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2022 ◽  
Author(s):  
KENAN DAGDELEN

Abstract PurposeThe aim of this study is to evaluate the accuracy, quality and reliability of the videos on trabeculectomy on YouTube which is an online video-sharing platform.Scope This paper aims to assess the quality and analyze the content of the videos on Trabeculectomy on YouTube. The material has been obtained by a video search carried out on the Youtube -online video platform- with the keyword “Trabeculectomy”. The material (videos) was examined and selected in accordance with the exclusion criteria (not being in English, duplicate videos, lack of title information of the video, being irrelevant to the subject, videos with only advertising content, videos with a pixel below 240, takes longer than 20 minutes). After the implementation of exclusion criteria, the first ten suitable videos were included in the evaluation.MethodologyRegarding the material, the parameters of the number of views, the number of likes, the number of dislikes, the number of comments, the video duration, the days since the video was uploaded were recorded. Thus, after carving out the secondary data, a number of statistical analyses were performed namely Shapiro-Wilks, Kruskall-Wallis, Mann-Whitney U and Backward Linear Regression. In this framework, statistical analyzes were made via using the Stata software. Statistical significance value (threshold) was accepted as %10 (p<0.1).ResultsAfter the videos were evaluated according to the upload source, it was found that 2 videos were downloaded by the individuals who is not a doctor, 6 videos from doctors and 2 videos from a commercial source. The number of subscribers of the YouTube channels on which these sources have been uploaded was significantly different from each other (p<0.1). When the videos were evaluated according to the information content they provided, it was found that 5 videos had low quality information content, 4 videos had medium quality information content, 1 video had good quality information content, and the video durations were also significantly different from each other (p<0.1). Moreover, it was statistically determined that the parameter affecting the number of views was the number of likes (p<0.1).ConclusionYouTube videos are essentially insufficient as an educational material and an English source of information for the Trabeculectomy. Health professionals need to pay more attention to online platforms so that patients can access accurate information.Categories:Medical Education, Medical Simulation, Surgery


2022 ◽  
Author(s):  
Diego Sasso Porto ◽  
Wasila Dahdul ◽  
Hilmar Lapp ◽  
James Balhoff ◽  
Todd Vision ◽  
...  

Morphology remains a primary source of phylogenetic information for many groups of organisms, and the only one for most fossil taxa. Organismal anatomy is not a collection of randomly assembled and independent "parts", but instead a set of dependent and hierarchically nested entities resulting from ontogeny and phylogeny. How do we make sense of these dependent and at times redundant characters? One promising approach is using ontologies---structured controlled vocabularies that summarize knowledge about different properties of anatomical entities, including developmental and structural dependencies. Here we assess whether the proximity of ontology-annotated characters within an ontology predicts evolutionary patterns. To do so, we measure phylogenetic information across characters and evaluate if it is hierarchically structured by ontological knowledge---in much the same way as phylogeny structures across-species diversity. We implement an approach to evaluate the Bayesian phylogenetic information (BPI) content and phylogenetic dissonance among ontology-annotated anatomical data subsets. We applied this to datasets representing two disparate animal groups: bees (Hexapoda: Hymenoptera: Apoidea, 209 chars) and characiform fishes (Actinopterygii: Ostariophysi: Characiformes, 463 chars). For bees, we find that BPI is not substantially structured by anatomy since dissonance is often high among morphologically related anatomical entities. For fishes, we find substantial information for two clusters of anatomical entities instantiating concepts from the jaws and branchial arch bones, but among-subset information decreases and dissonance increases substantially moving to higher-level subsets in the ontology. We further applied our approach to addressing particular evolutionary hypotheses with an example of morphological evolution in miniature fishes. While we show that ontology does indeed structure phylogenetic information, additional relationships and processes, such as convergence, likely play a substantial role in explaining BPI and dissonance, and merit future investigation. Our work demonstrates how complex morphological datasets can be interrogated with ontologies by allowing one to access how information is spread hierarchically across anatomical concepts, how congruent this information is, and what sorts of processes may structure it: phylogeny, development, or convergence.


Author(s):  
V. A. Ganchenko ◽  
E. E. Marushko ◽  
L. P. Podenok ◽  
A. V. Inyutin

This article describes evaluation the information content of metal objects surfaces for classification of fractures using 2D and 3D data. As parameters, the textural characteristics of Haralick, local binary patterns of pixels for 2D images, macrogeometric descriptors of metal objects digitized by a 3D scanner are considered. The analysis carried out on basis of information content estimation to select the features that are most suitable for solving the problem of metals fractures classification. The results will be used for development of methods for complex forensic examination of complex polygonal surfaces of solid objects for automated system for analyzing digital images.


2022 ◽  
Vol 8 ◽  
Author(s):  
Lisa M. W. Mogensen ◽  
Zhigang Mei ◽  
Yujiang Hao ◽  
Xavier A. Harrison ◽  
Ding Wang ◽  
...  

Conservation management requires evidence, but robust data on key parameters such as threats are often unavailable. Conservation-relevant insights might be available within datasets collected for other reasons, making it important to determine the information content of available data for threatened species and identify remaining data-gaps before investing time and resources in novel data collection. The Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis) has declined severely across the middle-lower Yangtze, but multiple threats exist in this system and the relative impact of different anthropogenic activities is unclear, preventing identification of appropriate mitigation strategies. Several datasets containing information on porpoises or potential threats are available from past boat-based and fishing community surveys, which might provide novel insights into causes of porpoise mortality and decline. We employed multiple analytical approaches to investigate spatial relationships between live and dead porpoises and different threats, reproductive trends over time, and sustainable offtake levels, to assess whether evidence-based conservation is feasible under current data availability. Our combined analyses provide new evidence that mortality is spatially associated with increased cargo traffic; observed mortality levels (probably a substantial underestimate of true levels) are unsustainable; and population recruitment is decreasing, although multiple factors could be responsible (pollutants, declining fish stocks, anthropogenic noise, reduced genetic diversity). Available data show little correlation between patterns of mortality and fishing activity even when analyzed across multiple spatial scales; however, interview data can be affected by multiple biases that potentially complicate attempts to reconstruct levels of bycatch, and new data are required to understand dynamics and sustainability of porpoise-fisheries interactions. This critical assessment of existing data thus suggests that in situ porpoise conservation management must target multiple co-occurring threats. Even limited available datasets can provide new insights for understanding declines, and we demonstrate the importance of an integrative approach for investigating complex conservation problems and maximizing evidence in conservation planning for poorly known taxa.


2022 ◽  
Author(s):  
Nakeeb un Nisa Yetoo ◽  
Aafreen Sakina ◽  
Najeebul Rehman* Sofi ◽  
Asif B. Shikari ◽  
Reyaz R. Mir ◽  
...  

Abstract Background: Characterization and evaluation of plant genetic resources play an important role for their utilization in the crop improvement programmes. Methods and results: This study entails the agro-morphological, cooking quality and molecular characterization of 51 genotypes / advance breeding lines of rice from Kashmir Himalayas. Significant variability was observed for all agro-morphological and cooking quality traits among all the studied genotypes. Cluster analysis using UPGMA method divided the genotypes into two major clusters having 15 and 36 genotypes. Thirty eight genotypes screened using 24 SSR markers detected 48 alleles with 2.0 alleles per locus and an average polymorphism information content (PIC) of 0.37. High polymorphism information content (PIC) values was observed for the primers RM263 (0.67), RM159 (0.59) and RM333 (0.50). Furthermore, out of 38 SSR markers screened on 192 temperate rice germpalsm lines, R4M17 accurately differentiated indica and temperate japonica genotypes amplifying 220 bp and 169bp, respectively. Accordingly, 15 genotypes were reported as indica and 28 temperate japonica in addition to 149 genotypes as intermediate types. Conclusion: The information on marker-based diversity and performance based on cooking quality and agronomic traits helped to select the most divergent lines for crossing and also the analysis was useful to generate information on indica - japonica classification of our germplasm.


2022 ◽  
Vol 10 (1) ◽  
pp. 63-83
Author(s):  
Wei Xiao ◽  
Jin Liu ◽  
Li Li

Recent years have witnessed a growing interest in research article (RA thereafter) introductions. Most previous studies focused on the macro structures, rhetorical functions and linguistic realizations of RA introductions, but few intended to investigate the information content distribution from the perspective of information theory. The current study conducted an entropy-based study on the distributional patterns of information content in RA introductions and their variations across disciplines (humanities, natural sciences, and social sciences). Three indices, that is, one-, two-, and three-gram entropies, were used to analyze 120 RA introductions (40 introductions from each disciplinary area). The results reveal that, first, in RA introductions, the information content is unevenly distributed, with the information content of Move 1 being the highest, followed in sequence by Move 3 and Move 2; second, the three entropy indices may reflect different linguistic features of RA introductions; and, third, disciplinary variations of information content were found. In Move 1, the RA introductions of natural sciences are more informative than those of the other two disciplines, and in Move 3 the RA introductions of social sciences are more informative as well. This study has implications for genre-based instruction in the pedagogy of academic writing, as well as the broadening of the applications of quantitative corpus linguistic methods into less touched fields.


2021 ◽  
Vol 4 ◽  
Author(s):  
David Hartmann ◽  
Daniel Franzen ◽  
Sebastian Brodehl

The ability of deep neural networks to form powerful emergent representations of complex statistical patterns in data is as remarkable as imperfectly understood. For deep ReLU networks, these are encoded in the mixed discrete–continuous structure of linear weight matrices and non-linear binary activations. Our article develops a new technique for instrumenting such networks to efficiently record activation statistics, such as information content (entropy) and similarity of patterns, in real-world training runs. We then study the evolution of activation patterns during training for networks of different architecture using different training and initialization strategies. As a result, we see characteristic- and general-related as well as architecture-related behavioral patterns: in particular, most architectures form bottom-up structure, with the exception of highly tuned state-of-the-art architectures and methods (PyramidNet and FixUp), where layers appear to converge more simultaneously. We also observe intermediate dips in entropy in conventional CNNs that are not visible in residual networks. A reference implementation is provided under a free license1.


2021 ◽  
Vol 4 (30) ◽  
pp. 42-48
Author(s):  
V. N. Kovregin ◽  
◽  
G. M. Kovregina ◽  

A unified method of disclosing blind ranges, reducing or eliminating measurement ambiguity has been proposed and investigated, which allows, within the framework of a typical long-range detection session of an air object by a pulse-Doppler radar, to reduce time costs, expand information content, and unify algorithmic support of a detection session. At the heart of: unified adaptive-robust procedures for controlling radiation parameters (guaranteeing the observability of an object) and processing ambiguous quasi-measurements of range (in absence of detection) and real measurement (in initial detection).


2021 ◽  
pp. 1-50
Author(s):  
Jose P. Mora Ortiz ◽  
Heather Bedle ◽  
Kurt J. Marfurt

Fault identification is critical in defining the structural framework for both exploration and reservoir characterization studies. Interpreters routinely use edge-sensitive attributes such as coherence to accelerate the manual picking process, where the actual choice of a particular edge-sensitive attribute varies with the seismic data quality and with the reflectivity response of the faulted geologic formations. CMY color blending provides an effective way to combine the information content of two or three edge-sensitive attributes when more than one attribute is sensitive to faults. We evaluate whether combining the information content of more than three attributes using probabilistic neural networks (PNN) provides any additional uplift. We employ a training data consisting of manually picked faults on a coarse grid of 3D seismic lines, and then we employ an exhaustive search PNN to identify the optimal set of attributes to create a fault probability volume for a 3D survey acquired over the Great South Basin, New Zealand. We construct a suite of candidate attributes using our understanding of the attribute response to faults seen in the data and examples extracted from the published literature to use the list as the analyzed attributes. Using a subset of picked faults as training data, we evaluate which suite of attributes and hyperparameters exhibit the highest validation on the remaining training data. When used together, we find that volume aberrancy magnitude, GLCM homogeneity, GLCM entropy, Sobel filter similarity, and envelope best predict the faults for this dataset. The PNN supervised classification creates a seismic image volume that exhibits fault probabilities providing a simple combination of multiple seismic attributes. We also find that applying a directional Laplacian of a Gaussian and skeletonization filters to the PNN fault volumes provides a superior result to simple CMY blending techniques.


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