Novel Bio-Imaging Techniques Based on Molecular Switching

Fluorescence microscopy has been a fundamental imaging tool for life science research. Fluorescence basically involves only two molecular states: the ground molecular state and the first singlet excited state (the fluorescent state). Astonishingly, it greatly diversified the applicability of fluorescence microscopy in many different ways by incorporating additional molecular states and switching fluorescent molecules through these three or even more molecular states during the fluorescence process. This switching mechanism between additional molecular states, either long lifetime or short lifetime, and two original molecular states actually adds nonlinearity into the linear fluorescence process, which empowers fluorescence microscopy additional imaging capabilities. Herein, we developed four distinct new imaging techniques by taking advantage of this molecular switching mechanism: dark state dynamics sensing and imaging by fluorescence anomalous phase advance, genetically-encoded microviscosity sensor using protein-flexibility mediated photochromism, deep tissue imaging with super-nonlinear fluorescence microscopy, and light-driven fluorescent timer for simultaneous spatial-temporal mapping of protein dynamics in live cells. The first technique, dark state dynamics sensing and imaging, effectively correlates the first triplet state of fluorescent organic dyes with the fluorescence process to produce fluorescence emission with unexpected phase advance compared with the excitation light, that reflects the real-time information of organic dyes' dark states. The last three techniques all harness the unique on-off switch capability of the optical highlighter fluorescent proteins: Dronpa, a photo-switchable fluorescent protein, is demonstrated to experience medium friction during the chromophore's cis-trans isomerization process while photo-switching from the bright state to the dark state; multiphoton fluorescence microscope could achieve higher order nonlinearity and thus deeper image depth in the scattering sample by the population transfer kinetics of the photoinduced molecular switches, such as photo-activatable fluorescent protein etc.; a photo-convertible fluorescent protein, mEos2, shows slow color conversion from green to red under extremely weak near-UV light, that could be used to time protein age. No matter fluorescent organic dyes or optical highlighter fluorescent proteins, the nonlinearity has been demonstrated to create new fluorescence imaging techniques by switching fluorescent molecules between additional molecular states and two original molecular states involved in the fluorescence process

Incorporating geostrophic wind information for improved space-time short-term wind speed forecasting

Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal (TDD) model. This paper proposes to incorporate the geostrophic wind as a new predictor in the TDD model. The geostrophic wind captures the physical relationship between wind and pressure through the observed approximate balance between the pressure gradient force and the Coriolis acceleration due to the Earth's rotation. Based on our numerical experiments with data from West Texas, our new method produces more accurate forecasts than does the TDD model using air pressure and temperature for 1- to 6-hour-ahead forecasts based on three different evaluation criteria. Furthermore, forecasting errors can be further reduced by using moving average hourly wind speeds to fit the diurnal pattern. For example, our new method obtains between 13.9% and 22.4% overall mean absolute error reduction relative to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction relative to the best previous space-time methods in this setting.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS756 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The association between victimization experiences and suicidality:The mediating roles of sleep and depression

Background Prior work suggests that multiple forms of victimization were associated with higher suicide risk among adolescents. However, the mechanisms underlying this association remain unclear. The present study aimed to understand the relationships between the multiple forms of victimization and suicidality by examining the potential mediators of sleep duration and depression. Methods Data for this study came from the 2019 Youth Risk Behavior Survey (YRBS). The hypothesized mediation model included 13,677 American adolescents in 9th through 12th-grade students (48.6 % female) were analyzed using Mplus 7.4, and suicidality (including suicidal ideation, plan, and attempts) as the outcome variables and the multiple forms of victimization (including bullying at school, being threatened at school, electronic bullying, sexual victimization, sexual dating victimization, and physical dating victimization) as the main explanatory variable. Results The relationships between the multiple forms of victimization and suicide risk were mediated by sleep duration, depression, and also serially mediated by sleep duration and depression. Limitations This is a cross-sectional study, and the results cannot inform the causality between these variables. This investigation only included adolescent sleep duration, and other specific sleep problem indicators should be included. Conclusions Longer sleep duration is an important protective factor, pointing the way forward for developing suicide prevention strategies and targeted interventions for adolescents

The Double Burden: The Digital Exclusion and Identity Crisis of Elderly Patients in Rural China

The rapid digitalization of China's healthcare system, a phenomenon that accelerated during the Covid-19 pandemic, had two negative consequences for a significant portion of elderly persons in China. The first is an unfortunate practical outcome: their exclusion from many health services such as online medical appointment platforms, e-prescription requests, obtaining e-referrals, and sharing electronic medical records. The second is an emotionally debilitating identity crisis as elderly persons' former status as knowledgeable senior mentors was replaced with social perceptions of them as helpless and ignorant souls reliant on more youthful persons for guidance and assistance. This article adopts a grounded theory to explore the phenomenon from the perception of excluded elderly persons using participatory observation and in-depth interviews of 44 elderly clients of a rural hospital in the Shandong province in eastern China. The study shows that the current focus on direct practical aspects of digital exclusion may fail to capture the impact and ancillary consequences such as a painful loss of self-esteem by the digitally excluded. As the study illustrates, the practical aspects can all be overcome through intervention by those who aid the digitally excluded but this help may exacerbate the rarely considered ancillary harms of digital exclusion. Studies of digital exclusion will make more significant contributions to our understanding of the phenomenon if they look beyond the obvious direct consequences of digital exclusion to consider possible ancillary and flow-on effects

Developmental pathways of suicidality and self-harm among youth

Suicidality and self-harm among youth are significant public health concerns. This thesis seeks to elucidate the developmental pathways and predictors underpinning these issues, with a particular emphasis on the roles of bullying victimisation (or peer victimisation), parental mental health, youth problem behaviours, and screen time use. Chapter 2 utilised the Zurich Project on the Social Development from Childhood to Adulthood (z-proso) and employed random-intercept cross-lagged panel models to investigate whether bullying victimisation has associations with suicidal ideation and self-harm. The analysis suggested a positive within-person effect between general bullying victimisation at age 15 and suicidal ideation at age 17. Intriguingly, this association is bidirectional, with suicidal ideation at age 17 subsequently leading to general bullying victimisation at age 20. Building on these findings, Chapter 3 examined the mediating roles of depressive symptoms, anxiety symptoms, and substance use in the associations between bullying victimisation and suicidal ideation, focusing on within-person effects. Contrary to expectations, bullying victimisation did not predict subsequent within-person increases in suicidal ideation through these mediators. In Chapter 4, data from the UK’s Millennium Cohort Study (MCS) and a parallel-process latent class growth analysis (LCGA) were utilised to examine the co-developmental trajectories of parental mental health issues and child internalising and externalising problems from early childhood to middle adolescence, as well as their associations with self-harm and suicidal attempts in adolescence. The findings highlight the significance of taking parental distress (especially maternal) and child problem behaviours into account when addressing negative outcomes such as self-harm and suicidal attempts. Chapter 5 examined the role of developmental patterns of screen time during adolescence in suicidality, self-harm, and other mental health and behavioural issues in young adults. Analysis of the z-proso study and a parallel-process LCGA indicated that youths in the trajectory group of increasing videogame and internet use displayed a higher risk for suicidal ideation and self-harm at age 20. This highlights the critical role of screen usage patterns as potential markers of later suicidality and self-harm risk; however, additional examinations are needed to test this association. Overall, this work illuminates the multifaceted developmental predictors of suicidality and self-harm in youth

Wind Speed Forecasting for Power System Operation

In order to support large-scale integration of wind power into current electric energy system, accurate wind speed forecasting is essential, because the high variation and limited predictability of wind pose profound challenges to the power system operation in terms of the efficiency of the system. The goal of this dissertation is to develop advanced statistical wind speed predictive models to reduce the uncertainties in wind, especially the short-term future wind speed. Moreover, a criterion is proposed to evaluate the performance of models. Cost reduction in power system operation, as proposed, is more realistic than prevalent criteria, such as, root mean square error (RMSE) and absolute mean error (MAE). Two advanced space-time statistical models are introduced for short-term wind speed forecasting. One is a modified regime-switching, space-time wind speed fore- casting model, which allows the forecast regimes to vary according to the dominant wind direction and seasons. Thus, it avoids a subjective choice of regimes. The other one is a novel model that incorporates a new variable, geostrophic wind, which has strong influence on the surface wind, into one of the advanced space-time statistical forecasting models. This model is motivated by the lack of improvement in forecast accuracy when using air pressure and temperature directly. Using geostrophic wind in the model is not only critical, it also has a meaningful geophysical interpretation. The importance of model evaluation is emphasized in the dissertation as well. Rather than using RMSE or MAE, the performance of both wind forecasting models mentioned above are assessed by economic benefits with real wind farm data from Pacific Northwest of the U.S and West Texas. Wind forecasts are incorporated into power system economic dispatch models, and the power system operation cost is used as a loss measure for the performance of the forecasting models. From another perspective, the new criterion leads to cost-effective scheduling of system-wide wind generation with potential economic benefits arising from the system-wide generation of cost savings and ancillary services cost savings. As an illustration, the integrated forecasts and economic dispatch framework are applied to the Electric Reliability Council of Texas (ERCOT) equivalent 24- bus system. Compared with persistence and autoregressive models, the first model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars. For the second model, numerical simulations suggest that the overall generation cost can be reduced by up to 6.6% using look-ahead dispatch coupled with spatio-temporal wind forecast as compared with dispatch with persistent wind forecast model

Live adaptation of privacy-enhancing technologies in connected vehicles' data pipelines

Connected vehicles are able to acquire and share enormous amount of information with various onboard sensors and network communication. The information sharing can cause privacy concerns. The uniqueness of these concerns lies in the mobility of connected vehicles, which encounter different situations regularly. Different situations may have different requirements on the intensity of privacy protection. Thus, a situational policy for privacy protection should be applied. To enable a connected vehicle to carry out the situational policy correctly and effectively, we propose “Adaptive Privacy in Flow”, a framework that utilizes stream processing technology to adapt the privacy-enhancing technologies within a connected vehicle’s data pipeline through continuous evaluation of the environments. Our framework provides a solution of applying the situational privacy policy accordingly to the sensor data. It reacts to situational changes automatically, without restart or user intervention. Besides, it allows simultaneous and individual dynamic privacy protection for different third-party applications. Moreover, it is capable of handling the diversity of the data types in connected vehicles, from simple scalar value to complex data type like the images, ensuring a comprehensive privacy protection.Vernetzte Fahrzeuge können mit verschiedenen Onboard-Sensoren und Netzwerkkommunikation enorme Mengen an Informationen erfassen und teilen. Das Teilen von Informationen kann Datenschutzbedenken hervorrufen. Die Einzigartigkeit des Bedenkens besteht in der Mobilität der vernetzten Fahrzeuge, die sich regelmäßig in unterschiedlichen Situationen befinden. Unterschiedliche Situationen können unterschiedliche Anforderungen an die Intensität des Datenschutzes besitzen. Daher sollte eine situative Richtlinie zum Schutz der Privatsphäre angewendet werden. Damit ein vernetztes Fahrzeug die situative Richtlinie korrekt und effektiv umzusetzen kann, schlagen wir "Adaptive Privacy in Flow" vor, ein Framework, das Stream-Processing-Technologie nutzt, um die datenschutzfördernden Technologien innerhalb der Datenpipeline eines vernetzten Fahrzeugs durch kontinuierliche Bewertung der Umgebungen anzupassen. Unser Framework bietet eine Lösung zur Anwendung der situativen Datenschutzrichtlinie entsprechend auf die Sensordaten. Es reagiert automatisch auf Veränderungen in den Situationen, ohne Neustart oder Benutzereingriff. Darüber hinaus, es ermöglicht gleichzeitigen und individuellen dynamischen Datenschutz für verschiedene Drittanbieter-Anwendungen. Außerdem kann es mit der Vielfalt der Datentypen in vernetzten Fahrzeugen umgehen, von einfachen Skalarwerten bis hin zu komplexen Datentypen wie Bildern und gewährleistet einen umfassenden Datenschutz