Hierarchical ordering of reticular networks

The structure of hierarchical networks in biological and physical systems has long been characterized using the Horton-Strahler ordering scheme. The scheme assigns an integer order to each edge in the network based on the topology of branching such that the order increases from distal parts of the network (e.g., mountain streams or capillaries) to the "root" of the network (e.g., the river outlet or the aorta). However, Horton-Strahler ordering cannot be applied to networks with loops because they they create a contradiction in the edge ordering in terms of which edge precedes another in the hierarchy. Here, we present a generalization of the Horton-Strahler order to weighted planar reticular networks, where weights are assumed to correlate with the importance of network edges, e.g., weights estimated from edge widths may correlate to flow capacity. Our method assigns hierarchical levels not only to edges of the network, but also to its loops, and classifies the edges into reticular edges, which are responsible for loop formation, and tree edges. In addition, we perform a detailed and rigorous theoretical analysis of the sensitivity of the hierarchical levels to weight perturbations. We discuss applications of this generalized Horton-Strahler ordering to the study of leaf venation and other biological networks.Comment: 9 pages, 5 figures, During preparation of this manuscript the authors became aware of a related work by Katifori and Magnasco, concurrently submitted for publicatio

Technology transfer and evaluation for Space Station telerobotics

The international space station (SS) must take advantage of advanced telerobotics in order to maximize productivity and safety and to reduce maintenance costs. The Automation and Robotics Division at the NASA Lyndon B. Johnson Space Center (JSC) has designed, developed, and constructed the Automated Robotics Maintenance of Space Station (ARMSS) facility for the purpose of transferring and evaluating robotic technology that will reduce SS operation costs. Additionally, JSC had developed a process for expediting the transfer of technology from NASA research centers and evaluating these technologies in SS applications. Software and hardware system developed at the research centers and NASA sponsored universities are currently being transferred to JSC and integrated into the ARMSS for flight crew personnel testing. These technologies will be assessed relative to the SS baseline, and, after refinements, those technologies that provide significant performance improvements will be recommended as upgrades to the SS. Proximity sensors, vision algorithms, and manipulator controllers are among the systems scheduled for evaluation

The Ursinus Weekly, May 8, 1903

A plea for pure sport • Audubon Science Club • Baseball • The farmer • YMCA • Valley Forge • Chorus concert • Dr. Crawford speaks • Philadelphia letter • Alumni Association of Ursinus College • Society noteshttps://digitalcommons.ursinus.edu/weekly/3086/thumbnail.jp

Tobramycin-Treated Pseudomonas aeruginosa PA14 Enhances Streptococcus constellatus 7155 Biofilm Formation in a Cystic Fibrosis Model System

Cystic fibrosis (CF) is a human genetic disorder which results in a lung environment that is highly conducive to chronic microbial infection. Over the past decade, deep-sequencing studies have demonstrated that the CF lung can harbor a highly diverse polymicrobial community. We expanded our existing in vitro model of Pseudomonas aeruginosa biofilm formation on CF-derived airway cells to include this broader set of CF airway colonizers to investigate their contributions to CF lung disease, particularly as they relate to the antibiotic response of the population. Using this system, we identified an interspecies interaction between P. aeruginosa, a bacterium associated with declining lung function and worsening disease, and Streptococcus constellatus, a bacterium correlated with the onset of pulmonary exacerbations in CF patients. The growth rate and cytotoxicity of S. constellatus 7155 and P. aeruginosa PA14 were unchanged when grown together as mixed biofilms in the absence of antibiotics. However, the addition of tobramycin, the frontline maintenance therapy antibiotic for individuals with CF, to a mixed biofilm of S. constellatus 7155 and P. aeruginosa PA14 resulted in enhanced S. constellatus biofilm formation. Through a candidate genetic approach, we showed that P. aeruginosa rhamnolipids were reduced upon tobramycin exposure, allowing for S. constellatus 7155 biofilm enhancement, and monorhamnolipids were sufficient to reduce S. constellatus 7155 biofilm viability in the absence of tobramycin. While the findings presented here are specific to a biofilm of S. constellatus 7155 and P. aeruginosa PA14, they highlight the potential of polymicrobial interactions to impact antibiotic tolerance in unanticipated ways

Temperature Effects on Metabolic Rate of Juvenile Pacific Bluefin Tuna \u3ci\u3eThunnus Orientalis\u3c/i\u3e

Pacific bluefin tuna inhabit a wide range of thermal environments across the Pacific ocean. To examine how metabolism varies across this thermal range, we studied the effect of ambient water temperature on metabolic rate of juvenile Pacific bluefin tuna, Thunnus thynnus, swimming in a swim tunnel. Rate of oxygen consumption (MO2) was measured at ambient temperatures of 8–25°C and swimming speeds of 0.75–1.75 body lengths (BL) s–1. Pacific bluefin swimming at 1 BL s–1 per second exhibited a U-shaped curve of metabolic rate vs ambient temperature, with a thermal minimum zone between 15°C to 20°C. Minimum MO2 of 175±29 mg kg–1 h–1–1 was recorded at 15°C, while both cold and warm temperatures resulted in increased metabolic rates of 331±62 mg kg–1 h–1–1 at 8°C and 256±19 mg kg–1 h–1–1 at 25°C. Tailbeat frequencies were negatively correlated with ambient temperature. Additional experiments indicated that the increase in MO2 at low temperature occurred only at low swimming speeds. Ambient water temperature data from electronic tags implanted in wild fish indicate that Pacific bluefin of similar size to the experimental fish used in the swim tunnel spend most of their time in ambient temperatures in the metabolic thermal minimum zone

Using Machine Learning to Diagnose Misaligned CT Scans

The usage of machine learning has grown exponentially in recent years; However, its applicable uses for medical diagnosis are still in an early stage. Conditions such as Developmental Dysplasia of the Hip (DDH), Cerebral Palsy (CP), and Femoracetabular Impingement (FAI) rely heavily on imaging techniques such as Ultrasound and Computed Tomography (CT) scans. Radiologists use multiple manually computed metrics using these images to diagnose conditions. This is time-intensive and requires an aligned image to get accurate diagnoses. The proposed application uses a deep learning detection algorithm to assist in the metric computation process. The algorithm is implemented using MATLAB R2023A and is trained on CT data gathered from 60 healthy participants. The algorithm performed well on images aligned according to the standard anteroposterior alignment used for radiological measurement. However, the variance of the metrics computation significantly increases when faced with severe misalignment in the craniocaudal or mediolateral axes. Additional algorithm improvements must be made to overcome this increased variance