Investigating water quality sensitivity to climate variability and its influencing factors in four Lake Erie watersheds.

Climate change alters weather patterns and hydrological cycle, thus potentially aggravating water quality impairment. However, the direct relationships between climate variability and water quality are complicated by a multitude of hydrological and biochemical mechanisms dominate the process. Thus, little is known regarding how water quality responds to climate variability in the context of changing meteorological conditions and human activities. Here, a longitudinal study was conducted using trend, correlation, and redundancy analyses to explore stream water quality sensitivity to temperature, precipitation, streamflow, and how the sensitivity was affected by watershed climate, land cover percentage, landscape configuration, fertilizer application, and tillage types. Specifically, daily pollutant concentration data of suspended solid (SS), total phosphorus (TP), soluble reactive phosphorus (SRP), total Kjeldahl nitrogen (TKN), nitrate and nitrite (NOx), and chloride (Cl) were used as water quality indicators in four Lake Erie watersheds from 1985 to 2017, during which the average temperature has increased 0.5 °C and the total precipitation has increased 9%. Results show that precipitation and flow were positively associated with SRP, NOx, TKN, TP, and SS, except for SRP and NOx in the urban basin. The rising temperatures led to increasing concentrations of SS, TKN, and TP in the urban basin. SRP and NOx sensitivity to precipitation was higher in the years with more precipitation and higher precipitation seasonality, and the basins with more spatially aggregated cropland. No-tillage and reduced tillage management could decrease both precipitation and temperature sensitivity for most pollutants. As one of the first studies leveraging multiple watershed environmental variables with long-term historical climate and water quality data, this study can assist target land use planning and management policy to mitigate future climate change effects on surface water quality.

Predicting bioretention pollutant removal efficiency with design features: A data-driven approach.

The objective of this study is to synthesize previous research findings from bioretention experiments and identify design features that lead to the best performance of bioretention pollutant removal with a data-driven approach. A bioretention database was built from 79 bioretention publications, composed of 182 records of bioretention cells with their design features and the corresponding pollutant removal efficiency data. Non-parametric correlation analysis, multiple linear regression (MLR), and decision tree classifiers were applied to investigate the relationships between bioretention design features and pollutant removal efficiencies. Non-parametric statistics and MLR results indicated that bioretention surface area, media depth, the presence of an internal water storage (IWS) layer, soil composition, and vegetation cover are all significantly correlated with pollutant removal efficiencies. The impacts of design features are significantly different under different climate and inflow conditions. Decision tree classifiers showed that non-vegetated bioretention cells with sand filter media generally have higher than 80% total suspended solid (TSS) mass removal efficiencies; bioretention cells with minimum organic matter and greater than 0.58 m soil media depth tend to remove more than 51% of total nitrogen (TN); and vegetated bioretention cells with minimum organic matter remove more than 67% of total phosphorus (TP). The overall accuracy of decision tree classifiers in the test set is around 70% to predict TSS, TN, and TP mass removal efficiency classes. This study suggests that the data-driven approach provides insights into understanding the complex relationship between bioretention design features and pollutant removal performance.

Pathways linking watershed development and riparian quality to stream water quality and fish communities: Insights from 233 subbasins of the Great Lakes region.

Anthropogenic stressors such as urban development, agricultural runoff, and riparian zone degradation impair stream water quality and biodiversity. However, the intricate pathways that connect these stressors at watershed and riparian scales to stream ecosystems-and their interplay with climate and hydrology-remain understudied. In this study, we used Partial Least Squares (PLS) path modeling to examine these pathways and their collective impacts on stream water quality and fish community structures across 233 watersheds in the Great Lakes region. Our study suggests that moderate levels of watershed development enhance overall fish richness, potentially due to increased water temperature and nutrient availability, but reduces both the percentages and richness of cold water and intolerant taxa. Riparian quality exerts indirect effects on water quality with climate and stream order serving as key mediators. Complementing our SEM analysis, we also used Multiple Linear Regression (MLR) models and identified a significant positive relationship between the proportion of clay and agricultural land with TN concentrations. However, TP concentrations are influenced by a more complex set of interactions involving developed areas, soil, and slope. These findings emphasize the necessity of adopting integrated management strategies to preserve the health and integrity of freshwater ecosystems in the Great Lakes region. These strategies should integrate watershed and riparian protection measures while also taking into account the effects of climate change and specific local conditions.

Computer-aided diagnosis system for optical diagnosis of colorectal polyps under white light imaging.

OBJECTIVES: This study presents a novel computer-aided diagnosis (CADx) designed for optically diagnosing colorectal polyps using white light imaging (WLI).We aimed to evaluate the effectiveness of the CADx and its auxiliary role among endoscopists with different levels of expertise. METHODS: We collected 2,324 neoplastic and 3,735 nonneoplastic polyp WLI images for model training, and 838 colorectal polyp images from 740 patients for model validation. We compared the diagnostic accuracy of the CADx with that of 15 endoscopists under WLI and narrow band imaging (NBI). The auxiliary benefits of CADx for endoscopists of different experience levels and for identifying different types of colorectal polyps was also evaluated. RESULTS: The CADx demonstrated an optical diagnostic accuracy of 84.49%, showing considerable superiority over all endoscopists, irrespective of whether WLI or NBI was used (P < 0.001). Assistance from the CADx significantly improved the diagnostic accuracy of the endoscopists from 68.84% to 77.49% (P = 0.001), with the most significant impact observed among novice endoscopists. Notably, novices using CADx-assisted WLI outperform junior and expert endoscopists without such assistance. CONCLUSIONS: The CADx demonstrated a crucial role in substantially enhancing the precision of optical diagnosis for colorectal polyps under WLI and showed the greatest auxiliary benefits for novice endoscopists.

Exercise Improves the Coordination of the Mitochondrial Unfolded Protein Response and Mitophagy in Aging Skeletal Muscle.

The mitochondrial unfolded protein response (UPRmt) and mitophagy are two mitochondrial quality control (MQC) systems that work at the molecular and organelle levels, respectively, to maintain mitochondrial homeostasis. Under stress conditions, these two processes are simultaneously activated and compensate for each other when one process is insufficient, indicating mechanistic coordination between the UPRmt and mitophagy that is likely controlled by common upstream signals. This review focuses on the molecular signals regulating this coordination and presents evidence showing that this coordination mechanism is impaired during aging and promoted by exercise. Furthermore, the bidirectional regulation of reactive oxygen species (ROS) and AMPK in modulating this mechanism is discussed. The hierarchical surveillance network of MQC can be targeted by exercise-derived ROS to attenuate aging, which offers a molecular basis for potential therapeutic interventions for sarcopenia.

Comparing stormwater quality and watershed typologies across the United States: A machine learning approach.

Watersheds continue to be urbanized across different regions of the United States, increasing the number of impaired waterbodies due to urban stormwater. Using machine learning techniques, this study examined how stormwater quality and watershed characteristics are related at a national scale and compared stormwater quality across watersheds in diverse climates. We analyzed a selection of data from the National Stormwater Quality Database (NSQD) comprising 1,881 stormwater samples taken from 182 watersheds in 26 metropolitan areas in the United States between 1992 and 2003. Using an ensemble clustering algorithm, the stormwater quality in these samples was classified into "stormwater signatures," defined as distinct combinations of 9 contaminants including metals (Pb, Zn, Cu), particulates (TSS, TDS), and nutrients (BOD, TP, TKN, NOx). Next, multinomial logistic regression was applied to the NSQD data now classified by signature and combined with climate, weather, land use, and imperviousness data obtained from multiple sources. The results yielded 5 stormwater signatures with distinct aquatic toxicity implications and relationships to climate, weather, land use, and imperviousness: Signature 1 ("Ecotoxic and Eutrophic"), defined by high median concentrations of contaminants, likely represents the first flush in moderate-to-high imperviousness watersheds; Signature 2 ("Reduced Nitrates") represents a wet season signature, particularly for dry climates; Signature 3 ("Potentially Eutrophic") represents the first flush in low imperviousness watersheds; Signature 4 ("Elevated Particulates and Metals") represents a wet season signature, particularly on warmer days; finally, Signature 5 ("Most Dilute") is primarily a regional signature associated with the warm, wet climate of the southeastern US. This study serves as a proof-of-concept demonstrating how machine learning techniques can be used to identify patterns in high-dimensional and highly variable data. Applied to stormwater quality, these techniques identify major patterns in stormwater quality across the United States using a stormwater signature approach, which examines how contaminants co-occur and under what climate, weather, land use, and impervious conditions. The findings point to dominant processes driving stormwater generation and inform watershed monitoring, green infrastructure planning, stormwater quality under climate change, and opportunities for public engagement.

A preferable modality for the differentiation of peripancreatic fluid collections: Endoscopic ultrasound.

Background and Objectives: Peripancreatic fluid collections (PFCs), including walled-off necrosis (WON) and pancreatic pseudocysts (PPCs), are categorized by imaging modalities, including EUS, computed tomography (CT), and magnetic resonance imaging. Our study aimed to evaluate the effectiveness of EUS in differentiating PFCs compared with that of other modalities. Subjects and Methods: Data were collected retrospectively from 99 patients at fourteen centers who were recruited to undergo lumen-apposing metal stent placement to treat PFCs. Results: PFCs were detected by CT and EUS in 51 WON and 48 PPC patients. The accuracy in differentiating PFCs by EUS was much higher than that of CT (90.9% vs. 50.5%, P < 0.001). The accuracy in identifying WON on EUS was much higher than that on CT (82.4% vs. 13.7%, P < 0.001), while the accuracy in identifying PPC was comparable in these two modalities (89.6% vs. 100%, P > 0.05). WON patients required more times of debridement than PPC patients (P < 0.001). Conclusion: EUS can categorize symptomatic PFCs with higher accuracy than CT and is a preferred imaging modality to detect solid necrotic debris.

Predicting stream water quality under different urban development pattern scenarios with an interpretable machine learning approach.

Urban development pattern significantly impacts stream water quality by influencing pollutant generation, build-up, and wash-off processes. It is thus necessary to understand and predict stream water quality in accordance with different urban development patterns to effectively advise urban growth planning and policies. To do so, we collected pollutant concentration data on nitrate (NO3--N), total phosphate (TP), and Escherichia coli (E. coli) from 1047 sampling stations in the Texas Gulf Region. We utilized a Random Forest (RF) machine learning model to predict stream water quality under four planning scenarios with different urban densities and configurations. SHapley Additive exPlanations (SHAP) was used to prove the importance of urban development pattern in influencing stream water quality. The spatial variations of the impact of these patterns were explored with Geographically Weighted Regression (GWR). SHAP results indicated that Largest Patch Index (LPI), Patch Cohesion Index (COHESION), Splitting Index (SPLIT), and Landscape Division Index (DIVISION) were the most important urban development pattern metrics affecting stream water quality. The spatial variations of such patterns were shown to impact stream water quality depending on pollutants, seasonality, climate, and urbanization level. RF prediction results suggested that high density aggregated development was more effective in reducing TP and NO3--N concentrations than the current sprawl development, but had the potential risk of increasing E. coli pollution in the wet season. The results of this study provide empirical evidence and a potential mechanistic explanation that stream water quality degradation is a consequence of urban sprawl. Lastly, machine learning is a powerful tool for scenario prediction in land use planning to forecast environmental impacts under different urban development pattern scenarios.

Creep-Fatigue Crack Initiation Simulation of a Modified 12% Cr Steel Based on Grain Boundary Cavitation and Plastic Slip Accumulation.

High-temperature components in power plants may fail due to creep and fatigue. Creep damage is usually accompanied by the nucleation, growth, and coalescence of grain boundary cavities, while fatigue damage is caused by excessive accumulated plastic deformation due to the local stress concentration. This paper proposes a multiscale numerical framework combining the crystal plastic frame with the meso-damage mechanisms. Not only can it better describe the deformation mechanism dominated by creep from a microscopic viewpoint, but also reflects the local damage of materials caused by irreversible microstructure changes in the process of creep-fatigue deformation to some extent. In this paper, the creep-fatigue crack initiation analysis of a modified 12%Cr steel (X12CrMoWvNBN10-1-1) is carried out for a given notch specimen. It is found that creep cracks usually initiate at the triple grain boundary junctions or at the grain boundaries approximately perpendicular to the loading direction, while fatigue cracks always initiate from the notch surface where stress is concentrated. In addition to this, the crack initiation life can be quantitatively described, which is affected by the average grain size, initial notch size, stress range and holding time.

Distinct Impacts of Fullerene on Cognitive Functions of Dementia vs. Non-dementia Mice.

Fullerene is a family of carbon materials widely applied in modern medicine and ecosystem de-contamination. Its wide application makes human bodies more and more constantly exposed to fullerene particles. Since fullerene particles are able to cross the blood-brain barrier (BBB) (Yamago et al. 1995), if and how fullerene would affect brain functions need to be investigated for human health consideration. For this purpose, we administered fullerene on subcortical ischemic vascular dementia (SIVD) model mice and sham mice, two types of mice with distinct penetration properties of BBB and hence possibly distinct vulnerabilities to fullerene. We studied the spatial learning and memory abilities of mice with Morris water maze (MWM) and the neuroplasticity properties of the hippocampus. Results showed that fullerene administration suppressed outcomes of MWM in sham mice, along with suppressed long-term potentiation (LTP) and dendritic spine densities. Oppositely, recoveries of MWM outcomes and neuroplasticity properties were observed in fullerene-treated SIVD mice. To further clarify the mechanism of the impact of fullerene on neuroplasticity, we measured the levels of postsynaptic density protein 95 (PSD-95), synaptophysin (SYP), brain-derived neurotrophic factor (BDNF), and tropomyosin receptor kinase B (TrkB) by western blot assay. Results suggest that the distinct impacts of fullerene on behavior test and neuroplasticity may be conducted through postsynaptic regulations that were mediated by BDNF.

Risk Factors And Epigenetic Markers Of Left Ventricular Diastolic Dysfunction With Preserved Ejection Fraction In A Community-Based Elderly Chinese Population.

PURPOSE: Left ventricular diastolic dysfunction with preserved ejection fraction (LVDD-PEF) is an early-stage manifestation but poorly understood in the process of heart failure. This study was designed to investigate risk factors and epigenetic markers for predicting LVDD-PEF. PATIENTS AND METHODS: A community-based study in 1568 residents over 65 years was conducted in Shanghai, People's Republic of China, from June 2014 to August 2015. Echocardiography was performed to diagnose LVDD-PEF. DNA methylation by whole-genome bisulfite sequencing was used to determine those potential epigenetic markers contributing to LVDD-PEF. RESULTS: A total of 177 participants (11.3%) were diagnosed with LVDD-PEF, and higher prevalence in females than in males (15.0% vs 6.5%, P<0.001). Multivariate logistic regression analysis indicated that female sex (OR 2.46, 95% CI 1.47-4.13), body mass index (BMI) (OR 1.09, 95% CI 1.04-1.14), pulse pressure (PP) (OR 1.03, 95% CI 1.01-1.05) and carotid intima-media thickness (CIMT) (OR 4.20, 95% CI 1.40-12.55) showed a significant association with LVDD-PEF. Overall, 638 CpG sites were differentially methylated in LVDD-PEF group compared to non-LVDD-PEF group (P<0.001); 242 sites were significantly hypermethylated (covering 238 genes) and 396 sites were significantly hypomethylated (covering 265 genes). CONCLUSION: Our findings found female, BMI, PP, and CIMT were independent predictors for LVDD-PEF in the community-dwelling elderly population. Regulation of DNA methylation might play a crucial role for LVDD-PEF.