Non-invasive assessment of periodontal inflammation by continuum-removal hemodynamic spectral indices.

BACKGROUND: Hyperspectral techniques have aroused great interest in non-invasively measuring periodontal tissue hemodynamics. However, current studies mainly focused on three typical inflammation stages (healthy, gingivitis and periodontitis) and practical approaches for using optical spectroscopy for early and precisely detection of periodontal inflammation at finer disease stages have not been well studied. METHODS: This study provided novel spectroscopic insights into periodontitis at different stages of disease, and developed six simple but physically meaning hemodynamic spectral indices (HSIs) including four spectral absorption depths of oxyhemoglobin ( D HbO 2 ), deoxyhemoglobin ( D Hb ), total hemoglobin ( t Hb ) and tissue water ( D water ), and two normalized difference indices of oxyhemoglobin( N D HbO 2 I ) and deoxyhemoglobin ( N D Hb I ) from continuum-removal spectra (400-1700 nm) of periodontal tissue collected from 47 systemically healthy subjects over different severities from healthy, gingivitis, slight, moderate to severe periodontitis for early and precision diagnostics of periodontitis. Typical statistical analyses were conducted to explore the effectiveness of the proposed HSIs. RESULTS: D Hb and t Hb exerted significant increasing trends as inflammation progressed, whereas D HbO 2 exhibited significant difference (P < 0.05) from the healthy sites only at moderate and severe periodontitis and D water presented unstable sensitives to disease severity. By contrast, N D HbO 2 I and N D Hb I showed more steadily downward trends as severity increased, and demonstrated the highest correlations with clinical gold standard parameters. Particularly, the proposed normalized HSIs ( N D HbO 2 I and N D Hb I ) yielded high correlations of - 0.49 and - 0.44 with probing depth, respectively, far outperforming results achieved by previous studies. The performances of the HSIs were also confirmed using the periodontal therapy group. CONCLUSIONS: These results indicated great potentials of combination optical spectroscopy and smart devices to non-invasively probe periodontitis at earlier stages using the simple and practical HSIs. Trial registration This study was retrospectively registered in the Chinese Clinical Trial Registry on October 24, 2021, and the clinical registration number is ChiCTR2100052306.

Role stress, occupational burnout and depression among emergency nurses: A cross-sectional study.

BACKGROUND: Emergency nurses are a professional group at a high risk of burnout and depression. This may influence their mental state and physical health. AIMS: The aim of the study was to explore the relationship between role stress, occupational burnout and depression among emergency nurses. METHODS: A cross-sectional study was carried out among 295 emergency nurses from eight tertiary hospitals in western China. The Role Stress Scale, the Maslach Burnout Inventory-Human Services Survey and the Center for Epidemiologic Studies Depression Scale were employed. Descriptive statistics, independent sample t-tests, one-way analysis of variance, Pearson's correlation analysis and regression analysis were performed. RESULTS: Of the study participants, 54.6% were found to have depressive symptoms and 48.1% had severe occupational burnout. Positive correlations were observed between the scores of role stress and burnout and depression. Approximately 37.1% of the variation in depression was explained by the components of occupational burnout. CONCLUSIONS: Emergency nurses experience high levels of role stress, occupational burnout and depression. Preventive approaches to role stress, burnout and depression are needed to improve their psychological condition and quality of work life.

The 10-m cotton maps in Xinjiang, China during 2018-2021.

Cotton maps (10 m) of Xinjiang (XJ_COTTON10), which is the largest cotton production region of China, were produced from 2018 to 2021 through supervised classification. A two-step mapping strategy, i.e., cropland mapping followed by cotton extraction, was employed to improve the accuracy and efficiency of cotton mapping for a large region of about 1.66 million km2 with high heterogeneity. Additionally, the time-series satellite data related to spectral, textural, structural, and phenological features were combined and used in a supervised random forest classifier. The cotton/non-cotton classification model achieved overall accuracies of about 95% and 90% on the test samples of the same and adjacent years, respectively. The proposed two-step cotton mapping strategy proved promising and effective in producing multi-year and consistent cotton maps. XJ_COTTON10 agreed well with the statistical areas of cotton at the county level (R2 = 0.84-0.94). This is the first cotton mapping for the entire Xinjiang at 10-meter resolution, which can provide a basis for high-precision cotton monitoring and policymaking in China.

How nursing students' risk perception affected their professional commitment during the COVID-19 pandemic: the mediating effects of negative emotions and moderating effects of psychological capital.

Nurses play a pivotal role in the delivery of medical services. Professional commitment is crucial for nursing professionals' long-term, healthy, and sustainable development. However, nursing students' professional commitment levels are currently unsatisfactory in China, especially given that the COVID-19 pandemic has posed unprecedented challenges to the profession. Therefore, studies investigating the professional commitment levels of nursing students and the underlying influencing factors are urgently required. This study explored how nursing students' risk perceptions, negative emotions, and psychological capital affected their professional commitment during the COVID-19 pandemic. A cross-sectional study was conducted among nursing students using risk perception, professional commitment, negative emotions, and psychological capital scales. An analysis of 1142 Chinese nursing students suggested that nursing students' risk perception positively impacted professional commitment and that negative emotions mediated this association. Importantly, psychological capital moderates the mediating effect of negative emotions and can buffer the negative emotions caused by risk perception. This study demonstrated that effective intervention strategies should be implemented in multiple dimensions such as education, individual, public and society to improve the professional commitment of nursing students.

Increasing temperature regulates the advance of peak photosynthesis timing in the boreal ecosystem.

The shift in vegetation phenology is an essential indicator of global climate change. Numerous researches based on reflectance-based vegetation index data have explored the changes in the start (SOS) and end (EOS) of vegetation life events at long time scales, while a huge discrepancy existed between the phenological metrics of vegetation structure and function. The peak photosynthesis timing (PPT), which is crucial in regulating terrestrial ecosystem carbon balance, has not received much attention. Using two global reconstructed solar-induced chlorophyll fluorescence data (CSIF and GOSIF) directly associated with vegetation photosynthesis, the spatio-temporal dynamics in PPT as well as the key environmental controls across the boreal ecosystem during 2001-2019 were systematically explored. Multi-year mean pattern showed that PPT mainly appeared in the first half of July. Compared to the northern Eurasia, later PPT appeared in the northern North America continent for about 4-5 days. Meanwhile, spatial trend in PPT exhibited an advanced trend during the last two decades. Especially, shrubland and grassland were obvious among all biomes. Spatial partial correlation analysis revealed that preseason temperature was the dominant environmental driver of PPT trends, occupying 81.32% and 78.04% of the total pixels of PPTCSIF and PPTGOSIF, respectively. Attribution analysis by ridge regression again emphasized the largest contribution of temperature to PPT dynamics in the boreal ecosystem by 52.22% (PPTCSIF) and 46.59% (PPTGOSIF), followed by radiation (PPTCSIF: 24.44%; PPTGOSIF: 28.66%) and precipitation (PPTCSIF: 23.34%; PPTGOSIF: 24.75%). These results have significant implications for deepening our understanding between vegetation photosynthetic phenology and carbon cycling with respect to future climate change in the boreal ecosystem.

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image.

Yield monitoring is an important parameter to evaluate cotton productivity during cotton harvest. Nondestructive and accurate yield monitoring is of great significance to cotton production. Unmanned aerial vehicle (UAV) remote sensing has fast and repetitive acquisition ability. The visible vegetation indices has the advantages of low cost, small amount of calculation and high resolution. The combination of the UAV and visible vegetation indices has been more and more applied to crop yield monitoring. However, there are some shortcomings in estimating cotton yield based on visible vegetation indices only as the similarity between cotton and mulch film makes it difficult to differentiate them and yields may be saturated based on vegetation index estimates near harvest. Texture feature is another important remote sensing information that can provide geometric information of ground objects and enlarge the spatial information identification based on original image brightness. In this study, RGB images of cotton canopy were acquired by UAV carrying RGB sensors before cotton harvest. The visible vegetation indices and texture features were extracted from RGB images for cotton yield monitoring. Feature parameters were selected in different methods after extracting the information. Linear and nonlinear methods were used to build cotton yield monitoring models based on visible vegetation indices, texture features and their combinations. The results show that (1) vegetation indices and texture features extracted from the ultra-high-resolution RGB images obtained by UAVs were significantly correlated with the cotton yield; (2) The best model was that combined with vegetation indices and texture characteristics RF_ELM model, verification set R 2 was 0.9109, and RMSE was 0.91277 t.ha-1. rRMSE was 29.34%. In conclusion, the research results prove that UAV carrying RGB sensor has a certain potential in cotton yield monitoring, which can provide theoretical basis and technical support for field cotton production evaluation.

Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province.

Accurate and timely estimation of cotton yield over large areas is essential for precision agriculture, facilitating the operation of commodity markets and guiding agronomic management practices. Remote sensing (RS) and crop models are effective means to predict cotton yield in the field. The satellite vegetation indices (VIs) can describe crop yield variations over large areas but can't take the exact environmental impact into consideration. Climate variables (CVs), the result of the influence of spatial heterogeneity in large regions, can provide environmental information for better estimation of cotton yield. In this study, the most important VIs and CVs for estimating county-level cotton yield across Xinjiang Province were screened out. We found that the VIs of canopy structure and chlorophyll contents, and the CVs of moisture, were the most significant factors for cotton growth. For yield estimation, we utilized four approaches: least absolute shrinkage and selection operator regression (LASSO), support vector regression (SVR), random forest regression (RFR) and long short-term memory (LSTM). Due to its ability to capture temporal features over the long term, LSTM performed best, with an R2 of 0.76, root mean square error (RMSE) of 150 kg/ha and relative RMSE (rRMSE) of 8.67%; moreover, an additional 10% of the variance could be explained by adding CVs to the VIs. For the within-season yield estimation using LSTM, predictions made 2 months before harvest were the most accurate (R2 = 0.65, RMSE = 220 kg/ha, rRMSE = 15.97%). Our study demonstrated the feasibility of yield estimation and early prediction at the county level over large cotton cultivation areas by integrating satellite and environmental data.

[L-carnitine-astaxanthin compound nutrients for the treatment of idiopathic oligospermia and asthenozoospermia: A multicenter clinical observation].

OBJECTIVE: To investigate the clinical effects of the L-carnitine-astaxanthin compound nutrients Menglankang (MLK) on idiopathic oligospermia (OS) and asthenospermia (AS). METHODS: This study included 73 cases of OS and 220 cases of AS treated with MLK once a bag, bid, for 3 successive months. Before and at 1, 2 and 3 months after treatment, we obtained and analyzed the semen parameters and sperm DNA fragmentation index (DFI) of the patients. RESULTS: Compared with the baseline, the OS patients showed remarkable increases after 1 and 2 months of treatment in the semen volume (ï¼»3.07 ± 1.47ï¼½ vs ï¼»3.26 ± 1.26ï¼½ and ï¼»3.30 ± 1.28ï¼½ ml), sperm concentration (ï¼»10.96 ± 6.09ï¼½ vs ï¼»16.74 ± 11.15ï¼½ and ï¼»17.56 ± 9.92ï¼½ ×106/ml, P < 0.05), total sperm count (ï¼»29.78 ± 17.48ï¼½ vs ï¼»52.98 ± 32.07ï¼½ and ï¼»57.67 ± 36.98ï¼½ ×106, P < 0.05) and the percentages of progressively motile sperm (PMS) (ï¼»39.8 ± 11.66ï¼½% vs ï¼»45.3 ± 14.03ï¼½% and ï¼»46.42 ± 10.69ï¼½%, P < 0.05) and morphologically normal sperm (MNS) (ï¼»1.71 ± 1.07ï¼½% vs ï¼»1.79 ± 0.91ï¼½% and ï¼»1.84 ± 0.96ï¼½%), and so did the AS patients in PMS (ï¼»19.23 ± 8.32ï¼½% vs ï¼»25.46 ± 13.86ï¼½% and ï¼»27.33 ± 12.88ï¼½%, P < 0.05). After 3 months of medication, the OS patients exhibited even more significant increases in the semen volume (ï¼»3.63 ± 1.39ï¼½ ml) (P < 0.05), sperm concentration (ï¼»20.56 ± 14.7ï¼½ ×106/ml) (P < 0.05), total sperm count (ï¼»66.35 ± 55.91ï¼½ ×106) (P < 0.05), PMS (ï¼»49.24 ± 13.45ï¼½%) (P < 0.05) and MNS (ï¼»2.59 ± 0.93ï¼½%) (P < 0.05), and so did the AS patients in the semen volume (ï¼»3.27 ± 1.42ï¼½ vs ï¼»3.85 ± 1.59ï¼½ ml, P < 0.05), PMS (ï¼»29.11 ± 13.58ï¼½%) (P < 0.05) and NMS (ï¼»2.01 ± 1.14ï¼½% vs ï¼»2.57 ± 1.15ï¼½%, P < 0.05). In comparison with the baseline, the sperm DFI was not significantly improved at 1 month after treatment, but remarkably decreased at 2 and 3 months in the OS patients (ï¼»25.87 ± 13.76ï¼½% vs ï¼»18.66 ± 10.83ï¼½% and ï¼»16.48 ± 11.46ï¼½%, P < 0.05) and the AS patients as well (ï¼»26.40 ± 12.28ï¼½% vs ï¼»19.35 ± 11.54ï¼½% and ï¼»15.32 ± 10.89ï¼½%, P < 0.05). CONCLUSIONS: The L-carnitine-astaxanthin compound nutrients Menglankang can significantly improve the semen quality of the patients with idiopathic oligospermia or asthenospermia.

Technologies and perspectives for achieving carbon neutrality.

Global development has been heavily reliant on the overexploitation of natural resources since the Industrial Revolution. With the extensive use of fossil fuels, deforestation, and other forms of land-use change, anthropogenic activities have contributed to the ever-increasing concentrations of greenhouse gases (GHGs) in the atmosphere, causing global climate change. In response to the worsening global climate change, achieving carbon neutrality by 2050 is the most pressing task on the planet. To this end, it is of utmost importance and a significant challenge to reform the current production systems to reduce GHG emissions and promote the capture of CO2 from the atmosphere. Herein, we review innovative technologies that offer solutions achieving carbon (C) neutrality and sustainable development, including those for renewable energy production, food system transformation, waste valorization, C sink conservation, and C-negative manufacturing. The wealth of knowledge disseminated in this review could inspire the global community and drive the further development of innovative technologies to mitigate climate change and sustainably support human activities.

Artificial intelligence: A powerful paradigm for scientific research.

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

[Correlation of sperm DNA fragmentation index with age and semen parameters and its influence on the outcomes of IVF-ET].

OBJECTIVE: To explore the correlation of the sperm DNA fragmentation index (DFI) with age and other semen parameters in infertile men and its influence on the outcomes of in vitro fertilization and embryo transplantation (IVF-ET). METHODS: Semen samples were obtained from 6 162 infertile males in our hospital between July 2017 and December 2018. Sperm concentration, the percentages of progressively motile sperm (PMS) and morphologically normal sperm (MNS) and sperm DFI were determined by computer-assisted semen analysis, modified Papanicolaou staining and sperm chromatin structure assay, respectively. According to the sperm DFI, the samples were divided into three groups: DFI≤15%, 15%30%. Comparisons were made among different groups in the rates of fertilization, embryo cleavage, embryo formation and pregnancy. RESULTS: Spearman correlation analysis showed that DFI was correlated positively with age (r = 0.508, P < 0.05), but negatively with sperm concentration (r = －0.155, P < 0.05) and the percentages of PMS (r = －0.111, P < 0.05) and MNS (r = －0.315, P < 0.05). With sperm DFI >20%, the clinical pregnancy rate of IVF-ET was significantly reduced, while with sperm DFI >30%, the rate of available embryos markedly decreased and that of biochemical pregnancy remarkably increased. No correlation was found between sperm DFI and the rates of fertilization, embryo cleavage and high-quality embryos in IVF-ET. CONCLUSIONS: In infertile males, sperm DFI is correlated positively with age but negatively with sperm concentration and the percentages of PMS and MNS, and it significantly affects the outcomes of IVF-ET.

[Monitor of Cyanobacteria Bloom in Lake Taihu from 2001 to 2013 Based on MODIS Temporal Spectral Data].

Algal bloom highly impacts the ecological balance of inland lakes. Remote sensing provides real-time and large-scale observations, which plays an increasingly significant role in the monitoring of algal bloom. Various Vegetation Indices (VIs) derived from satellite images have been used to monitor algae. With threshold segmentation of VI, the area of algal bloom can be extracted from images. However, the result of threshold segmentation only reflects the condition of algae when images were generated. Compared to separated VI data obtained at a particular moment of time, temporal spectral VI data contains phonological information of algae, which may be used to evaluate algal bloom more accurately and comprehensively. This study chose MODIS NDVI data of the Lake Taihu from 2001 to 2013, and constructed temporal spectral data for each year. Then, we determined the feature temporal spectra of severe cyanobacteria bloom, moderate cyanobacteria bloom, slight cyanobacteria bloom and aquatic plants, and separated these four kinds of objects using SVM (Support Vector Machine) algorithm, getting the spatial distribution and area of them. In order to compare the results of our method with traditional threshold segmentation method, we chose 8 separated NDVI images from the temporal spectral data of 2007. With the threshold 0.2 and 0.4, cyanobacteria bloom was classified into three degrees: severe cyanobacteria bloom, moderate cyanobacteria bloom, and slight cyanobacteria bloom. By comparison, it showed that our method reflected cyanobacteria bloom more comprehensively, and could distinguish cyanobacteria and aquatic plants using the phonological information provided by NDVI temporal spectra. This study provides important information for monitoring the algal bloom trends and degrees of inland lakes, and temporal spectral method may be used in the forecast of algal bloom in the future.

[Research on the model of spectral unmixing for minerals based on derivative of ratio spectroscopy].

The precise analysis of mineral abundance is a key difficulty in hyperspectral remote sensing research. In the present paper, based on linear spectral mixture model, the derivative of ratio spectroscopy (DRS) was introduced for spectral unmixing of visible to short-wave infrared (Vis-SWIR; 0.4 - 2.5 microm) reflectance data. The mixtures of different proportions of plaster and allochite were analyzed to estimate the accuracy of the spectral unmixing model based on DRS. For the best 5 strong linear bands, the Pearson correlation coefficient (PCC) of the abundances and the actual abundances were higher than 99.9%, while the root mean square error (RMSE) is less than 2.2%. The result shows that the new spectral unmixing model based on DRS is simple, of rigorous mathematical proof, and highly precise. It has a great potential in high-precision quantitative analysis of spectral mixture with fixed endmembers.

Laboratory calibration of a field imaging spectrometer system.

A new Field Imaging Spectrometer System (FISS) based on a cooling area CCD was developed. This paper describes the imaging principle, structural design, and main parameters of the FISS sensor. The FISS was spectrally calibrated with a double grating monochromator to determine the center wavelength and FWHM of each band. Calibration results showed that the spectral range of the FISS system is 437-902 nm, the number of channels is 344 and the spectral resolution of each channel is better than 5 nm. An integrating sphere was used to achieve absolute radiometric calibration of the FISS with less than 5% calibration error for each band. There are 215 channels with signal to noise ratios (SNRs) greater than 500 (62.5% of the bands). The results demonstrated that the FISS has achieved high performance that assures the feasibility of its practical use in various fields.

[Fresh and frozen-thawed meat discrimination based on FISS imaging spectral data].

In the present paper, a self-developed Field imaging spectrometer system (FISS) was used to detect whether pork has been frozen and thawed. The preservation time of fresh pork has also been identified. Fresh and frozen-thawed pork was scanned and imaged and hyperspectral image cubes were acquired using FISS. To eliminate high-frequency random noise and baseline offset and improve the multi-collinearity, all samples were preprocessed by MNF (Minimum noise fraction) transform and first derivative. Multiple analysis models were built by using Wilks' lambda stepwise method to select proper wavelengths. Fisher LDA (linear discriminant analysis) was performed to discriminate fresh and frozen-thawed pork. Eight selected bands gave 99% correct results of fresh or frozen-thawed pork samples. For the freshness by the day, classification accuracy reached 98% with 6 selected bands, while for the freshness by the hour, classification accuracy reached 93.6% with all 28 selected bands. The results showed that FISS might be used as a screening method to identify the quality of meat.

[Study on discrimination of varieties of milk based on FISS imaging spectral data].

Using a self-developed field imaging spectrometer system (FISS), hyperspectral images of 14 typical kinds of milk were acquired, based on which the discrimination of varieties of milk was studied. Firstly, removing 2 abnormal samples, the remaining 12 kinds of milk were randomly sampled, a total of 1 200 pixel samples. To eliminating high-frequency random noises and baseline offset and decrease the multi-collinearity, all samples were preprocessed by smooth-moving average and first derivative. Secondly, multiple discriminant analysis models for milk were built using characteristic wavelengths selected by the stepwise method. Results demonstrated that the overall identification accuracy for 1 200 spectral samples put together reached 95.5%, of which the overall distinguishing rate of Mengniu, Yili and Guangming acidophilous milk was 88.3%. The discriminant models for the three kinds of acidophilous milk subset, 300 spectral samples in all, were built, with the overall distinguishing rate of 88.7%. This explicated that FISS would be useful for discriminating milk varieties, and to accomplish specific discrimination of milk varieties, it would be best for milk of the same type from different manufacturers to form a subset, which may not only reduce the model variables, improving operational efficiency and the stability of the model, but improve their overall discriminant accuracy.