High-Throughput Workflow for Computer-Assisted Human Parsing of Biological Specimen Label Data

4th International Conference on Open RepositoriesThis presentation was part of the session : Conference PostersHundreds of thousands of specimens in herbaria and natural history museums worldwide are potential candidates for digitization, making them more accessible to researchers. An herbarium contains collections of preserved plant specimens created for scientific use. Herbarium specimens are ideal natural history objects for digitization, as the plants are pressed flat and dried, and mounted on individual sheets of paper, creating a nearly two-dimensional object. Building digital repositories of herbarium specimens can increase use and exposure of the collections while simultaneously reducing physical handling. As important as the digitized specimens are, the data contained on the associated specimen labels provide critical information about each specimen (e.g., scientific name, geographic location of specimen, etc.). The volume and heterogeneity of these printed label data present challenges in transforming them into meaningful digital form to support research. The Apiary Project is addressing these challenges by exploring and developing transformation processes in a systematic workflow that yields high-quality machine-processable label data in a cost- and time-efficient manner. The University of North Texas's Texas Center for Digital Knowledge (TxCDK) and the Botanical Research Institute of Texas (BRIT), with funding from an Institute of Museum and Library Services National Leadership Grant, are conducting fundamental research with the goal of identifying how human intelligence can be combined with machine processes for effective and efficient transformation of specimen label information. The results of this research will yield a new workflow model for effective and efficient label data transformation, correction, and enhancement.Institute of Museum and Library Services, National Leadership Gran

Enrichment of OpenStreetMap data completeness with sidewalk geometries using data mining techniques

Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset ("ground truth dataset"). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions

A Brief Overview of Food Insecurity in Southern New Jersey

Atlantic County, located off the Jersey Shore, faces the highest level of food insecurity in New Jersey, with a rate of 11.2% as of 2021, surpassing the state\u27s overall rate of 8.8%. This situation contributes to health issues such as hypertension, diabetes, and heart disease. The objective of this research is to understand and address the persistence of food insecurity in Atlantic County. Community stakeholders and local physicians were interviewed to identify barriers and disparities contributing to food insecurity. Methods included a literature review using keywords like food insecurity and southern New Jersey . Proposed interventions suggest collaboration with health centers for screenings and the creation of informational handouts on SNAP benefits and local resources. Efforts should made to ensure sustainability by training volunteers from the undergraduate campus. These efforts highlight the impact of food insecurity on health and the importance of collaboration with existing community efforts for lasting change

Lacticaseibacillus rhamnosus GG Survival and Quality Parameters in Kefir Produced from Kefir Grains and Natural Kefir Starter Culture

The study aimed to determine the effect of starter cultures (kefir grains and natural kefir starter culture without grains) on Lacticaseibacillus rhamnosus GG (LGG) survival and on the quality characteristics of kefir. To this end, the viability of probiotic L. rhamnosus GG strain and the rheological properties and quality parameters of kefir beverages were tested during storage over 21 days at 4 °C. The final LGG counts were 7.71 and 7.55 log cfu/mL in natural kefir starter culture and kefir grain, respectively. When prepared with probiotic bacteria, the syneresis values of kefir prepared using natural kefir starter culture was significantly lower (p 0.05). Moreover, all samples showed shear-thinning behavior. The flavor scores for kefir prepared using natural kefir starter culture were significantly higher than for the other samples (p 0.05). Overall, the results indicate that natural kefir starter culture could be a potential probiotic carrier

Lacticaseibacillus rhamnosus GG Survival and Quality Parameters in Kefir Produced from Kefir Grains and Natural Kefir Starter Culture

The study aimed to determine the effect of starter cultures (kefir grains and natural kefir starter culture without grains) on Lacticaseibacillus rhamnosus GG (LGG) survival and on the quality characteristics of kefir. To this end, the viability of probiotic L. rhamnosus GG strain and the rheological properties and quality parameters of kefir beverages were tested during storage over 21 days at 4 °C. The final LGG counts were 7.71 and 7.55 log cfu/mL in natural kefir starter culture and kefir grain, respectively. When prepared with probiotic bacteria, the syneresis values of kefir prepared using natural kefir starter culture was significantly lower (p 0.05). Moreover, all samples showed shear-thinning behavior. The flavor scores for kefir prepared using natural kefir starter culture were significantly higher than for the other samples (p 0.05). Overall, the results indicate that natural kefir starter culture could be a potential probiotic carrier

A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian undersampling of 2D cardiac MR images, the proposed method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI) reconstruction, in terms of reconstruction error, perceptual quality and reconstruction speed for both 3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the method proposed is approximately twice as small, allowing to preserve anatomical structures more faithfully. Using our method, each image can be reconstructed in 23 ms, which is fast enough to enable real-time applications

Efficient Link Prediction Model For Real-World Complex Networks Using Matrix-Forest Metric With Local Similarity Features

Link prediction in a complex network is a difficult and challenging issue to address. Link prediction tries to better predict relationships, interactions and friendships based on historical knowledge of the complex network graph. Many link prediction techniques exist, including the common neighbour, Adamic-Adar, Katz and Jaccard coefficient, which use node information, local and global routes, and previous knowledge of a complex network to predict the links. These methods are extensively used in various applications because of their interpretability and convenience of use, irrespective of the fact that the majority of these methods were designed for a specific field. This study offers a unique link prediction approach based on the matrix-forest metric and vertex local structural information in a real-world complex network. We empirically examined the proposed link prediction method over 13 real-world network datasets obtained from various sources. Extensive experiments were performed that demonstrated the superior efficacy of the proposed link prediction method compared to other methods and outperformed the existing state-of-the-art in terms of prediction accuracy