Estimating long-term time-resolved indoor PM2.5 of outdoor and indoor origin using easily obtainable inputs

To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM_2.5 in buildings. This study used an algorithm to automatically estimate the long-term time-resolved indoor PM_2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM_2.5 concentrations and occupants’ window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM_2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi'an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long-term outdoor PM_2.5 monitoring using low-cost light-scattering sensors.     

Role of Endocrine-Disrupting Chemicals in the Pathogenesis of Non-Alcoholic Fatty Liver Disease: A Comprehensive Review

Non-alcoholic fatty liver disease (NAFLD) is considered the most common liver disorder, affecting around 25% of the population worldwide. It is a complex disease spectrum, closely linked with other conditions such as obesity, insulin resistance, type 2 diabetes mellitus, and metabolic syndrome, which may increase liver-related mortality. In light of this, numerous efforts have been carried out in recent years in order to clarify its pathogenesis and create new prevention strategies. Currently, the essential role of environmental pollutants in NAFLD development is recognized. Particularly, endocrine-disrupting chemicals (EDCs) have a notable influence. EDCs can be classified as natural (phytoestrogens, genistein, and coumestrol) or synthetic, and the latter ones can be further subdivided into industrial (dioxins, polychlorinated biphenyls, and alkylphenols), agricultural (pesticides, insecticides, herbicides, and fungicides), residential (phthalates, polybrominated biphenyls, and bisphenol A), and pharmaceutical (parabens). Several experimental models have proposed a mechanism involving this group of substances with the disruption of hepatic metabolism, which promotes NAFLD. These include an imbalance between lipid influx/efflux in the liver, mitochondrial dysfunction, liver inflammation, and epigenetic reprogramming. It can be concluded that exposure to EDCs might play a crucial role in NAFLD initiation and evolution. However, further investigations supporting these effects in humans are required.     

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness (K_IC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of K_IC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R², RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R² value and lower errors.     

Nacre-Inspired,Liquid Metal-Based Ultrasensitive Electronic Skin by Spatially Regulated Cracking Strategy

The realization of liquid metal-based wearable systems will be a milestone toward high-performance, integrated electronic skin. However, despite the revolutionary progress achieved in many other components of electronic skin, liquid metal-based flexible sensors still suffer from poor sensitivity due to the insufficient resistance change of liquid metal to deformation. Herein, a nacre-inspired architecture composed of a biphasic pattern (liquid metal with Cr/Cu underlayer) as “bricks” and strain-sensitive Ag film as “mortar” is developed, which breaks the long-standing sensitivity bottleneck of liquid metal-based electronic skin. With 2 orders of magnitude of sensitivity amplification while maintaining wide (>85%) working range, for the first time, liquid metal-based strain sensors rival the state-of-art counterparts. This liquid metal composite features spatially regulated cracking behavior. On the one hand, hard Cr cells locally modulate the strain distribution, which avoids premature cut-through cracks and prolongs the defect propagation in the adjacent Ag film. On the other hand, the separated liquid metal cells prevent unfavorable continuous liquid-metal paths and create crack-free regions during strain. Demonstrated in diverse scenarios, the proposed design concept may spark more applications of ultrasensitive liquid metal-based electronic skins, and reveals a pathway for sensor development via crack engineering.     

A Voltage Control Method for a Distributed Line Feeder by means of Multiagent Approach

This paper proposes a method for the coordinated control of power factor by means of a multiagent approach. The proposed multiagent system consists of two types of agent: single feeder agent (F_AG) and bus agent (B_AG). In the proposed system, an F_AG plays as an important role, which decides the power factors of all distributed generators by executing the load flow calculations repeatedly. The voltage control strategies are implemented as the class definition of Java into the system. In order to verify the performance of the proposed method, it has been applied to a typical distribution model system. The simulation results show that the system is able to control very violent fluctuation of the demands and the photovoltaic (PV) generations.     

Omics Analysis of Blood-Responsive Regulon in Bordetella pertussis Identifies a Novel Essential T3SS Substrate

Bacterial pathogens sense specific cues associated with different host niches and integrate these signals to appropriately adjust the global gene expression. Bordetella pertussis is a Gram-negative, strictly human pathogen of the respiratory tract and the etiological agent of whooping cough (pertussis). Though B. pertussis does not cause invasive infections, previous results indicated that this reemerging pathogen responds to blood exposure. Here, omics RNA-seq and LC–MS/MS techniques were applied to determine the blood-responsive regulon of B. pertussis. These analyses revealed that direct contact with blood rewired global gene expression profiles in B. pertussis as the expression of almost 20% of all genes was significantly modulated. However, upon loss of contact with blood, the majority of blood-specific effects vanished, with the exception of several genes encoding the T3SS-secreted substrates. For the first time, the T3SS regulator BtrA was identified in culture supernatants of B. pertussis. Furthermore, proteomic analysis identified BP2259 protein as a novel secreted T3SS substrate, which is required for T3SS functionality. Collectively, presented data indicate that contact with blood represents an important cue for B. pertussis cells.     

Estimates of pollutant temporal and spatial variability in commercial buildings from the joint urban 2003 field experiments

In this paper, we report on the indoor concentrations from a suite of full-scale outdoor tracer-gas point releases conducted in the downtown area of Oklahoma City in 2003. A point release experiment consisted of releases of sulfur hexafluoride (SF₆) in multiple buildings and from different outdoor locations. From the measurements, we are able to estimate the concentration variations indoors for a building operating under “typical” operating conditions. The mean indoor spatial coefficients of variation are 30% to 45% from a daytime outdoor release are around 80% during an outdoor evening release. Having estimates of the spatial coefficient of variation provides stakeholders, including first responders, with the likely range of concentrations in the building when little is known about the building characteristics and operating behavior, such as developing urban-scale hazard and consequence analyses. We show differences in indoor measurements at different distances to the release points, floors of the building, and heating, ventilation, and air conditioning system (HVAC) operation. We also show estimates at different time resolutions. The statistics show that in the studied medium to large commercial buildings, spatial differences would result in peak indoor concentrations in certain parts of the buildings that may be substantially higher than the building average. To our knowledge, very few tracer gas measurements have been conducted in buildings of this scope, particularly with measurements on multiple floors and within a floor. The resulting estimates of spatial variability provide a unique opportunity for hazard assessment, and comparison to multi-zone models.     

Magnetic Resonance Signal Amplification by Reversible Exchange of Selective PyFALGEA Oligopeptide Ligands Towards Epidermal Growth Factor Receptors

The biorelevant PyFALGEA oligopeptide ligand, which is selective towards the epidermal growth factor receptor (EGFR), has been successfully employed as a substrate in magnetic resonance signal amplification by reversible exchange (SABRE) experiments. It is demonstrated that PyFALGEA and the iridium catalyst IMes form a PyFALGEA:IMes molecular complex. The interaction between PyFALGEA:IMes and H₂ results in a ternary SABRE complex. Selective 1D EXSY experiments reveal that this complex is labile, which is an essential condition for successful hyperpolarization by SABRE. Polarization transfer from parahydrogen to PyFALGEA is observed leading to significant enhancement of the ¹H NMR signals of PyFALGEA. Different iridium catalysts and peptides are inspected to discuss the influence of their molecular structures on the efficiency of hyperpolarization. It is observed that PyFALGEA oligopeptide hyperpolarization is more efficient when an iridium catalyst with a sterically less demanding NHC ligand system such as IMesBn is employed. Experiments with shorter analogues of PyFALGEA, that is, PyLGEA and PyEA, show that the bulky phenylalanine from the PyFALGEA oligopeptide causes steric hindrance in the SABRE complex, which hampers hyperpolarization with IMes. Finally, a single-scan ¹H NMR SABRE experiment of PyFALGEA with IMesBn revealed a unique pattern of NMR lines in the hydride region, which can be treated as a fingerprint of this important oligopeptide.