Performance evaluation of hybrid constructed wetlands for nitrogen removal and statistical approaches.

Nitrogen pollution in water bodies has become a pressing environmental and public health issue worldwide, demanding the implementation of effective nitrogen removal strategies. This research paper delves into the performance evaluation of hybrid constructed wetlands (HCWs) as a sustainable and innovative approach for nitrogen removal, employing a comprehensive year-long dataset gathered from a practical setup. The study collected data under diverse operating conditions to investigate the effectiveness of HCWs in removing nitrogen. Results revealed that HCWs achieved nitrogen removal efficiencies ranging from 28% to 65%, influenced by temperature and hydraulic retention time. Optimal removal occurred at an average temperature of 28°C and a 4-day hydraulic retention time. Notably, performance declined during colder periods, with temperatures below 15°C. The study also aims to predict nitrogen removal by three modeling techniques, that is, artificial neural networks (ANNs), support vector machines Pearson VII kernel function (SVM PUK), and multiple linear regression (MLR). Prediction has been done considering temperature (TEMP), hydraulic loading rate (HLR), initial concentration of chemical oxygen demand (COD) (CODin), initial concentration of total nitrogen (TNin ), initial concentration of total phosphorous (TPin ), and initial concentration of turbidity (TBin ) as input parameters, whereas reduction of total nitrogen (RED TN) is regarded as output parameter. The performance of the soft computing techniques has been compared in terms of coefficient of determination (R2 ), root mean square error (RMSE), and mean absolute error (MAE). The analysis revealed that the performance of the SVM (PUK) model (R2 : 0.572, RMSE: 0.0359, MAE: 0.0294) for the prediction of TN reduction is superior followed by MLR (R2 : 0.562, RMSE: 0.0365, MAE: 0.0294) and ANN (R2 : 0.597, RMSE: 0.0377, MAE: 0.0301). The present study concludes that the treated effluent by the HCWs, using water hyacinth and water lettuce, is of fair quality, thus having potential application for the treatment of rice mill wastewater in warmer climates. Further, machine learning approaches employed in estimating the total nitrogen reduction by HCWs technology have shown promising applicability and utilization in such studies. PRACTITIONER POINTS: Hybrid constructed wetlands (HCWs) are effective in removing nitrogen from wastewater. The performance of HCWs in nitrogen removal can vary due to physical, chemical, and biological processes. The performance of the HCWs highly depends on temperature and hydraulic retention time. Artificial neural networks (ANNs) and support vector machines (SVMs) provided better predictions of nitrogen removal with high accuracy and low root mean square error.

Transformation of PET raw data into images for event classification using convolutional neural networks.

In positron emission tomography (PET) studies, convolutional neural networks (CNNs) may be applied directly to the reconstructed distribution of radioactive tracers injected into the patient's body, as a pattern recognition tool. Nonetheless, unprocessed PET coincidence data exist in tabular format. This paper develops the transformation of tabular data into n-dimensional matrices, as a preparation stage for classification based on CNNs. This method explicitly introduces a nonlinear transformation at the feature engineering stage and then uses principal component analysis to create the images. We apply the proposed methodology to the classification of simulated PET coincidence events originating from NEMA IEC and anthropomorphic XCAT phantom. Comparative studies of neural network architectures, including multilayer perceptron and convolutional networks, were conducted. The developed method increased the initial number of features from 6 to 209 and gave the best precision results (79.8) for all tested neural network architectures; it also showed the smallest decrease when changing the test data to another phantom.

Detection of range shifts in proton beam therapy using the J-PET scanner: a patient simulation study.

OBJECTIVE: The Jagiellonian PET (J-PET) technology, based on plastic scintillators, has been proposed as a cost effective tool for detecting range deviations during proton therapy. This study investigates the feasibility of using J-PET for range monitoring by means of a detailed Monte Carlo simulation study of 95 patients who underwent proton therapy at the Cyclotron Centre Bronowice (CCB) in Krakow, Poland. Approach: Discrepancies between prescribed and delivered treatments were artificially introduced in the simulations by means of shifts in patient positioning and in the Hounsfield unit to the relative proton stopping power calibration curve. A dual-layer, cylindrical J-PET geometry was simulated in an in-room monitoring scenario and a triple-layer, dual-head geometry in an in-beam protocol. The distribution of range shifts in reconstructed PET activity was visualised in the beam's eye view. Linear prediction models were constructed from all patients in the cohort, using the mean shift in reconstructed PET activity as a predictor of the mean proton range deviation. Main results: Maps of deviations in the range of reconstructed PET distributions showed agreement with those of deviations in dose range in most patients. The linear prediction model showed a good fit, with coefficient of determination r^2 = 0.84 (in-room) and 0.75 (in-beam). Residual standard error was below 1 mm: 0.33 mm (in-room) and 0.23 mm (in-beam). Significance: The precision of the proposed prediction models shows the sensitivity of the proposed J-PET scanners to shifts in proton range for a wide range of clinical treatment plans. Furthermore, it motivates the use of such models as a tool for predicting proton range deviations and opens up new prospects for investigations into the use of intra-treatment PET images for predicting clinical metrics that aid in the assessment of the quality of delivered treatment. .

ProTheRaMon - a GATE simulation framework for proton therapy range monitoring using PET imaging.

OBJECTIVE: This paper reports on the implementation and shows examples of the use of the ProTheRaMon framework for simulating the delivery of proton therapy treatment plans and range monitoring using positron emission tomography (PET). ProTheRaMon offers complete processing of proton therapy treatment plans, patient CT geometries, and intra-treatment PET imaging, taking into account therapy and imaging coordinate systems and activity decay during the PET imaging protocol specific to a given proton therapy facility. We present the ProTheRaMon framework and illustrate its potential use case and data processing steps for a patient treated at the Cyclotron Centre Bronowice (CCB) proton therapy center in Krakow, Poland. APPROACH: The ProTheRaMon framework is based on GATE Monte Carlo software, the CASToR reconstruction package and in-house developed Python and bash scripts. The framework consists of five separated simulation and data processing steps, that can be further optimized according to the user's needs and specific settings of a given proton therapy facility and PET scanner design. MAIN RESULTS: ProTheRaMon is presented using example data from a patient treated at CCB and the J-PET scanner to demonstrate the application of the framework for proton therapy range monitoring. The output of each simulation and data processing stage is described and visualized. SIGNIFICANCE: We demonstrate that the ProTheRaMon simulation platform is a high-performance tool, capable of running on a computational cluster and suitable for multi-parameter studies, with databases consisting of large number of patients, as well as different PET scanner geometries and settings for range monitoring in a clinical environment. Due to its modular structure, the ProTheRaMon framework can be adjusted for different proton therapy centers and/or different PET detector geometries. It is available to the community via github.

Insights into the Molecular Mechanisms of Histone Code Recognition by the BRPF3 Bromodomain.

Bromodomains are evolutionarily conserved reader modules that recognize acetylated lysine residues on the histone tails to facilitate gene transcription. The bromodomain and PHD finger containing protein 3 (BRPF3) is a scaffolding protein that forms a tetrameric complex with HBO1 histone acetyltransferase (HAT) and two other subunits, which is known to regulate the HAT activity and substrate specificity. However, its molecular mechanism, histone ligands, and biological functions remain unknown. Herein, we identify mono- (H4K5ac) and di- (H4K5acK12ac) acetylated histone peptides as novel interacting partners of the BRPF3 bromodomain. Consistent with this, pull-down assays on purified histones from human cells confirm the interaction of BRPF3 bromodomain with acetylated histone H4. Further, MD simulation studies highlight the binding mode of acetyllysine (Kac) and the stability of bromodomain-histone peptide complexes. Collectively, our findings provide a key insight into how histone targets of the BRPF3 bromodomain direct the recruitment of HBO1 complex to chromatin for downstream transcriptional regulation.

A prospective study of a new prediction model of vaginal birth after cesarean section at a tertiary care centre.

OBJECTIVE: To create a new and simple model for predicting the likelihood of vaginal birth after cesarean (VBAC) section using variables available at the time of admission. MATERIALS AND METHODS: A prospective observational study was performed at a tertiary care centre in Haryana over a period of 12 months (January 2018 - December 2018) in pregnant women attending the labour room with one previous cesarean section fulfilling the criteria for undergoing trial of labour after cesarean (TOLAC). The sample size was 150. A VBAC score was calculated for each patient using a new prediction model that included variables available at the time of admission such as maternal age, gestational age, Bishop's score, body mass index, indication for primary cesarean section, and clinically estimated fetal weight. The results of the VBAC scores were correlated with outcomes i.e. successful VBAC or failed VBAC. The chi-square test and Student's t-test was used for comparison among the groups. Descriptive and regression analysis was performed for the study variables. RESULTS: Out of 150 TOLAC cases, 78% had successful VBAC and the remainder (22%) had failed VBAC. The observed probability of having a successful VBAC for a VBAC score of 0-3 was 34%, 4-6 was 68%, 7-9 was 90%, and ≥10 was 97%. The prediction model performed well with an area under the curve of 0.77 (95% CI: 0.68 to 0.85) of the receiver operating characteristics receiver operating characteristic curve. CONCLUSION: The present study shows that the proposed VBAC prediction model is a good tool to predict the outcome of TOLAC and can be used to counsel women regarding the mode of delivery in the current and subsequent pregnancies. Further studies of this model and other such models with different permutations and combinations of variables are required.

An assessment of risks associated with obstructive sleep apnea and its relationship with adverse health outcomes among pregnant women. A multi-hospital based study.

INTRODUCTION: Physiological changes in pregnancy increase the vulnerability of antenatal women to develop obstructive sleep apnoea (OSA). It is a known cause of several adverse health outcomes in pregnancy. OBJECTIVES: To assess the risk status of OSA in pregnant women and to study its association with adverse maternal outcomes, fatigability, and daytime sleepiness. MATERIAL AND METHODS: Pregnant women were interviewed to assess for the risk of OSA, fatigability, and daytime sleepiness. STOP BANG, the fatigue severity scale, and the Epworth sleepiness scale were used to assess these parameters. RESULTS: The mean age of the 214 participants was 27.2 ± 4.7 years. 7 (3.3%) participants had a history of snoring louder than the volume of normal talking, or of being loud enough to be heard past closed doors. A moderate risk status of OSA was present among 3 (1.4%) participants. 45 (21.0%) pregnancies were high risk in nature. The risk status of OSA was associated with a high risk status of pregnancies among the participants (p = 0.0088). 41 (19.2%) participants had a history of significant fatigue over the previous week of the study. 7 (3.3%) participants reported mild to severe excessive daytime sleepiness. A history of snoring loudly (p = 0.0179) and a OSA risk status (p = 0.0027) was associated with excessive daytime sleepiness. CONCLUSIONS: A risk status for OSA was associated with a high risk pregnancy status and excessive daytime sleepiness among pregnant women in the current setting. Therefore, pregnant women with these conditions need to be evaluated for OSA. They also need to be suitably managed to ensure the healthy well-being of the mother and the baby.

Magnesium bistrifluoromethanesulfonimide as a new and efficient acylation catalyst.

Magnesium bistrifluoromethanesulfonimide catalyzed the acetylation of phenols, alcohols, and thiols under solvent-free conditions at room temperature and in short times. Electron-deficient and sterically hindered phenols provided excellent yields. The catalyst was found to be general for acylation with other anhydrides, such as propionic, isobutyric, pivalic, chloroacetic, and benzoic anhydrides. The rate of acylation was influenced by the electronic and steric factors associated with the anhydride. The reaction with less electrophilic anhydrides (e.g., chloroacetic and benzoic anhydrides) required higher temperature (approximately 80 degrees C). Chemoselective acetylation, pivalation, and benzoylation took place with acid-sensitive alcohols without any competitive dehydration/rearrangement.