Tables of moments of sample extremes of order statistics from discrete uniform distribution

In this paper, moments of sample extremes of order statistics from discrete uniform distribution are given. For n up to 15, algebraic expressions for the expected values and variances of sample extremes of order statistics from discrete uniform distribution are obtained. It is shown that with the help of the sum s (k) n , one can obtain all moments for sample extremes of order statistics from a discrete uniform distribution. Furthermore, for sample size k = 20 and n =1(1)20 , numerical results calculated by using Matlab.Makalede, kesikli düzgün dağılımdaki sıra istatistiklerin örnek ekstremlerinin momentleri verilmiştir. Kesikli düzgün dağılımdaki sıra istatistiklerin örnek ekstremlerinin beklenen değer ve varyansları için n=15’ e kadar cebirsel ifadeler bulunmuştur. s (k) n toplamı yardımıyla kesikli düzgün dağılımdaki sıra istatistiklerin örnek ekstremlerinin bütün momentlerinin bulunabileceği görülmüştür. Ayrıca, Matlab kullanılarak k = 20 ve n =1(1)20 örnek boyutu için sayısal sonuçlar hesaplanmıştır

Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays

BACKGROUND: Tissue microarrays (TMAs) have become a valuable resource for biomarker expression in translational research. Immunohistochemical (IHC) assessment of TMAs is the principal method for analysing large numbers of patient samples, but manual IHC assessment of TMAs remains a challenging and laborious task. With advances in image analysis, computer-generated analyses of TMAs have the potential to lessen the burden of expert pathologist review. METHODS: In current commercial software computerised oestrogen receptor (ER) scoring relies on tumour localisation in the form of hand-drawn annotations. In this study, tumour localisation for ER scoring was evaluated comparing computer-generated segmentation masks with those of two specialist breast pathologists. Automatically and manually obtained segmentation masks were used to obtain IHC scores for thirty-two ER-stained invasive breast cancer TMA samples using FDA-approved IHC scoring software. RESULTS: Although pixel-level comparisons showed lower agreement between automated and manual segmentation masks (κ=0.81) than between pathologists' masks (κ=0.91), this had little impact on computed IHC scores (Allred; [Image: see text]=0.91, Quickscore; [Image: see text]=0.92). CONCLUSIONS: The proposed automated system provides consistent measurements thus ensuring standardisation, and shows promise for increasing IHC analysis of nuclear staining in TMAs from large clinical trials

Prediction of nasal morphology in facial reconstruction: Validation and recalibration of the Rynn method

Background Prediction of the nose from the skull remains an important issue in forensic facial approximation. In 2010, Rynn et al. published a method of predicting nose projection from the skull. With this method, three craniometric measurements (x, y, z) are taken, and these are then used in regression formulae to estimate the nasal dimensions. Aim The purpose of this study was to examine and test the accuracy of the Rynn et al. method and if necessary to adapt the formulae for this population. Subjects and methods A sample of 90 CT scans of Turkish adults was used in the study. The actual and predicted dimensions were compared using t-test. The age of the individuals ranged from 20 to 40 years by sex. Results The descriptive statistics and correlations were calculated, and the actual and predicted measurements were compared. The differences between the actual and predicted values were statistically significant (p < 0.01), with −1 mm for males and −1.5 mm for females. Validation accuracies ranged from 76 to 92% in females and 72 to 82% in males. Recalibration equation accuracies ranged from 88 to 100% in females and 90 to 100% in males. Conclusion The results showed that the recalibration of the Rynn et al. method and its formulae gave satisfactory results with less error and can be employed in facial approximation cases

Assessment of algorithms for mitosis detection in breast cancer histopathology images

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists

Convolutional neural network-based clinical predictors of oral dysplasia: class activation map analysis of deep learning results

Oral cancer/oral squamous cell carcinoma is among the top ten most common cancers globally, with over 500,000 new cases and 350,000 associated deaths every year worldwide. There is a critical need for objective, novel technologies that facilitate early, accurate diagnosis. For this purpose, we have developed a method to classify images as “suspicious” and “normal” by performing transfer learning on Inception-ResNet-V2 and generated automated heat maps to highlight the region of the images most likely to be involved in decision making. We have tested the developed method’s feasibility on two independent datasets of clinical photographic images of 30 and 24 patients from the UK and Brazil, respectively. Both 10-fold cross-validation and leave-one-patient-out validation methods were performed to test the system, achieving accuracies of 73.6% (±19%) and 90.9% (±12%), F1-scores of 97.9% and 87.2%, and precision values of 95.4% and 99.3% at recall values of 100.0% and 81.1% on these two respective cohorts. This study presents several novel findings and approaches, namely the development and validation of our methods on two datasets collected in different countries showing that using patches instead of the whole lesion image leads to better performance and analyzing which regions of the images are predictive of the classes using class activation map analysis

The battle over Syria's reconstruction

Reconstruction is becoming the new battleground in the Syrian conflict—its continuation by other means. It is instrumentalized by the regime as a way to reconsolidate its control over the country and by rival regional and international powers to shape the internal balance of power and establish spheres of influence in the country. The paper examines the Asad regime’s practices, including co-optation of militia leaders via reconstruction concessions and use of reconstruction to clear strategic areas of opposition-dominated urban settlements. The paper then surveys how the geopolitical struggle in Syria has produced an asymmetry as regards reconstruction: those powers that lost the geo-political contest on the ground seek to use geo-economic superiority to reverse the geo-political outcome. Then the impact of proxy wars and spheres of influence in the country on the security context for reconstruction is examined. Finally, the reconstruction initiatives of the various external parties are assessed, including Russia, Iran and Turkey as well as the spoiler role by which the US seeks to obstruct reconstruction that would spell victory in Syria for its Russian and Iranian rivals.PostprintPeer reviewe