Refined Sea Salt Markers for Coastal Cities Facilitating Quantification of Aerosol Aging and PM2.5 Apportionment.

Sea salt (ss) aerosols in PM2.5 are often quantified through source apportionment by applying sodium (Na+) and chloride (Cl-) as the markers, but both markers can be substantially emitted from anthropogenic sources. In this study, we differentiate ss from nonss (nss) portions of Na+ and Cl- to better apportion PM2.5 in a coastal tropical urban environment. Size-resolved ionic profiles accounting for Cl- depletion of aged ss were applied to 162-day measurements during 2012 and 2018-2019. Results show that the nss (likely anthropogenic) portions, on average, account for 50-80% of total Na+ and Cl- in submicron aerosols (PM1). This corresponds to up to 2.5 µg/m3 of ss in submicron aerosols that can be ∼10 times overestimated if one attributes all Na+ and Cl- in PM1 to ss. Employing the newly speciated ss- and nss-portions of Na+ and Cl- to source apportionment of urban PM2.5 via positive matrix factorization uncovers a new source of transported anthropogenic emissions during the southwest monsoon, contributing to 12-15% of PM2.5. This increases anthropogenic PM2.5 by ≥19% and reduces ss-related PM2.5 by >30%. In addition to demonstrating Cl- depletion (aging) in submicron aerosols and quantifying ssNa+, nssNa+, ssCl-, as well as nssCl- therein, the refined PM2.5 apportionment resolves new insights on PM2.5 of anthropogenic origins in urban environments, useful to facilitate policy making.

Infrared and Visible Image Fusion via Attention-Based Adaptive Feature Fusion.

Infrared and visible image fusion methods based on feature decomposition are able to generate good fused images. However, most of them employ manually designed simple feature fusion strategies in the reconstruction stage, such as addition or concatenation fusion strategies. These strategies do not pay attention to the relative importance between different features and thus may suffer from issues such as low-contrast, blurring results or information loss. To address this problem, we designed an adaptive fusion network to synthesize decoupled common structural features and distinct modal features under an attention-based adaptive fusion (AAF) strategy. The AAF module adaptively computes different weights assigned to different features according to their relative importance. Moreover, the structural features from different sources are also synthesized under the AAF strategy before reconstruction, to provide a more entire structure information. More important features are thus paid more attention to automatically and advantageous information contained in these features manifests itself more reasonably in the final fused images. Experiments on several datasets demonstrated an obvious improvement of image fusion quality using our method.

ZnO-Decorated In/Ga Oxide Nanotubes Derived from Bimetallic In/Ga MOFs for Fast Acetone Detection with High Sensitivity and Selectivity.

The development of acetone gas sensors is desirable but challenging for both air quality monitoring and medical diagnosis. Herein, starting from bimetallic In/Ga metal-organic frameworks (MOFs) (MIL-68 (In/Ga)), a facile strategy is proposed to couple with zinc ions to design In/Ga oxide (IGO)@ZnO core-shell nanotubes for efficient acetone detection. In such a heterostructure, tiny ZnO nanoparticles are closely decorated on IGO nanotubes, which is beneficial to enlarge the specific surface area and create rich oxygen vacancies and heterojunction interfaces. Benefiting from the structural merits and synergetic effects, the IGO@ZnO-based gas sensor exhibits a low detection limitation (200 ppb), a high response, good linearity relationship between the sensing responses and wide testing acetone concentrations, and fast response and recovery time (6.8/6.1 s) with good selectivity and stability. These sensing performances strongly indicate the practical application to quantitatively detect acetone.

A nickel foam modified with electrodeposited cobalt and phosphor for amperometric determination of dopamine.

Considering the importance of dopamine (DA) detection for neuroscience and disease diagnosis, herein, an electrochemical sensor for dopamine is described. It is based on the use of a Ni-Co-P nanostructure fabricated on nickel foam via electrode position from cobalt chloride and ammonium phosphate for 10 min. Time-dependent experiments show the transformation of Ni-Co-P nanoparticles to spheres. The resulting electrode display excellent electrochemical response to DA. Figures of merit include (a) a working potential of 0.55 V (vs. Ag/AgCl); (b) an electrochemical sensitivity of 5262 µA mM-1 cm-2; (c) a wide linear range (from 0.5 to 2350 µM), and (d) a 1 µM detection limit. The outstanding electrochemical performance is explained by the synergistic effects of large surface area, improved electron transfer, presence of free binders, and the presence of three active components (nickel, cobalt and phosphonium ion). Graphical abstract A Ni-Co-P nanostructure was electrodeposited on nickel foam to obtain an electrochemical sensor for amperometric determination of dopamine with outstanding performance.

MOFs-Derived Porous NiFe2O4 Nano-Octahedrons with Hollow Interiors for an Excellent Toluene Gas Sensor.

Toluene is extensively used in many industrial products, which needs to be effectively detected by sensitive gas sensors even at low-ppm-level concentrations. Here, NiFe2O4 nano-octahedrons were calcinated from NiFe-bimetallic metal-organic framework (MOFs) octahedrons synthesized by a facile refluxing method. The co-existence of p-Phthalic acid (PTA) and 3,3-diaminobenzidine (DAB) promotes the formation of smooth NiFe-bimetallic MOFs octahedrons. After subsequent thermal treatment, a big weight loss (about 85%) transformed NiFe2O4 nanoparticles (30 nm) into NiFe2O4 porous nano-octahedrons with hollow interiors. The NiFe2O4 nano-octahedron based sensor exhibited excellent gas sensing properties for toluene with a nice stability, fast response, and recovery time (25 s/40 s to 100 ppm toluene), and a lower detection limitation (1 ppm) at 260 °C. The excellent toluene-sensing properties can not only be derived from the hollow interiors combined with porous nano-octahedrons to favor the diffusion of gas molecules, but also from the efficient catalytic activity of NiFe2O4 nanoparticles.

A Pilot Study on a Smart Home for Elders Based on Continuous In-Home Unobtrusive Monitoring Technology.

PURPOSE: This article reports on a pilot study of a smart home for elders (SHfE) designed to continuously monitor senior adults' daily behaviors and the living environment of their residential homes using the application of unobtrusive sensors. SHfE users include older adults, their family members, and healthcare staff. BACKGROUND: Globally, countries are experiencing the challenges of an increasingly aging population. A healthy environment is essential to support aging in place. By applying information and communications technology to building environments to support health, smart homes may be an option to provide a low-cost, comfortable, and user-friendly living environment for older adults. METHOD: A pilot study was conducted in a capital city in the Yangtze River Delta Agglomerations in China to verify the feasibility of the SHfE. One female older adult participated in the pilot study, which was conducted from November 2015 to January 2016. RESULTS: The results indicated that the SHfE is a feasible way to analyze the behaviors (e.g., sleeping, cooking, water usage) of the elder and monitor the built environment (e.g., temperature, windows, and doors). CONCLUSIONS: The pilot study can be used as a baseline for further comprehensive experiments, case studies, and surveys to gain a better understanding of a smart healthy environment for older adults. On the basis of the current study, several recommendations are put forward for further implementation of the SHfE, including integrating multiple unobtrusive sensing devices; detecting fall accidents; monitoring indoor lighting, noise, and ventilation; remotely controlling electrical appliances; and developing the system with various languages. It is anticipated that the SHfE will be adopted in seniors' residential homes in countries around the world which face an increasingly aging population.

Developing discriminate model and comparative analysis of differentially expressed genes and pathways for bloodstream samples of diabetes mellitus type 2.

BACKGROUND: Diabetes mellitus of type 2 (T2D), also known as noninsulin-dependent diabetes mellitus (NIDDM) or adult-onset diabetes, is a common disease. It is estimated that more than 300 million people worldwide suffer from T2D. In this study, we investigated the T2D, pre-diabetic and healthy human (no diabetes) bloodstream samples using genomic, genealogical, and phonemic information. We identified differentially expressed genes and pathways. The study has provided deeper insights into the development of T2D, and provided useful information for further effective prevention and treatment of the disease. RESULTS: A total of 142 bloodstream samples were collected, including 47 healthy humans, 22 pre-diabetic and 73 T2D patients. Whole genome scale gene expression profiles were obtained using the Agilent Oligo chips that contain over 20,000 human genes. We identified 79 significantly differentially expressed genes that have fold change ≥ 2. We mapped those genes and pinpointed locations of those genes on human chromosomes. Amongst them, 3 genes were not mapped well on the human genome, but the rest of 76 differentially expressed genes were well mapped on the human genome. We found that most abundant differentially expressed genes are on chromosome one, which contains 9 of those genes, followed by chromosome two that contains 7 of the 76 differentially expressed genes. We performed gene ontology (GO) functional analysis of those 79 differentially expressed genes and found that genes involve in the regulation of cell proliferation were among most common pathways related to T2D. The expression of the 79 genes was combined with clinical information that includes age, sex, and race to construct an optimal discriminant model. The overall performance of the model reached 95.1% accuracy, with 91.5% accuracy on identifying healthy humans, 100% accuracy on pre-diabetic patients and 95.9% accuract on T2D patients. The higher performance on identifying pre-diabetic patients was resulted from more significant changes of gene expressions among this particular group of humans, which implicated that patients were having profound genetic changes towards disease development. CONCLUSION: Differentially expressed genes were distributed across chromosomes, and are more abundant on chromosomes 1 and 2 than the rest of the human genome. We found that regulation of cell proliferation actually plays an important role in the T2D disease development. The predictive model developed in this study has utilized the 79 significant genes in combination with age, sex, and racial information to distinguish pre-diabetic, T2D, and healthy humans. The study not only has provided deeper understanding of the disease molecular mechanisms but also useful information for pathway analysis and effective drug target identification.