Greenhouse gas from moving bed based integrated fixed film activated sludge membrane bioreactors

The present paper reports the results of a nitrous oxide production investigation in a moving bed based integrated fixed film activated sludge (IFAS) membrane bioreactor (MBR) pilot plant designed in accordance with the UCT layout for biological phosphorous removal. Samples of gas and liquid were collected in order to measure the gaseous, as well as the dissolved concentration of N2O. Furthermore, the gas flow rate from each reactor was measured and the gas flux was estimated. The results confirmed that the anoxic reactor represents the main source of nitrous oxide production. A significant production of N2O was, however, also found in the anaerobic reactor, thus indicating a probable occurrence of the DPAOs activity

Nitrous oxide emissions in a membrane bioreactor treating saline wastewater contaminated by hydrocarbons

The joint effect of wastewater salinity and hydrocarbons on nitrous oxide emission was investigated. The membrane bioreactor pilot plant was operated with two phases: i. biomass acclimation by increasing salinity from 10 gNaCl L−1 to 20 gNaCl L−1 (Phase I); ii. hydrocarbons dosing at 20 mg L−1 with a constant salt concentration of 20 gNaCl L−1 (Phase II). The Phase I revealed a relationship between nitrous oxide emissions and salinity. During the end of the Phase I, the activity of nitrifiers started to recover, indicating a partial acclimatization. During the Phase II, the hydrocarbon shock induced a temporary inhibition of the biomass with the suppression of nitrous oxide emissions. The results revealed that the oxic tank was the major source of nitrous oxide emission, likely due to the gas stripping by aeration. The joint effect of salinity and hydrocarbons was found to be crucial for the production of nitrous oxid

A Topology-aware Analysis of Graph Collaborative Filtering

The successful integration of graph neural networks into recommender systems (RSs) has led to a novel paradigm in collaborative filtering (CF), graph collaborative filtering (graph CF). By representing user-item data as an undirected, bipartite graph, graph CF utilizes short- and long-range connections to extract collaborative signals that yield more accurate user preferences than traditional CF methods. Although the recent literature highlights the efficacy of various algorithmic strategies in graph CF, the impact of datasets and their topological features on recommendation performance is yet to be studied. To fill this gap, we propose a topology-aware analysis of graph CF. In this study, we (i) take some widely-adopted recommendation datasets and use them to generate a large set of synthetic sub-datasets through two state-of-the-art graph sampling methods, (ii) measure eleven of their classical and topological characteristics, and (iii) estimate the accuracy calculated on the generated sub-datasets considering four popular and recent graph-based RSs (i.e., LightGCN, DGCF, UltraGCN, and SVD-GCN). Finally, the investigation presents an explanatory framework that reveals the linear relationships between characteristics and accuracy measures. The results, statistically validated under different graph sampling settings, confirm the existence of solid dependencies between topological characteristics and accuracy in the graph-based recommendation, offering a new perspective on how to interpret graph CF

Evaluation of HIV-DNA and inflammatory markers in HIV-infected individuals with different viral load patterns

Abstract Background: Persistent residual viremia (RV) and low grade inflammation and immune activation have been associated with non-AIDS defining events. The impact of persistent RV and HIV-DNA load on immune activation/ inflammation remains unclear. The purpose of this study was to gain new insights into the relation between viremia, markers of inflammation and HIV-DNA levels. Methods: Three hundred and twenty-one HIV-infected patients were studied. A retrospective analysis of viremia values, prospectively collected for 48 months, was performed. Patients were separated into three groups: 113 TND (Target Not Detected, patients with sustained undetectable viremia); 113 RV (Residual Viremia, patients who had at least three detectable viral load (VL) values <37 copies/ml); 95 LLV (Low Level Viremia, patients with at least two VL values >37 but <200 copies/ml). HIV-DNA, TNF-α, IL-6 and sCD14 were analyzed. Results: HIV-DNA, sCD14 and TNF-α were significantly lower in the TND group than in the RV and LLV groups. In addition, RV patients showed lower levels of HIV-DNA and sCD14 than LLV individuals. HIV-DNA load was not related to markers of inflammation. The ordinal logistic analysis showed that two independent variables were significantly associated with VL pattern: sCD14, HIV-DNA. In addition NRTIs plus NNRTIs and NRTIs plus PIs were negatively associated to VL pattern compared to INI-containing regimen. Conclusions: Persistent undetectable viremia was associated with lower levels of inflammatory markers and HIVDNA. However, the lack of normalization of these biomarkers in the TND group and the fact that HIV-DNA load was not associated with inflammation strongly suggest that other mechanisms play a major role in maintaining inflammation over time

Diagnostic value of qualitative and strain ratio elastography in the differential diagnosis of non-palpable testicular lesions

The purpose of this study was to evaluate prospectively the accuracy of qualitative and strain ratio elastography (SE) in the differential diagnosis of non-palpable testicular lesions. The local review board approved the protocol and all patients gave their consent. One hundred and six patients with non-palpable testicular lesions were consecutively enrolled. Baseline ultrasonography (US) and SE were correlated with clinical and histological features and ROC curves developed for diagnostic accuracy. The non-palpable lesions were all ≤1.5 cm; 37/106 (34.9%) were malignant, 38 (35.9%) were benign, and 31 (29.2%) were non-neoplastic. Independent risk factors for malignancy were as follows: size (OR 17.788; p = 0.002), microlithiasis (OR 17.673, p &lt; 0.001), intralesional vascularization (OR 9.207, p = 0.006), and hypoechogenicity (OR, 11.509, p = 0.036). Baseline US had 89.2% sensitivity (95% CI 74.6-97.0) and 85.5% specificity (95% CI 75.0-92.8) in identifying malignancies, and 94.6% sensitivity (95% CI 86.9-98.5) and 87.1% specificity (95% CI 70.2-96.4) in discriminating neoplasms from non-neoplastic lesions. An elasticity score (ES) of 3 out of 3 (ES3, maximum hardness) was recorded in 30/37 (81.1%) malignant lesions (p &lt; 0.001). An intermediate score of 2 (ES2) was recorded in 19/38 (36.8%) benign neoplastic lesions and in 22/31 (71%) non-neoplastic lesions (p = 0.005 and p = 0.001 vs. malignancies). None of the non-neoplastic lesions scored ES3. Logistic regression analysis revealed a significant association between ES3 and malignancy (χ2 = 42.212, p &lt; 0.001). ES1 and ES2 were predictors of benignity (p &lt; 0.01). Overall, SE was 81.8% sensitive (95% CI 64.8-92.0) and 79.1% specific (95% CI 68.3-88.4) in identifying malignancies, and 58.6% sensitive (95% CI 46.7-69.9) and 100% specific (95% CI 88.8-100) in discriminating non-neoplastic lesions. Strain ratio measurement did not improve the accuracy of qualitative elastography. Strain ratio measurement offers no improvement over elastographic qualitative assessment of testicular lesions; testicular SE may support conventional US in identifying non-neoplastic lesions when findings are controversial, but its added value in clinical practice remains to be proven

Prenatal predictors of adverse perinatal outcome in congenital cytomegalovirus infection: a retrospective multicenter study

Objectives To identify predictors of adverse perinatal outcome in congenital cytomegalovirus (CMV) infection. Methods In a multicenter study fetuses with congenital CMV infection diagnosed by PCR on amniotic fluid and normal prenatal imaging at the time of diagnosis were included. Primary outcome was the occurrence of structural anomalies at follow-up ultrasound or prenatal magnetic resonance imaging (MRI). Secondary outcomes were the occurrence of anomalies detected exclusively postnatally and the rate of symptomatic infection. Results One hundred and four fetuses with congenital CMV were included in the study. Anomalies were detected at follow-up ultrasound or MRI in 18.3% (19/104) cases. Additional anomalies were found after birth in 11.9% (10/84) of cases and 15.5% (13/85) of newborns showed clinical symptoms related to CMV infection. There was no difference in either maternal age (p=0.3), trimester (p=0.4) of infection and prenatal therapy (p=0.4) between fetuses with or whiteout anomalies at follow-up. Conversely, median viral load in the amniotic fluid was higher in fetuses with additional anomalies at follow-up (p=0.02) compared to those without. At multivariate logistic regression analysis, high viral load in the amniotic fluid, defined as &gt;= 100,000 copies/mL was the only independent predictor for the occurrence of anomalies detected exclusively at follow-up ultrasound assessment or MRI, with an OR of 3.12. Conclusions Viral load in the amniotic fluid is a strong predictor of adverse perinatal outcome in congenital CMV infection. The results of this study emphasize the importance of adequate follow up even in case of negative neurosonography to better predict postnatal adverse outcomes of infected newborns, especially in amniotic fluid high viral load

How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework: Data Processing, Model Selection, and Performance Evaluation

Recommender Systems have shown to be an efective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of proposed recommendation algorithms, splitting strategies, evaluation protocols, metrics, and tasks, has made rigorous experimental evaluation particularly challenging. ELLIOT is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple confguration fle. The framework loads, flters, and splits the data considering a vast set of strategies. Then, it optimizes hyperparameters for several recommendation algorithms, selects the best models, compares them with the baselines, computes metrics spanning from accuracy to beyond-accuracy, bias, and fairness, and conducts statistical analysis. The aim is to provide researchers a tool to ease all the experimental evaluation phases (and make them reproducible), from data reading to results collection. ELLIOT is freely available on GitHub at https://github.com/sisinflab/ellio