Depression and risk of arthritis: A Mendelian randomization study.

INTRODUCTION: Observational studies have found that most patients with arthritis have depression. We aimed to determine the causal relationship between various types of arthritis and depression. METHODS: We conducted a two-sample bidirectional Mendelian randomized (MR) analysis to determine whether there was a significant causal relationship between depression and multiple types of arthritis. The data of our study were derived from the publicly released genome-wide association studies (GWASs) and the largest GWAS meta-analysis. MR analysis mainly used inverse-variance weighted method; supplementary methods included weighted median, weighted mode, and MR-Egger using MR pleiotropy residual sum and outlier to detect and correct for the presence of pleiotropy. RESULTS: After adjusting for heterogeneity and horizontal pleiotropy, we found that depression was associated with an increased risk of osteoarthritis (OA) (OR = 1.02, 95%CI: 1.01-1.02, p = 2.96 × E - 5). In the reverse analysis, OA was also found to increase the risk of depression (OR = 1.10, 95%CI: 1.04-1.15, p = .0002). Depression only increased the risk of knee OA (KOA) (OR = 1.25, 95%CI: 1.10-1.42, p = 6.46 × E - 4). Depression could potentially increase the risk of spondyloarthritis (OR = 1.52, 95%CI: 1.19-1.94, p ≤ 8.94 × E - 4). CONCLUSION: There is a bidirectional causal relationship of depression with OA. However, depression only augments the risk of developing KOA. Depression may increase the risk of spondyloarthritis and gout.

Greenhouse gas emissions and reduction strategies for the world's largest greenhouse gas emitters.

In the context of climate change, it is crucial to examine the contributions of leading countries in greenhouse gas (GHG) emissions. This research provides an overview of global GHG emissions from 1970 to 2022 for the world's most polluting countries: the United States, China, India, Russia, Brazil, Indonesia, Japan, Iran, Mexico, and Saudi Arabia. These countries collectively account for approximately 64% of GHG emissions. The aim is to understand the impact of various economic sectors, such as industry, energy, agriculture, and transportation, on overall emissions. The analysis highlights the disparity in per capita emissions, with smaller but major oil-producing countries in the Persian Gulf, such as Qatar and the United Arab Emirates, exhibiting high per capita emission levels, while more populated countries like the United States and South Korea show lower per capita values but significant total emission volumes. The study suggests that transitioning to renewable energy, improving energy efficiency in industry, promoting sustainable agriculture, reforestation, and electrifying transportation are key methods to achieve United Nations Sustainable Development Goals (UN SDG). Recommendations include encouraging technological innovations, implementing stringent government regulations and standards, and garnering active support for GHG reduction programs from governments, financial institutions, and the business community. The urgency is emphasized for global efforts to combat climate change for ensuring a sustainable future.

Numerical Optimization Study of Combustion and Heat Transfer Characteristics in Municipal Solid Waste Incinerators.

The efficient and clean utilization of urban waste can substitute for partial fossil fuels and reduce total carbon emissions. Fuel combustion is divided into three stages. Before the fire, the fuel is put into the furnace to reach the preparation stage of the fire temperature, the combustion stage takes place after the ignition temperature is reached, and finally, the combustion is completed. This article employs numerical simulation methods to comprehensively study the effects of various factors on the combustion characteristics of waste in a mechanical grate incinerator, including the inclination angle of the front arch, fuel properties, height of the front and rear arches, air distribution methods, and speed of the grate chain rotation. The results indicate that when the rear arch angle is set at 25°, the airflow distribution within the furnace is uniform and the high-temperature flue gas exhibits an ideal "L" shaped flow, achieving favorable characteristics of airflow distribution inside the furnace. With this structure, the airflow from the rear arch can adequately penetrate deep into the front arch area, thereby forming an efficient T-shaped combustion flame.

Weissella cibaria Relieves Gut Inflammation Caused by Escherichia coli through Inflammation Modulation and Gut Microbiota Regulation.

The emergence of multi-drug-resistant (MDR) pathogens has considerably challenged the development of new drugs. Probiotics that inhibit MDR pathogens offer advantages over chemical antibiotics and drugs due to their increased safety and fewer side effects. This study reported that Weissella cibaria P-8 isolated from pickles showed excellent antibacterial activity against intestinal pathogens, particularly the antibacterial activity against MDR Escherichia coli B2 was the highest. This study showed that the survival rates of W. cibaria P-8 at pH 2.0 and 0.3% bile salt concentration were 72% and 71.56%, respectively, and it still had antibacterial activity under pepsin, trypsin, protease K, and catalase hydrolysis. Moreover, W. cibaria P-8 inhibits the expression of inflammatory factors interleukin-1ß, tumor necrosis factor-α, and interleukin-6, upregulates the interleukin-10 level, and increases total antioxidant capacity and superoxide dismutase enzyme activity in serum. W. cibaria P-8 also efficiently repairs intestinal damage caused by E. coli infection. The gut microbiota analysis demonstrated that W. cibaria P-8 colonizes the intestine and increases the abundance of some beneficial intestinal microorganisms, particularly Prevotella. In conclusion, W. cibaria P-8 alleviated MDR E. coli-induced intestinal inflammation by regulating inflammatory cytokine and enzyme activity and rebalancing the gut microbiota, which could provide the foundation for subsequent clinical analyses and probiotic product development.

Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features.

Purpose: We investigated the value of magnetic resonance imaging (MRI) histogram features, a non-invasive method, in assessing the changes in chemoresistance of colorectal cancer xenografts in rats. Methods: A total of 50 tumor-bearing mice with colorectal cancer were randomly divided into two groups: control group and 5-fluorouracil (5-FU) group. The MRI histogram characteristics and the expression levels of p53 protein and MRP1 were obtained at 24 h, 48 h, 72 h, 120 h, and 168 h after treatment. Results: Sixty highly repeatable MRI histogram features were obtained. There were 16 MRI histogram parameters and MRP1 resistance protein differences between groups. At 24 h after treatment, the MRI histogram texture parameters of T2-weighted imaging (T2WI) images (10%, 90%, median, energy, and RootMeanSquared) and D images (10% and Range) were positively correlated with MRP1 (r = 0.925, p = 0.005). At 48 h after treatment, histogram texture parameters of apparent diffusion coefficient (ADC) images (Energy) were positively correlated with the presence of MRP1 resistance protein (r = 0.900, p = 0.037). There was no statistically significant difference between MRI histogram features and p53 protein expression level. Conclusions: MRI histogram texture parameters based on T2WI, D, and ADC maps can help to predict the change of 5-FU resistance in colorectal cancer in the early stage and provide important reference significance for clinical treatment.

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer.

Deep stromal invasion is an important pathological factor associated with the treatments and prognosis of cervical cancer patients. Accurate determination of deep stromal invasion before radical hysterectomy (RH) is of great value for early clinical treatment decision-making and improving the prognosis of these patients. Machine learning is gradually applied in the construction of clinical models to improve the accuracy of clinical diagnosis or prediction, but whether machine learning can improve the preoperative diagnosis accuracy of deep stromal invasion in patients with cervical cancer was still unclear. This cross-sectional study was to construct three preoperative diagnostic models for deep stromal invasion in patients with early cervical cancer based on clinical, radiomics, and clinical combined radiomics data using the machine learning method. We enrolled 229 patients with early cervical cancer receiving RH combined with pelvic lymph node dissection (PLND). The least absolute shrinkage and selection operator (LASSO) and the fivefold cross-validation were applied to screen out radiomics features. Univariate and multivariate logistic regression analyses were applied to identify clinical predictors. All subjects were divided into the training set (n = 160) and testing set (n = 69) at a ratio of 7:3. Three light gradient boosting machine (LightGBM) models were constructed in the training set and verified in the testing set. The radiomics features were statistically different between deep stromal invasion < 1/3 group and deep stromal invasion ≥ 1/3 group. In the training set, the area under the curve (AUC) of the prediction model based on radiomics features was 0.951 (95% confidence interval (CI) 0.922-0.980), the AUC of the prediction model based on clinical predictors was 0.769 (95% CI 0.703-0.835), and the AUC of the prediction model based on radiomics features and clinical predictors was 0.969 (95% CI 0.947-0.990). The AUC of the prediction model based on radiomics features and clinical predictors was 0.914 (95% CI 0.848-0.980) in the testing set. The prediction model for deep stromal invasion in patients with early cervical cancer based on clinical and radiomics data exhibited good predictive performance with an AUC of 0.969, which might help the clinicians early identify patients with high risk of deep stromal invasion and provide timely interventions.

Efficiency and toxicity of nab-paclitaxel and camrelizumab in the second or above line treatment of advanced non-small cell lung cancer: a retrospective cohort study.

Background: Paclitaxel-based chemotherapy represented by nanoparticle albumin-bound paclitaxel (nab-ptx) combined with programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) inhibitors has become the standard model for the 1st treatment of advanced non-small cell lung cancer (NSCLC) with negative driver genes (such as EGFR, ALK, etc.), indicating that nab-ptx and PD-1/PD-L1 inhibitors are synergistic. Considering PD-1/PD-L1 inhibitors alone or chemotherapy single has limited efficiency in the 2nd line or above of NSCLC, so it is of great significance to explore the combination of PD-1/PD-L1 inhibitors and nab-ptx to further improve the therapeutic efficiency in such field. Methods: We retrospectively collected the date of these advanced NSCLC patients who accept the combination treatment of PD-1/PD-L1 inhibitor and nab-ptx in the 2nd or above line. We further analysed baseline clinical characteristics, therapeutic efficacy, treatment-related adverse events (AEs) and followed up survival. The main parameters of the study were objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS) and AEs. Results: A total of 53 patients were enrolled in this study. The preliminary results indicated that the ORR of the combination of camrelizumab and nab-ptx was about 36% in the 2nd or above line of NSCLC, with 19 cases of partial response (PR), 16 of stable disease (SD), and 18 cases of progressive disease (PD); the mean PFS and OS were 5 months and 10 months, respectively. Further subgroup analysis demonstrated that the expression of PD-L1 level and the decrease of regulatory T cell (Treg) correlated with the efficiency. the main adverse reactions were neuropathy, bone marrow suppression, fatigue, and hypothyroidism, most of which were mild and tolerable, indicating such regimen was higher efficiency and lower cytotoxicity for NSCLC. Conclusions: The combination of nab-ptx and camrelizumab shows promising efficiency and lower toxicities for advanced NSCLC in the 2nd or above line treatment. The mechanism of action may be related to depleting Treg ratio; such a regimen may have the potential to become an effective treatment approach for NSCLC. However, due to the limitation of sample size, the real value of this regimen needs to be further confirmed in the future.

Treatment options for recurrent platinum-resistant ovarian cancer: A systematic review and Bayesian network meta-analysis based on RCTs.

Background: There are a variety of treatment options for recurrent platinum-resistant ovarian cancer, and the optimal specific treatment still remains to be determined. Therefore, this Bayesian network meta-analysis was conducted to investigate the optimal treatment options for recurrent platinum-resistant ovarian cancer. Methods: Pubmed, Cochrane, Embase, and Web of Science were searched for articles published until 15 June 2022. The outcome measures for this meta-analysis were overall survival (OS), progression-free survival (PFS), and adverse events (AEs) of Grade 3-4. The Cochrane assessment tool for risk of bias was used to evaluate the risk of bias of the included original studies. The Bayesian network meta-analysis was conducted. This study was registered on PROSPERO (CRD42022347273). Results: Our systematic review included 11 RCTs involving 1871 patients and 11 treatments other than chemotherapy. The results of meta-analysis showed that the overall survival (OS) was the highest in adavosertib + gemcitabine compared with conventional chemotherapy, (HR=0.56,95%CI:0.35-0.91), followed by sorafenib + topotecan (HR=0.65, 95%CI:0.45-0.93). In addition, Adavosertib + Gemcitabine regimen had the highest PFS (HR=0.55,95%CI:0.34-0.88), followed by Bevacizumab + Gemcitabine regimen (HR=0.48,95%CI:0.38-0.60) and the immunotherapy of nivolumab was the safest (HR=0.164,95%CI:0.312-0.871) with least adverse events of Grades 3-4. Conclusions: The results of this study indicated that Adavosertib (WEE1 kinase-inhibitor) + gemcitabine regimen and Bevacizumab + Gemcitabine regimen would be significantly beneficial to patients with recurrent platinum-resistant ovarian cancer, and could be preferred for recurrent platinum-resistant ovarian cancer. The immunotherapeutic agent, Nivolumab, is of considerable safety, with a low risk for grade-III or IV adverse events. Its safety is comparable to Adavosertib + gemcitabine regimen. Pazopanib + Paclitaxel (weekly regimen), Sorafenib + Topotecan/Nivolumab could be selected if there are contraindications of the above strategies. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022347273.

Value of IVIM in Differential Diagnoses between Benign and Malignant Solitary Lung Nodules and Masses: A Meta-analysis.

Purpose: This study aims to evaluate the accuracy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in distinguishing malignant and benign solitary pulmonary nodules and masses. Methods: Studies investigating the diagnostic accuracy of IVIM-DWI in lung lesions published through December 2020 were searched. The standardized mean differences (SMDs) of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The sensitivity, specificity, area under the curve (AUC), publication bias, and heterogeneity were then summarized, and the source of heterogeneity and the reliability of combined results were explored by meta-regression and sensitivity analysis. Results: A total of 16 studies including 714 malignant and 355 benign lesions were included. Significantly lower ADC, D, and f values were found in malignant pulmonary lesions compared to those in benign lesions. The D value showed the best diagnostic performance (sensitivity = 0.90, specificity = 0.71, AUC = 0.91), followed by ADC (sensitivity = 0.84, specificity = 0.75, AUC = 0.88), f (sensitivity = 0.70, specificity = 0.62, AUC = 0.71), and D * (sensitivity = 0.67, specificity = 0.61, AUC = 0.67). There was an inconspicuous publication bias in ADC, D, D* and f values, moderate heterogeneity in ADC, and high heterogeneity in D, D*, and f values. Subgroup analysis suggested that both ADC and D values had a significant higher sensitivity in "nodules or masses" than that in "nodules." Conclusions: The parameters derived from IVIM-DWI, especially the D value, could further improve the differential diagnosis between malignant and benign solitary pulmonary nodules and masses.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/#myprospero, identifier: CRD42021226664.

Measuring air pollution from the 2021 Canary Islands volcanic eruption.

The eruption of the Cumbre Vieja volcano on the island of La Palma (Canary Islands, Spain) began on September 19, 2021 and ended on December 13, 2021. It lasted continuously for 85 days with short periods of calm when lava did not exit the cone of the volcano. Vast amounts of volcanic material, including ash and gases, were emitted into the environment. This research focuses on these emissions. The main objective is to use available open-source data to examine the impact on regional and local air quality. Data from the following sources were used: 1) Copernicus Atmosphere Monitoring Service (CAMS) data was used to track the transfer of volcanic SO2 in the troposphere in early October over long distances from the source of the eruption, including Western and Eastern Europe, across the Atlantic Ocean and the Caribbean; 2) Data from ground monitoring stations measured the concentrations of SO2 and PM10 near the source; 3) AErosol RObotic NETwork (AERONET) data from the La Palma station that showed high Aerosol Optical Depth (AOD) values (over 0.4) during the active phase of emissions on September 24 and 28, as well as on October 3; 4) Ångström Exponent (AE) values indicated the presence of particles of different sizes. On September 24, high AE values (>1.5), showed the presence of fine-mode fraction scattering aerosols such as sulfates; 5) Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data additionally confirmed the presence of sulfate and dust aerosols in the atmosphere over the region. However, the influence of Saharan dust on the atmosphere of the entire region could not be excluded. This research helps forecast air pollution resulting from large-scale volcanic eruptions and associated health risks to humans.

Assessing spatio-temporal patterns and driving force of ecosystem service value in the main urban area of Guangzhou.

Increasing human activity around the world has greatly changed the natural ecosystem and the services it provides. In the past few decades, a series of significant changes have taken place in land use/land cover (LULC) in China due to the rapid growth in population, particularly in the cities of the Zhujiang Deita. However, there have been few attempts to study the co-evolution of land use/land cover change and ecosystem service value (ESV) in the main urban area of Guangzhou. Therefore, based on Landsat TM/OLI images from 1987, 1993, 1999, 2005, 2011 and 2017, the weight vector AdaBoost (WV AdaBoost) multi-classification algorithm was utilized to extract LULC data sets, and the spatiotemporal patterns of LULC over these periods were studied. The ESV was estimated and the driving force was analysed. The effect of LULC dynamics on the ESV was evaluated. The results showed that great changes have taken place in LULC in the main urban area of Guangzhou from 1987 to 2017, of which the most significant was the large-scale expansion of the built-up area that occurred through degradation of the forest and cultivated land. The proportion of forest and cultivated land decreased from 43.12% and 34.23% to 25.88% and 12.59%, respectively. The results between periods revealed a decrease in total ESVs from 5.63 × 109 yuan in 1987 to 5.27, 4.16, 4.62, 3.76 and 4.47 × 109 yuan in 1993, 1999, 2005, 2011 and 2017, respectively. In total, ESVs decreased by 1.16 billion yuan (20.61%) from 1987 to 2017. Water supply, food production, nutrient cycling and gas regulation were the four principal ecosystem service functions that affected the total ESVs. Forest, water body and cultivated land areas played a key role in ecosystem services. Therefore, we advocate that when protecting natural ecosystems in the future land use management in Guangzhou should be prioritized.

Impact of Covid-19 lockdown on air quality in the Poland, Eastern Europe.

The first case of COVID-19 in Poland was registered on 4 March 2020. Governmental measures significantly restricted social and economic activities. This study investigates the impact on air quality resulting from the preventive measures taken by the government to manage Covid-19. The study was carried out with use of aerosol optical depth (AOD) retrieved from Moderate Resolution Imaging Spectrometer (MODIS) satellite and tropospheric column NO2 observed by Ozone Monitoring Instrument (OMI). Concentrations of atmospheric pollutants (PM2.5, PM10, NO2 and SO2) retrieved from ground-based air quality stations, located in five large cities of the country, were also used for quantitative assessment of air quality change. Ground-based and satellite data demonstrated the reduction of pollutants in the period of lockdown as compared to the same periods in 2018 and 2019. In particular, AOD data shows reductions of aerosol concentrations in the air column in April and May of approximately by -23% and -18% as compared to 2018-2019. The greatest contraction was for PM2.5 in April and May with reductions of -11.1% to -26.4% and from -8.7 to -21.1% respectively. For PM10, the reductions were from -8.6% to -33.9% and from -8.5% to -31.5% as compared to the same months in 2019. The results showed that restrictions imposed to prevent the spread of COVID-19 significantly improved Poland's air quality.

Camrelizumab Plus Apatinib in Patients With Advanced Cervical Cancer (CLAP): A Multicenter, Open-Label, Single-Arm, Phase II Trial.

PURPOSE: Camrelizumab is an antibody against programmed death protein 1. We assessed the activity and safety of camrelizumab plus apatinib, a tyrosine kinase inhibitor of vascular endothelial growth factor receptor-2, in patients with advanced cervical cancer. METHODS: This multicenter, open-label, single-arm, phase II study enrolled patients with advanced cervical cancer who progressed after at least one line of systemic therapy. Patients received camrelizumab 200 mg every 2 weeks and apatinib 250 mg once per day. The primary end point was objective response rate (ORR) assessed by investigators per RECIST version 1.1. Key secondary end points were progression-free survival (PFS), overall survival (OS), duration of response, and safety. RESULTS: Forty-five patients were enrolled and received treatment. Median age was 51.0 years (range, 33-67 years), and 57.8% of patients had previously received two or more lines of chemotherapy for recurrent or metastatic disease. Ten patients (22.2%) had received bevacizumab. Median follow-up was 11.3 months (range, 1.0-15.5 months). ORR was 55.6% (95% CI, 40.0% to 70.4%), with two complete and 23 partial responses. Median PFS was 8.8 months (95% CI, 5.6 months to not estimable). Median duration of response and median OS were not reached. Treatment-related grade 3 or 4 adverse events (AEs) occurred in 71.1% of patients, and the most common AEs were hypertension (24.4%), anemia (20.0%), and fatigue (15.6%). The most common potential immune-related AEs included grade 1-2 hypothyroidism (22.2%) and reactive cutaneous capillary endothelial proliferation (8.9%). CONCLUSION: Camrelizumab plus apatinib had promising antitumor activity and manageable toxicities in patients with advanced cervical cancer. Larger randomized controlled trials are warranted to validate our findings.

An Improved Pulse-Coupled Neural Network Model for Pansharpening.

Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fidelity is considered. A key problem is that most fusion methods using PCNNs usually focus on the selection mechanism either in the space domain or in the transform domain, rather than a details injection mechanism, which is of utmost importance in multispectral image fusion. Thus, a novel pansharpening PCNN model for multispectral image fusion is proposed. The new model is designed to acquire the spectral fidelity in terms of human visual perception for the fusion tasks. The experimental results, examined by different kinds of datasets, show the suitability of the proposed model for pansharpening.

Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data.

Accurate diagnostic classification of cancers can greatly help physicians to choose surveillance and treatment strategies for patients. Following the explosive growth of huge amounts of biological data, the shift from traditional biostatistical methods to computer-aided means has made machine-learning methods as an integral part of today's cancer prognosis prediction. In this work, we proposed a classification model by leveraging the power of extreme gradient boosting (XGBoost) and using increasingly complex multi-omics data with the aim to separate early stage and late stage cancers. We applied XGBoost model to four kinds of cancer data downloaded from TCGA and compared its performance with other popular machine-learning methods. The experimental results showed that our method obtained statistically significantly better or comparable predictive performance. The results of this study also revealed that DNA methylation outperforms other molecular data (mRNA expression and miRNA expression) in terms of accuracy and stability for discriminating between early stage and late stage groups. Furthermore, integration of multi-omics data by autoencoder can enhance the classification accuracy of cancer stage. Finally, we conducted bioinformatics analyses to assess the medical utility of the significant genes ranked by their importance using XGBoost algorithm. Extensively comparative experiments demonstrated that the XGBoost method has a remarkable performance in predicting the stage of cancer patients with multi-omics data. Moreover, identification of novel candidate genes associated with cancer stages would contribute to further elucidate disease pathogenesis and develop novel therapeutics.

Author Correction: Combined use of satellite and surface observations to study aerosol optical depth in different regions of China.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Combined use of satellite and surface observations to study aerosol optical depth in different regions of China.

Aerosol optical depth (AOD) is one of essential atmosphere parameters for climate change assessment as well as for total ecological situation study. This study presents long-term data (2000-2017) on time-space distribution and trends in AOD over various ecological regions of China, received from Moderate Resolution Imaging Spectroradiometer (MODIS) (combined Dark Target and Deep Blue) and Multi-angle Imaging Spectroradiometer (MISR), based on satellite Terra. Ground-based stations Aerosol Robotic Network (AERONET) were used to validate the data obtained. AOD data, obtained from two spectroradiometers, demonstrate the significant positive correlation relationships (r = 0.747), indicating that 55% of all data illustrate relationship among the parameters under study. Comparison of results, obtained with MODIS/MISR Terra and AERONET, demonstrate high relation (r = 0.869 - 0.905), while over 60% of the entire sampling fall within the range of the expected tolerance, established by MODIS and MISR over earth (±0.05 ± 0.15 × AODAERONET and 0.05 ± 0.2 × AODAERONET) with root-mean-square error (RMSE) of 0.097-0.302 and 0.067-0.149, as well as low mean absolute error (MAE) of 0.068-0.18 and 0.067-0.149, respectively. The MODIS search results were overestimated for AERONET stations with an average overestimation ranging from 14 to 17%, while there was an underestimate of the search results using MISR from 8 to 22%.