Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.

[Characteristics of Carbonaceous Species in PM2.5 in Chengdu Under the Background of Emission Reduction].

The Air Pollution Prevention and Control Action Plan and Three-year Plan on Defending the Blue Sky promulgated by the State Council of the People's Republic of China have played an important role in the overall improvement of air quality in China. However, few studies have evaluated the implementation effects of these two policies in Sichuan Basin and the new characteristics of PM2.5 chemical components after the implementation of these policies. The key periods for evaluating the implementation effects of these two pollution reduction policies are 2017 and 2020, respectively. In order to study the atmospheric PM2.5 and carbonaceous species in Chengdu during these two periods, this study sampled the PM2.5 in Chengdu from October 2016 to July 2017 and December 2020, respectively, and the organic carbon (OC) and elemental carbon (EC) were analyzed. The results showed that the annual ρ(PM2.5) from 2016-2017 in Chengdu was (114.0±76.4) µg·m-3. The maximum value of the ρ(PM2.5) appeared in winter[(193.3±98.5) µg·m-3], and the minimum value appeared in spring[(73.8±32.3) µg·m-3]. By contrast, the ρ(PM2.5) in winter decreased significantly in 2020, with a value of (96.0±39.3) µg·m-3. The annual ρ(OC) and ρ(EC) from 2016-2017 were (21.1±16.4) µg·m-3 and (1.9±1.3) µg·m-3, which accounted for 18.5% and 1.7% of the PM2.5 mass, respectively. The seasonal variation characteristic of ρ(OC) was:winter[(40.6±21.5) µg·m-3]>autumn[(17.0±7.0) µg·m-3]>summer[(14.4±3.9) µg·m-3]>spring[(12.6±6.0) µg·m-3], whereas the ρ(EC) in the four seasons were close, ranging from 1.3 to 2.4 µg·m-3. The annual ρ(SOC) was (9.4±9.1) µg·m-3, which accounted for 44.5% of the OC mass. Compared with that in winter 2016, the ρ(OC) decreased by 52.7% in winter 2020, whereas the ρ(EC) increased by 26.1%. With the aggravation of pollution, the change trends of carbon species and their contributions were different. Compared with that in winter 2016, the variation in the contribution of OC with the aggravation of pollution in winter 2020 was more stable, whereas the proportion of SOC increased more obviously. There were obvious differences in the direction of air masses and the potential source area of pollutants in each season. Although there was no significant change in the direction of air masses in winter 2020 compared with those in winter 2016, the pollutant concentrations corresponding to each cluster decreased significantly, and the potential source area of pollutants expanded significantly to the eastern area.

Characteristics of fine particulate matter (PM2.5) at Jinsha Site Museum, Chengdu, China.

Air pollution is a serious threat to ancient sites and cultural relicts. In this study, we collected indoor and outdoor PM2.5 samples and individual particles at the Exhibition Hall of Jinsha Site Museum in June 2020, and then the chemical components, sources, morphology, and mixing state of the fine particulate matter were analyzed. Our results show that the indoor and outdoor PM2.5 concentrations at the Exhibition Hall were 33.3±6.6 and 39.4±11.4 µg m-3, respectively. Although the indoor and outdoor concentrations of OC and EC were close, the proportion of secondary organic carbon in OC outdoor (33%) was higher than that indoor (27%). The PM2.5 was alkaline both indoors and outdoors, and the outdoor alkalinity was stronger than the indoor alkalinity. SNA (SO42-, NO3-, and NH4+) was the dominant component in the water-soluble inorganic ions; Na+, Mg2+, and Ca2+ were well correlated (R2> 0.9), and Cl- and K+ were also highly correlated (R2> 0.8). Enrichment factor analysis showed that Cu (indoor) and Cd were the main anthropogenic elements and that Cd was heavily enriched. Principal components analysis showed that the main sources of PM2.5 at Jinsha Site Museum were motor vehicles, dust, secondary sources, and combustion sources. The individual particles were classified as organic matter, S-rich, soot, mineral, and fly ash/metal particles, and most of these particles were internally mixed with each other. At last, we proposed pollution control measures to improve the air quality of museums and the preservation of cultural relicts.

[Characteristics of Carbonaceous Species in PM2.5 in Southern Beijing].

To investigate the characteristics of carbonaceous species in PM2.5 in Beijing after the implementation of the Action Plan for the Prevention and Control of Air Pollution, PM2.5 was continuously sampled in the heavily polluted southern urban area of Beijing from December 2017 to December 2018. The characteristics of organic carbon (OC) and element carbon (EC) were then determined. The results showed that the annual concentrations of PM2.5, OC, and EC in Beijing varied in wide ranges of 4.2-366.3, 0.9-74.5, and 0.0-5.5 µg ·m-3, respectively, and the average mass concentration were (77.1±52.1), (11.2±7.8), and (1.2±0.8) µg ·m-3. Overall, the carbonaceous species (OC and EC) accounted for 16.1% of the PM2.5 mass. The seasonal characteristics of the OC mass concentrations were: winter [(13.8±8.7) µg ·m-3] > spring [(12.7±9.6) µg ·m-3] > autumn [(11.8±6.2) µg ·m-3] > summer [(6.5±2.1) µg ·m-3]. The concentration of the EC during the four seasons was low, ranging from 0.8 to 1.5 µg ·m-3. The annual average mass concentration and contribution of secondary organic carbon (SOC) were (5.4±5.8) µg ·m-3 and 48.2%, respectively, highlighting the significant contribution of the secondary process. With the aggravation of pollution, although the contribution proportion of OC and EC decreased, their mass concentrations during "heavily polluted" days were 6.3 and 3.2 times that of "excellent" days, respectively. Compare to non-heating period, the mass concentrations of PM2.5, OC, and SOC increased by 14.4%, 47.9%, and 72.1% in heating period, respectively, which emphasized the importance of carbonaceous species during heating periods. Potential source contribution function (PSCF) analysis showed that the southwest areas of Beijing (such as Shanxi and Henan province) were the main potential source areas of PM2.5 and OC. The high value area of the PSCF of EC was less and the main potential source area was in the south of Beijing (such as Shandong and Henan province).

A simple method of transport parameter estimation for slug injecting tracer tests in porous media.

Slug (instantaneous injection) tracer tests can be used effectively to determinate solute transport parameters in porous media such as pore velocities and dispersivities, which are usually estimated with curve-fitting methods. This study proposes a simple method to estimate conservative and reactive solute transport parameters in one-, two- and three- dimensional domains with uniform flow fields based on peak times of slug tracer tests. This method requires fewer measured data than traditional curve-fitting methods. The accuracy of the method depends on the time-interval of measurement that is the time interval used in collecting observed concentrations of solutes. The error of the pore velocity estimate is very small (less than 3%) even for a relatively large time-interval of measurement. The error of the dispersivity estimate increases with the time-interval (Δt) of measurement significantly. For 1-D case, the relative error increases from 0.29% at ∆t of 0.1 min to 17.12% at ∆t of 6 min. Such an error can be reduced by refining the time-interval of measurement near the actual peak time of breakthrough curves. The error of the dispersivity estimate decreases when the retardation factor increases. The first-order decay rate constant in the liquid hardly influences the accuracies of both pore velocity and dispersivity estimates. The proposed method is applied on laboratory sand column tests. The results indicate that the estimated pore velocities and dispersivities are almost the same to that of the curves-fitting method. This method can be employed easily by scientists and practitioners for parameter estimations in laboratory column experiments if advection-dispersion equation is applicable. This method can also be used for parameter estimation of heat transport in a laboratory column experiment if a slug heat source is injected into a porous media with the presence of a uniform flow field. Limitations of the study have also been addressed.