Mutationsscanning der Transaktivierungsdomäne von CLOCK Exon 19

In mammalian circadian clocks, the CLOCK:BMAL1 heterodimer transactivates E-box enhancer elements to regulate the transcription of circadian clock targets. The CLOCK exon 19 deletion (CLOCKΔ19) lacks 51 amino acids and exhibits an arrhythmic phenotype. For more than 27 years, we had no clear insight into the essential amino acids of CLOCK exon 19-domain that are required for normal transactivation. To investigate this, we developed a CLOCK rescue system based on a human-derived reporter cell line with CLOCK-knockout using CRISPR/Cas9 technology. We performed alanine mutation scanning for the entire exon 19-domain. The CLOCK-knockout cell line is arrhythmic, and the rhythmic phenotype was rescued by transduction with wild-type CLOCK but not with CLOCK∆19. We identified 10 CLOCK variants that recapitulate the deletion of exon 19, suggesting that these residues are essential for CLOCK protein functionality. We also identified certain residues where mutations shortened the period. Some of those mutations showed a dominant phenotype in wild-type cell line. Interestingly, many of the identified mutations play a role in the hydrophobic interaction of the predicted dimer of CLOCK exon 19-domains. These results reveal critical residues responsible for CLOCK functionality. Our data also indicate the importance of exon 19-domain dimerization to serve as a platform for activator and repressor binding, which is critical for normal circadian rhythms.In zirkadianen Uhren von Säugetieren transaktiviert das CLOCK:BMAL1-Heterodimer E-Box-Enhancer-Elemente, um die Transkription zirkadian exprimierter Zielgene zu regulieren. Der CLOCKΔ19-Mutante fehlen 51 Aminosäuren und sie weist einen arrhythmischen Phänotyp auf. Mehr als 27 Jahre lang fehlte ein klarer Einblick, welche Aminosäuren des CLOCK-Exons 19 für eine normale Transaktivierung erforderlich sind. Um dies zu untersuchen, haben wir ein CLOCK-Rescue-System entwickelt, das auf einer humanen, mittels CRISPR/Cas9-Technologie generierten CLOCK-Knockout- Reporterzelllinie basiert. Die CLOCK-Knockout-Zelllinie ist arrhythmisch, und der rhythmische Phänotyp wurde durch Transduktion mit Wildtyp-CLOCK, nicht aber mit CLOCKΔ19 Rescue. Mittels Alanin-Mutationsscanning konnten wir 10 CLOCKVarianten identifizieren, die die Deletion von Exon 19 rekapitulieren, was darauf schließen lässt, dass diese Reste für die Funktionalität des CLOCK-Proteins wesentlich sind. Des Weiteren konnten wir Punktmutationen identifizieren, die die Periode verkürzten. Einige dieser Mutationen zeigten einen dominanten Phänotyp in Wildtyp-Zelllinien. Viele dieser Reste spielen eine Rolle bei der hydrophoben Interaktion eines postulierten Dimers der CLOCK-Exon-19-Domänen. Diese Studie identifiziert somit entscheidende Aminosäurereste, die für die Funktionalität von CLOCK verantwortlich sind. Unsere Daten deuten zudem darauf hin, wie wichtig die Dimerisierung des Exons 19 ist, um als Plattform für die Bindung von Aktivatoren und Repressoren zu dienen, was für normale zirkadiane Rhythmen entscheidend ist

Improving frequency response for AC interconnected microgrids containing renewable energy resources

Interconnecting two or more microgrids can help improve power system performance under changing operational circumstances by providing mutual and bidirectional power assistance. This study proposes two interconnected AC microgrids based on three renewable energy sources (wind, solar, and biogas). The wind turbine powers a permanent magnet synchronous generator. A solar photovoltaic system with an appropriate inverter has been installed. In the biogas generator, a biogas engine is connected to a synchronous generator. M1 and M2, two interconnected AC microgrids, are investigated in this study. M2 is connected to a hydro turbine, which provides constant power. The distribution power loss, frequency, and voltage of interconnected AC microgrids are modeled as a multi-objective function (OF). Minimizing this OF will result in optimal power flow and frequency enhancement in interconnected AC microgrids. This research is different from the rest of the research works that talk about the virtual inertia control (VIC) method, as it not only improves frequency using an optimal controller but also achieves optimal power flow in microgrids. In this paper, the following five controllers have been studied: proportional integral controller (PI), fractional-order PI controller (FOPI), fuzzy PI controller (FPI), fuzzy fractional-order PI controller (FFOPI), and VIC based on FFOPI controller. The five controllers are tuned using particle swarm optimization (PSO) to minimize the (OF). The main contribution of this paper is the comprehensive study of the performance of interconnected AC microgrids under step load disturbances, the eventual grid following/forming contingencies, and the virtual inertia control of renewable energy resources used in the structure of the microgrids, and simulation results are recorded using the MATLAB™ platform. The voltages and frequencies of both microgrids settle with zero steady-state error following a disturbance within 0.5 s with less overshoots/undershoots (3.7e-5/-0.12e-3) using VIC. Moreover, the total power losses of two interconnected microgrids must be considered for the different controllers to identify which one provides the best optimal power flow

Optimal Power Management of Interconnected Microgrids Using Virtual Inertia Control Technique

Two interconnected AC microgrids are proposed based on three renewable energy sources (RESs): wind, solar, and biogas. The wind turbine drives a permanent magnet synchronous generator (PMSG). A solar photovoltaic system (SPVS) with an appropriate inverter was incorporated. The biogas genset (BG) consists of a biogas engine coupled with a synchronous generator. Two interconnected AC microgrids, M1 and M2, were considered for study in this work. The microgrid M2 is connected to a diesel engine (DE) characterized by a continuous power supply. The distribution power loss of the interconnected AC microgrids comprises in line loss. The M1 and M2 losses are modeled as an objective function (OF). The power quality enhancement of the interconnected microgrids will be achieved by minimizing this OF. This research also created a unique frequency control method called virtual inertia control (VIC), which stabilizes the microgrid frequency using an optimal controller. In this paper, the following five controllers are studied: a proportional integral controller (PI), a fractional order PI controller (FOPI), a fuzzy PI controller (FPI), a fuzzy fractional order PI controller (FFOPI), and a VIC based on FFOPI controller. The five controllers were tuned using particle swarm optimization (PSO) to minimize the (OF). The main contribution of this paper is the comprehensive study of the performance of interconnected AC microgrids under step load disturbances, stepn changes in wind/solar input power, and eventually grid following/forming contingencies as well as the virtual inertia control of renewable energy resources used in the structure of the microgrid

Microgripper design and evaluation for automated µ-wire assembly: a survey

Microgrippers are commonly used for micromanipulation of micro-objects from 1 to 100 µm and attain features of reliable accuracy, low cost, wide jaw aperture and variable applied force. This paper aim is to review the design of different microgrippers which can manipulate and assemble µ-wire to PCB connectors. A review was conducted on microgrippers’ technologies, comparing fundamental components of structure and actuators’ types, which determined the most suitable design for the required micromanipulation task. Various microgrippers’ design was explored to examine the suitability and the execution of requirements needed for successful micromanipulation

GestureMoRo: an algorithm for autonomous mobile robot teleoperation based on gesture recognition

Gestures are a common way people communicate. Gesture-based teleoperation control systems tend to be simple to operate and suitable for most people’s daily use. This paper employed a LeapMotion sensor to develop a mobile robot control system based on gesture recognition, which mainly established connections through a client/server structure. The principles of gesture recognition in the system were studied and the relevant self-investigated algorithms—GestureMoRo, for the association between gestures and mobile robots were designed. Moreover, in order to avoid the unstably fluctuated movement of the mobile robot caused by palm shaking, the Gaussian filter algorithm was used to smooth and denoise the collected gesture data, which effectively improved the robustness and stability of the mobile robot’s locomotion. Finally, the teleoperation control strategy of the gesture to the WATER2 mobile robot was realized, and the effectiveness and practicability of the designed system were verified through multiple experiments

Recent Advances In Stimuli-Responsive Drug release and targeting concepts using mesoporous silica nanoparticles

Being a developed and promising approach, nanotechnology has attracted a lot of attention in biomedical and pharmaceutical therapy applications. Among nanostructured materials, mesoporous silica nanoparticles (MSNs) are effectively used as nanocarriers for drug delivery systems. MSNs can be tailored-designed by different synthetic techniques. Their morphological characteristics dictate the type of application of such materials. Recently, polymer-based materials have been employed to functionalize the MSNs surface. These modified nanocarriers are loaded with the drug and can unload their “cargo” upon exposure to either endogenous or exogenous types of stimuli. In this study, different targeting concepts, including passive, active, vascular, nuclear, and multistage targeting, are discussed

Enhancing Self-consumption of PV-battery Systems Using a Predictive Rule-based Energy Management

A predictive real-time Energy Management System (EMS) is proposed which improves PV self-consumption and operating costs using a novel rule-based battery scheduling algorithm. The proposed EMS uses the day-ahead demand and PV generation forecasting to determine the best battery scheduling for the next day. The proposed method optimizes the use of the battery storage and extends battery lifetime by only storing the required energy by considering the forecasted day-ahead energy at peak time. The proposed EMS has been implemented in MATLAB software and using Active Office Building on the Swansea University campus as a case study. Results are compared favorably with published state-of-the-arts algorithms to demonstrate its effectiveness. Results show a saving of 20% and 41% in total energy cost over six months compared to a forecast-based EMS and to a conventional EMS, respectively. Furthermore, a reduction of 54% in the net energy exchanged with the utility by avoiding the unnecessary charge/discharge cycles

Investigation of Electric Vehicles Contributions in an Optimized Peer-to-Peer Energy Trading System

The rapid increase in integration of Electric Vehicles (EVs) and Renewable Energy Sources (RESs) at the consumption level poses many challenges for network operators. Recently, Peer-to-Peer (P2P) energy trading has been considered as an effective approach for managing RESs, EVs, and providing market solutions. This paper investigates the effect of EVs and shiftable loads on P2P energy trading with enhanced Vehicle to Home (V2H) mode, and proposes an optimized Energy Management Systems aimed to reduce the net energy exchange with the grid. Mixed-integer linear programming (MILP) is used to find optimal energy scheduling for smart houses in a community. Results show that the V2H mode reduces the overall energy costs of each prosumer by up to 23% compared to operating without V2H mode (i.e., EVs act as a load only). It also reduces the overall energy costs of the community by 15% compared to the houses operating without the V2H mode. Moreover, it reduces the absolute net energy exchanged between the community and the grid by 3%, which enhances the energy independence of the community

Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II

Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of the EAF-based steel-making process based on a multi-objective optimisation using evolutionary computing and machine learning. Using a large amount of real-world historical data containing 6423 consecutive EAF heats collected from a melt shop in an established steel plant this work not only creates machine learning models for both EAF and ladle furnaces but also simultaneously minimises the total scrap cost and EAF energy consumption per ton of scrap. In the modelling process, several algorithms are tested, tuned, evaluated and compared before selecting Gradient Boosting as the best option to model the data analysed. A similar approach is followed for the selection of the multi-objective optimisation algorithm. For this task, six techniques are tested and compared based on the hypervolume performance indicator to just then select the Non-dominated Sorting Genetic Algorithm II ( NSGA-II ) as the best option. Given this applied research focus on a real manufacturing process, real-world constraints and variables such as individual scrap price, scrap availability, tap additives and ambient temperature are used in the models developed here. A comparison with an equivalent EAF model from the literature showed a 13% improvement using the mean absolute error in the EAF energy usage prediction as a comparative metric. The multi-objective optimisation resulted in reductions in the energy consumption costs that ranged from 1.87% up to 8.20% among different steel grades and scrap cost reductions ranging from 1.15% up to 5.2%. The machine learning models and the optimiser were ultimately deployed with a graphical user interface allowing the melt-shop staff members to make informed decisions while controlling the EAF operation

Generation of Human CRY1 and CRY2 Knockout Cells Using Duplex CRISPR/Cas9 Technology

Circadian clocks are endogenous oscillators essential for orchestrating daily rhythms in physiology, metabolism and behavior. While mouse models have been instrumental to elucidate the molecular mechanism of circadian rhythm generation, our knowledge about the molecular makeup of circadian oscillators in humans is still limited. Here, we used duplex CRISPR/Cas9 technology to generate three cellular models for studying human circadian clocks: CRY1 knockout cells, CRY2 knockout cells as well as CRY1/CRY2 double knockout cells. Duplex CRISPR/Cas9 technology efficiently removed whole exons of CRY genes by using two guide RNAs targeting exon-flanking intron regions of human osteosarcoma cells (U-2 OS). Resulting cell clones did not express CRY proteins and showed short period, low-amplitude rhythms (for CRY1 knockout), long period rhythms (for CRY2 knockout) or were arrhythmic (for CRY1/CRY2 double knockout) similar to circadian phenotypes of cells derived from classical knockout mouse models