Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer's disease

IntroductionAlzheimer's disease (AD) is a neurodegenerative disease that significantly impacts the quality of life of patients and their families. Neuroimaging-driven brain age prediction has been proposed as a potential biomarker to detect mental disorders, such as AD, aiding in studying its effects on functional brain networks. Previous studies have shown that individuals with AD display impaired resting-state functional connections. However, most studies on brain age prediction have used structural magnetic resonance imaging (MRI), with limited studies based on resting-state functional MRI (rs-fMRI).MethodsIn this study, we applied a graph neural network (GNN) model on controls to predict brain ages using rs-fMRI in patients with AD. We compared the performance of the GNN model with traditional machine learning models. Finally, the post hoc model was also used to identify the critical brain regions in AD.ResultsThe experimental results demonstrate that our GNN model can predict brain ages of normal controls using rs-fMRI data from the ADNI database. Moreover the differences between brain ages and chronological ages were more significant in AD patients than in normal controls. Our results also suggest that AD is associated with accelerated brain aging and that the GNN model based on resting-state functional connectivity is an effective tool for predicting brain age.DiscussionOur study provides evidence that rs-fMRI is a promising modality for brain age prediction in AD research, and the GNN model proves to be effective in predicting brain age. Furthermore, the effects of the hippocampus, parahippocampal gyrus, and amygdala on brain age prediction are verified

Cluster Analysis of Submicron Particle Number Size Distributions at the SORPES Station in the Yangtze River Delta of East China

Submicron particles in polluted regions have received much attention because of their influences on human health and climate. A k-means clustering technique was performed on a data set of particle number size distributions (PNSD) that was obtained over more than 3 years in the Yangtze River Delta (YRD) region of East China. With simultaneous measurements of meteorological conditions, trace gases and aerosol compositions, seven clusters were categorized and interpreted. Cluster 1 and cluster 2, which accounted for 9.9% of the total PNSD data, were attributed to new particle formation (NPF) and vehicle exhaust emissions with different intensities; Cluster 3 and Cluster 4, which accounted for 10.5% of the total PNSD data, were related to the growth of nucleation mode particles; Cluster 5, which accounted for 37.9% of the total data, was attributed to the humid YRD background; and Cluster 6 and Cluster 7, which accounted for 41.6% of the total data set, were both pollution-related clusters with similar mass concentrations but completely different PNSD. Although the PM2.5 mass concentrations were somewhat similar, the particle number concentrations of the accumulation mode particles could vary by more than one order of magnitude from the urban background cluster to the pollution-related clusters. The cluster proximity diagram and conversion flow chart of clusters clearly show the influence of NPF and growth on haze, as well as the conversion between background and polluted conditions. This study highlights the importance of PNSD for understanding urban air quality and recommends the clustering technique for analyzing complex PNSD datasets. Plain Language Summary Submicron particles in polluted regions have significant influences on human health and climate. Based on long-term field measurements, we used the k-means clustering technique to characterize the number size distributions of submicron particles in the Yangtze River Delta (YRD) of China. Seven clusters were categorized and interpreted. New particle formation (NPF), fossil fuel combustion and biomass burning are the main sources of submicron particles in the YRD. The influences of NPF and growth on haze, as well as the conversion between background and polluted conditions, were found. Key Points New particle formation (NPF), fossil fuel combustion and biomass burning are the main sources of submicron particles in Nanjing The influences of NPF and growth on haze, and the conversion between background and pollution conditions were found The k-means cluster technique is an effective tool to categorize particle number size distribution data setPeer reviewe

Toward Building a Physical Proxy for Gas-Phase Sulfuric Acid Concentration Based on Its Budget Analysis in Polluted Yangtze River Delta, East China

Gaseous sulfuric acid (H2SO4) is a crucial precursor for secondary aerosol formation, particularly for new particle formation (NPF) that plays an essential role in the global number budget of aerosol particles and cloud condensation nuclei. Due to technology challenges, global-wide and long-term measurements of gaseous H2SO4 are currently very challenging. Empirical proxies for H2SO4 have been derived mainly based on short-term intensive campaigns. In this work, we performed comprehensive measurements of H2SO4 and related parameters in the polluted Yangtze River Delta in East China during four seasons and developed a physical proxy based on the budget analysis of gaseous H2SO4. Besides the photo-oxidation of SO2, we found that primary emissions can contribute considerably, particularly at night. Dry deposition has the potential to be a non-negligible sink, in addition to condensation onto particle surfaces. Compared with the empirical proxies, the newly developed physical proxy demonstrates extraordinary stability in all the seasons and has the potential to be widely used to improve the understanding of global NPF fundamentally.Peer reviewe

Photoinduced Production of Chlorine Molecules from Titanium Dioxide Surfaces Containing Chloride

Titanium dioxide (TiO2) is extensively used with the process of urbanization and potentially influences atmospheric chemistry, which is yet unclear. In this work, we demonstrated strong production of Cl-2 from illuminated KCl-coated TiO2 membranes and suggested an important daytime source of chlorine radicals. We found that water and oxygen were required for the reactions to proceed, and Cl-2 production increased linearly with the amount of coated KCl, humidity of the carrier gas, and light intensity. These results suggested that water promotes the reactivity of coated KCl via interaction with the crystal lattice to release free chloride ions (Cl-). The free Cl- transfer charges to O-2 via photoactivated TiO2 to form Cl-2 and probably the O-2(-) radical. In addition to Cl-2, ClO and HOCl were also observed via the complex reactions between Cl/Cl-2 and HOx. An intensive campaign was conducted in Shanghai, during which evident daytime peaks of Cl-2 were observed. Estimated Cl-2 production from TiO2 photocatalysis can be up to 0.2 ppb/h when the TiO2-containing surface reaches 20% of the urban surface, and highly correlated to the observed Cl-2. Our results suggest a non-negligible role of TiO2 in atmospheric photochemistry via altering the radical budget.Peer reviewe

Urbanization and Its Effects on Industrial Pollutant Emissions: An Empirical Study of a Chinese Case with the Spatial Panel Model

Urbanization is considered a main indicator of regional economic development due to its positive effect on promoting industrial development; however, many regions, especially developing countries, have troubled in its negative effect—the aggravating environmental pollution. Many researchers have addressed that the rapid urbanization stimulated the expansion of the industrial production and increased the industrial pollutant emissions. However, this statement is exposed to a grave drawback in that urbanization not only expands industrial production but also improves labor productivity and changes industrial structure. To make up this drawback, we first decompose the influence of urbanization impacts on the industrial pollutant emissions into the scale effect, the intensive effect, and the structure effect by using the Kaya Identity and the LMDI Method; second, we perform an empirical study of the three effects by applying the spatial panel model on the basis of the data from 282 prefecture-level cities of China from 2003 to 2014. Our results indicate that (1) there are significant reverse U-shapes between China’s urbanization rate and the volume of industrial wastewater discharge, sulfur dioxide emissions and soot (dust) emissions; (2) the relationship between China’s urbanization and the industrial pollutant emissions depends on the scale effect, the intensive effect and the structure effect jointly. Specifically, the scale effect and the structure effect tend to aggravate the industrial wastewater discharge, the sulfur dioxide emissions and the soot (dust) emissions in China’s cities, while the intensive effect results in decreasing the three types of industrial pollutant emissions; (3) there are significant spatial autocorrelations of the industrial pollutant emissions among China’s cities, but the spatial spillover effect is non-existent or non-significant. We attempt to explain this contradiction due to the fact that the vast rural areas around China’s cities serve as sponge belts and absorb the spatial spillover of the industrial pollutant emissions from cities. According to the results, we argue the decomposition of the three effects is necessary and meaningful, it establishes a cornerstone in understanding the definite relationship between urbanization and industrial pollutant emissions, and effectively contributes to the relative policy making

BgCut: Automatic Ship Detection from UAV Images

Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches

Characterization of the planar differential mobility analyzer (DMA P5): resolving power, transmission efficiency and its application to atmospheric relevant cluster measurements

International audienceThe planar differential mobility analyzer (DMA), functioning as a particle sizer, exhibits superior transmission and selection accuracy at ambient pressure relative to its cylindrical counterparts. It also presents integration potential with atmospheric pressure interface mass spectrometry (API-MS) for enhanced cluster detection with an additional ion mobility dimension. In this study, the performance of a commercially available planar DMA (DMA P5) was evaluated. The device is capable of sizing particles below 3.9 nm, with larger sizes measurable through a sheath gas flow restrictor. The resolving power was appraised under various recirculation arrangements, including suction and counterflow modes along with different sheath flow rates, using electrosprayed tetra-alkyl ammonium salts. The peak resolving powers for tetrahexylammonium (THA+) achieved in suction and counterflow modes were 61.6 and 84.6, respectively. The DMA P5 offers a sizing resolution that is 5 to 16 times greater than that of cylindrical DMAs. Resolving power displayed a near-linear relationship with the square root of the applied voltage (VDMA) in counterflow mode. Conversely, the resolving power for THA+ ceased its linear enhancement with VDMA beyond a VDMA of 3554.3 V, entering a plateau which is ascribed to the perturbations in sample flow impacting the laminar nature of sheath flow. The DMA P5 transmission efficiency reaches 54.3 %, markedly surpassing that of conventional DMAs by nearly 1 order of magnitude. Moreover, the mobility spectrum of various electrosprayed tetra-alkyl ammonium salts and the mass-to-charge versus mobility 2D spectrum of sulfuric acid clusters were characterized using the DMA P5 MS system