Search Results (4)

An apparent proliferation of filamentous algal blooms (FABs) in pristine lakes around the world is a source of concern. However, little is known about the predominant drivers and effects of such FABs on lake ecosystems. We observed FABs in a large clear-water lake (Bear Lake, UT/ID, USA) and analyzed long-term lake monitoring data and algal stable isotopes for changes in climate, food webs and anthropogenic nutrient loading, respectively, as potential local drivers of FAB formation. Furthermore, we quantified in situ metabolism rates on rocks with and without FABs at two locations. Long-term monitoring data revealed increasing summer water temperatures (2009 to 2020) and decreasing winter ice cover (1923 to 2021). The FABs had δ¹⁵N values that were higher than 0 ‰, indicating a potential nutrient influx to Bear Lake from livestock or human waste. Climate change and anthropogenic nutrients may thus have facilitated FAB occurrence. Contrary to expectation, the FABs exhibited significantly lower gross primary production rates compared to low-biomass periphyton communities, indicating potentially negative effects of FAB proliferations on lake food webs. Our results highlight the need for expanding lake monitoring programs to include littoral zones to detect and mitigate changes occurring in lakes. Full article

In companion animal medicine, urinary tract infection (UTI) is one of the most common indications for antimicrobial therapy. Definitive diagnosis of UTI requires isolation of bacteria with routine urine culture from an animal with concurrent clinical signs. Urine culture is typically performed at reference laboratories where paired susceptibility testing can be performed, but delays in shipment or processing can affect results. This study evaluated the use of a selective chromogenic, point-of-care culture system (UTid+) compared to conventional urine culture. A total of 119 (73 canine and 46 feline) cystocentesis urine samples were evaluated. Conventional urine culture was positive for 28 (23.5%) of the 119 cultures and UTid+ culture was positive for 26 (21.8%). The overall sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 92.3%, 97.8%, 92.3%, 97.8 and 96.6% for UTid+ respectively. Overall, the UTid+ culture system showed an acceptable level of accuracy when compared to conventional urine culture. Agreement of identification results was high (κ = 0.90) with an important exception being Proteus spp. which was only identified in 1/3 positive cultures. UTid+ may be useful in scenarios where a common UTI pathogen is expected and identification within 24 h is ideal; however, conventional urine culture remains the gold standard. Full article

Continuous water level records are required to detect long-term trends and analyse the climatological mechanisms responsible for extreme events. This paper compiles nine ocean water level records from gauges located along the New South Wales (NSW) coast of Australia. These gauges represent the longest and most complete records of hourly—and in five cases 15-min—water level data for this region. The datasets were adjusted to the vertical Australian Height Datum (AHD) and had the rainfall-related peaks removed from the records. The Unified Tidal Analysis and Prediction (Utide) model was subsequently used to predict tides for datasets with at least 25 years of records to obtain the associated tidal residuals. Finally, we provide a series of examples of how this dataset can be used to analyse trends in tidal anomalies as well as extreme events and their causal processes. Full article

Accurate tidal current prediction plays a critical role with increasing utilization of tidal energy. The classical prediction approach of the tidal current velocity adopts the harmonic analysis (HA) method. The performance of the HA approach is not ideal to predict the high frequency components of tidal currents due to the lack of capability processing the non-astronomic factor. Recently, machine learning algorithms have been applied to process the non-astronomic factor in the prediction of tidal current. In this paper, a tidal current velocity prediction considering the effect of the multi-layer current velocity method is proposed. The proposed method adopts three machine learning algorithms to establish the prediction models for comparative investigations, namely long-short term memory (LSTM), back-propagation (BP) neural network, and the Elman regression network. In the case study, the tidal current data collected from the real ocean environment were used to validate the proposed method. The results show that the proposed method combined with the LSTM algorithm had higher accuracy than both the commercial tidal prediction tool (UTide) and the other two algorithms. This paper presents a novel tidal current velocity prediction considering the effect of the multi-layer current velocity method, which improves the accuracy of the power flow prediction and contributes to the research in the field of tidal current velocity prediction and the capture of tidal energy. Full article