An indirect estimation of x-ray spectrum via convolutional neural network and transmission measurement.

Objective.In this work, we aim to propose an accurate and robust spectrum estimation method by synergistically combining x-ray imaging physics with a convolutional neural network (CNN).Approach.The approach relies on transmission measurements, and the estimated spectrum is formulated as a convolutional summation of a few model spectra generated using Monte Carlo simulation. The difference between the actual and estimated projections is utilized as the loss function to train the network. We contrasted this approach with the weighted sums of model spectra approach previously proposed. Comprehensive studies were performed to demonstrate the robustness and accuracy of the proposed approach in various scenarios.Main results.The results show the desirable accuracy of the CNN-based method for spectrum estimation. The ME and NRMSE were -0.021 keV and 3.04% for 80 kVp, and 0.006 keV and 4.44% for 100 kVp, superior to the previous approach. The robustness test and experimental study also demonstrated superior performances. The CNN-based approach yielded remarkably consistent results in phantoms with various material combinations, and the CNN-based approach was robust concerning spectrum generators and calibration phantoms.Significance. We proposed a method for estimating the real spectrum by integrating a deep learning model with real imaging physics. The results demonstrated that this method was accurate and robust in estimating the spectrum, and it is potentially helpful for broad x-ray imaging tasks.

A comparison of the Mini-Mental State Examination (MMSE) with the Montreal Cognitive Assessment (MoCA) for mild cognitive impairment screening in Chinese middle-aged and older population: a cross-sectional study.

BACKGROUND: The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are the most commonly used scales to detect mild cognitive impairment (MCI) in population-based epidemiologic studies. However, their comparison on which is best suited to assess cognition is scarce in samples from multiple regions of China. METHODS: We conducted a cross-sectional analysis of 4923 adults aged ≥55 years from the Community-based Cohort Study on Nervous System Diseases. Objective cognition was assessed by Chinese versions of MMSE and MoCA, and total score and subscores of cognitive domains were calculated for each. Education-specific cutoffs of total score were used to diagnose MCI. Demographic and health-related characteristics were collected by questionnaires. Correlation and agreement for MCI between MMSE and MoCA were analyzed; group differences in cognition were evaluated; and multiple logistic regression model was used to clarify risk factors for MCI. RESULTS: The overall MCI prevalence was 28.6% for MMSE and 36.2% for MoCA. MMSE had good correlation with MoCA (Spearman correlation coefficient = 0.8374, p < 0.0001) and moderate agreement for detecting MCI with Kappa value of 0.5973 (p < 0.0001). Ceiling effect for MCI was less frequent using MoCA versus MMSE according to the distribution of total score. Percentage of relative standard deviation, the measure of inter-individual variance, for MoCA (26.9%) was greater than for MMSE (19.0%) overall (p < 0.0001). Increasing age (MMSE: OR = 2.073 for ≥75 years; MoCA: OR = 1.869 for≥75 years), female (OR = 1.280 for MMSE; OR = 1.163 for MoCA), living in county town (OR = 1.386 and 1.862 for MMSE and MoCA, respectively) or village (OR = 2.579 and 2.721 for MMSE and MoCA, respectively), smoking (OR = 1.373 and 1.288 for MMSE and MoCA, respectively), hypertension (MMSE: OR = 1.278; MoCA: OR = 1.208) and depression (MMSE: OR = 1.465; MoCA: OR = 1.350) were independently associated with greater likelihood of MCI compared to corresponding reference group in both scales (all p < 0.05). CONCLUSIONS: MoCA is a better measure of cognitive function due to lack of ceiling effect and with good detection of cognitive heterogeneity. MCI prevalence is higher using MoCA compared to MMSE. Both tools identify concordantly modifiable factors for MCI, which provide important evidence for establishing intervention measures.

Assessment of the impact of geogenic and climatic factors on global risk of urinary stone disease.

Urinary Stone Disease (USD) or urolithiasis has plagued humans for centuries, and its prevalence has increased over the past few decades. Although USD pathology could vary significantly among individuals, previous qualitative assessments using limited survey data demonstrated that the prevalence of USD might exhibit a distinctive geographical distribution (the so-called "stone belt"), without any knowledge about the characteristics and contribution factors of the belt. Here, we argue that the spatial distribution of USD can at least partly be explained by geogenic and climatic factors, as it correlates with the ambient geo-environmental conditions modulated by lithology/mineralogy, water quality and climate. Using a Bayesian risk model, we assessed the global risk of USD based on updated big data of four key geogenic factors: phosphorite mines (inventory >1600 points), carbonate rocks (at the scale of 1:40 million), Ca2+/Mg2+ molar ratio of river water (1.27 million samples distributed over 17,000 sampling locations), and mean air temperature (0.5o × 0.5° resolution) representing the climate. We quantitatively identified possible contributions of the factors to USD and delineated the regions with the high USD risk which stretched from southern North America, via the Mediterranean region, northeastern Africa, southern China to Australia, and roughly coincide with the world's major areas of carbonate outcropping. Under current climate conditions, the areas with the probabilities for the USD prevalence of ≥50% and ≥30% covered 3.7% and 20% of the Earth's land surface, respectively. By the end of the 21st century, such total areas could rise to 4.4% and 25% as a result of global warming. Since the USD data used in this study were quite heterogeneous, the prediction results needed further calibration with additional high-quality prevalence data in the future.

Multiple isotope geochemistry and hydrochemical monitoring of karst water in a rapidly urbanized region.

Karst water is an important resource for drinking water supply. To determine the impacts of urbanization on karst water quality, we performed a case study in the rapidly urbanized Guiyang-Anshun region, Guizhou province, southwestern China. We interpret data from regional reconnaissance and long-term monitoring related to major ion chemistry, stable isotopes (Sr, C, S (for SO42-), and N and O (for NO3-)), remote sensing, and socio-economic development. We identify groundwater SO42- and NO3- sources by combined use of δ34S and Ca2+/Na+ molar ratio and δ15N and NO3-/Na+ ratio, respectively. We find that carbonate, sulfide, silicate, and gypsum weathering, anthropogenic inputs, and hydrodynamic conditions account for karst water composition and its seasonal variations. Atmospheric N and S deposition, nitrification of soil N, and sulfide oxidation control the background levels of groundwater NO3- and SO42-. The elevated concentrations of NO3- and SO42- at residential sites in rural and urban areas mainly arise from domestic sewage. Nitrification and fertilizer application are major reasons for the high levels of NO3- in regional groundwater systems. Vegetated/forested land area decreases as constructed land area increases, which results in declining biogenic CO2 production and inputs into the aquifer. Although the local government has attempted to control SO2 emissions, substantial increases in fossil fuel utilization and fertilizer consumption as well as population growth may have increased atmospheric HNO3 deposition and induced increasingly severe contamination of groundwater with NO3- and SO42-. Our results improve the understanding of urbanization impacts on water quality and are important for water resource management in karst regions.

Occurrence and risk assessment of antibiotics in surface water and groundwater from different depths of aquifers: A case study at Jianghan Plain, central China.

The occurrence of 14 antibiotics (fluoroquinolones, tetracyclines, macrolides and sulfonamides) in groundwater and surface water at Jianghan Plain was investigated during three seasons. The total concentrations of target compounds in the water samples were higher in spring than those in summer and winter. Erythromycin was the predominant antibiotic in surface water samples with an average value of 1.60µg/L, 0.772µg/L and 0.546µg/L respectively in spring, summer and winter. In groundwater samples, fluoroquinolones and tetracyclines accounted for the dominant proportion of total antibiotic residues. The vertical distributions of total antibiotics in groundwater samples from three different depths boreholes (10m, 25m, and 50m) exhibited irregular fluctuations. Consistently decreasing of antibiotic residues with increasing of depth was observed in four (G01, G02, G03 and G05) groundwater sampling sites over three seasons. However, at the sampling sites G07 and G08, the pronounced high concentrations of total antibiotic residues were detected in water samples from 50m deep boreholes instead of those at upper aquifer in winter sampling campaign, with the total concentrations of 0.201µg/L and 0.100µg/L respectively. The environmental risks posed by the 14 antibiotics were assessed by using the methods of risk quotient and mixture risk quotient for algae, daphnids and fish in surface water and groundwater. The results suggested that algae might be the aquatic organism most sensitive to the antibiotics, with the highest risk levels posed by erythromycin in surface water and by ciprofloxacin in groundwater among the 14 antibiotics. In addition, the comparison between detected antibiotics in groundwater samples and the reported effective concentrations of antibiotics on denitrification by denitrifying bacteria, indicating this biogeochemical process driven by microorganisms won't be inhibitory influenced by the antibiotic residues in groundwater.

Venous thromboembolism risk in patients with cancer receiving chemotherapy: a real-world analysis.

UNLABELLED: The occurrence of malignant disease increases the risk for venous thromboembolism (VTE). Here we evaluate the risk for VTE in a large unselected cohort of patients with cancer receiving chemotherapy. METHODS: The United States IMPACT health care claims database was retrospectively analyzed to identify patients with a range of solid tumors who started chemotherapy from January 2005 through December 2008. International Classification of Diseases, 9th revision, Clinical Modification Codes were used to identify cancer location, presence of VTE 3.5 months and 12 months after starting chemotherapy, and incidence of major bleeding complications. Health care costs were assessed one year before initiation of chemotherapy and one year after initiation of chemotherapy. RESULTS: The overall incidence of VTE 3.5 months after starting chemotherapy was 7.3% (range 4.6%-11.6% across cancer locations) rising to 13.5% at 12 months (range 9.8%-21.3%). The highest VTE risk was identified in patients with pancreatic, stomach, and lung cancer. Patients in whom VTE developed had a higher risk for major bleeding at 3.5 months and at 12 months (11.0% and 19.8% vs. 3.8% and 9.6%, respectively). Health care costs were significantly higher in patients in whom VTE developed. CONCLUSION: Those undergoing chemotherapy as outpatients are at increased risk for VTE and for major bleeding complications. Thromboprophylaxis may be considered for such patients after carefully assessing the risks and benefits of treatment.

A cost-effective system for in-situ geological arsenic adsorption from groundwater.

An effective and low-cost in-situ geological filtration system was developed to treat arsenic-contaminated groundwater in remote rural areas. Hangjinhouqi in western Hetao Plain of Inner Mongolia, China, where groundwater contains a high arsenic concentration, was selected as the study area. Fe-mineral and limestone widely distributed in the study area were used as filter materials. Batch and column experiments as well as field tests were performed to determine optimal filtration parameters and to evaluate the effectiveness of the technology for arsenic removal under different hydrogeochemical conditions. A mixture containing natural Fe-mineral (hematite and goethite) and limestone at a mass ratio of 2:1 was found to be the most effective for arsenic removal. The results indicated that Fe-mineral in the mixture played a major role for arsenic removal. Meanwhile, limestone buffered groundwater pH to be conducive for the optimal arsenic removal. As(III) adsorption and oxidation by iron mineral, and the formation of Ca-As(V) precipitation with Ca contributed from limestone dissolution were likely mechanisms leading to the As removal. Field demonstrations revealed that a geological filter bed filled with the proposed mineral mixture reduced groundwater arsenic concentration from 400 µg/L to below 10 µg/L. The filtration system was continuously operated for a total volume of 365,000L, which is sufficient for drinking water supplying a rural household of 5 persons for 5 years at a rate of 40 L per person per day.