Mobile Tracking Research Articles

Exosomal long noncoding RNA MLETA1 promotes tumor progression and metastasis by regulating the miR-186-5p/EGFR and miR-497-5p/IGF1R axes in non-small cell lung cancer

BackgroundLung cancer is the most common and deadliest cancer worldwide, and approximately 90% of all lung cancer deaths are caused by tumor metastasis. Tumor-derived exosomes could potentially promote tumor metastasis through the delivery of metastasis-related molecules. However, the function and underlying mechanism of exosomal long noncoding RNA (lncRNA) in lung cancer metastasis remain largely unclear.MethodsCell exosomes were purified from conditioned media by differential ultracentrifugation and observed using transmission electron microscopy, and the size distributions were determined by nanoparticle tracking analysis. Exosomal lncRNA sequencing (lncRNA-seq) was used to identify long noncoding RNAs. Cell migration and invasion were determined by wound-healing assays, two-chamber transwell invasion assays and cell mobility tracking. Mice orthotopically and subcutaneously xenografted with human cancer cells were used to evaluate tumor metastasis in vivo. Western blot, qRT‒PCR, RNA-seq, and dual-luciferase reporter assays were performed to investigate the potential mechanism. The level of exosomal lncRNA in plasma was examined by qRT‒PCR. MS2-tagged RNA affinity purification (MS2-TRAP) assays were performed to verify lncRNA-bound miRNAs.ResultsExosomes derived from highly metastatic lung cancer cells promoted the migration and invasion of lung cancer cells with low metastatic potential. Using lncRNA-seq, we found that a novel lncRNA, lnc-MLETA1, was upregulated in highly metastatic cells and their secreted exosomes. Overexpression of lnc-MLETA1 augmented cell migration and invasion of lung cancer. Conversely, knockdown of lnc-MLETA1 attenuated the motility and metastasis of lung cancer cells. Interestingly, exosome-transmitted lnc-MLETA1 promoted cell motility and metastasis of lung cancer. Reciprocally, targeting lnc-MLETA1 with an LNA suppressed exosome-induced lung cancer cell motility. Mechanistically, lnc-MLETA1 regulated the expression of EGFR and IGF1R by sponging miR-186-5p and miR-497-5p to facilitate cell motility. The clinical datasets revealed that lnc-MLETA1 is upregulated in tumor tissues and predicts survival in lung cancer patients. Importantly, the levels of exosomal lnc-MLETA1 in plasma were positively correlated with metastasis in lung cancer patients.ConclusionsThis study identifies lnc-MLETA1 as a critical exosomal lncRNA that mediates crosstalk in lung cancer cells to promote cancer metastasis and may serve as a prognostic biomarker and potential therapeutic target for lung cancer diagnosis and treatment.

An Evaluation of the User Experience and Privacy Concerns of Individuals Misusing Opioids Using a Location Tracking Mobile Application

Opioid use disorder is a growing public health concern in the United States, causing economic burden and hindered by stigma. New forms of data, including location data, may improve the effectiveness of interventions for preventing and treating opioid use disorder and/or misuse, increase access to treatment and address racial and ethnic disparities. This qualitative study aimed to identify factors that contribute to users’ experience with a publicly available location-tracking mobile app - and investigate their privacy and ethical concerns. The study was conducted through two 15-minute interviews within a 48-h time frame. Participants were recruited from a pool of past research participants, Facebook ads, and referrals, and had to meet certain inclusion criteria related to opioid use disorder and/or misuse. The study had a final sample of 30 participants, 15 male and 15 female. The study suggests that a simple onboarding process and convenient experience can enhance participant adherence to the study app and other similar location-based research apps. However, the study also found that participants had concerns about privacy and transparency about locational privacy when sharing their location data. To improve the app, researchers suggest incorporating user behavior earlier in the app development stage. The study also highlights the importance of addressing ethical and privacy concerns such as limiting the types of collected data, incorporating data encryption and retention strategies, giving access to research staff only, and not sharing the data with third-party companies or law enforcement agencies to increase user satisfaction.

Utilizing Mobility Tracking to Identify Hotspots for Contagious Disease Spread

A significant global health problem nowadays is the incidence of serious infectious illnesses. An extraordinary humanitarian crisis has been brought on by the current COVID-19 pandemic, which has spread around the world. The spread of new viruses has put established healthcare institutions under tremendous strain and created a number of pressing problems. It is important to predict the future movement and pattern of the illness in order to decrease infectious instances and maximize recovered cases. This research paper aims to utilize mobility tracking as a means to identify hotspots for contagious disease spread. The study focuses on collecting and analyzing mobility data from UNITEN students using Google Map data over a period of two weeks. The paper describes the data collection process, data pre-processing steps, and the application of the HDBSCAN algorithm for hotspot clustering. The results demonstrate the effectiveness of HDBSCAN in identifying hotspots based on the mobility data. The findings highlight the potential of mobility tracking for disease surveillance and provide insights for public health interventions and preventive measures.

A Mobile Health Application Using Geolocation for Behavioral Activity Tracking.

The increasing popularity of mHealth presents an opportunity for collecting rich datasets using mobile phone applications (apps). Our health-monitoring mobile application uses motion detection to track an individual's physical activity and location. The data collected are used to improve health outcomes, such as reducing the risk of chronic diseases and promoting healthier lifestyles through analyzing physical activity patterns. Using smartphone motion detection sensors and GPS receivers, we implemented an energy-efficient tracking algorithm that captures user locations whenever they are in motion. To ensure security and efficiency in data collection and storage, encryption algorithms are used with serverless and scalable cloud storage design. The database schema is designed around Mobile Advertising ID (MAID) as a unique identifier for each device, allowing for accurate tracking and high data quality. Our application uses Google's Activity Recognition Application Programming Interface (API) on Android OS or geofencing and motion sensors on iOS to track most smartphones available. In addition, our app leverages blockchain and traditional payments to streamline the compensations and has an intuitive user interface to encourage participation in research. The mobile tracking app was tested for 20 days on an iPhone 14 Pro Max, finding that it accurately captured location during movement and promptly resumed tracking after inactivity periods, while consuming a low percentage of battery life while running in the background.

Broiler Mobility Assessment via a Semi-Supervised Deep Learning Model and Neo-Deep Sort Algorithm.

Mobility is a vital welfare indicator that may influence broilers' daily activities. Classical broiler mobility assessment methods are laborious and cannot provide timely insights into their conditions. Here, we proposed a semi-supervised Deep Learning (DL) model, YOLOv5 (You Only Look Once version 5), combined with a deep sort algorithm conjoined with our newly proposed algorithm, neo-deep sort, for individual broiler mobility tracking. Initially, 1650 labeled images from five days were employed to train the YOLOv5 model. Through semi-supervised learning (SSL), this narrowly trained model was then used for pseudo-labeling 2160 images, of which 2153 were successfully labeled. Thereafter, the YOLOv5 model was fine-tuned on the newly labeled images. Lastly, the trained YOLOv5 and the neo-deep sort algorithm were applied to detect and track 28 broilers in two pens and categorize them in terms of hourly and daily travel distances and speeds. SSL helped in increasing the YOLOv5 model's mean average precision (mAP) in detecting birds from 81% to 98%. Compared with the manually measured covered distances of broilers, the combined model provided individual broilers' hourly moved distances with a validation accuracy of about 80%. Eventually, individual and flock-level mobilities were quantified while overcoming the occlusion, false, and miss-detection issues.

Characterization of superspreaders movement in a bidirectional corridor using a social force model.

During infectious disease outbreaks, some infected individuals may spread the disease widely and amplify risks in the community. People whose daily activities bring them in close proximity to many others can unknowingly become superspreaders. The use of contact tracking based on social networks, GPS, or mobile tracking data can help to identify superspreaders and break the chain of transmission. We propose a model that aims at providing insight into risk factors of superspreading events. Here, we use a social force model to estimate the superspreading potential of individuals walking in a bidirectional corridor. First, we applied the model to identify parameters that favor exposure to an infectious person in scattered crowds. We find that low walking speed and high body mass both increase the expected number of close exposures. Panic events exacerbate the risks while social distancing reduces both the number and duration of close encounters. Further, in dense crowds, pedestrians interact more and cannot easily maintain the social distance between them. The number of exposures increases with the density of person in the corridor. The study of movements reveals that individuals walking toward the center of the corridor tend to rotate and zigzag more than those walking along the edges, and thus have higher risks of superspreading. The corridor model can be applied to designing risk reduction measures for specific high volume venues, including transit stations, stadiums, and schools.

BIM-based smart safety monitoring system using a mobile app: a case study in an ongoing construction site

Purpose Construction safety is a crucial aspect that has far-reaching impacts on economic development. But safety monitoring is often reliant on labor-based observations, which can be prone to errors and result in numerous fatalities annually. This study aims to address this issue by proposing a cloud-building information modeling (BIM)-based framework to provide real-time safety monitoring on construction sites to enhance safety practices and reduce fatalities. Design/methodology/approach This system integrates an automated safety tracking mobile app to detect hazardous locations on construction sites, a cloud-based BIM system for visualization of worker tracking on a virtual construction site and a Web interface to visualize and monitor site safety. Findings The study’s results indicate that implementing a comprehensive automated safety monitoring approach is feasible and suitable for general indoor construction site environments. Furthermore, the assessment of an advanced safety monitoring system has been successfully implemented, indicating its potential effectiveness in enhancing safety practices in construction sites. Practical implications By using this system, the construction industry can prevent accidents and fatalities, promote the adoption of new technologies and methods with minimal effort and cost and improve safety outcomes and productivity. This system can reduce workers’ compensation claims, insurance costs and legal penalties, benefiting all stakeholders involved. Originality/value To the best of the authors’ knowledge, this study represents the first attempt in Bangladesh to develop a mobile app-based technological solution aimed at reforming construction safety culture by using BIM technology. This has the potential to change the construction sector’s attitude toward accepting new technologies and cultures through its convenient choice of equipment.

Айтрекинг-исследование трудностей обучения детей с нарушением слуха

<p>The article analyzes the specifics of the organization of the perceptual activity of preschoolers with and without hearing impairment with different forms of instruction in the learning process. A comparative study of a sample of children aged 4-6 years was carried out: typically developing and with sensorineural hearing loss after cochlear implantation. During the experiment, the combinations of verbal and non-verbal instructions were varied. During the training task, eye movements were recorded by a Pupil Labs mobile tracker in the form of glasses. When changing the forms of instructions in 4 series of the experiment, it was found that a measure of reducing visual attention in the learning process in children with hearing impairment is changes in the periods of fixations when they are focused on the regions targeted for the learning task (such as a form for completing the task, a sample, an adult's face) . In children with hearing impairment, during the learning process, a transformation of perceptual processes was recorded depending on the form of the instruction: whether fixation on non-target stimuli decreases, fixation occurs faster or slower, whether the cognitive complexity of information decreases, whether fixation will be longer in target areas, whether there is constant attention and shared attention with an adult. It is shown how a different form of instruction allows you to restructure the perception of a child with hearing impairment, focusing attention on the elements relevant to the task. Differences in the change in the perceptual activity of typically developing preschoolers and preschoolers with hearing impairment were analyzed with different forms of instruction. It was found that the movement of the eyes of children with hearing impairment, unlike their peers, can be characterized by a significant reduction in orienting perceptual actions. The most effective for children with hearing impairment is the simultaneous use of multimodal means of explaining instructions or separately non-verbal forms of instructions (showing an action or a sample). For typically developing children, non-verbal forms of instruction without verbal accompaniment are not as effective.</p>

‘I like to run to feel’: embodiment and wearable mobile tracking devices in distance running

ABSTRACT Many experienced runners consider the use of wearable devices an important element of the training process. A key techno-utopic promise of wearables lies in the use of proprietary algorithms to identify training load errors in real-time and alert users to risks of running-related injuries. Such real-time ‘knowing’ is claimed to obviate the need for athletes’ subjective judgements by telling runners how they have deviated from a desired or optimal training load or intensity. This realist-contoured perspective is, however, at odds with sociological research indicating that users of wearables engage in active ‘data sense-making’ that is highly contextualised. To investigate how athletes use (or not) algorithmic analysis to understand, make sense of, and improve their performance in real-time, we undertook qualitative interviews with distance runners to explore lived experiences of running with wearables. The runners described how they actively interpreted data from wearables, drawing on their own experience, ‘somatic knowledge’, and embodied ways of knowing. This allowed them to assess the relevance and usefulness of data in relation to their own goals, intentions, and feelings. Our findings challenge the techno-utopic promises of real-time and predictive analytics.

Human mobility tracks as FAIR data: Designing a privacy-preserving repository for GNSS-based activity tracking data

Abstract. Devices with integrated global navigation satellite system (GNSS) receivers have enabled citizens to accurately record activities such as bicycle trips, runs, and walks. Due to its spatiotemporal extent and high level of detail, GNSS-based activity tracking data is a valuable source of information on active modes of transportation. At the same time, movement recordings of individuals are sensitive data and are associated with privacy concerns. In this work, we present a privacy-aware platform where citizens can contribute GNSS tracks to an open repository. The repository is published according to the FAIR data principles: findable, accessible, interoperable, and reusable. This provides the opportunity to use the data as a benchmark for the development of GNSS trajectory processing methods. The platform’s privacy module processes each track before publication, concealing stay points, generalizing the tracks in the temporal dimension, and suppressing tracks in sparsely populated areas. This approach mitigates the most likely re-identification attacks and limits the amount of information that could leak if an attacker succeeds with re-identification. As a residual risk remains, the platform sensitizes users to privacy risks and enables them to make informed decisions about publishing their data.

A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups.

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper aims to reveal the complete silent transmission dynamics of COVID-19 by agent-based simulations overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, by inferring individual professions, visited locations and age group, we found that retailing, catering and hotel staff are more likely to get infected than other professions, and elderly and retirees are more likely to get infected at home than outside home.

Timeliness and completeness of weekly surveillance data reporting on epidemic prone diseases in Uganda, 2020–2021

IntroductionDisease surveillance provides vital data for disease prevention and control programs. Incomplete and untimely data are common challenges in planning, monitoring, and evaluation of health sector performance, and health service delivery. Weekly surveillance data are sent from health facilities using mobile tracking (mTRAC) program, and synchronized into the District Health Information Software version 2 (DHIS2). The data are then merged into district, regional, and national level datasets. We described the completeness and timeliness of weekly surveillance data reporting on epidemic prone diseases in Uganda, 2020–2021.MethodsWe abstracted data on completeness and timeliness of weekly reporting of epidemic-prone diseases from 146 districts of Uganda from the DHIS2.Timeliness is the proportion of all expected weekly reports that were submitted to DHIS2 by 12:00pm Monday of the following week. Completeness is the proportion of all expected weekly reports that were completely filled and submitted to DHIS2 by 12:00pm Wednesday of the following week. We determined the proportions and trends of completeness and timeliness of reporting at national level by year, health region, district, health facility level, and facility ownership.ResultsNational average reporting timeliness and completeness was 44% and 70% in 2020, and 49% and 75% in 2021. Eight of the 15 health regions achieved the target for completeness of ≥ 80%; Lango attained the highest (93%) in 2020, and Karamoja attained 96% in 2021. None of the regions achieved the timeliness target of ≥ 80% in either 2020 or 2021. Kampala District had the lowest completeness (38% and 32% in 2020 and 2021, respectively) and the lowest timeliness (19% in both 2020 and 2021). Referral hospitals and private owned health facilities did not attain any of the targets, and had the poorest reporting rates throughout 2020 and 2021.ConclusionWeekly surveillance reporting on epidemic prone diseases improved modestly over time, but timeliness of reporting was poor. Further investigations to identify barriers to reporting timeliness for surveillance data are needed to address the variations in reporting.

Homing tracking control of autonomous underwater vehicle based on adaptive integral event-triggered nonlinear model predictive control

The homing problem of autonomous underwater vehicle with constraints and bounded disturbances is studied in this paper. More specifically, a nonlinear model predictive control (NMPC) framework based on adaptive integral event-triggered (AIET) mechanism is designed for AUV tracking mobile docking station (DS). Firstly, according to the characteristics of AUV homing, a guidance system based on mobile DS is designed, which can provide a feasible reference trajectory for the subsequent AUV tracking independent of the vehicle model. Then, in the framework of AIET-NMPC control, the designed tracking controller can reduce the computational cost while guaranteeing the control accuracy. The proposed AIET mechanism can dynamically adjust the trigger threshold according to the integral error between the real state and the optimal state of the system. In addition, by designing exponential robust constraints in the NMPC optimization problem, the system stability margin is increased. Finally, it strictly proves the Zeno free behavior, the feasibility of the algorithm, and the stability of the system. Simulation results demonstrate that the proposed control approach yields better effects in terms of tracking control performance and computational performance compared to other methods.

Development of a wireless wearable Health Monitoring System with weband mobile tracking system

Health Monitoring Systems (HMS) is becoming increasingly ubiquitous supported by recent technological advances and influenced by high healthcare costs. Health is a necessary aspect in our daily routine and it would make it easier for people to act upon the condition that they are having with these systems. This research paper aims to develop and design a scalable and feasible model of an HMS through advanced sensor and communication technology. The Proof of Concept can be achieved by utilizing low-cost sensors and microprocessors such as heart rate sensors, MAX30205 body temperature sensor, and the NodeMCU. The NodeMCU is an amazing little chip that has onboard WiFi capabilities and will be the way in communicating to Adafruit IO through MQTT protocol. Adafruit IO has feeds and dashboards that will serve as a visual representation of the metadata about the sensor data you push to Adafruit IO from the NodeMCU board. This technology can increase the capacity of physicians to treat and monitor more patients, can offer better access to healthcare, and assures patients to get that daily information about their conditions.

Human Activity Detection Using Smart Wearable Sensing Devices with Feed Forward Neural Networks and PSO

Hospitals must continually monitor their patients’ actions to lower the chance of accidents, such as patient falls and slides. Human behavior is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating a static link that is used to influence human behavior is challenging, since it is hard to forecast how individuals will think or act in response to a certain event. Mobility tracking depends on intelligent monitoring systems that apply artificial intelligence (AI) applications referred to as “categories”. Because motion sensors, such as gyroscopes and accelerometers, output unconnected data that lack labels, event detection is a vital task. The fall feature parameters of tridimensional accelerometers and gyroscope sensors are presented and used, and the classification technique is based on distinguishing characteristics. This study focuses on the age-old problem of tracking turbulence in motion to improve detection precision. We trained the model, considering that detection accuracy is limited by factors such as the subject’s mass, velocity, and gait style. This is performed by employing an experimental dataset. When we used the sophisticated technique of particle swarm optimization (PSO) in combination with a four-stage forward neural network (4SFNN) to forecast four different types of turbulent motion, we observed that the total prediction accuracy was 98.615% accurate.

Menstrual Tracking Mobile App Review by Consumers and Health Care Providers: Quality Evaluations Study.

Women's menstrual cycle is an important component of their overall health. Physiological cycles and associated symptoms can be monitored continuously and used as indicators in various fields. Menstrual apps are accessible and can be used to promote overall female health. However, no study has evaluated these apps' functionality from both consumers' and health care providers' perspectives. As such, the evidence indicating whether the menstrual apps available on the market provide user satisfaction is insufficient. This study was performed to investigate the key content and quality of menstrual apps from the perspectives of health care providers and consumers. We also analyzed the correlations between health care provider and consumer evaluation scores. On the basis of this analysis, we offer technical and policy recommendations that could increase the usability and convenience of future app. We searched the Google Play Store and iOS App Store using the keywords "period" and "menstrual cycle" in English and Korean and identified relevant apps. An app that met the following inclusion criteria was selected as a research app: nonduplicate; with >10,000 reviews; last updated ≤180 days ago; relevant to this topic; written in Korean or English; available free of charge; and currently operational. App quality was evaluated by 6 consumers and 4 health care providers using Mobile Application Rating Scale (MARS) and user version of the Mobile Application Rating Scale (uMARS). We then analyzed the correlations among MARS scores, uMARS scores, star ratings, and the number of reviews. Of the 34 apps, 31 (91%) apps could be used to predict the menstrual cycle, and 2 (6%) apps provided information pertinent to health screening. All apps that scored highly in the MARS evaluation offer a symptom logging function and provide the user with personalized notifications. The "Bom Calendar" app had the highest MARS (4.51) and uMARS (4.23) scores. The MARS (2.22) and uMARS (4.15) scores for the "Menstrual calendar-ovulation & pregnancy calendar" app were different. In addition, there was no relationship between MARS and uMARS scores (r=0.32; P=.06). We compared consumer and health care provider ratings for menstrual apps. Continuous monitoring of app quality from consumer and health care provider perspectives is necessary to guide their development and update content.

OPTIMIZATION OF INTELLIGENT TELECOMMUNICATION NETWORKS

The paper presents the results of research on the use of machine learning in telecommunication networks and describes the basics of the theory of artificial intelligence. The impact of dynamic Bayesian network (DBN) and DNN on the development of many technologies, including user activity detection, channel estimation, and mobility tracking, is determined. The indicators of the effectiveness of communications based on the theory of information bottlenecks, which is at the junction of machine learning and forecasting, statistics and information theory, are considered. A neural network model that is pretrained for high-level tasks and divided into transmitter-side and receiver-side uses is investigated. The process of learning the model, which is performed after its adjustment, taking ino account the existing transmission channels, is considered. New ANN learning techniques capable of predicting or adapting to sudden changes in a wireless network, such as federated learning and multiagent reinforcement learning (MARL), are reviewed. The DBN model, which describes a system that dynamically changes or develops over time, is studied. The considered model provides constant monitoring of work and updating of the system and prediction of its behavior. Distributed forecasting of channel states and user locations as a key component in the development of reliable wireless communication systems is studied. The possibility of increasing the number of degrees of freedom of the generalized wireless channel G(E) in terms of: the physical propagation channel, the directional diagram of the antenna array and mutual influence, electromagnetic physical characteristics is substantiated. The impact of ultra-highresolution theory on the development of many technologies, including localization algorithms, compressed sampling, and wireless imaging algorithms, is also identified. Mathematical expressions for optimizing the functional characteristics of 5G/6G radio networks are presented using new, sufficiently formal and at the same time universal mathematical tools with an emphasis on deep learning technologies, which allow systematic, reliable and interpretable analysis of large random networks and a wide range of their network models and practical networks.

MiR-455-5p suppresses PDZK1IP1 to promote the motility of oral squamous cell carcinoma and accelerate clinical cancer invasion by regulating partial epithelial-to-mesenchymal transition

BackgroundLymph node and distant metastasis contribute to poor outcomes in patients with oral squamous cell carcinoma (OSCC). The mechanisms regulating cancer migration and invasion play a key role in OSCC.MethodsWe determined migration and invasion ability of OSCC by wound-healing assay, two-chamber transwell invasion assay and cell mobility tracking and evaluated tumor metastasis in vivo. Western blot (WB), qRT-PCR, RNA-seq, dual-luciferase reporter assays and nuclear/cytoplasmic fractionation were performed to investigate the potential mechanism. Immunohistochimical (IHC) staining determined vimentin and PDZK1IP1 expression in OSCC tissues.Results and conclusionIn this study, we determined that miR-455-5p was associated with lymph node metastasis and clinical invasion, leading to poor outcomes in patients with OSCC. MiR-455-5p promoted oral cancer cell migration and invasion and induced epithelial-to-mesenchymal transition (EMT). We also identified a new biomarker, PDZK1IP1 (MAP17), that was targeted by miR-455-5p. PDZK1IP1 knockdown led to migration, metastasis, EMT, and increased transforming growth factor-β signaling in OSCC. In addition, miR-455-5p overexpression and PDZK1IP1 inhibition promoted collective OSCC cell migration. According to data from the Cancer Genome Atlas database and the NCKU-OrCA-40TN data set, miR-455-5p and PDZK1IP1 are positively and negatively correlated, respectively, with partial EMT score. High miR-455-5p expression was associated with high vimentin levels and low MAP17 H-scores. The patients with low MAP17 expression had higher rates of disease recurrence than did patients with high MAP17 expression, especially for patients with clinical invasion risk factors and low MAP17 expression. These results suggest that miR-455-5p suppresses PDZK1IP1 expression and mediates OSCC progression. MiR-455-5p and PDZK1IP1 may therefore serve as key biomarkers and be involved in regulating partial EMT in OSCC cells. PDZK1IP1 expression may also serve as an independent factor that impacts outcomes in patients with clinical risk factors for recurrence.

A Communication, Management and Tracking Mobile Application for Enhancing Earthquake Preparedness and Situational Awareness in the Event of an Earthquake

The presented DepApp is an application that provides information about the intensity, time and whereabouts of a recent earthquake. In addition, the presented application is a mobile application that contains emergency phone numbers and can provide general information about an earthquake which people can benefit from before and after the earthquake. With the presented application, people will be able to easily access information about earthquakes. Moreover, safe areas in which to shelter after an earthquake are offered to the user. Information about an earthquake kit is given to user in the application. The earthquake kit should be prepared before the earthquake. The application also includes emergency sounds that can be used in the event of an earthquake. A survey system is designed to determine the intensity of the earthquake as it is felt by the users. After the earthquake, the application presents the intensity, time, and location of the earthquake to the user. Thus, with this application, users will be more knowledgeable and prepared for an earthquake.

Immersive Remote Collaboration and Workplace Tracking Systems, Mobile Biometric and Sentiment Data, and Algorithmic Monitoring and Wearable Augmented Reality Technologies in Generative Artificial Intelligence-based Virtual Human Resource Management

Immersive Remote Collaboration and Workplace Tracking Systems, Mobile Biometric and Sentiment Data, and Algorithmic Monitoring and Wearable Augmented Reality Technologies in Generative Artificial Intelligence-based Virtual Human Resource Management