance covered, calories burned, average speed, and total duration in real time. Additionally, users can register as blood donors through the app, making it easier for those needing donations. FitTrack BloodLink also includes a map feature displaying nearby blood donors, allowing users to view donor details and contact them directly via phone with a single click on the marker.

Water suppression 101 for benchtop NMR–An accessible guide and primer including fully interactive training videos

Benchtop NMR is enjoying a renaissance with numerous manufacturers bringing products to the market over the last decade. The improved accessibility, lower cost of ownership and ease of use (vs high field NMR), is attracting new users into NMR spectroscopy, which is highly beneficial for the field in general. As benchtop NMR systems seldom require deuterated solvents, this allows samples to be analyzed “as is”, without extraction or alteration. However, many interesting samples, be it an organic reaction mixture, beer, or a biofluid, contain one or more solvent/water signals, which often require suppression. Due to the lower spectral dispersion of benchtop NMR's (vs high field) the frequency of solvent/water is much closer to the analytes of interest, making solvent suppression more challenging. As such, there is a conundrum, where novel users wish to analyze unaltered samples but are quickly faced with the challenge of water suppression, and the wealth of options in the high field literature can be overwhelming. It is important to note that all manufacturers offer some sort of automated water suppression that can be performed with a “single click” that are sufficient for “walk-up” applications or occasional users. This primer is aimed as an accessible guide to those wishing to take the next step and is suitable for users who; 1) would like to pick the optimal water suppression approach for their sample type and 2) wish to understand how water suppression works. The guide focuses on water suppression approaches that are easy to apply, namely presaturation based sequences, binomial sequences for aggressive suppression, and WET for multiple signal suppression, across a range of samples including sucrose standards, espresso, human blood serum and wine. The primer finishes with a flow chart that can be used to guide users in choosing the optimal water suppression approach for their specific sample type, with considerations, including exchangeable signals and the preservation of macromolecular signals, amongst others. In addition, the primer includes 3 fully interactive videos based on H5P technology, focusing on how to acquire data using the approaches described here. The videos include quizzes, with a first-person-perspective of the spectrometer software with interactive elements, as if the users were acquiring the data themselves. In summary, the primer is aimed at advanced undergraduates, graduate students, new users, or users wishing to expand their water/solvent suppression skills/knowledge using benchtop NMR.

Peer assessment to promote self-regulated learning with technology in higher education: systematic review for improving course design

Peer assessment is one of the approaches to develop self-regulation of learning. When evaluating the work of peers, metacognitive strategies of critical reflection are employed. They improve their own learning especially if evaluative feedback and/or suggestions for modification are provided. The aim of this systematic review is to learn how technology can facilitate self-regulation of learning, using peer assessment activities. We focus on higher education. To achieve the objective, we searched WoS and Scopus, obtaining 15 publications that concatenate the four search terms: self-regulated learning, peer assessment, higher education, and technology. These four terms must appear in the title, abstract or keywords. In this way, we ensure that the topic to be reviewed is central to the publication. The results are analyzed using the model for systematic review, which has three phases: description, synthesis, and critique. A proposal has been made to improve the design of courses in virtual classrooms, focusing on Moodle, and to include peer evaluation to improve self-regulated learning. It highlights the possibility of virtual classrooms to configure a rubric to guide the evaluation, together with the request for mandatory comments to justify the evaluation. This helps the student reflect on what is wrong and why, and how to improve. It also highlights the facility to randomly assign a specific number of tasks per reviewer or per task, and to make the whole process completely anonymous. The technology allows short deadlines for submission and review times to be maintained for instant feedback, as it can be configured with a single click. Finally, and related to this, Moodle can reopen the submission phase, to send an improved version based on feedback, and the evaluation phase, to check that the proposed improvements have been made. This helps to a greater extent to apply metacognitive strategies.

Photon number resolving detection with a single-photon detector and adaptive storage loop

Photon number resolving (PNR) measurements are beneficial or even necessary for many applications in quantum optics. Unfortunately, PNR detectors are usually large, slow, expensive, and difficult to operate. However, if the input signal is multiplexed, photon ‘click’ detectors, that lack an intrinsic PNR capability, can still be used to realize photon number resolution. Here, we investigate the operation of a single click detector, together with a storage line with tunable outcoupling. Using adaptive feedback to adjust the storage outcoupling rate, the dynamic range of the detector can in certain situations be extended by up to an order of magnitude relative to a purely passive setup. An adaptive approach can thus allow for photon number variance below the quantum shot noise limit under a wider range of conditions than using a passive multiplexing approach. This can enable applications in quantum enhanced metrology and quantum computing.

Impact of Perceived Benefits and Perceived Risks on Online Shopping Behaiour with Mediating Role of Purchase Intention

Online purchasing has revolutionized consumer market. It allows individuals to access a variety of products with a single click. With evolution of e-commerce understanding the factors influencing online shopping behavior is imperative for firms to remain competitive. This study investigates the relationship between perceived benefits (PB) and perceived risks (PR), mediated by purchase intention (PI) on online buying behavior (OSB). Six key elements—shopping convenience, product variety, price advantage, product risk, privacy risk, and financial risk—were evaluated through data collected from 300 internet shoppers via a structured questionnaire. The data is analyzed with SPSS. Results underline the significance of convenience, product variety, and price advantage in stimulating online purchases, with PB positively impacting customers' online purchasing behavior (OPB). Conversely, perceived risks negatively affect online shopping decisions, necessitating efficient risk management strategies to bolster consumer confidence. The mediation role of PI was examined using the Hayes macro process, revealing its significance in the relationship between PB, PR, and OSB. Notably, PI mediates the impact of both PB and PR on OSB, highlighting its pivotal role in shaping consumers' online purchasing behavior. These findings offer actionable insights for businesses, emphasizing the importance of leveraging PB to encourage beneficial purchasing intentions and mitigate PR to foster online shopping behavior. Ultimately, this study contributes to a deeper understanding of consumer decision-making in online settings, facilitating the development of targeted marketing strategies and enhancing overall online shopping experiences.

Abstract 887: Fast, interactive, AI-assisted 3D lung tumour segmentation

Abstract Segmentation of lung tumors is an important step in the planning of clinical treatment and operative procedures. Automatic segmentation of advanced lung tumors is challenging due to local invasion and heterogeneous metastasis. Fully automatic deep learning models often fail to accurately delineate advanced disease boundaries and do not allow the user to provide hints to improve segmentation performance. In contrast, interactive segmentation accepts user inputs and is an efficient way to segment lesions while reducing radiologist effort. This paper describes an interactive segmentation approach for lung lesion segmentation in CT scans. Our pipeline is built atop the SwinUNETR neural network architecture and can be expanded to support alternate models. The backbone model accepts radiologists' hints in the form of mouse clicks that indicate whether a location on the scan corresponds to a lesion. Our approach has the following novel contributions: (1) Context-aware encoding of guidance clicks by combining geodesic and Gaussian smoothing, resulting in improved segmentation of lesion boundaries. (2) An iterative prompting strategy to achieve higher accuracy with fewer clicks at inference time. (3) Guidance-aware Conditional Random Fields to refine the produced segmentation masks. (4) Volume cropping around guidance clicks at inference to improve segmentation precision and inference speed. (5) Linearly scaling our approach on multiple GPUs, to improve inference speed. On a public non-small cell lung cancer dataset predominantly containing advanced stage cancers (68\% stage III, and 27\% stage IV), our interactive approach delivers a Dice score of 0.74 with a single click, a 21\% improvement from the fully automatic approach (Dice Score = 0.61), and a Dice score of 0.81 with 5 clicks, which is a 33\% improvement. Inference speed is an important metric for an interactive pipeline. On a typical 350 $\times$ 350 $\times$ 150 voxel chest CT scan volume, our interactive approach infers 5 times faster than the original fully automated approach on a single GPU. After scaling to 4 GPUs, we infer in only 0.5 seconds; 20 times faster than the original approach. Qualitative evaluation from three independent radiologists indicates that our interactive pipeline significantly improved their standard clinical segmentation experience. Citation Format: Mayank Patwari, Yi Wei, Meng Xu, Zhenning Zhang, Konstantinos Sidiropoulos, Balaji Selvaraj, Georgia Hughes, Nadine Garibli, Kamsiriochukwu Ojiako, Mohan Lella, Leon Fedden, James Parkin, Michael Parker, Shaneil Patel, Qin Li, Kedar Patwardhan. Fast, interactive, AI-assisted 3D lung tumour segmentation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 887.

Sleep- and sleep deprivation-related changes of vertex auditory evoked potentials during the estrus cycle in female rats

The estrus cycle in female rodents has been shown to affect a variety of physiological functions. However, little is known about its presumably thorough effect on auditory processing during the sleep–wake cycle and sleep deprivation. Vertex auditory evoked potentials (vAEPs) were evoked by single click tone stimulation and recorded during different stages of the estrus cycle and sleep deprivation performed in metestrus and proestrus in female rats. vAEPs showed a strong sleep-dependency, with the largest amplitudes present during slow wave sleep while the smallest ones during wakefulness. Higher amplitudes and longer latencies were seen in the light phase during all vigilance stages. The largest amplitudes were found during proestrus (light phase) while the shortest latencies were seen during estrus (dark phase) compared to the 2nd day diestrus baseline. High-amplitude responses without latency changes were also seen during metestrus with increased homeostatic sleep drive. More intense and faster processing of auditory information during proestrus and estrus suggesting a more effective perception of relevant environmental cues presumably in preparation for sexual receptivity. A 4-h sleep deprivation resulted in more pronounced sleep recovery in metestrus compared to proestrus without difference in delta power replacement suggesting a better tolerance of sleep deprivation in proestrus. Sleep deprivation decreased neuronal excitability and responsiveness in a similar manner both during metestrus and proestrus, suggesting that the negative consequences of sleep deprivation on auditory processing may have a limited correlation with the estrus cycle stage.