Mouse Movements Research Articles

Ultra-fast photoelectron transfer in bimetallic porphyrin optoelectrode for single neuron modulation

Shrinking the size of photoelectrodes into the nanoscale will enable the precise modulation of cellular and subcellular behaviors of a single neuron and neural circuits. However, compared to photovoltaic devices, the reduced size causes the compromised efficiencies. Here, we present a highly efficient nanoelectrode based on bimetallic zinc and gold porphyrin (ZnAuPN). Upon light excitation, we observe ultrafast energy transfer (~66 ps) and charge transfer (~0.5 ps) through the porphyrin ring, enabling 97% efficiency in separating and transferring photoinduced charges to single Au-atom centers. Leveraging these isolated Au atoms as stimulating electrode arrays, we achieve significant photocurrent injection in single neurons, triggering action potential with millisecond light pulses. Notably, Extracranial near-infrared light irradiation of the motor cortex induces neuronal firing and enhances mouse movement. These results show the potential of nanoscale optoelectrodes for high spatiotemporal modulation of neuronal networks without the need for gene transfection in optogenetics.

IOT Gesture Recognition-Based Virtual Keyboard and Mouse

Recognition-based virtual mouse and keyboard systems use gesture recognition technology to control mouse movements and keyboard inputs without physical devices. Instead of using a physical mouse or keyboard, users can perform hand or finger gestures in the air, which are captured and interpreted by a camera or sensor system. Gesture abstracts in this context refer to the specific hand or finger movements that are recognized and associated with certain mouse or keyboard actions. These gestures are predefined and programmed into the recognition system, allowing users to perform specific movements to simulate the actions of a traditional mouse or keyboard

Olfactory bulb tracks breathing rhythms and place in freely behaving mice.

Vertebrates sniff to control the odor samples that enter their nose. These samples can not only help identify odorous objects, but also locations and events. However, there is no receptor for place or time. Therefore, to take full advantage of olfactory information, an animal's brain must contextualize odor-driven activity with information about when, where, and how they sniffed. To better understand contextual information in the olfactory system, we captured the breathing and movements of mice while recording from their olfactory bulb. In stimulus- and task-free experiments, mice structure their breathing into persistent rhythmic states which are synchronous with statelike structure in ongoing neuronal population activity. These population states reflect a strong dependence of individual neuron activity on variation in sniff frequency, which we display using "sniff fields" and quantify using generalized linear models. In addition, many olfactory bulb neurons have "place fields" that display significant dependence of firing on allocentric location, which were comparable with hippocampal neurons recorded under the same conditions. At the population level, a mouse's location can be decoded from olfactory bulb with similar accuracy to hippocampus. Olfactory bulb place sensitivity cannot be explained by breathing rhythms or scent marks. Taken together, we show that the mouse olfactory bulb tracks breathing rhythms and self-location, which may help unite internal models of self and environment with olfactory information as soon as that information enters the brain.

Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning

Objective.Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from neural injury and disease. A critical step in implementing a BMI is to decode movement intention from recorded neural activity patterns in sensorimotor areas. Optical imaging, including two-photon (2p) calcium imaging, is an attractive approach for recording large-scale neural activity with high spatial resolution using a minimally-invasive technique. However, relating slow two-photon calcium imaging data to fast behaviors is challenging due to the relatively low optical imaging sampling rates. Nevertheless, neural activity recorded with 2p calcium imaging has been used to decode information about stereotyped single-limb movements and to control BMIs. Here, we expand upon prior work by applying deep learning to decode multi-limb movements of running mice from 2p calcium imaging data.Approach.We developed a recurrent encoder-decoder network (LSTM-encdec) in which the output is longer than the input.Main results.LSTM-encdec could accurately decode information about all four limbs (contralateral and ipsilateral front and hind limbs) from calcium imaging data recorded in a single cortical hemisphere.Significance.Our approach provides interpretability measures to validate decoding accuracy and expands the utility of BMIs by establishing the groundwork for control of multiple limbs. Our work contributes to the advancement of neural decoding techniques and the development of next-generation optical BMIs.

Exacerbating orthodontic tooth movement in mice with salt-sensitive hypertension

Background/purposeOrthodontic tooth movement (OTM) is a critical aspect of dental treatment that requires the precise control of bone remodeling processes. Hypertension (HTN) can affect the effectiveness of OTM. Salt-sensitive hypertension (SSHTN) is of particular concern due to its detrimental effects on bone health, potentially altering orthodontic outcomes. This study aimed to investigate the effects of SSHTN on OTM using a mouse model. Materials and methodsMale mice were divided into a normal and an SSHTN group. The SSHTN model was generated by administering N(ω)-nitro-l-arginine methyl ester (l-NAME) followed by a high-salt diet. The OTM was performed using a nickel-titanium (Ni-Ti) closed-coil spring, and the tooth movement was measured after 12 days. Silicone imprinting was used to estimate the OTM distance. Osteoclast activity was assessed using tartrate-resistant acid phosphatase (TRAP) staining of decalcified maxillary sections. ResultsSSHTN mice exhibited significantly increased tooth movement compared to normal mice. This enhanced movement was associated with more osteoclasts in the SSHTN group than in the control group. These findings suggest that SSHTN increases OTM levels by promoting bone resorption. ConclusionSSHTN significantly affected OTM by enhancing osteoclast activity and increasing tooth movement. These results underscore the importance of considering hypertensive conditions in orthodontic treatment planning as they may require adjustments in force application to prevent potential adverse effects.

Hierarchical analysis of the sense of agency in schizophrenia: motor control, control detection, and self-attribution

The sense of agency refers to the feeling of initiating and controlling one’s actions and their resulting effects on the external environment. Previous studies have uncovered behavioral evidence of excessive self-attribution and, conversely, a reduction in the sense of agency in patients with schizophrenia. We hypothesize that this apparent paradox is likely to result from impairment in lower-level processes underlying the sense of agency, combined with a higher-level compensational bias. The present study employed three behavioral tasks utilizing the same stimuli and experimental design to systematically evaluate multiple factors that influence the sense of agency, including motor control, sensorimotor processing, and self-attribution. Participants’ real-time mouse movements were combined with prerecorded motions of others in ratios of 30/70, 55/45, or 80/20, with an additional angular bias of either 0° or 90°. Twenty-six patients with schizophrenia and 27 health control volunteers participated in the three tasks. Patients with schizophrenia performed significantly worse in the reaching and control detection tasks than healthy controls. However, their self-attribution in the control judgment task was comparable to that of the healthy controls. Patients with schizophrenia were impaired in motor control components and in the detection of control using sensorimotor information, but their evaluation of agency remained relatively less affected. This underscores the importance of distinguishing between different subcomponents when addressing the abnormal sense of agency in patients with schizophrenia. Subsequent cluster analysis revealed that the combined task performance accurately distinguished between the patients and healthy control participants.

Application of self powered black phosphorus nanosheets-based hydrogel skin patch in wound healing of diabetes

Diabetes mellitus-related chronic non-healing wounds have greatly lowered the quality of life for people afflicted and placed a heavy financial burden on the healthcare system. In order to hasten the healing of diabetic wounds, a wearable hydrogel-based triboelectric nanogenerator (BP-TENG) was created as a skin patch, which combines the beneficial effects of electrical and photothermal stimulation. The triboelectric output performance of the BP-TENG was adjusted and photothermal conversion capacity was brought about by the black phosphorus nanosheets (BPNS) doped in PVA hydrogel. In just 60 seconds, BP-TENG managed to bring the temperature of the mouse wound region up to 48.6°C under the irradiation of 808 nm near-infrared light. BP-TENG also exhibited spectral antibacterial activity against E. coli, S. aureus, and P. aeruginosa. Furthermore, BP-TENG had the ability to capture the energy from mechanical movements of mice and transform it into electrical stimulation. Under 808 nm near-infrared light irradiation, BP-TENG was shown to significantly speed the healing of skin wounds in the incision mouse model of type I diabetes. According to in vitro research, these benefits may be ascribed to photothermal and electric stimulation, which can both repair the healthy microenvironment of wound area and speed up the deposition and remodeling of collagen during the healing process of diabetic wounds. As far as we are aware, this is the first report on the creation and use of hydrogel-based TENG doped with BP for wound healing. We believe that this study improves the quality of life for patients with persistent non-healing wounds and opens up new possibilities for 2D materials and TENG in the application of wearable healthcare devices.

ALS-associated C21ORF2 variant disrupts DNA damage repair, mitochondrial metabolism, neuronal excitability and NEK1 levels in human motor neurons

Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease leading to motor neuron loss. Currently mutations in > 40 genes have been linked to ALS, but the contribution of many genes and genetic mutations to the ALS pathogenic process remains poorly understood. Therefore, we first performed comparative interactome analyses of five recently discovered ALS-associated proteins (C21ORF2, KIF5A, NEK1, TBK1, and TUBA4A) which highlighted many novel binding partners, and both unique and shared interactors. The analysis further identified C21ORF2 as a strongly connected protein. The role of C21ORF2 in neurons and in the nervous system, and of ALS-associated C21ORF2 variants is largely unknown. Therefore, we combined human iPSC-derived motor neurons with other models and different molecular cell biological approaches to characterize the potential pathogenic effects of C21ORF2 mutations in ALS. First, our data show C21ORF2 expression in ALS-relevant mouse and human neurons, such as spinal and cortical motor neurons. Further, the prominent ALS-associated variant C21ORF2-V58L caused increased apoptosis in mouse neurons and movement defects in zebrafish embryos. iPSC-derived motor neurons from C21ORF2-V58L-ALS patients, but not isogenic controls, show increased apoptosis, and changes in DNA damage response, mitochondria and neuronal excitability. In addition, C21ORF2-V58L induced post-transcriptional downregulation of NEK1, an ALS-associated protein implicated in apoptosis and DDR. In all, our study defines the pathogenic molecular and cellular effects of ALS-associated C21ORF2 mutations and implicates impaired post-transcriptional regulation of NEK1 downstream of mutant C21ORF72 in ALS.

Behaviorally Measuring Usability by Analyzing Users’ Mouse Movement Efficiency

Usability—the extent to which a system can be operated to achieve specified goals with effectiveness, efficiency, and satisfaction—is a hallmark of many successful systems. Identifying usability concerns with existing measures, however, can be problematic. We use biased competition theory (BCT) to explain how interfaces with low usability create attentional interference, which can be measured through mouse movement efficiency (MME). We test our hypotheses in four studies. Study 1 manipulates attentional interference and examines its influence on MME using eye tracking to validate BCT as an appropriate theoretical lens for our research. Study 2 manipulates usability and shows similar markers of attentional interference in MME on a website with two different populations. Study 3 is a field test that compares the reliability of MME in ranking the usability of different components of a commercial web application to rankings produced by a common perceptual measure of usability: perceived ease of use. Collaborating with a Fortune 100 software vendor, Study 4 further demonstrates the efficacy of MME in ranking the usability of various system components compared to a standard industry measure of usability across three different products. The results consistently show that MME can help identify usability differences among parts of systems. The results have implications for facilitating more efficient and temporally precise usability research and mass-deployable usability testing.

PuppetMouse: Practical and Contactless Mouse Manipulation Attack via Intentional Electromagnetic Interference Injection

Mouse is a ubiquitous input tool crucial for user-computer interaction in modern society. However, the inherent trust in the mouse may pose security risks. If the mouse is maliciously manipulated, the connected computer could be secretly controlled, endangering personal privacy and property. In this paper, we introduce PuppetMouse, the first intentional electromagnetic interference (IEMI) injection-based attack that can effectively manipulate mouse clicks and movements without physical contact. By adjusting the parameters of IEMI signals, PuppetMouse can precisely control the click side, as well as the movement direction and speed. We demonstrate PuppetMouse's effectiveness on 14 wired and wireless mice from popular brands. The short response delay (within 4 ms) affirms the real-time performance of PuppetMouse. Robustness analysis across different attack distances and material occlusions validate the stability and reliability of PuppetMouse. Two case studies on firewall disabling and malicious WiFi connection further prove the severe threats of PuppetMouse in the real world. We also propose an integrated set of hardware and software-based defensive mechanisms to mitigate the risks posed by PuppetMouse.

Studying Orthodontic Tooth Movement in Mice.

Orthodontic tooth movement (OTM) represents a dynamic process in which the alveolar bone undergoes resorption at compression sites and deposition at tension sites, orchestrated by osteoclasts and osteoblasts, respectively. This mechanism serves as a valuable model for studying various aspects of bone adaptation, including root resorption and the cellular response to mechanical force stimuli. The protocol outlined here offers a straightforward approach to investigate OTM, establishing 0.35 N as the optimal force in a mouse model employing a nickel-titanium (NiTi) coil spring. Utilizing micro-computed tomography analysis, we quantified OTM by assessing the discrepancy in the linear distance at the cement-enamel junction. The evaluation also included an analysis of orthodontic-induced inflammatory root resorption, assessing parameters such as root mineral density and the percentage of root volume per total volume. This comprehensive protocol contributes to advancing our understanding of bone remodeling processes and enhancing the ability to develop effective orthodontic treatment strategies.

Co-coding of head and whisker movements by both VPM and POm thalamic neurons

Rodents continuously move their heads and whiskers in a coordinated manner while perceiving objects through whisker-touch. Studies in head-fixed rodents showed that the ventroposterior medial (VPM) and posterior medial (POm) thalamic nuclei code for whisker kinematics, with POm involvement reduced in awake animals. To examine VPM and POm involvement in coding head and whisker kinematics in awake, head-free conditions, we recorded thalamic neuronal activity and tracked head and whisker movements in male mice exploring an open arena. Using optogenetic tagging, we found that in freely moving mice, both nuclei equally coded whisker kinematics and robustly coded head kinematics. The fraction of neurons coding head kinematics increased after whisker trimming, ruling out whisker-mediated coding. Optogenetic activation of thalamic neurons evoked overt kinematic changes and increased the fraction of neurons leading changes in head kinematics. Our data suggest that VPM and POm integrate head and whisker information and can influence head kinematics during tactile perception.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Keypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute because keypoint data are susceptible to high-frequency jitter that clustering algorithms can mistake for transitions between actions. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules (‘syllables’) from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to identify syllables whose boundaries correspond to natural sub-second discontinuities in pose dynamics. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq also works in multiple species and generalizes beyond the syllable timescale, identifying fast sniff-aligned movements in mice and a spectrum of oscillatory behaviors in fruit flies. Keypoint-MoSeq, therefore, renders accessible the modular structure of behavior through standard video recordings.

Time association study on a sub-acute mouse model of Parkinson’s disease

Parkinson's disease (PD) is a severe neurodegenerative disease that disturbs human health. In the laboratory researches about PD, the mice model induced by intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) was widely used. However, there has been controversy about the model effectiveness to simulate PD symptoms and pathology, and the time-varying development of behavioral and pathological characteristic after MPTP treatment remains unclear. In order to solve these problems, we designed a series of experiments to evaluate this PD model at different time points. We constructed the subacute PD mouse model by intraperitoneal injection of MPTP for 5 consecutive days. The rotarod test, open field test and the immunohistochemical staining of tyrosine hydroxylase were conducted at −5, 1, 5, 7, 14, 21 and 28 days after the last injection of MPTP. The results showed that 5 days after the last MPTP administration, typical motor disorders with significant balance function damage in rotarod test began to appear and remained stable throughout the entire experiment. Simultaneously, we also observed the loss of tyrosine hydroxylase (TH) positive cells in the substantia nigra compacta and reduction of TH content in the striatum but this pathological change in the substantia nigra compacta reversed 21 days after injection. Besides, the spontaneous movement of mice in open field test remained unchanged by MPTP. This research indicated the time-dependence of MPTP neurotoxicity that impair the motor function and histological features and confirmed the symptom occurrence time after MPTP injection, which provides a reference for the future research about MPTP-induced PD.

THE IMPACT OF INTERPERSONAL COMMUNICATION EVOLUTION ON THE RELATIONSHIPS OF ERICA AND MARK ON FACEBOOK: A STUDY OF 'THE SOCIAL NETWORK

This study examines two pivotal scenes from "The Social Network" to analyze the nuanced dynamics of communication, emotions, and relationships depicted in the film. The first scene illustrates the consequences of Mark Zuckerberg's creation of Facemask, a website for rating the attractiveness of Harvard students, and its subsequent impact on interpersonal dynamics. Through a rapid sequence of events, the scene showcases the power of communication technology in shaping social interactions, while also highlighting the potential harm of misusing technology and exploiting personal information. Mark's unapologetic attitude and determination to push boundaries underscore the complex interplay of verbal and non-verbal communication, emphasizing the consequences of miscommunication and the misuse of technology within interpersonal relationships. In the second scene, Mark's actions on Facebook symbolize his internal thoughts and emotions regarding Erica Albright. The use of the Facebook platform becomes a metaphor for modern communication and social dynamics, as Mark navigates the complexities of reaching out in the digital realm. Through visual elements such as mouse movements and on-screen prompts, the scene effectively conveys Mark's emotional state and the uncertainties of online connections. This study offers insights into the evolving landscape of communication in the digital age, highlighting the complexities and consequences of interpersonal interactions within online environments.

Enhancing data tables user experience via automated adaptations

User experience is a crucial aspect of software quality, and while various guidelines and heuristics have been proposed to enhance it, tables have not received as much attention as other elements. Additionally, many developers are unaware of the guidelines they should follow in this regard. The objective of this article is to enhance the user experience on websites by streamlining the development process of usable tables. To achieve this, we propose a set of heuristics integrated into a tool called BetterTable that enables automatic application of usability improvements while parsing the HTML document. The complexity involved in developing a table with applied usability guidelines is evaluated using different implementations: the proposed approach, DataTables, and AntDesign. Furthermore, tests are conducted to validate the effectiveness of the proposed heuristics through user interactions with 13 table usage scenarios, measuring time, mouse movement, and clicks. Each user is randomly presented with 13 scenarios that either meet or do not meet a specific guideline. The results indicate that our proposal reduces implementation complexity by 43.83% and 24.69% compared to DataTables and AntDesign, respectively. Moreover, user testing reveals that 6 out of the 13 guidelines show improvements in at least 2 of the 3 calculated metrics. Guidelines fulfilled show 40.37% less average completion time and 20.39% less mouse movement than the baseline. Based on the conducted studies, we observed that compliant tables, on average, exhibit reduced user time and mouse movements compared to non-compliant tables. However, no significant differences are found in terms of clicks.

Performance evaluation of lightweight network-based bot detection using mouse movements

Confronted with the challenge of malicious bot abuse disrupting daily life, bot detection has become a crucial field of research. Among the various methods proposed, a behavior-driven framework analyzing mouse movements stands out for its widespread applicability, adaptability, and non-intrusive nature. Traditionally, this area of research has relied on manually extracted features from mouse dynamics and classical machine learning techniques, which often struggle to identify complex behavior patterns. To address these limitations, our research turns to Convolutional Neural Networks (CNNs), which have proven effective in image processing, to explore their application in bot detection using mouse movement data. We employ various visual representation techniques to convert mouse movements into trajectory images suitable for CNN analysis. Our empirical findings show that among all the lightweight networks tested, EfficientNet_b3 not only achieves the highest accuracy, with an impressive precision of 99.82%, but also surpasses more complex models like ResNet50 in terms of detection accuracy and modeling speed. These results not only enhance the practical application of deep learning in bot detection but also offer valuable insights into more effective strategies for advancing bot detection technologies.

Using memes and emoji-scales in a web survey: experimental assessment of consequences for multimodal cognitive effort and data quality

ABSTRACT Decreasing web survey participation rates lead to higher importance of ways to increase respondents’ motivation to participate and gamification is seen as one of possible ways to do so. However, there is no clear understanding on how it affects respondents’ cognitive load. This study investigates whether gamification reduces cognitive load among 18–25-year-old survey respondents. A laboratory experiment was conducted with 128 randomly assigned students to either a non-gamified or gamified web survey design. Gamification was implemented using emojis for ordinal scales and motivating memes between sets of questions. Cognitive load was measured through paradata, subjective evaluation, and neurophysiological indicator (pupil diameter dynamics). Results show higher cognitive effort for the gamification group based on pupil diameter dynamics, but subjective evaluation, mouse movements, and completion times show increased cognitive effort for the non-gamified group. Attention was higher for the gamification group, and data quality did not differ. Therefore, gamification may increase cognitive load, but it also increases motivation and engagement, leading to similar data quality compared to non-gamified web surveys.

Ventricular Netrin-1 deficiency leads to defective pyramidal decussation and mirror movement in mice.

The corticospinal tract (CST) is the principal neural pathway responsible for conducting voluntary movement in the vertebrate nervous system. Netrin-1 is a well-known guidance molecule for midline crossing of commissural axons during embryonic development. Families with inherited Netrin-1 mutations display congenital mirror movements (CMM), which are associated with malformations of pyramidal decussation in most cases. Here, we investigated the role of Netrin-1 in CST formation by generating conditional knockout (CKO) mice using a Gfap-driven Cre line. A large proportion of CST axons spread laterally in the ventral medulla oblongata, failed to decussate and descended in the ipsilateral spinal white matter of Ntn1Gfap CKO mice. Netrin-1 mRNA was expressed in the ventral ventricular zone (VZ) and midline, while Netrin-1 protein was transported by radial glial cells to the ventral medulla, through which CST axons pass. The level of transported Netrin-1 protein was significantly reduced in Ntn1Gfap CKO mice. In addition, Ntn1Gfap CKO mice displayed increased symmetric movements. Our findings indicate that VZ-derived Netrin-1 deletion leads to an abnormal trajectory of the CST in the spinal cord due to the failure of CST midline crossing and provides novel evidence supporting the idea that the Netrin-1 signalling pathway is involved in the pathogenesis of CMM.

Virtual Mouse

Now a days the use of hand gesture recognition became popular due to the advance technology of artificial Intelligence (AI). In recent years, there has been a growing interest in developing alternative methods for human-computer interaction (HCI) that go beyond traditional input devices like keyboards and mouse. Hand gesture recognition has emerged as a promising approach, offering intuitive and natural ways for users to interact with digital interfaces. The proposed system utilizes computer vision techniques to detect and track hand gestures in real-time. By mapping specific gestures to mouse control commands, users can navigate and interact with graphical user interfaces (GUIs) without the need for physical input devices. In this paper A hand gesture- controlled virtual mouse system utilizes the AI and Machine learning algorithms to recognize the proper hand gestures and translate them into the mouse movements. The system we are designed that the people who have problems or difficulty using a traditional mouse or keyboard it will be appropriate for them. For this we are using camera that capture images of the user’s hand, which are processed by an AI and ML algorithm to recognize the gesture of hands being made. We trained the system by using a dataset of hand gestures. Keywords — Hand gesture recognition, Machine Learning, Artificial Intelligence, Virtual Mouse, Python, Media Pipe