195 research outputs found

    Groundwater augmentation through the site selection of floodwater spreading using a data mining approach (case study: Mashhad Plain, Iran)

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    © 2018 by the authors. It is a well-known fact that sustainable development goals are difficult to achieve without a proper water resources management strategy. This study tries to implement some state-of-the-art statistical and data mining models i.e., weights-of-evidence (WoE), boosted regression trees (BRT), and classification and regression tree (CART) to identify suitable areas for artificial recharge through floodwater spreading (FWS). At first, suitable areas for the FWS project were identified in a basin in north-eastern Iran based on the national guidelines and a literature survey. Using the same methodology, an identical number of FWS unsuitable areas were also determined. Afterward, a set of different FWS conditioning factors were selected for modeling FWS suitability. The models were applied using 70% of the suitable and unsuitable locations and validated with the rest of the input data (i.e., 30%). Finally, a receiver operating characteristics (ROC) curve was plotted to compare the produced FWS suitability maps. The findings depicted acceptable performance of the BRT, CART, and WoE for FWS suitability mapping with an area under the ROC curves of 92, 87.5, and 81.6%, respectively. Among the considered variables, transmissivity, distance from rivers, aquifer thickness, and electrical conductivity were determined as the most important contributors in the modeling. FWS suitability maps produced by the proposed method in this study could be used as a guideline for water resource managers to control flood damage and obtain new sources of groundwater. This methodology could be easily replicated to produce FWS suitability maps in other regions with similar hydrogeological conditions

    Non-linear height-diameter models for oriental beech (Fagus orientalis Lipsky) in the Hyrcanian forests, Iran

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    The relationship between tree height and diameter is an important element in growth and yield models, in carbon budget and timber volume models, and in the description of stand dynamics. Six non-linear growth functions (i.e. Chapman-Richards, Schnute, Lundqvist/Korf, Weibull, Modified Logistic and Exponential) were fitted to tree height-diameter data of oriental beech in the Hyrcanian mixed hardwood forests of Iran. The predictive performance of these models was in the first place assessed by means of different model evaluation criteria such as adjusted R squared (adjR2), root mean square error (RMSE), Akaike information criterion (AIC), mean difference (MD), mean absolute difference (MAD) and mean square (MS) error criteria. Although each of the six models accounted for approximately 75% of total variation in height, a large difference in asymptotic estimates was observed. Apart from this, the predictive performance of the models was also evaluated by means of cross-validation and by splitting the data into 5-cm diameter classes. Plotting the MD in relation to these diameter at breast height (DBH) classes showed for all growth functions, except for the Modified Logistic function, similar mean prediction errors for small- and medium-sized trees. Large-sized trees, however, showed a higher mean prediction error. The Modified Logistic function showed the worst performance due to a large model bias. The Exponential and Lundqvist/Korf models were discarded due to their showing biologically illogical behavior and unreasonable estimates for the asymptotic coefficient, respectively. Considering all the above-mentioned criteria, the Chapman-Richards, Weibull, and Schnute functions provided the most satisfactory height predictions. However, we would recommend the Chapman-Richards function for further analysis because of its higher predictive performance

    Flood susceptibility assessment using extreme gradient boosting (EGB), Iran

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    Flood occurs as a result of high intensity and long-term rainfalls accompanied by snowmelt which flow out of the main river channel onto the flood prone areas and damage the buildings, roads, and facilities and cause life losses. This study aims to implement extreme gradient boosting (EGB) method for the first time in flood susceptibility modelling and compare its performance with three advanced benchmark models including Frequency Ratio (FR), Random Forest (RF), and Generalized Additive Model (GAM). Flood susceptibility map is an efficient tool to make decision for flood control. To do this, the altitude, slope degree, profile curvature, topographic wetness index (TWI), distance from rivers, normalized difference vegetation index, plan curvature, rainfall, land use, stream power index, and lithology were fed to the models. To run the models, 243 flood locations were detected by field surveys and national reports. The same number of locations were randomly created in the study regions and considered as non-flood locations. The flood and non-flood locations were split in 70% ratio for the training dataset and 30% ratio for the testing dataset. Both flood and non-flood locations were fed into the models and output flood susceptibility maps were produced. In order to evaluate the performance of the algorithms, receiver operating characteristics (ROC) curve was implemented. The results of the current research show that the RF model and EGB have the best performances with the area under ROC curve (AUC) of 0.985, and 0.980, followed by the GAM and FR algorithms with AUC values of 0.97, and 0.953, respectively. The results of variable importance by the RF model show that distance from rivers has an important influence on flood susceptibility mapping (FSM), followed by profile curvature, slope, TWI, and altitude. Considering the high performances of the RF and EGB models in flood susceptibility modelling, application of these models is recommended for such studies

    Sexual dimorphism in relation to adipose tissue and intrahepatocellular lipid deposition in early infancy

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    Sexual dimorphism in adiposity is well described in adults, but the age at which differences first manifest is uncertain. Using a prospective cohort, we describe longitudinal changes in directly measured adiposity and intrahepatocellular lipid (IHCL) in relation to sex in healthy term infants. At median ages of 13 and 63 days, infants underwent quantification of adipose tissue depots by whole-body magnetic resonance imaging and measurement of IHCL by in vivo proton magnetic resonance spectroscopy. Longitudinal data were obtained from 70 infants (40 boys and 30 girls). In the neonatal period girls are more adipose in relation to body size than boys. At follow-up (median age 63 days), girls remained significantly more adipose. The greater relative adiposity that characterises girls is explained by more subcutaneous adipose tissue and this becomes increasingly apparent by follow-up. No significant sex differences were seen in IHCL. Sex-specific differences in infant adipose tissue distribution are in keeping with those described in later life, and suggest that sexual dimorphism in adiposity is established in early infancy

    The role of chemotherapeutic drugs in the evaluation of breast tumour response to chemotherapy using serial FDG-PET

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    INTRODUCTION: The aims of this study were to investigate whether drug sequence (docetaxel followed by anthracyclines or the drugs in reverse order) affects changes in the maximal standard uptake volume (SUVmax) on [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) during neoadjuvant chemotherapy in women with locally advanced breast cancer. METHODS: Women were randomly assigned to receive either drug sequence, and FDG-PET scans were taken at baseline, after four cycles and after eight cycles of chemotherapy. Tumour response to chemotherapy was evaluated based on histology from a surgical specimen collected upon completion of chemotherapy. RESULTS: Sixty women were enrolled into the study. Thirty-one received docetaxel followed by anthracyclines (Arm A) and 29 received drugs in the reverse order (Arm B). Most women (83%) had ductal carcinoma and 10 women (17%) had lobular or lobular/ductal carcinoma. All but one tumour were downstaged during therapy. Overall, there was no significant difference in response between the two drug regimens. However, women in Arm B who achieved complete pathological response had mean FDG-PET SUVmax reduction of 87.7% after four cycles, in contrast to those who had no or minor pathological response. These women recorded mean SUVmax reductions of only 27% (P < 0.01). Women in Arm A showed no significant difference in SUVmax response according to pathological response. Sensitivity, specificity, accuracy and positive and negative predictive values were highest in women in Arm B. CONCLUSIONS: Our results show that SUVmax uptake by breast tumours during chemotherapy can be dependent on the drugs used. Care must be taken when interpreting FDG-PET in settings where patients receive varied drug protocols

    The role of leadership in salespeople’s price negotiation behavior

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    Salespeople assume a key role in defending firms’ price levels in price negotiations with customers. The degree to which salespeople defend prices should critically depend upon their leaders’ influence. However, the influence of leadership on salespeople’s price defense behavior is barely understood, conceptually or empirically. Therefore, building on social learning theory, the authors propose that salespeople might adopt their leaders’ price defense behavior given a transformational leadership style. Furthermore, drawing on the contingency leadership perspective, the authors argue that this adoption fundamentally depends on three variables deduced from the motivation–ability–opportunity (MAO) framework, that is, salespeople’s learning motivation, negotiation efficacy, and perceived customer lenience. Results of a multi-level model using data from 92 salespeople and 264 salesperson–customer interactions confirm these predictions. The first to explore contingencies of salespeople’s adoption of their transformational leaders’ price negotiation behaviors, this study extends marketing theory and provides actionable guidance to practitioners

    Glycans in Sera of Amyotrophic Lateral Sclerosis Patients and Their Role in Killing Neuronal Cells

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    Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease caused by degeneration of upper and lower motor neurons. To date, glycosylation patterns of glycoproteins in fluids of ALS patients have not been described. Moreover, the aberrant glycosylation related to the pathogenesis of other neurodegenerative diseases encouraged us to explore the glycome of ALS patient sera. We found high levels of sialylated glycans and low levels of core fucosylated glycans in serum-derived N-glycans of patients with ALS, compared to healthy volunteer sera. Based on these results, we analyzed the IgG Fc N297-glycans, as IgG are major serum glycoproteins affected by sialylation or core fucosylation and are found in the motor cortex of ALS patients. The analyses revealed a distinct glycan, A2BG2, in IgG derived from ALS patient sera (ALS-IgG). This glycan increases the affinity of IgG to CD16 on effector cells, consequently enhancing Antibody-Dependent Cellular Cytotoxicity (ADCC). Therefore, we explore whether the Fc-N297-glycans of IgG may be involved in ALS disease. Immunostaining of brain and spinal cord tissues revealed over-expression of CD16 and co-localization of intact ALS-IgG with CD16 and in brain with activated microglia of G93A-SOD1 mice. Intact ALS-IgG enhanced effector cell activation and ADCC reaction in comparison to sugar-depleted or control IgG. ALS-IgG were localized in the synapse between brain microglia and neurons of G93A-SOD1 mice, manifesting a promising in vivo ADCC reaction. Therefore, glycans of ALS-IgG may serve as a biomarker for the disease and may be involved in neuronal damage
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