Fourier Transform Infrared (FT-IR) Spectroscopy and Simple Algorithm Analysis for Rapid and Non-Destructive Assessment of Cotton Fiber Maturity and Crystallinity for Plant Mapping.

Information on boll distribution within a cotton plant is critical to evaluate the adaptation and response of cotton plants to environmental and biotic stress in cotton production. Cotton researchers have applied available conventional fiber measurements, such as the high volume instrument (HVI) and advanced fiber information system (AFIS), to map the location and the timing of boll development and distribution within plants and further to determine within-plant variability of cotton fiber properties. Both HVI and AFIS require numerous cotton bolls combined for the measurement. As an alternative approach, attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy was proposed to measure fiber maturity (MIR) and crystallinity (CIIR) of a sample as little as 0.5 mg lint. Extending fiber maturity and crystallinity measurement into a single boll for node-by-node mapping, FT-IR method might be advantageous due to less sampling amount compared with HVI and AFIS methods. Results showed that FT-IR technique enabled the evaluation of fiber MIR and CIIR at a boll level, which resulted in average MIR and CIIR values highly correlated with HVI micronaire (MIC) and AFIS maturity ratio (M). Hence, FT-IR technique possesses a good potential for a rapid and non-destructive node-by-node mapping of cotton boll maturity and crystallinity distribution.

The pollution characteristics and risk assessment of microplastics in mollusks collected from the Bohai Sea.

Microplastics (MPs) pollution in the marine environment has become a global problem. In this study, a number of 21 mollusk species (n = 2006) with different feeding habits were collected from 11 sites along the Bohai Sea for MPs uptake analysis. The MPs in mollusk samples were isolated and identified by micro-Fourier Transform Infrared Spectroscopy (µ-FTIR), and an assessment of the health risks of MPs ingested by mollusk consumption is also conducted. Approximately 91.9 % of the individuals among all the collected species inhaled MPs, and there was an average abundance of 3.30 ± 2.04 items·individual-1 or 1.04 ± 0.74 items·g-1 of wet weight. The shape of MPs was mainly fiber, and a total number of 8 polymers were detected, of which rayon had the highest detection rate (58.3 %). The highest abundance, uptake rate and polymer composition of MPs was observed in creeping types, suggesting that they might ingest these MPs from their food. The gastropod Siphonalia subdilatata contains the highest levels of MPs, which may increase the risk of human exposure if consumed whole without removing the digestive gland. The polymer risk level of MPs in these mollusks was Level III (H = 299), presenting harmful MPs such as polyvinyl chloride. In terms of human exposure risk, the average risk of human exposure to MPs through consumption of Bohai mollusks is estimated to be 3399 items·(capita·year)-1 (424-9349 items·(capita·year)-1). Overall, this study provides a basis for the ecological and health Risk assessment of MPs in mollusks collected from the coastline of China.

Source identification and risk assessment of trace metals in surface sediment of China Sea by combining APCA-MLR receptor model and lead isotope analysis.

This study aimed to investigate the distribution, pollution, risk and sources of trace metals in sediments along China Sea. Clear spatial variations were found for Cr, Mn, Co, Ni, Cu, Zn, Se, Mo, Ag, Cd, and Pb, whereas As did not show spatial variation. East China Sea (ECS) contained the highest concentrations of Mn, Co, Ni, Cu, Zn, South China Sea (SCS) shallow sea contained the highest concentrations of Zn, Se, Mo, Ag, Cd, and Pb, whereas coral reefs contained the lowest concentrations of trace metals. Spatial variations could be explained by economic development characteristics along China Sea. As, Se and Cd exhibited low to moderate pollution in China Sea sediment, yet pollution for Cu, Zn, Ni, and Ag appeared in some regions. Sediment in ECS had moderate ecological risks and other regions at low ecological risks. The absolute principle component score-multiple linear regression (APCS-MLR) and Pb stable isotope indicated that 43-74% of trace metals (Ni, Cu, Zn, As, Se, Cd, and Pb) were derived from anthropogenic sources like traffic emission, agricultural activities, industrial source. No pollution and ecological risk were observed in coral reefs, yet 39-71% (Pb) was derived from anthropogenic activities such as motor vessels.

Performance of an artificial intelligence system for bone age assessment in Tibet.

OBJECTIVE: To investigate whether bone age (BA) of children living in Tibet Highland could be accurately assessed using a fully automated artificial intelligence (AI) system. METHODS：: Left hand radiographs of 385 children (300 Tibetan and 85 immigrant Han) aged 4-18 years who presented to the largest medical center of Tibet between September 2013 and November 2019 were consecutively collected. From these radiographs, BA was determined using the Greulich and Pyle (GP) method by experts in a consensus manner; furthermore, BA was estimated by a previously reported artificial intelligence (AI) BA system based on Han children from southern China. The performance of the AI system was compared with that of experts by using statistical analysis. RESULTS: Compared with the experts' results, the accuracy of the AI system for Tibetan and Han children within 1 year was 84.67 and 89.41%, respectively, and its mean absolute difference (MAD) was 0.65 and 0.56 years, respectively. The discrepancy in hand-wrist bone maturation was the main cause of low accuracy of the system in the 4- to 6-year-old group. CONCLUSION: The AI BA system developed for Han Chinese children living in flat regions could enable to assess BA accurately in Tibet where medical resources are limited. ADVANCES IN KNOWLEDGE: AI-based BA system may serve as an effective and efficient solution to assess BA in Tibet.

Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development.

OBJECTIVE: Bone age (BA) is a crucial indicator for revealing the growth and development of children. This study tested the performance of a fully automated artificial intelligence (AI) system for BA assessment of Chinese children with abnormal growth and development. MATERIALS AND METHODS: A fully automated AI system based on the Greulich and Pyle (GP) method was developed for Chinese children by using 8,000 BA radiographs from five medical centers nationwide in China. Then, a total of 745 cases (360 boys and 385 girls) with abnormal growth and development from another tertiary medical center of north China were consecutively collected between January and October 2018 to test the system. The reference standard was defined as the result interpreted by two experienced reviewers (a radiologist with 10 years and an endocrinologist with 15 years of experience in BA reading) through consensus using the GP atlas. BA accuracy within 1 year, root mean square error (RMSE), mean absolute difference (MAD), and 95% limits of agreement according to the Bland-Altman plot were statistically calculated. RESULTS: For Chinese pediatric patients with abnormal growth and development, the accuracy of this new automated AI system within 1 year was 84.60% as compared to the reference standard, with the highest percentage of 89.45% in the 12- to 18-year group. The RMSE, MAD, and 95% limits of agreement of the AI system were 0.76 years, 0.58 years, and -1.547 to 1.428, respectively, according to the Bland-Altman plot. The largest difference between the AI and experts' BA result was noted for patients of short stature with bone deformities, severe osteomalacia, or different rates of maturation of the carpals and phalanges. CONCLUSIONS: The developed automated AI system could achieve comparable BA results to experienced reviewers for Chinese children with abnormal growth and development.

Fourier Transform Infrared Spectroscopy (FT-IR) and Simple Algorithm Analysis for Rapid and Non-Destructive Assessment of Developmental Cotton Fibers.

With cotton fiber growth or maturation, cellulose content in cotton fibers markedly increases. Traditional chemical methods have been developed to determine cellulose content, but it is time-consuming and labor-intensive, mostly owing to the slow hydrolysis process of fiber cellulose components. As one approach, the attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy technique has also been utilized to monitor cotton cellulose formation, by implementing various spectral interpretation strategies of both multivariate principal component analysis (PCA) and 1-, 2- or 3-band/-variable intensity or intensity ratios. The main objective of this study was to compare the correlations between cellulose content determined by chemical analysis and ATR FT-IR spectral indices acquired by the reported procedures, among developmental Texas Marker-1 (TM-1) and immature fiber (im) mutant cotton fibers. It was observed that the R value, CIIR, and the integrated intensity of the 895 cm-1 band exhibited strong and linear relationships with cellulose content. The results have demonstrated the suitability and utility of ATR FT-IR spectroscopy, combined with a simple algorithm analysis, in assessing cotton fiber cellulose content, maturity, and crystallinity in a manner which is rapid, routine, and non-destructive.

Use of Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy in Direct, Nondestructive, and Rapid Assessment of Developmental Cotton Fibers Grown In Planta and in Culture.

Cotton fibers are routinely harvested from cotton plants (in planta), and their end-use qualities depend on their development stages. Cotton fibers are also cultured in controlled laboratory environments, so that cotton researchers can investigate many aspects of experimental protocols in cotton breeding programs at reduced expenses. In this work, attenuated total reflection Fourier transform infrared (ATR FT-IR) spectra of cotton fibers grown in planta and in culture were collected to explore the potential of FT-IR technique as a simple, rapid, and direct method for characterizing the fiber development. Complementary to visual inspection of spectral variations, principal component analysis (PCA) of ATR FT-IR spectra revealed the occurrence of phase transition from primary to secondary cell wall synthesis and also the difference of starting the phase transition between two types of fibers. Like PCA observation, three simple algorithms were capable of monitoring the secondary cell wall formation effectively. Interestingly and uniquely, simple algorithms were able to detect the subtle discrepancies in fibers older than 25 days post-anthesis, which was not apparent from PCA results. The observation indicated the feasibility of FT-IR technique in rapid, routine, nondestructive, and direct assessment of fiber development for cotton physiology and breeding applications.