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A cost-saving, picture-assisted method for toric intraocular lens alignment versus manual self-leveling marking via RoboMarker
Purpose
To determine the accuracy of two axis-marking methods for toric intraocular lens (IOL) implantation, one picture-assisted approach based on scleral vessel vectors, and the other based on a self-leveling device for manual marking.
Methods
This retrospective single-center study involved 60 eyes of 51 participants, who underwent phacoemulsification with toric IOL implantation. In all eyes, preoperative markings were made in a seated position both manually via a self-leveling corneal marker (RoboMarker), and digitally on slit-lamp photographs, defining scleral vessels as landmarks, aiding to find the correct intraoperative orientation for an angular graduation instrument. The axis of IOL alignment at the end of surgery was determined from high resolution, intraoperative footage from a microscope-integrated camera and the axis-marking error served as an outcome measurement for both marking techniques. The endpoint was the alignment of the lens at the end of surgery.
Results
The average axis-marking error was 2.5 ± 1.9 degrees for picture-assisted marking, which was significantly less than that of the self-leveling corneal marker, being 5.4 ± 4.4 degrees.
Conclusion
Our results indicate that scleral vessel vector marking leads to highly accurate toric IOL alignments, while being an inexpensive technique, as solely a slit-lamp camera is required for preoperative preparation.
Journal Article
The flunking of Joshua T. Bates
Driving home from the beach on Labor Day, Joshua receives some shocking news from his mother: he must repeat third grade.
Book
Using heterogeneous camera-trapping sites to obtain the first density estimates for the transboundary Eurasian lynx (Lynx lynx) population in the Dinaric Mountains
Estimating abundance of wild animal populations is crucial for their management and conservation. While spatial capture-recapture models are becoming increasingly common to assess the densities of elusive species, recent studies have indicated potential bias that can be introduced by unaccounted spatial variation of detectability. We used camera-trapping data collected in collaboration with local hunters from a transnational population survey of the Eurasian lynx (Lynx lynx) in Slovenia and Croatia, to provide the first density estimate for the threatened Eurasian lynx population in the Northern Dinaric Mountains. Population density was 0.83 (95% CI: 0.60–1.16) lynx/100 km2, which is comparable to other reintroduced Eurasian lynx populations in Europe. Furthermore, we showed that baseline detection rate was influenced by the type of site used, as well as by sex of the individual and local behavioural response. Scent-marking sites had on average a 1.6- and 2.5-times higher baseline detection rate compared to roads and other locations, respectively. Scent-marking behaviour is common for several mammals, and selecting sites that attracts the targeted species is used to increase detection rates, especially for rare and cryptic species. But we show that the use of different location types for camera trapping can bias density estimates if not homogenously distributed across the surveyed area. This highlights the importance of incorporating not only individual characteristics (e.g., sex), but also information on the type of site used in camera trapping surveys into estimates of population densities.
Journal Article
Signal detection theory applied to giant pandas: Do pandas go out of their way to make sure their scent marks are found?
Inter‐animal communication allows signals released by an animal to be perceived by others. Scent‐marking is the primary mode of such communication in giant pandas (Ailuropoda melanoleuca). Signal detection theory propounds that animals choose the substrate and location of their scent marks so that the signals released are transmitted more widely and last longer. We believe that pandas trade‐off scent‐marking because they are an energetically marginal species and it is costly to generate and mark chemical signals. Existing studies only indicate where pandas mark more frequently, but their selection preferences remain unknown. This study investigates whether the marking behavior of pandas is consistent with signal detection theory. Feces count, reflecting habitat use intensity, was combined with mark count to determine the selection preference for marking. The results showed that pandas preferred to mark ridges with animal trails and that most marked tree species were locally dominant. In addition, marked plots and species were selected for lower energy consumption and a higher chance of being detected. Over 90% of the marks used were the longest‐surviving anogenital gland secretion marks, and over 80% of the marks were oriented toward animal trails. Our research demonstrates that pandas go out of their way to make sure their marks are found. This study not only sheds light on the mechanisms of scent‐marking by pandas but also guides us toward more precise conservation of the panda habitat. This study provides a more rigorous approach to the study of scent marking in giant pandas and concludes that it is indeed consistent with signal detection theory.
Journal Article
The internet of animals : discovering the collective intelligence of life on Earth
\"All we need to do is give animals a voice and our perception of the world could change forever. That's what author Martin Wikelski and his team of scientists believe, and this book shares their story for the first time. As they tag animals around the world with minuscule tracking devices, they link their movements to The International Space Station, which taps into the 'internet of animals': an astonishing network of information made up of thousands of animals communicating with each other and their environments. Called the International Cooperation for Animal Research Using Space, or ICARUS, this phenomenal project is poised to change our world. Down on the ground, Wikelski describes animals' sixth sense first-hand. Farm animals become restless when earthquakes are imminent. Animals on the African plains sense when poachers are on the move. Frigatebirds in South America depart before hurricanes arrive.\"-- Provided by publisher.
Book
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered \"de facto\" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages - from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reflect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.
Journal Article