Evaluation of pollutant removal efficiency of urban stormwater wet ponds and the application of machine learning algorithms.

Wet ponds have been extensively used for controlling stormwater pollutants, such as sediment and nutrients, in urban watersheds. The removal of pollutants relies on a combination of physical, chemical, and biological processes. It is crucial to assess the performance of wet ponds in terms of removal efficiency and develop an effective modeling scheme for removal efficiency prediction to optimize water quality management. To achieve this, a two-year field program was conducted at two wet ponds in Calgary, Alberta, Canada to evaluate the wet ponds' performance. Additionally, machine learning (ML) algorithms have been shown to provide promising predictions in datasets with intricate interactions between variables. In this study, the generalized linear model (GLM), partial least squares (PLS) regression, support vector machine (SVM), random forest (RF), and K-nearest neighbors (KNN) were applied to predict the outflow concentrations of three key pollutants: total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP). Generally, the concentrations of inflow pollutants in the two study ponds are highly variable, and a wide range of removal efficiencies are observed. The results indicate that the concentrations of TSS, TN, and TP decrease significantly from the inlet to outlet of the ponds. Meanwhile, inflow concentration, rainfall characteristics, and wind are important indicators of pond removal efficiency. In addition, ML algorithms can be an effective approach for predicting outflow water quality: PLS, GLM, and SVM have shown strong potential to capture the dynamic interactions in wet ponds and predict the outflow concentration. This study highlights the complexity of pollutant removal dynamics in wet ponds and demonstrates the potential of data-driven outflow water quality prediction.

Impact of rainfall characteristics on urban stormwater quality using data mining framework.

Understanding the impact of rainfall characteristics on urban stormwater quality is important for stormwater management. Even though significant attempts have been undertaken to study the relationship between rainfall and urban stormwater quality, the knowledge developed may be difficult to apply in commercial stormwater management models. A data mining framework was proposed to study the impacts of rainfall characteristics on stormwater quality. A rainfall type-based calibration approach was developed to improve water quality model performance. Specifically, the relationship between rainfall characteristics and stormwater quality was studied using principal component analysis and correlation analysis. Rainfall events were classified using a K-means clustering method based on the selected rainfall characteristics. A rainfall type-based (RTB) model was independently calibrated for each rainfall type to obtain optimal parameter sets of stormwater quality models. The results revealed that antecedent dry days, average rainfall intensity, and rainfall duration were the most critical rainfall characteristics affecting the event mean concentrations (EMCs) of total suspended solids, total nitrogen, and total phosphorus, while total rainfall was found to be of negligible importance. The K-means method effectively clustered the rainfall events into four types that could represent the rainfall characteristics in the study areas. The rainfall type-based calibration approach can considerably improve water quality model accuracy. Compared to the traditional continuous simulation model, the relative error of the RTB model was reduced by 11.4 % to 16.4 % over the calibration period. The calibrated stormwater quality parameters can be transferred to adjacent catchments with similar characteristics.

Knocking down of lncRNA SBF2-AS1 inhibits proliferation, invasion and induces apoptosis of colorectal cancer through regulating PTEN.

To illustrate the potential function of lncRNA SBF2-AS1 in the progression of colorectal cancer (CRC) and the molecular mechanism. The relative level of SBF2-AS1 in CRC tissues and cell lines was determined by qRT-PCR. Its level in CRC patients with different tumor stages and tumor sizes was examined. After the knockdown of SBF2-AS1, proliferative, invasive abilities and apoptotic rate of CRC cells were evaluated. The correlation between SBF2-AS1 and PTEN was analyzed in CRC tissues. Furthermore, the subcellular distribution of SBF2-AS1 was assessed. Through RIP and ChIP assay, the interaction between SBF2-AS1 and PTEN was identified. Finally, the involvement of PTEN in SBF2-AS1-mediated CRC progression was analyzed. SBF2-AS1 was upregulated in CRC tissues and cell lines. Its level remained higher in CRC with worse tumor stage and larger tumor size. Knockdown of SBF2-AS1 attenuated proliferative, invasive abilities, but induced apoptotic rate of SW480 and DLD1 cells. A negative correlation was identified between expression levels of SBF2-AS1 and PTEN in CRC tissues. PTEN level was negatively regulated by SBF2-AS1. Subcellular distribution analysis indicated that SBF2-AS1 was mainly expressed in the nucleus. Furthermore, the RIP assay proved the binding of SBF2-AS1 to EZH2 and SUZ12. Knockdown of SBF2-AS1 attenuated the recruitment ability of EZH2 to PTEN. Notably, inhibited proliferation by transfection of sh-SBF2-AS1 1# was partially reversed after co-transfection of sh-PTEN. LncRNA SBF2-AS1 is upregulated in CRC. Knocking down of lncRNA SBF2-AS1 inhibits proliferation, and invasion and induces apoptosis of colorectal cancer by interacting with EZH2 to downregulate PTEN level.

ß-elemene induced apoptosis and senescence of triple-negative breast cancer cells through IGF1/IGF1R pathway.

PURPOSE: ß-elemene has a wide range of anticancer effects and can be used in a variety of cancer types. This study mainly explored its mechanism of action on TNBC cells and provided theoretical basis for the treatment of TNBC. METHODS: Firstly, TNBC cells were treated with different concentrations of ß-elemene, and screened out an appropriate concentration for subsequent research. Then, through the bioinformatics website, predicted genes that have a binding relationship with ß-elemene. Then, the overexpression vector of the selected gene was transfected into the cell. The effects of ß-elemene and its target genes on the proliferation and apoptosis of TNBC cells were detected by CCK-8, Edu assay, and flow cytometry, and the senescence of cells was determined by SA-ß-gal experiment. Western blotting was used to detect the expression of apoptosis and aging-related proteins. RESULTS: ß-elemene inhibited TNBC cell viability and proliferation in a concentration-dependent manner, and induces apoptosis and senescence. Through the screening of candidate genes, IGF1 was finally determined to be an effective target gene of ß-elemene. The expression level of IGF1 was decreased in cells treated with ß-elemene. Overexpression of IGF1 significantly alleviated ability of ß-elemene to inhibit cell viability, proliferation, and induced cell apoptosis and senescence. In addition, ß-elemene inhibited the expression of IGF1R and Bcl-2, and promoted the expression of Cleaved Caspase-3 and senescence-related proteins (p27, p16, p53 and p21), and these effects were reversed by overexpression of IGF1. CONCLUSION: ß-elemene induced apoptosis and senescence of triple-negative breast cancer cells through IGF1/IGF1R pathway.

Land cover based simulation of urban stormwater runoff and pollutant loading.

Urban stormwater models such as PCSWMM are important tools for evaluating urban stormwater quantity and quality. However, due to the lack of consideration of land covers, traditional catchment delineation methods have defects in model precision, parameter transferability and assessment of contribution from individual land cover types. This paper used PCSWMM model as a foundation, built a new land-cover based (LCB) model and made a systematic comparison with the traditional watershed delineation tool (WDT) model to study the impacts of land cover on the simulation of stormwater runoff and pollutant loading. The models were applied to two urban catchments in Calgary, Canada. The results revealed that the LCB model performed better than the WDT model in hydrological simulation, and land cover consideration can considerably improve model accuracy. The two models showed comparable performances in simulation of total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) loading. The LCB model parameters could be regionalized based on land cover types. The hydrologic-hydraulic parameters can be satisfactorily transferred from neighboring gauged catchments to similar ungauged catchments. The transferring of water quality parameters did not perform as satisfactory. The LCB model could quantitively evaluate the contribution to runoff and pollutant loads of different land covers. Roads and roofs were found to be the major contributors to urban runoff and pollutants in the two urban catchments. Green space became important only during large storms events and its contribution could be ignored during dryer years.

The therapeutic effects of X-ray devitalization and replantation and alcoholic devitalization and replantation in adolescent patients with lower limb osteosarcoma.

OBJECTIVE: To compare and analyze the therapeutic effects of X-ray devitalization and replantation and alcoholic devitalization and replantation in adolescent patients with lower limb osteosarcoma. METHODS: We collected clinical data for 43 osteosarcoma patients with limb salvage treatment treated in our hospital from February 2014 to February 2018. The patients were divided into x-ray devitalization and replantation group (n=23) and alcoholic devitalization and replantation group (n=20) based on the treatment methods. The two groups were compared in operation duration, intraoperative blood loss, postoperative fracture healing time, length of tumor bones, MSTS score and ISOLS score, postoperative complications, postoperative follow-ups and postoperative recurrence and metastases. RESULTS: Operation duration and intraoperative blood loss of the alcoholic group were less than that of the X-ray group, while postoperative fracture healing time of the alcoholic group was longer than that of the X-ray group (P<0.05). For the X-ray group, MSTS score and ISOLO score of the final follow-up were 26.13±2.65 and 32.53±3.73 respectively. For the alcoholic group, MSTS score and ISOLO score of the final follow-up were 23.69±3.27 and 30.98±3.56 respectively. MSTS score of the X-ray group was higher than that of the alcoholic group (P<0.05). There were 2 cases of internal fixation failure and 2 cases of adhesive knee joints stiffness in the X-ray group. As for the alcoholic group, there were 2 cases of internal fixation failure and 2 cases of incision soft tissue infection. There were no statistically significant differences in postoperative complications, recurrence, and metastases between the two groups (P>0.05). CONCLUSION: Both methods are convenient, inexpensive, and effective for adolescent patients with lower limb osteosarcoma. Alcoholic devitalization and replantation results in shorter operation duration and less intraoperative blood loss, while X-ray devitalization and replantation results in better postoperative limb function restoration.

Five centuries of reconstructed streamflow in Athabasca River Basin, Canada: Non-stationarity and teleconnection to climate patterns.

Given the challenge to estimate representative long-term natural variability of streamflow from limited observed data, a hierarchical, multilevel Bayesian regression (HBR) was developed to reconstruct the 1489-2006 annual streamflow data at six Athabasca River Basin (ARB) gauging stations based on 14 tree ring chronologies. Seven nested models were developed to maximize the applications of available tree ring predictors. Based on results of goodness-of-fit tests, the HBR developed was skillful and reliable in reconstructing the streamflow of ARB. From five centuries of reconstructed streamflow for ARB, five or six abrupt change points are detected. The streamflow time series obtained from a backward moving, 46-year window for six gauging sites in ARB vary significantly over five centuries (1489-2006) and at times could exceed the 90% and/or 95% confidence intervals, denoting significant non-stationarities. Apparently changes in the mean state and the lag-1 autocorrelation of reconstructed streamflow across the gauging sites can be similar or radically different from each other. These nonstationary features imply that the default stationary assumption is not applicable in ARB. Further, the reconstructed streamflow shows statistically significant oscillations at interannual, interdecadal and multidecadal time scales and are teleconnected to climate patterns such as El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO). A composite analysis shows that La Niña (El Niño), cold (warm) PDO, and cold (warm) AMO events are typically associated with increased (decreased) streamflow anomalies of ARB. The reconstructed streamflow data provides us the full range of streamflow variability and recurrence characteristics of extremes spanned over five centuries from which it is useful for us to evaluate and manage the current water systems of ARB more effectively and a better risk analysis of future droughts of ARB.

miR-340 alleviates chemoresistance of osteosarcoma cells by targeting ZEB1.

Chemoresistance during treatment of osteosarcoma (OS) is attracting more and more attention as the main clinical obstacle. The purpose of this study was to elucidate the role of miR-340 in chemoresistance of OS. Plasmid construction and transfection, miRNA arrays, PCR analyses, and western blot analysis, as well as MTT, apoptosis, and luciferase assays were carried out in MG-63 cells and MG-63/cisplatin (DDP)-resistant cells. The results showed that miR-340 was downregulated in OS tissues and drug-resistant OS cells. Moreover, a negative correlation was observed between miR-340 and ZEB1 expression in OS tissues. Forced expression of miR-340 in drug-resistant OS cells significantly reduced multidrug resistance-1 and P-gp expression. Overexpression of miR-340 enhanced sensitivity to DDP by inhibiting viability and promoting apoptosis. The luciferase assay and western blot analysis identified ZEB1 as a direct target of miR-340, and miR-340 negatively regulated ZEB1 expression. Ectopic expression of ZEB1 reversed the effects of miR-340 on P-gp expression, cell viability, and apoptosis. miR-340 alleviated chemoresistance of OS cells by targeting ZEB1. Our results indicate that targeting miR-340 may be a potential therapeutic approach to treat drug-resistant OS.

Prototype-Based Discriminative Feature Learning for Kinship Verification.

In this paper, we propose a new prototype-based discriminative feature learning (PDFL) method for kinship verification. Unlike most previous kinship verification methods which employ low-level hand-crafted descriptors such as local binary pattern and Gabor features for face representation, this paper aims to learn discriminative mid-level features to better characterize the kin relation of face images for kinship verification. To achieve this, we construct a set of face samples with unlabeled kin relation from the labeled face in the wild dataset as the reference set. Then, each sample in the training face kinship dataset is represented as a mid-level feature vector, where each entry is the corresponding decision value from one support vector machine hyperplane. Subsequently, we formulate an optimization function by minimizing the intraclass samples (with a kin relation) and maximizing the neighboring interclass samples (without a kin relation) with the mid-level features. To better use multiple low-level features for mid-level feature learning, we further propose a multiview PDFL method to learn multiple mid-level features to improve the verification performance. Experimental results on four publicly available kinship datasets show the superior performance of the proposed methods over both the state-of-the-art kinship verification methods and human ability in our kinship verification task.