Some E-J Generalized Hausdorff Matrices Not of Type

We show that there exists a regular E-J generalized Hausdorff matrix which has no zero elements on the main diagonal and which is not of type and establish several other related theorems

Red Cell Distribution Width and Acute Complications of Diabetes

Context. Red cell distribution width (RDW) has been associated with type 2 diabetes (T2DM), however data in relation to diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar non-ketotic acidosis (HONK) remains unclear. Objective. The aim of this study was to evaluate the association between RDW, MCV, and RDW/MVC values and acute complications in T2DM. Patients and Methods. RDW was measured in 90 T2DM patients (30 DKA, 30 HONK and 30 T2DM without acute complications). Clinical variables were analyzed by One -Way ANOVA, Kruskal-Wallis and Pearson analysis with SPSS software. Diagnostic screening tests and ROC curve analysis determined the cut-off point of MCV,RDW and RDW/MCV values. Results. DKA patients had higher levels of plasma glucose (524.20 +/- 201.43mg/dL, p<0.001), HbA1c (10.73 +/- 2.29%, p<0.001), osmotic pressure (310.32 mosm/L, p<0.001), RDW (14.61 +/- 1.75g/L, p<0.01), and the RDW/MCV ratio (0.17 +/- 0.04%, p<0.01), compared to HONK patients. RDW/MCV cut-off value was 0.15 with 90% sensitivity 50% specifity these values for only MCV were 76.67%-70%, for only RDW were 76.67%-63.33% respectively. The area under curve values for the ability to reflect DKA for RDW and the RDW/MCV ratio were 0.708 and 0.766, respectively (p<0.001). Conclusions. RDW and RDW/MCV ratio were found associated with DKA and valuable in predicting DKA. However these parameters were not valuable in predicting HONK

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors

PURPOSE We aimed to compare the accuracy and repeatability of emerging machine learning-based (i.e., deep learning) automatic segmentation algorithms with those of well-established interactive semi-automatic methods for determining liver volume in living liver transplant donors at computed tomography (CT) imaging. METHODS A total of 12 methods (6 semi-automatic, 6 full-automatic) were evaluated. The semi-automatic segmentation algorithms were based on both traditional iterative models including watershed, fast marching, region growing, active contours arid modern techniques including robust statistics segmenter and super-pixels. These methods entailed some sort of interaction mechanism such as placing initialization seeds on images or determining a parameter range. The automatic methods were based on deep learning and included three framework templates (DeepMedic, NiftyNet and U-Net), the first two of which were applied with default parameter sets and the last two involved adapted novel model designs. For 20 living donors (8 training and 12 test datasets), a group of imaging scientists and radiologists created ground truths by performing manual segmentations on contrast-enhanced CT images. Each segmentation was evaluated using five metrics (i.e., volume overlap and relative volume errors, average/root-mean-square/maximum symmetrical surface distances). The results were mapped to a scoring system and a final grade was calculated by taking their average. Accuracy and repeatability were evaluated using slice-by-slice comparisons and volumetric analysis. Diversity and complementarily were observed through heatmaps. Majority voting (MV) and simultaneous truth and performance level estimation (STAPLE) algorithms were utilized to obtain the fusion of the individual results. RESULTS The top four methods were automatic deep learning models, with scores of 79.63, 79.46, 77.15, and 74.50. Intra-user score was determined as 95.14. Overall, automatic deep learning segmentation outperformed interactive techniques on all metrics. The mean volume of liver of ground truth was 1409.93 +/- 271.28 mL, while it was calculated as 1342.21 +/- 231.24 mL using automatic and 1201.26 +/- 258.13 mL using interactive methods, showing higher accuracy and less variation with automatic methods. The qualitative analysis of segmentation results showed significant diversity and complementarity, enabling the idea of using ensembles to obtain superior results. The fusion score of automatic methods reached 83.87 with MV and 86.20 with STAPLE, which my slightly less than fusion of all methods (MV, 86.70) and (STAPLE, 88.74). CONCLUSION Use of the new deep learning-based automatic segmentation algorithms substantially increases the accuracy and repeatability for segmentation and volumetric measurements of liver. Fusion of automatic methods based on ensemble approaches exhibits best results with almost no additional time cost due to potential parallel execution of multiple models

Effects of kefir on coccidial oocysts excretion and performance of dairy goat kids following weaning

The aim of this study was to investigate effects of kefir, a traditional source of probiotic, on coccidial oocysts excretion and on the performance of dairy goat kids following weaning. Twin kids were randomly allocated to one of two groups at weaning. Kids of the first group received 20 ml of kefir daily for 6 weeks (KEF), while kids in the control group were given a placebo (CON). Individual faecal samples were regularly (n = 18 per kid) taken to quantify the number of coccidial oocysts per gram of faeces (OpG). There were no differences between the groups in terms of body weight development (P > 0.05) and feed consumption. Kids of both groups were not able to consume enough feed to meet their nutrient requirements during the first 3 weeks following weaning. KEF had a lower frequency of OpG positive samples than CON (P = 0.043). Kefir did not affect the maximum oocyst excretion and age of the kids at the highest oocyst excretion (P > 0.05). KEF shed numerically 35% lower coccidial oocysts than the controls, which corresponded to a statistical tendency (P = 0.074) in lowering Log-OpG in comparison to CON. While KEF had a lower frequency of OpG positive samples and tended to shed lower OPG by around one-third, the frequency of diarrhea, level of highest oocyst excretion, and performance of the kids remained unaffected. Therefore, it is concluded that overall effects of kefir do not have a significant impact on sub-clinical infection and performance in weaned kids under relatively high-hygienic farming conditions

The impact of automated hippocampal volumetry on diagnostic confidence in patients with suspected Alzheimer's disease: an EADC study

INTRODUCTION: Hippocampal volume is a core biomarker of Alzheimer's disease (AD). However, its contribution over the standard diagnostic workup is unclear. METHODS: Three hundred fifty-six patients, under clinical evaluation for cognitive impairment, with suspected AD and Mini–Mental State Examination ≥20, were recruited across 17 European memory clinics. After the traditional diagnostic workup, diagnostic confidence of AD pathology (DCAD) was estimated by the physicians in charge. The latter were provided with the results of automated hippocampal volumetry in standardized format and DCAD was reassessed. RESULTS: An increment of one interquartile range in hippocampal volume was associated with a mean change of DCAD of −8.0% (95% credible interval: [−11.5, −5.0]). Automated hippocampal volumetry showed a statistically significant impact on DCAD beyond the contributions of neuropsychology, 18F-fluorodeoxyglucose positron emission tomography/single-photon emission computed tomography, and cerebrospinal fluid markers (−8.5, CrI: [−11.5, −5.6]; −14.1, CrI: [−19.3, −8.8]; −10.6, CrI: [−14.6, −6.1], respectively). DISCUSSION: There is a measurable effect of hippocampal volume on DCAD even when used on top of the traditional diagnostic workup

(lambda, mu)-statistical convergence of double sequences in n-normed spaces

In this paper, we introduce the concept of (lambda, μ)-statistical convergence in n-normed spaces, where = (r) and μ = (μs) be two non-decreasing sequences of positive realnumbers, each tending to ∞ and such that r+1 ≤ r + 1, 1 = 1; μs+1 ≤ μs + 1, μ1 = 1.Some inclusion relations between the sets of statistically convergent and (, μ)-statisticallyconvergent double sequences are established. We find its relation to statistical convergence,(C, 1, 1)-summability and strong (V, lambda, μ)-summability in n-normed spaces