Influence Of Fish Presence And Removal On Woodland Pond Breeding Amphibians

U okviru rada izrađena je programska potpora te provedeno istraživanje učenja programskih koncepata korištenjem blokova. Ispitivanje je provedeno nad učenicima prvog razreda osnovne škole koji su korištenjem programske potpore rješavali problemske zadatke prikladne njihovoj dobi. Zadatci su podijeljeni u četiri skupine od kojih je svaka povezana s gradivom određenih predmeta, a svaka od njih ispituje različite programske koncepte algoritamsko-računalnog načina razmišljanja. Radom je dan pregled razvoja korištene aplikacije, od postavljanja zahtjeva do implementacije koja je usko vezana uz pojedine programske biblioteke i okvire. Predložena su poboljšanja te su dani savjeti za razvoj sličnog rješenja. Ispitivanje je pokazalo korelacije među nekim općenitim pokazateljima uspješnosti, ali na generalnoj razini ne postoji povezanost zadataka sa procjenom matematičke i logičke vještine ispitanika.For the purpose of analysing how well students use basic programming concepts, a research was conducted using custom-built application software. The tested students were enrolled in the first grade of primary school and had to solve problem tasks using predefined blocks which represented certain programming concepts. Problem tasks were divided into four groups depending on which concept was tested as well as which course its problem was related to. Each experiment tested one problem task group and consisted of students using the custom-built application to solve its problems. The thesis describes in detail the development process of the created software as well as points out certain design decisions that it, and similar solutions, should have. The research showed that among some variables there is significant correlation, but generally it was found that defined success variables aren’t connected to the mathematical and logical skill assessment

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The Renaissance rapier -- history and use

This project analyzed the fencing techniques of the rapier as taught by five different fencing masters from Renaissance Europe. A web page was then constructed which incorporated the analyses, a basic history of the rapier and rapier techniques, and animations of different attacks and defenses. The user-friendly website can be used as a basis for studying rapier techniques, or one of the individual masters, by people around the world

Measurement of Endotracheal Tube Positioning on Chest X-Ray Using Object Detection.

Patients who are intubated with endotracheal tubes often receive chest x-ray (CXR) imaging to determine whether the tube is correctly positioned. When these CXRs are interpreted by a radiologist, they evaluate whether the tube needs to be repositioned and typically provide a measurement in centimeters between the endotracheal tube tip and carina. In this project, a large dataset of endotracheal tube and carina bounding boxes was annotated on CXRs, and a machine-learning model was trained to generate these boxes on new CXRs and to calculate a distance measurement between the tube and carina. This model was applied to a gold standard annotated dataset, as well as to all prospective data passing through our radiology system for two weeks. Inter-radiologist variability was also measured on a test dataset. The distance measurements for both the gold standard dataset (mean error = 0.70 cm) and prospective dataset (mean error = 0.68 cm) were noninferior to inter-radiologist variability (mean error = 0.70 cm) within an equivalence bound of 0.1 cm. This suggests that this model performs at an accuracy similar to human measurements, and these distance calculations can be used for clinical report auto-population and/or worklist prioritization of severely malpositioned tubes

Detection of Critical Spinal Epidural Lesions on CT Using Machine Learning.

BACKGROUND: Critical spinal epidural pathologies can cause paralysis or death if untreated. Although magnetic resonance imaging is the preferred modality for visualizing these pathologies, computed tomography (CT) occurs far more commonly than magnetic resonance imaging in the clinical setting. OBJECTIVE: A machine learning model was developed to screen for critical epidural lesions on CT images at a large-scale teleradiology practice. This model has utility for both worklist prioritization of emergent studies and identifying missed findings. MATERIALS AND METHODS: There were 153 studies with epidural lesions available for training. These lesions were segmented and used to train a machine learning model. A test data set was also created using previously missed epidural lesions. The trained model was then integrated into a teleradiology workflow for 90 days. Studies were sent to secondary manual review if the model detected an epidural lesion but none was mentioned in the clinical report. RESULTS: The model correctly identified 50.0% of epidural lesions in the test data set with 99.0% specificity. For prospective data, the model correctly prioritized 66.7% of the 18 epidural lesions diagnosed on the initial read with 98.9% specificity. There were 2.0 studies flagged for potential missed findings per day, and 17 missed epidural lesions were found during a 90-day time period. These results suggest almost half of critical spinal epidural lesions visible on CT imaging are being missed on initial diagnosis. CONCLUSION: A machine learning model for identifying spinal epidural hematomas and abscesses on CT can be implemented in a clinical workflow