Crystalline phases in Zr9Ni11 and Hf9Ni11 intermetallics; Investigations by perturbed angular correlation spectroscopy and ab initio calculations

Crystalline phases formed in stoichiometric Zr9Ni11 and Hf9Ni11 have been studied by perturbed angular correlation (PAC) spectroscopy, XRD and TEM/SAED measurements. In Zr9Ni11, the phases Zr9Ni11 (∼89%) and Zr8Ni21 (∼11%) have been found at room temperature from PAC measurements. At 773 K, Zr9Ni11 partially decomposes to Zr7Ni10 and at 973 K, it is completely decomposed to ZrNi and Zr7Ni10. In Hf9Ni11, a predominant phase (∼81%) due to HfNi is found at room temperature while the phase Hf9Ni11 is produced as a minor phase (∼19%). No compositional phase change at higher temperature is found in Hf9Ni11. Phase components found from XRD and TEM/SAED measurements are similar to those observed from PAC measurements. Electric field gradients in Zr9Ni11 and Hf9Ni11 have been calculated by density functional theory (DFT) using all electron full potential (linearized) augmented plane wave plus local orbitals [FP-(L)APW+lo] method in order to assign the phase components.This is the preprint version of the following article: Dey, S. K., C. C. Dey, S. Saha, G. Bhattacharjee, J. Belošević-Čavor, and D. Toprek. "Crystalline phases in Zr9Ni11 and Hf9Ni11 intermetallics; investigations by perturbed angular correlation spectroscopy and ab initio calculations." Journal of Solid State Chemistry (2018). http://dx.doi.org/10.1016/j.jssc.2018.10.00

A multi-centre qualitative study exploring the experiences of UK South Asian and White Diabetic Patients referred for renal care

Background An exploration of renal complications of diabetes from the patient perspective is important for developing quality care through the diabetic renal disease care pathway. Methods Newly referred South Asian and White diabetic renal patients over 16 years were recruited from nephrology outpatient clinics in three UK centres - Luton, West London and Leicester – and their experiences of the diabetes and renal care recorded. A semi-structured qualitative interview was conducted with 48 patients. Interview transcripts were analysed thematically and comparisons made between the White and South Asian groups. Results 23 South Asian patients and 25 White patients were interviewed. Patient experience of diabetes ranged from a few months to 35 years with a mean time since diagnosis of 12.1 years and 17.1 years for the South Asian and White patients respectively. Confusion emerged as a response to referral shared by both groups. This sense of confusion was associated with reported lack of information at the time of referral, but also before referral. Language barriers exacerbated confusion for South Asian patients. Conclusions The diabetic renal patients who have been referred for specialist renal care and found the referral process confusing have poor of awareness of kidney complications of diabetes. Healthcare providers should be more aware of the ongoing information needs of long term diabetics as well as the context of any information exchange including language barriers

Significant benefits of AIP testing and clinical screening in familial isolated and young-onset pituitary tumors

Context Germline mutations in the aryl hydrocarbon receptor-interacting protein (AIP) gene are responsible for a subset of familial isolated pituitary adenoma (FIPA) cases and sporadic pituitary neuroendocrine tumors (PitNETs). Objective To compare prospectively diagnosed AIP mutation-positive (AIPmut) PitNET patients with clinically presenting patients and to compare the clinical characteristics of AIPmut and AIPneg PitNET patients. Design 12-year prospective, observational study. Participants & Setting We studied probands and family members of FIPA kindreds and sporadic patients with disease onset ≤18 years or macroadenomas with onset ≤30 years (n = 1477). This was a collaborative study conducted at referral centers for pituitary diseases. Interventions & Outcome AIP testing and clinical screening for pituitary disease. Comparison of characteristics of prospectively diagnosed (n = 22) vs clinically presenting AIPmut PitNET patients (n = 145), and AIPmut (n = 167) vs AIPneg PitNET patients (n = 1310). Results Prospectively diagnosed AIPmut PitNET patients had smaller lesions with less suprasellar extension or cavernous sinus invasion and required fewer treatments with fewer operations and no radiotherapy compared with clinically presenting cases; there were fewer cases with active disease and hypopituitarism at last follow-up. When comparing AIPmut and AIPneg cases, AIPmut patients were more often males, younger, more often had GH excess, pituitary apoplexy, suprasellar extension, and more patients required multimodal therapy, including radiotherapy. AIPmut patients (n = 136) with GH excess were taller than AIPneg counterparts (n = 650). Conclusions Prospectively diagnosed AIPmut patients show better outcomes than clinically presenting cases, demonstrating the benefits of genetic and clinical screening. AIP-related pituitary disease has a wide spectrum ranging from aggressively growing lesions to stable or indolent disease course

Anticancer activity of Indigofera aspalathoides and Wedelia calendulaceae in swiss albino mice

Abstract The methanolic extracts of Indigofera aspalathoides (MEIA) and Wedelia calendulaceae (MEWC) were evaluated for their anticancer activity against Ehrlich Ascites Carcinoma (EAC) in Swiss albino mice. On day 1, the extract of Indigofera aspalathoides at a dose of 250 and 500 mg/kg body weight and the extract of Wedelia calendulaceae at a dose of 250 and 500 mg/ kg body weight were administered orally and continued for 9 consecutive days. The anticancer activity of MEIA and MEWC were examined by determining the tumor volume, tumor cell count, viable tumor cell count, nonviable tumor cell count, mean survival time and increase in life span in experimental animal models. Both these extracts increased the life span of EAC treated mice and restored the hematological parameters as compared with the EAC bearing mice. Thus, the present study revealed that the MEIA and MEWC showed anticancer activity in the tested animal models

Sanfilippo syndrome (mucopolysaccharidosis-III)

Clinical, radiological, electromyographic, histopathological and biochemical findings of 2 cases of MPS III syndrome are described. Both these patients showed a clinical resemblance to MPS I, the more striking resemblance being noted in Case 1. On biochemical studies, both the patients showed a marked disturbance of heparan sulphate. In addition, in Case 1, a mild disturbance of dermatan sulphate was noted in the brain. At autopsy, the neuronal involvement in the disease process was noted at all levels of the central nervous system. Peripheral nerve and muscle biopsies done on 1 patient showed evidence of demyelination and "group muscle fibre atrophy". Electron-microscopic findings on the brain tissue are described in 1 of the patients. These cases are compared with other reported cases of this syndrome and it is suggested that these patients differ phenotypically from some of the cases described in the literature. The literature on various aspects (clinical, radiological, histopathological, and biochemical) of this syndrome is reviewed

An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery

Very high spatial resolution commercial satellite imagery can inform observation, mapping, and documentation of micro-topographic transitions across large tundra regions. The bridging of fine-scale field studies with pan-Arctic system assessments has until now been constrained by a lack of overlap in spatial resolution and geographical coverage. This likely introduced biases in climate impacts on, and feedback from the Arctic region to the global climate system. The central objective of this exploratory study is to develop an object-based image analysis workflow to automatically extract ice-wedge polygon troughs from very high spatial resolution commercial satellite imagery. We employed a systematic experiment to understand the degree of interoperability of knowledge-based workflows across distinct tundra vegetation units—sedge tundra and tussock tundra—focusing on the same semantic class. In our multi-scale trough modelling workflow, we coupled mathematical morphological filtering with a segmentation process to enhance the quality of image object candidates and classification accuracies. Employment of the master ruleset on sedge tundra reported classification accuracies of correctness of 0.99, completeness of 0.87, and F1 score of 0.92. When the master ruleset was applied to tussock tundra without any adaptations, classification accuracies remained promising while reporting correctness of 0.87, completeness of 0.77, and an F1 score of 0.81. Overall, results suggest that the object-based image analysis-based trough modelling workflow exhibits substantial interoperability across the terrain while producing promising classification accuracies. From an Arctic earth science perspective, the mapped troughs combined with the ArcticDEM can allow hydrological assessments of lateral connectivity of the rapidly changing Arctic tundra landscape, and repeated mapping can allow us to track fine-scale changes across large regions and that has potentially major implications on larger riverine systems

Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery

Deep learning (DL) convolutional neural networks (CNNs) have been rapidly adapted in very high spatial resolution (VHSR) satellite image analysis. DLCNN-based computer visions (CV) applications primarily aim for everyday object detection from standard red, green, blue (RGB) imagery, while earth science remote sensing applications focus on geo object detection and classification from multispectral (MS) imagery. MS imagery includes RGB and narrow spectral channels from near- and/or middle-infrared regions of reflectance spectra. The central objective of this exploratory study is to understand to what degree MS band statistics govern DLCNN model predictions. We scaffold our analysis on a case study that uses Arctic tundra permafrost landform features called ice-wedge polygons (IWPs) as candidate geo objects. We choose Mask RCNN as the DLCNN architecture to detect IWPs from eight-band Worldview-02 VHSR satellite imagery. A systematic experiment was designed to understand the impact on choosing the optimal three-band combination in model prediction. We tasked five cohorts of three-band combinations coupled with statistical measures to gauge the spectral variability of input MS bands. The candidate scenes produced high model detection accuracies for the F1 score, ranging between 0.89 to 0.95, for two different band combinations (coastal blue, blue, green (1,2,3) and green, yellow, red (3,4,5)). The mapping workflow discerned the IWPs by exhibiting low random and systematic error in the order of 0.17–0.19 and 0.20–0.21, respectively, for band combinations (1,2,3). Results suggest that the prediction accuracy of the Mask-RCNN model is significantly influenced by the input MS bands. Overall, our findings accentuate the importance of considering the image statistics of input MS bands and careful selection of optimal bands for DLCNN predictions when DLCNN architectures are restricted to three spectral channels

An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery

Very high spatial resolution commercial satellite imagery can inform observation, mapping, and documentation of micro-topographic transitions across large tundra regions. The bridging of fine-scale field studies with pan-Arctic system assessments has until now been constrained by a lack of overlap in spatial resolution and geographical coverage. This likely introduced biases in climate impacts on, and feedback from the Arctic region to the global climate system. The central objective of this exploratory study is to develop an object-based image analysis workflow to automatically extract ice-wedge polygon troughs from very high spatial resolution commercial satellite imagery. We employed a systematic experiment to understand the degree of interoperability of knowledge-based workflows across distinct tundra vegetation units—sedge tundra and tussock tundra—focusing on the same semantic class. In our multi-scale trough modelling workflow, we coupled mathematical morphological filtering with a segmentation process to enhance the quality of image object candidates and classification accuracies. Employment of the master ruleset on sedge tundra reported classification accuracies of correctness of 0.99, completeness of 0.87, and F1 score of 0.92. When the master ruleset was applied to tussock tundra without any adaptations, classification accuracies remained promising while reporting correctness of 0.87, completeness of 0.77, and an F1 score of 0.81. Overall, results suggest that the object-based image analysis-based trough modelling workflow exhibits substantial interoperability across the terrain while producing promising classification accuracies. From an Arctic earth science perspective, the mapped troughs combined with the ArcticDEM can allow hydrological assessments of lateral connectivity of the rapidly changing Arctic tundra landscape, and repeated mapping can allow us to track fine-scale changes across large regions and that has potentially major implications on larger riverine systems