Fatigue in multiple sclerosis is a diagnostic challenge: A cross-sectional study

Introduction: Multiple sclerosis (MS) is a chronic and unpredictable demyelinating disease of the central nervous system (CNS). While MS is mostly known for muscle weakness, numbness, and pain, but fatigue is the most common complaint of this condition. Despite this fact, MS related fatigue is one of the most misunderstood symptoms. Methods: A non-interventional study of 100 individuals was conducted in the MS clinic, Tabriz University of Medical Sciences. Patients were divided into groups with and without complaints of fatigue. The course of the disease was determined for all patients. To quantify fatigue, the Modified Fatigue Impact Scale (MFIS) was used. Furthermore, mood disorders, pain, disability, nocturia, insomnia, and spasticity were evaluated among the patients. Results: Overall, fatigue was diagnosed in 61 through 100 patients. Depression was reported in 23 patients of whom 19 had fatigue (P=0.015). 40 patients showed anxiety 33 of which had fatigue (P>0.001). 53 patients of whom reported to have pain (76 patients) showed fatigue (P=0.001). Insomnia was reported in 27 patients, where 21 of them had fatigue (P=0.036). Nocturia was reported in 10 patients, of whom 9 had fatigue (P=0.047). Spasticity was detected in 9 patients, all of whom had fatigue (P=0.012). Conclusion: There are several factors directly and indirectly associated with fatigue that are either fatigue-induced, caused by fatigue, or showing a two-way relationship with it. Understanding these links and attempting to reduce them will improve the quality of life for these patients

Development and analysis of the Soil Water Infiltration Global database

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements ( ∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type ( ∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it

Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed

A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), generalized linear model (GLM), and multiple linear regression (MLR) models are appropriate for prediction of soil wet aggregate stability (as quantified by the mean weight diameter, MWD) in a highly mountainous watershed (Bazoft watershed, southwestern Iran). Three different sets of easily available properties were used as inputs. The first set (denoted as SP) consisted of soil properties including clay content, calcium carbonate equivalent, and soil organic matter content. The second set (denoted as TVA) included topographic attributes (slope and aspect) and the normalized difference vegetation index (NDVI). The third set (denoted as STV) was a combination of soil properties, slope, and NDVI. The ANN and ANFIS models predicted MWD more accurately than the GLM and MLR models. Estimation of MWD using TVA data set resulted in the lowest model efficiency values. The observed model efficiency values for the developed MLR, GLM, ANN, and ANFIS models using the SP data set were 60.76, 62.98, 77.68 and 77.15, respectively. Adding slope and NDVI to soil data (i.e. STV data set) improved the predictions of all four methods. The obtained correlation coefficient values between the predicted and measured MWD for the developed MLR, GLM, ANN, and ANFIS models using STV data set were 0.24, 0.35, 0.84 and 0.73, respectively. In conclusion, the ANN and ANFIS models showed greater potential in predicting soil aggregate stability from soil and site characteristics, whereas linear regression methods did not perform well.ISSN:0341-8162ISSN:1872-688

Impacts of Clay Content and Type on Shear Strength and Splash Erosion of Clay–Sand Mixtures

Soil characteristics, especially clay content and clay type, have significant impacts on splash erosion. This investigation was conducted to determine the effects of clay content and clay type (zeolite, phlogopite, bentonite, and kaolinite) on the shear strength and splash erosion of clay–sand mixtures compared with a clay soil under controlled conditions. Clay–sand mixtures were prepared by mixing 15, 30, and 45 kg 100 kg−1 of the selected clays with pure sand and a clay soil; these mixtures were pre-treated with three levels of wetting and drying (W&D) cycles, and then shear strength was measured. The splash erosion rate was measured at three levels of water status: air-dry, plastic limit (PL), and liquid limit (LL). The highest values of splash erosion were observed in the samples without the W&D cycle, and after applying the W&D cycles, splash erosion decreased. Moreover, splash erosion was higher in the air-dry and LL groups. Splash erosion decreased with increased clay content because of the structure development in the mixtures. In general, as clay content increased, splash erosion was reduced and shear strength was significantly increased. From the highest to lowest mean of cohesion, the results showed the following order: bentonite > kaolinite > phlogopite > clay soil > zeolite. However, splash erosion showed the following order for the studied clays: zeolite > clay soil > phlogopite > kaolinite > bentonite. Nonlinear power models, best-fitted relations between splash erosion and shear strength, and the shear strength could explain about 30–33% of the splash erosion variability in this study

Breakthrough Curve of Escherichia Coli Released from Organic Manures as Influenced by Soil Properties

Organic manures are the source of many pathogenic bacteria which could be dangerous for human health. In this study, the effects of soil texture and structure on transmitting and filtering of manure-borne Escherichia Coli were investigated. The intact soil samples (25 cm in height and 16 cm in diameter) were taken from a sandy clay loam soil and a loamy sand soil. Three manures including: cow manure, poultry manure and sewage sludge were applied on the surface of the soil cores at the rate of 10 Mg ha-1 on dry basis. With controlled steady-state unsaturated water flow, the influent and effluent concentration of Escherichia Coli were determined vs. time up to four pore volumes (PV). In spite of greater adsorptive sites of sandy clay loam soil, more bacteria have been transmitted and polluted the effluent of the soil. The loamy sand soil filtered more Escherichia Coli compared with the sandy clay loam soil. The effluent contamination of poultry manure-treated columns was greater than the cow manure and that of treated sewage sludge. In the majority of the columns, the difference between cow manure and sewage sludge was negligible. The filtration of Escherichia Coli in loamy sand soil was greater due to weaker structure and discontinuity of pores which are responsible for physical filtering. In sandy clay loam soil, the stable structure and preferential pathways are believed to cause funneling of the bacteria towards the bottom of the columns and the early appearance of Escherichia Coli in the drain water. The results demonstrated the importance of soil structure and preferential (macroporous) flow in bacteria transport which could diminish the impacts of soil texture and adsorptive sites on the transmission mechanisms