Mechanism underlying synergic activation of Tyrosinase promoter by MITF and IRF4

Background: The transcription factor interferon regulatory factor 4 (IRF4) was identified to be involved in human pigmentation by genome-wide association studies (GWASs). The rs12203592-[T/C], which is located in intron 4 of IRF4, shows the strongest link to these pigmentation phenotypes including freckling, sun sensitivity, eye and hair color. Previous studies indicated a functional cooperation of IRF4 with Microphthalmia-associated transcription factor (MITF), a causing gene of Waardenburg syndrome (WS), to synergistically trans-activate Tyrosinase (TYR). However, the underlying mechanism is still unknown. Methods: To investigate the importance of DNA binding in the synergic effect of IRF4. Reporter plasmids with mutant TYR promoters was generated to locate the IRF4 DNA binding sites in the Tyrosinase minimal promoter. By building MITF and IRF4 truncated mutations plasmids, the necessary regions of the synergy functions of these two proteins were also located. Results: The cooperative effect between MITF and IRF4 was specific for TYR promoter. The DNA-binding of IRF4 was critical for the synergic function. IRF4 DNA binding sites in TYR promoter were identified. The Trans-activation domains in IRF4 (aa134-207, aa300-420) were both important for the synergic function, whereas the auto-mask domain (aa207-300) appeared to mask the synergic effect. Mutational analysis in MITF indicated that both DNA-binding and transcriptional activation domains were both required for this synergic effect. Conclusions: Here we showed that IRF4 potently synergized with MITF to activate the TYR promoter, which was dependent on DNA binding of IRF4. The synergic domains in both IRF4 and MITF were identified by mutational analysis. This identification of IRF4 as a partner for MITF in regulation of TYR may provide an important molecular function for IRF4 in the genesis of melanocytes and the pathogenic mechanism in WS

Rethinking the Framework of Smart Water System: A Review

Throughout the past years, governments, industries, and researchers have shown increasing interest in incorporating smart techniques, including sensor monitoring, real-time data transmitting, and real-time controlling into water systems. However, the design and construction of such a smart water system are still not quite standardized for massive applications due to the lack of consensus on the framework. The major challenge impeding wide application of the smart water network is the unavailability of a systematic framework to guide real-world design and deployment. To address this challenge, this review study aims to facilitate more extensive adoption of the smart water system, to increase effectiveness and efficiency in real-world water system contexts. A total of 32 literature pieces including 1 international forum, 17 peer-reviewed papers, 10 reports, and 4 presentations that are directly related to frameworks of smart water system have been reviewed. A new and comprehensive smart water framework, including definition and architecture, was proposed in this review paper. Two conceptual metrics (smartness and cyber wellness) were defined to evaluate the performance of smart water systems. Additionally, three pieces of future research suggestions were discussed, calling for broader collaboration in the community of researchers, engineers, and industrial and governmental sectors to promote smart water system applications

Influence of sleep disruption on protein accumulation in neurodegenerative diseases

Abnormal accumulation of disease proteins in the central nervous system is a neuropathological feature in neurodegenerative disorders. Recently, a growing body of evidence has supported a role of disruption of the sleep-wake cycle in disease development, pathological changes and abnormal protein accumulation in neurodegenerative diseases, especially in Alzheimer’s disease and Parkinson’s disease. Sleep deprivation promotes abnormal accumulation of disease proteins. Interestingly, amyloid-β (Aβ) has daily oscillations in human cerebral spinal fluid (CSF) and is cleared more in sleep. Both circadian genes and circadian hormones are associated with disease protein deposition. Recently, the glymphatic pathway and meningeal lymphatics have been shown to play a critical role in Aβ clearance, which is mediated by the aquaporin (AQP-4) water channel on astrocytes. The rate of the clearance of Aβ by the glymphatic pathway is different during the sleep/wake cycle. Most importantly, circadian rhythms facilitate glymphatic clearance of solutes and Aβ in the CSF and interstitial fluid in an AQP-4-dependent manner, which further provides evidence for the involvement of circadian rhythms in disease protein clearance

The Online DQM of BESIII

The BESIII is a general-purpose experiment for studying electron-positron collisions at BEPCII which is located at IHEP, Beijing, China. It works in the τ-charm region mainly. Several world’s largest samples in this region had been collected. The BESIII DQM is a lightweight online data quality monitoring (DQM) solution at BESIII. It uses the full offline reconstruction software to reconstruct a part of data for real-time monitoring the data quality. The document gives an overview of the BESIII DQM system, including the framework, main components and data flow. The DQM system separates online DAQ and offline software environment as much as possible and is easy to expand

The Online DQM of BESIII

The BESIII is a general-purpose experiment for studying electron-positron collisions at BEPCII which is located at IHEP, Beijing, China. It works in the τ-charm region mainly. Several world’s largest samples in this region had been collected. The BESIII DQM is a lightweight online data quality monitoring (DQM) solution at BESIII. It uses the full offline reconstruction software to reconstruct a part of data for real-time monitoring the data quality. The document gives an overview of the BESIII DQM system, including the framework, main components and data flow. The DQM system separates online DAQ and offline software environment as much as possible and is easy to expand

Is Clustering Time-Series Water Depth Useful? An Exploratory Study for Flooding Detection in Urban Drainage Systems

As sensor measurements emerge in urban water systems, data-driven unsupervised machine learning algorithms have drawn tremendous interest in event detection and hydraulic water level and flow prediction recently. However, most of them are applied in water distribution systems and few studies consider using unsupervised cluster analysis to group the time-series hydraulic-hydrologic data in stormwater urban drainage systems. To improve the understanding of how cluster analysis contributes to flooding location detection, this study compared the performance of K-means clustering, agglomerative clustering, and spectral clustering in uncovering time-series water depth dissimilarity. In this work, the water depth datasets are simulated by an urban drainage model and then formatted for a clustering problem. Three standard performance evaluation metrics, namely the silhouette coefficient index, Calinski–Harabasz index, and Davies–Bouldin index are employed to assess the clustering performance in flooding detection under various storms. The results show that silhouette coefficient index and Davies–Bouldin index are more suitable for assessing the performance of K-means and agglomerative clustering, while the Calinski–Harabasz index only works for spectral clustering, indicating these clustering algorithms are metric-dependent flooding indicators. The results also reveal that the agglomerative clustering performs better in detecting short-duration events while K-means and spectral clustering behave better in detecting long-duration floods. The findings of these investigations can be employed in urban stormwater flood detection at the specific junction-level sites by using the occurrence of anomalous changes in water level of correlated clusters as flood early warning for the local neighborhoods

Based on a single-sided hertzian-contact forced oscillator model design method of traveling wave rotary ultrasonic motors drive circuit under high voltage excitation

No abstract available

Revealing the Challenges of Smart Rainwater Harvesting for Integrated and Digital Resilience of Urban Water Infrastructure

Smart rainwater harvesting (RWH) systems can automatically release stormwater prior to rainfall events to increase detention capacity on a household level. However, impacts and benefits of a widespread implementation of these systems are often unknown. This works aims to investigate the effect of a large-scale implementation of smart RWH systems on urban resilience by hypothetically retrofitting an Alpine municipality with smart rain barrels. Smart RWH systems represent dynamic systems, and therefore, the interaction between the coupled systems RWH units, an urban drainage network (UDN) and digital infrastructure is critical for evaluating resilience against system failures. In particular, digital parameters (e.g., accuracy of weather forecasts, or reliability of data communication) can differ from an ideal performance. Therefore, different digital parameters are varied to determine the range of uncertainties associated with smart RWH systems. As the results demonstrate, smart RWH systems can further increase integrated system resilience but require a coordinated integration into the overall system. Additionally, sufficient consideration of digital uncertainties is of great importance for smart water systems, as uncertainties can reduce/eliminate gained performance improvements. Moreover, a long-term simulation should be applied to investigate resilience with digital applications to reduce dependence on boundary conditions and rainfall patterns