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Thyroid cancer (TC) is prone to recurrence, and biomarkers for predicting progression-free interval (PFI) are poorly explored. The study investigates the predictive value and underlying biological mechanisms of lncRNA CDKN2B-AS1 in TC. Combining RNA-seq and survival data, we identified that CDKN2B-AS1 was upregulated in TC samples and could be an excellent prognostic indicator. To exclude confounding factors, we divided patients into different subgroups using median CDKN2B-AS1 expression, and the effects of CDKN2B-AS1 on PFI and clinical features were explored in different clinical subgroups. Meanwhile, ssGSEA and ESTIMATE algorithms revealed that CDKN2B-AS1 overexpression may suggest the active state of the immune microenvironment. On the other hand, enrichment analysis also proved the potential influence of CDKN2B-AS1 on immune regulation in TC. Finally, we created a CDKN2B-AS1, microRNAs, and TFs network and discovered a new biomarker (CDKN2B-AS1) that might be employed as a therapeutic target in TC patients.

SZJ-1207 is an antidepressant natural product with a steroidal structure extracted from Stephanotis mucronata. It is a novel antidepressant candidate molecule and all its pharmacokinetic properties have not been reported. In this pharmacokinetic study in mice following oral administration, an accurate and sensitive UPLC-MS/MS method was established and evaluated to measure SZJ-1207 concentrations in mouse plasma and brain samples. The results provide information regarding the pharmacokinetics of SZJ-1207 as a potential antidepressant.

The Japanese government has announced a commitment to net-zero greenhouse gas emissions by 2050. It envisages an important role for hydrogen in the nation’s future energy economy. This paper explores the possibility that a significant source for this hydrogen could be produced by electrolysis fueled by power generated from offshore wind in China. Hydrogen could be delivered to Japan either as liquid, or bound to a chemical carrier such as toluene, or as a component of ammonia. The paper presents an analysis of factors determining the ultimate cost for this hydrogen, including expenses for production, storage, conversion, transport, and treatment at the destination. It concludes that the Chinese source could be delivered at a volume and cost consistent with Japan’s idealized future projections., Hydrogen is regarded as important to Japan’s clean energy transition. Here the authors consider the production of hydrogen by electrolysis fueled by offshore wind power in China, and the potential for delivery to Japan as part of Japan’s transition.

Hydrogen can play a key role in facilitating the transition to a future deeply decarbonized energy system and can help accommodate higher penetrations of renewables in the power system. Arguments to justify this conclusion are supported by an analysis based on realworld data from China’s Western Inner Mongolia (WIM). The economic feasibility and decarbonization potential of renewable-based hydrogen production are discussed through an integrated power-hydrogen-emission analytical framework. The framework combines a high-resolution wind resource analysis with hourly simulation for the operation of power systems and hydrogen production considering technical and economic specifications on selection of three different types of electrolyzers and two operating modes. The results indicate that using wind power to produce hydrogen could provide a cost-competitive alternative (

To smoothen the power fluctuation of the grid, various smart control methods, e.g. vehicle-to-grid (V2G), are developed to rearrange charging and discharging behaviors of Electric Vehicles (EVs) for peak shaving and valley filling. In this paper, a rolling global optimization method is proposed to control the charging/discharging behavior of the EV fleet. Coupled with thorough EV behavior simulation through a trip chain model, the optimization results are more trustworthy and the optimized charging/discharging arrangement could flatten the fluctuation of the grid observably. It proves that the proposed rolling global optimization method could solve the multi-period optimization problem resulted from the uncertainty of EV behavior.

The development of low cost, portable, and disposable biosensors for equipment-free and naked-eye biosensing is in eager demand for their widespread application in biomedical field, but it is still a challenge. Herein, we propose a novel paper analytical device (PAD) for truly equipment-free and naked-eye biosensing using dopamine as the chromogenic agent based on target-initiated catalyzed oxidation reaction. The dopamine-functionalized PAD (DPAD) possesses a significant three-dimensional net structure, excellent hydrophilicity, and unique response toward G-quadruplex DNAs against other DNAs, benefiting the bio/chemo reaction occurrence to assay target biomolecules. In light of the exceptional properties, the fabricated DPAD was applied in the analysis of Dam MTase through target-triggered exponential isothermal amplification. The recognition and methylation of H1 by Dam MTase contribute to formation of abundant hemin/G-quadruplexes, which catalyze oxidation of dopamine into dopachrome and reduce the dopamine amount on the DPAD surface. In comparison with the case in which Dam MTase is absent, an evident deep pink signal originating from dopachrome is observed directly by the naked eye and relied on Dam MTase concentrations. Therefore, truly equipment-free and naked-eye detection of Dam MTase is achieved with a detection limit of 1.46 U/mL. The fabricated DPAD not only achieves Dam MTase-visualized detection but also permits the accurate determination of other analytes by varying recognizable DNA's sequences, thus offering a universal biosensor and depicting significant potential for widespread applications in biomedical field.

Marrow fat accumulates in diabetic conditions but remains elusive. The published works on the relationships between marrow fat phenotypes and glucose homeostasis are controversial.To detect the association of insulin resistance with marrow adiposity in postmenopausal women with newly diagnosed type 2 diabetes (T2D) using chemical shift-encoded water-fat MRI.We measured vertebral proton density fat fraction (PDFF) by 3T-MRI in 75 newly diagnosed T2D and 20 nondiabetic postmenopausal women. Bone mineral density (BMD), whole body fat mass and lean mass were determined by dual-energy X-ray absorptiometry. Insulin sensitivity was estimated using the homeostasis model assessment of insulin resistance (HOMA-IR).Lumbar spine PDFF was higher in women with T2D (65.9 ± 6.8%) than those without diabetes (59.5 ± 6.1%, P = 0.009). There was a consistent inverse association between the vertebral PDFF and BMD. PDFF had a positive association with glycated hemoglobin and HOMA-IR but not with fasting plasma glucose and insulin. PDFF was significantly increased, and BMD was decreased in a linear trend from the lowest (1.90) to highest (≥2.77) HOMA-IR quartile. Multivariate linear regression analyses revealed a positive association between log-transformed HOMA-IR and PDFF after adjustment for multiple covariates (ß = 0.382, P 0.001). The positive association of HOMA-IR with PDFF remained robust when total body lean mass and fat mass, BMD was entered into the multivariate regression model, respectively (ß = 0.293 and ß = 0.251, respectively; all P0.05).Elevated HOMA-IR was linked to higher marrow fat fraction in postmenopausal women with newly diagnosed T2D independently of body compositions.

A cellulose paper-based biosensor was developed for the equipment-free and naked-eye detection of a transcription factor (TF) using dopamine as the signal-responsive material based on exonuclease III-assisted cycling amplification. Compared with other TF biosensors, the proposed biosensor demonstrates low-cost, portable, disposable, and naked-eye detection features.

In this paper, a multi-agent system (MAS) was developed to simulate the operation of an energy hub (EH) with different penetration rates (PRs) and various charging patterns of electric vehicle (EV) ...

This paper aims to explore the charging demand of electric vehicles (EVs) in multi-locations. To this aim, a model, which involves 4 charging locations: residential, work, business and recreation area, and 7 trip purposes: end trip, home linger, work, shopping, meal, pick / drop and public recreation, were developed to study the charging of EVs using agent-based trip chain method. The driving patterns of EVs are modelled based on 2017 NHTS data and then validated. The charging profile of 4 locations under uncontrolled charging are obtained, the difference caused by locations and supply equipment (normal charging and rapid charging) are analyzed. The rapid charging profile of 2 public locations and the relationship between normal and rapid charging are investigated. Finally, suggestions on charging stations planning in different area are provided after the analysis.

The idea that electric vehicles can connect to grid to serve as energy storage energy devices is compelling, especially in situations where traditional forms of storage, back-up energy supply is unavailable or expensive, or considering the volatility of frequency. So far, the viability and reliability of vehicle-to-grid(V2G) has become a very popular research topic. Previous studies mainly focused on the maximum potential of V2G capacity, which, however, is usually unrealistic to achieve. The potential capacity of V2G is relevant to the size of batteries and the penetration rates of electric vehicle. This article developed an agent-based model to estimate the real amount of electricity that vehicles can discharge to the grid. Three battery sizes under the penetration rate of 90% and the penetration rate from 10% to 90% were investigated. The results show that the increase of battery capacity led to an increase of V2G potential capacity from 1650.2 kW to 1868.5 kW and that increase from 10% to 90% in penetration rate could account for V2G potential capacity change from 162.8 kW to 1650.2 kW.

The prevailing High-rise Residential Buildings (HRBs) alleviate land shortage in modern cities, but energy sustainability in these energy-intensive constructions remains a big problem. The large available surface area of HRB makes rooftop attached PV technique a competent solution to utilize the local renewable energy. In the future, this can be expanded through facade installations. However, to which degree could PV generation support the HRB electricity consumption remains unclear. With more variations coming from renewable energy sources to the grid, peak-load electricity reduction will become critical. In this paper, the peak load shaving potential of rooftop attached PVs in HRB is estimated. Multi-agent system framework is employed to identify the building and occupant characteristics of HRB electricity consumption. A series of comparisons between demand and net demand profiles of HRB in a typical summer week are conducted. The results show that PV could effectively reduce the peak load during daytime, however, the maximum PV generation in the midday may cause new valleys in the net demand profiles. The results suggest that energy storage should be used to optimally match demand and supply in order to smoothen the net demand profiles.

In order to optimize the configuration and operation of multi-energy complementary distributed energy systems, based on the existing optimization methods, this paper proposes an improved comprehensive evaluation model: the improved model comprehensively evaluates energy-saving, economic and environmental performance of the system, and factors including off-design condition performance and start-stop performance have also been considered. After the model is established, a certain virtual building is optimized based on the genetic algorithm, and equipment capacity and hourly equipment operating load are obtained. By comparing the improved model proposed with the simplified model, it can be found that the results obtained from the simplified one cannot meet the actual needs of the system and is unreasonable, thus validating the rationality of the improved model.

Solar thermal heaters (STs), photovoltaics (PVs) and ground source heat pumps (GSHPs) are widely used nowadays due to their advantages of environmental protection and energy saving. The combination of solar thermal, photovoltaic and GSHP can achieve an even better performance and such a system is referred to as PV/ST-GSHP in this study. The overall efficiency of the PV/ST-GSHP system is highly dependent on the capacity size which is large or small would affect the overall efficiency and the power of PVs and STs affects the efficiency as well. This paper established an optimization model for a PV/ST-GSHP system based on the environment, energy and economy criteria. The model focused on the optimal planning of PV and ST under the restriction of a limited size of area and of the practical loads of heat and cooling, for which the rated capacity of GSHP is included. Genetic algorithm is employed to solve the optimization problem. After optimization, the multi-objective value is 26.88%.

Autoimmune polyglandular syndrome (APS) is a rare disease that is characterized by autoimmune reactions to multiple endocrine and non-endocrine organs, which can be divided into four main types. The principal manifestations of APS-3 are autoimmune thyroid disease and other autoimmune diseases, such as type 1 diabetes, atrophic gastritis, pernicious anemia, vitiligo, alopecia, and myasthenia gravis, but not Addison’s disease or hypoparathyroidism. Here we report a case demonstrating the rare coexistence of growth hormone deficiency and hyperthyroidism with sexual dysgenesis, secondary amenorrhea, cardiomegaly, splenomegaly, hypoproteinemia, pleural effusion, seroperitoneum, pericardial effusion, anasarca, osteoporosis, vitamin D deficiency, iron-deficiency anemia, poor blood coagulation, leucocytopenia, peripheral neuropathy, hyperuricemia, ichthyosis, tinea cruris, and onychomycosis.

Intra-city Public Charging Stations (PCSs) play a crucial role in promoting the mass deployment of Electric Vehicles (EVs). To motivate the investment on PCSs, this work proposes a novel framework to find the optimal location and size of PCSs, which can maximize the benefit of the investment. The impacts of charging behaviors and urban land uses on the income of PCSs are taken into account. An agent-based trip chain model is used to represent the travel and charging patterns of EV owners. A cell-based geographic partition method based on Geographic Information System is employed to reflect the influence of land use on the dynamic and stochastic nature of EV charging behaviors. Based on the distributed charging demand, the optimal location and size of PCSs are determined by mixed-integer linear programming. Vasteras, a Swedish city, is used as a case study to demonstrate the model's effectiveness. It is found that the charging demand served by a PCS is critical to its profitability, which is greatly affected by the charging behavior of drivers, the location and the service range of PCS. Moreover, charging price is another significant factor impacting profitability, and consequently the competitiveness of slow and fast PCSs.

OBJECTIVE We aimed to determine the effects of recombinant human growth hormone (rhGH) replacement on cognitive function in subjects with poststroke cognitive impairment using resting-state functional magnetic resonance imaging. METHODS We included 60 patients with a first-ever stroke for 3 months and a diagnosis of cognitive impairment who were randomized 1:1 to receive either rhGH subcutaneously or placebo injection for 6 months. All subjects were required to receive the same rehabilitative therapy program. Both groups were subjected to pretreatment and posttreatment neuropsychological assessment using the Montreal Cognitive Assessment, serum neurotrophic factors, biomarkers of glucose and lipid metabolism, and functional magnetic resonance imaging during 6 months of the study period. The pattern of brain activity was determined by examining the functional connectivity and amplitude of low-frequency fluctuations (ALFF) of blood oxygen level dependent signal. RESULTS Forty-three (82.7%) completed the study. Treatment with rhGH reduced levels of triglycerides and low-density lipoprotein cholesterol but did not significantly altered plasma concentrations of glucose and glycated hemoglobin. We found a significant increase in serum insulin-like growth factor 1 levels (32.6%; P < 0.001) in the rhGH-treated group compared with that in the controls. After 6 months of rhGH treatment, mean Montreal Cognitive Assessment score improved from 16.31 (5.32) to 21.19 (6.54) (P < 0.001). The rhGH group showed significant increased area of activation with increased ALFF values in the regions of the frontal lobe, putamen, temporal lobe, and thalamus (P < 0.05), relative to the baseline conditions. The correlation analysis revealed that the ALFF and functional connectivity of default mode network was positively correlated with the ΔMoCA score and ΔIGF-1 levels; that is, the more the scale score increased, the higher the functional connection strength. No undesirable adverse effects were observed. CONCLUSIONS The rhGH replacement has a significant impact on global and domain cognitive functions in poststroke cognitive impairment.

The idea that electric vehicles (EVs) can connect to the grid to serve as energy storage energy devices is compelling, especially in situations where traditional forms of storage, back-up energy supply is unavailable or expensive, or considering the volatility of frequency. So far, the viability and reliability of vehicle-grid integration, which represents smart charging (SC) and vehicle to grid (V2G) of electric vehicles, has become a very popular research topic. Previous studies mainly focused on the maximum potential of SC and V2G capacity on account of too many assumptions, which, however, is usually unrealistic to achieve. The charging and V2G flexibility of EVs are estimated from both adjustable power and adjustable amount aspects with model derived from real-world data. Overall, the EVs at home locations have the largest charging and V2G flexibility under the uncontrolled charging (UC) strategy, only in the regular working time, the maximum adjustable charging power at home location is smaller than that at work locations. Meanwhile, the V2G flexibility, is generally larger than charging flexibility at work and public locations.

Uncertainty is a common and critical problem for planning the capacity and operation of integrated energy systems (IESs). This study evaluates the effects of uncertainties on the capacity and operation of an IES. To this aim, system planning and operation with uncertainties are optimized by a two-stage stochastic programming model and compared with a referencing deterministic case. Specifically, the uncertainties of photovoltaic (PV) generation and energy demand are investigated. Regarding system capacity, a larger energy storage capacity is needed to accommodate a higher uncertainty. The superimposed uncertainties have a higher effect on system capacity than the sum of the effect of each uncertainty. The uncertainty of energy demand has a higher impact than the uncertainty of PV generation. Regarding system operation, the increase in operation cost is smaller than the increase in investment cost and total cost. In addition, the average flexibility provided by the energy storage increases with uncertainty and uncertainties affect the change rate for power charging/discharging of the electric energy storage. Regarding the effect on the grid, the uncertainties increase not only the magnitude of ramping-rate, but also the frequency of power-dispatch.

This paper proposed a novel approach to optimize the operation of an energy hub based on a multi-agent based model which includes hub element agents, a multi-energy demands agent, a commercial agent, an integrated dispatch agent and an environment agent. The novel optimization architecture is a combination and evolution of day-ahead and hour-ahead management method based on agents’ pre-defined roles and their interactions. The off-design condition performance and start-stop loss of hub units are considered in the model. A simple case study is taken to enforce the optimization architecture. The results validated the efficient operation of the developed system, proving its ability to optimize the operation of energy hub.

Climate Change is known to have a great impact on the energy consumption in buildings. This paper studied the cooling load of a residential building in Jinan, China, in 2020, 2050, 2080 by considering the impacts of climate change on the influential parameters for cooling loads. The Hadley Centre Coupled Model 3(HadCM3) was employed to investigate the impact of climate change on the outdoor temperature, solar radiation and wind speed. In addition, this study also differentiated the cooling loads according to occupants’ age, since the energy usage patterns and the requirements on cooling loads are closely related to occupants’ age. The results suggest that climate change will create significant impacts on the cooling loads in residential buildings. Regarding occupants’ age, the impact on the occupants under 55-year-old is larger than the people older than 55.

This paper developed an agent-based model (ABM) to explore the energy saving potentials (ESPs) of various types of appliances in offices under different pricing mechanisms. The model included four types of commonly used appliances in office buildings: an air conditioner (AC), computers, lights and a basic load. The total ESPs of the entire office are 6.7% and 17.4% on the second and the third price tier of the tiered pricing mechanism (TEP), while the ESPs are 11.8% and 14.2% under the peak-valley pricing (PVP) and critical peak pricing (CPP), respectively. Within different types of appliances, AC consumes the largest amount of electricity, over 50%, while the ESPs of the AC under different pricing mechanisms are only 6.9–12.1%. In contrast, the lights have the biggest ESP, i.e. 14.1–53.4%, under various pricing levels. Both the pricing mechanisms of PVP and CPP only have the effect of peak clipping and do not have a significant effect of valley filling, since there is no people working in the office during the valley price period. The maximum ESP, which is based on people’s maximum-saving behavior, is much larger than the ESPs on the basis of people’s ordinary consumption patterns. This implies the importance of improving people’s awareness of energy saving and refining their behaviors. Lastly, the model developed in this study provides a generic platform for simulating many types of energy systems and is very effective for handling the complicated relations between different types of technology and the way how they are used and interacted with each other. ABMs have very good adaptability and capacity in simulating energy systems.

An agent-based model is developed to estimate the potential of energy savings of an office room under different pricing mechanisms. For this aim, an energy balance model was developed to simulate the operation of air conditioner operation, the heat flux of different behavior and the electricity consumption of different types of appliances according different energy consumption patterns was investigated in detail. The total electricity consumption reduced 3.9%-17.1% under different pricing mechanisms. Light's energy saving potential is the biggest among the electric appliances. The maximum-saving behavior is investigated and the total energy saving potential of it is 26.91%.

Conventional approaches for modelling energy consumption for data center are limited to deal with the randomness, emergence and interdependence in energy systems. To fill the gap, an agent-based method was proposed and implemented to simulate the electricity consumption of a data center and to explore the potential of energy saving. The study found that a large amount of energy can be saved by dynamically operating the cooling load according to the practical load of the servers, namely 13% of the total energy consumption can be saved in the dynamic energy saving case (DESC) and 17% in the enhanced dynamic energy saving case (EDESC). The power usage effectiveness (PUE) of the data center can decrease from 1.71 in the original situation to 1.49 in the DESC and 1.42 in the EDESC. By contrast, the frequency of data backup has a heavy impact on the electricity consumption of the data center, while the frequency of computing tasks do not.

In this paper, we developed an agent-based model to simulate the electricity consumption in an office building under a tiered pricing mechanism. In the model, electricity users’ behavior is significantly influenced by the electricity price. The price and the range of each tier have great impacts on daily and total consumption, these parameters are discussed by sensitivity analysis. The result was compared with the consumption under single pricing. We found the daily consumption reduced more than 20% under the third tier price, the price change's influence has huge difference among different electric appliances.

Photoelectrochemical (PEC) sensing techniques have attracted considerable concerns because of the intrinsic merit of complete separation between the excitation light and responsive current but still remain a great challenge for further potential application. It is assigned to the scarcity of photoactive materials with narrow band gap, good biosafety, and high photon-to-electron conversion efficiency and unfavorable processing methods for photoactive materials on indium tin oxide. Herein, we employed a perylene-based polymer (PTC-NH2) with exceptional photoelectrical properties to develop a red-light-driven PEC sensor for ultrasensitive biosensing based on its superior electrostatic intercalation efficiency in double-stranded DNA to that in single-stranded DNA, with DNA adenine methyltransferase (Dam MTase) as the model target. The prepared PTC-NH2 was characterized by Fourier transform infrared spectroscopy, ultraviolet–visible spectroscopy, and PEC techniques, and the results demonstrated that PTC-NH2 ra...

Electric Vehicles (EVs) have achieved a significant development because of the continuous technology revolution and policy supports in recent years, which leads to a larger demand of EV charging stations (EVCSs). Strategies about optimal siting and sizing of public EVCSs are urgently needed in order to further assist the development of EVs. This paper focus on the return of investments on EVCSs and proposes a Mixed Integer Linear Programming (MILP) model based on Geographic Information System (GIS) to identify the optimal location and size of EVCS in cities. Traffic flow data, aggregated charging profiles and land-use classifications are used as important inputs together with important constraints, are included in the MILP model with the objective function of maximizing the total profits of new charging stations. The effectiveness of the proposed method is then demonstrated by implementing a case study in Vasteras, Sweden.

Surface plasmon resonance (SPR) biosensing strategies have drawn substantial attention due to their advantages of label free, real time, and high performance, but complicated modification procedures and strict reaction conditions of amplification tags associated with current SPR biosensors hinder their potential utilizations. Herein, an in situ prepared AgNPs-based SPR biosensor for MicroRNA (miRNA) sensitive detection was developed based on hybridization chain reaction (HCR) without the biomodification on amplification tags. The target miRNA initiated the HCR of the hairpin probes to generate long double strand DNA (dsDNA) chains that were immobilized on SPR disk. Thus, with Ag+ intercalating into dsDNA chains, large numbers of AgNPs generated after NaBH4 reduction, resulting in the significantly elevated SPR angle. Further, the SPR angle is positively proportional to target miRNA concentrations. As a result, the in situ generated AgNPs-based SPR biosensor realized exceptional let-7a detection with linear range of 0.001–0.1 pM and detection limit (LOD) of 0.35 fM, lower than that of other SPR biosensors that used modifiable amplification tags. Featured with the modification-free characteristic and excellent performance, the proposed strategy provides new way for ultrasensitive detection of miRNA, and allows to detect other biomarkers by simply verifying the target responsive substances, and thus has a great potential for health and early disease diagnosis.

In developing countries with a large population and fast urbanization, High-rise Residential Buildings (HRBs) have unavoidably become a very common, if not the most, accommodation solution. The paradigm of HRB energy consumption is characterized by high-density energy consumption, severe peak effects and a limited site area for integrating renewable energy, which constitute a hindrance to the low-carbon transition. This review paper investigates low-carbon transition efforts in the HRB sector from the perspective of urban energy systems to get a holistic view of their approaches. The HRB sector plays a significant role in reducing carbon emission and improving the resilience of urban energy systems. Different approaches to an HRB low-carbon transition are investigated and a brief overview of potential solutions is offered from the perspectives of improving energy efficiency, self-sufficiency and system resilience. The trends of decarbonization, decentralization and digitalization in the HRB sector allow a better alignment with transitioning urban energy systems and create cross-sectoral integration opportunities for low-carbon transition. It is also found that policy tools are powerful driving forces in China for incentivizing transition behaviors among utilities, end users and developers. Based on a comprehensive policy review, the policy implications are given. The research is geared for the situation in China but could also be used as an example for other developing countries that have similar urbanization patterns. Future research should focus on quantitative analysis, life-cycle analysis and transdisciplinary planning approaches.