Applying machine learning random forest (RF) method in predicting the cement products with a co-processing of input materials: Optimizing the hyperparameters.

Co-processing recycled waste during cement production, i.e., using alternative materials such as secondary raw materials or secondary raw fuels, is widely practiced in developed countries. Alternative raw materials or fuels contain high concentrations of heavy metals and other hazardous chemicals, which might lead to the potential for dangerous heavy metals and hazardous chemicals to be transferred to clinker or cement products, resulting in exposure and emissions to people or the environment. Managing input materials and predicting which inputs affect the final concentration is essential to prevent potential hazards. We used the data of six heavy metals by input raw materials and input fuels of cement manufacturers in 2016-2017. The concentrations of Pb and Cu in cement were about 10-200 times and 4 to 200 times higher than other heavy metals (Cr, As, Cd, Hg), respectively. We profiled the influence of heavy metal concentration of each input material using the principal component analysis (PCA), which analyzed the leading causes of each heavy metal. The Random Forest (RF) ensemble model predicted cement heavy metal concentrations according to input materials. In the case of Cu, Cd, and Cr, the training performance showed R square values of 0.71, 0.71, and 0.92, respectively, as a result of predicting the cement heavy metal concentration according to the heavy metal concentration of each cement input material using the RF model, which is a machine learning model. The results of this study show that the RF model can be used to predict the amount and concentration of alternative raw materials and alternative fuels by controlling the concentration of heavy metals in cement through the concentration of heavy metals in the input materials.

Machine Learning-Based Early Warning Level Prediction for Cyanobacterial Blooms Using Environmental Variable Selection and Data Resampling.

Many countries have attempted to mitigate and manage issues related to harmful algal blooms (HABs) by monitoring and predicting their occurrence. The infrequency and duration of HABs occurrence pose the challenge of data imbalance when constructing machine learning models for their prediction. Furthermore, the appropriate selection of input variables is a significant issue because of the complexities between the input and output variables. Therefore, the objective of this study was to improve the predictive performance of HABs using feature selection and data resampling. Data resampling was used to address the imbalance in the minority class data. Two machine learning models were constructed to predict algal alert levels using 10 years of meteorological, hydrodynamic, and water quality data. The improvement in model accuracy due to changes in resampling methods was more noticeable than the improvement in model accuracy due to changes in feature selection methods. Models constructed using combinations of original and synthetic data across all resampling methods demonstrated higher prediction performance for the caution level (L-1) and warning level (L-2) than models constructed using the original data. In particular, the optimal artificial neural network and random forest models constructed using combinations of original and synthetic data showed significantly improved prediction accuracy for L-1 and L-2, representing the transition from normal to bloom formation states in the training and testing steps. The test results of the optimal RF model using the original data indicated prediction accuracies of 98.8% for L0, 50.0% for L1, and 50.0% for L2. In contrast, the optimal random forest model using the Synthetic Minority Oversampling Technique-Edited Nearest Neighbor (ENN) sampling method achieved accuracies of 85.0% for L0, 85.7% for L1, and 100% for L2. Therefore, applying synthetic data can address the imbalance in the observed data and improve the detection performance of machine learning models. Reliable predictions using improved models can support the design of management practices to mitigate HABs in reservoirs and ultimately ensure safe and clean water resources.

Uterine Cervical Angioleiomyoma Mimicking Squamous Cell Carcinoma.

Angioleiomyoma, a rare variant of leiomyoma, is a benign tumor of mesenchymal origin. Angioleiomyomas of the female urogenital tract are extremely rare, with only six cases of uterine cervical angioleiomyoma previously reported in the literature. In this case study, we report on a 49-year-old female patient who presented with menorrhagia whose initial magnetic resonance imaging (MRI) findings suggested cervical squamous cell carcinoma (SCC). However, following the hysterectomy, histological examination confirmed the lesion to be angioleiomyoma. To the best of our knowledge, there have been no previously reported cases of angioleiomyomas presenting with MRI findings that are suggestive of uterine SCC. Recognizing that angioleiomyomas can mimic uterine malignancies on MRI may prove beneficial for future diagnostic and treatment strategies.

Postoperative Adjuvant Chemoradiotherapy Versus Chemotherapy Alone for Stage III Endometrial Cancer: A Multicenter Retrospective Study.

INTRODUCTION: We aimed to evaluate the efficacy and toxicity of the combination of 6 cycles of chemotherapy and radiation therapy compared with chemotherapy alone as postoperative adjuvant therapy for patients with stage III endometrial cancer. METHODS: This retrospective cohort study included patients with stage III endometrial cancer who received postoperative chemoradiotherapy or chemotherapy alone at 6 hospitals between January 2009 and December 2019. The progression-free survival (PFS) and overall survival (OS) for each treatment group were analyzed using the Kaplan-Meier method. We also assessed differences in toxicity profiles between the treatment groups. RESULTS: A total of 133 patients met the inclusion criteria. Of these, 80 patients (60.2%) received adjuvant chemoradiotherapy and 53 (39.8%) received chemotherapy alone. The PFS and OS did not differ significantly between the groups. For patients with stage IIIC endometrioid subtype, the chemoradiotherapy group had significantly longer PFS rate than did the chemotherapy alone group (log-rank test, P = .019), although there was no significant difference in the OS (log-rank test, P = .100). CRT was identified as a favorable prognostic factor for PFS in multivariate analysis (adjusted HR, .37; 95% CI, .16-.87; P = .022). Patients treated with chemoradiotherapy more frequently suffered from grade 4 neutropenia (73.8% vs 52.8%; P = .018) and grade 3 or worse thrombocytopenia (36.3% vs 9.4%; P = .001) compared with the chemotherapy alone group. There were no differences between the 2 treatment groups in the frequency of toxicity-related treatment discontinuation or dose reduction. CONCLUSION: We confirmed that chemoradiotherapy yields longer progression-free survival than does chemotherapy alone for patients with stage IIIC endometrioid endometrial cancer, with an acceptable toxicity profile.

Preoperative neutrophil-to-lymphocyte, platelet-to-lymphocyte and monocyte-to-lymphocyte ratio as a prognostic factor in non-endometrioid endometrial cancer.

Objective: Evaluate the prognostic value of neutrophil-lymphocyte ratio (NMR), platelet-lymphocyte ratio (PLR), and monocyte-lymphocyte ratio (MLR) in patients with non-endometrioid endometrial cancer. Method: Laboratory and clinicopathological data from 118 patients with non-endometrioid endometrial cancer who underwent surgical resection between January 2010 and December 2019 were reviewed. NLR, PLR and MLR were analyzed for correlations with recurrence and survival. The receiver operating characteristic (ROC) curves were generated for the NLR, PLR, and MLR. Optimal cut-off values were determined as the points at which the Youden index (sensitivity + specificity - 1) was maximal. Based on the results of the ROC curve analysis, the patients were grouped into high MLR and low MLR groups. Recurrence rate, disease-free survival, and overall survival were compared between the two groups. The prognostic factors were investigated using univariate and multivariate Cox proportional hazards model. Results: The optimal cut-off value of MLR was 0.191 (AUC, 0.718; p < 0.001). Significantly more patients in the high MLR group experienced recurrence (60.3% vs. 15.6%, p < 0.0001) and cancer-related deaths (46.6% vs. 13.3%, p = 0.003). In multivariate analysis, advanced stage and high MLR were independent prognostic factors for disease-free survival and overall survival. Conclusion: Elevated MLR was significantly associated poor clinical outcomes in patients with non endometrioid endometrial cancer. Our findings suggest that MLR may be clinically reliable and useful as an independent prognostic marker for patients with non-endometrioid endometrial cancer.

Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method.

Many countries have attempted to monitor and predict harmful algal blooms to mitigate related problems and establish management practices. The current alert system-based sampling of cell density is used to intimate the bloom status and to inform rapid and adequate response from water-associated organizations. The objective of this study was to develop an early warning system for cyanobacterial blooms to allow for efficient decision making prior to the occurrence of algal blooms and to guide preemptive actions regarding management practices. In this study, two machine learning models: artificial neural network (ANN) and support vector machine (SVM), were constructed for the timely prediction of alert levels of algal bloom using eight years' worth of meteorological, hydrodynamic, and water quality data in a reservoir where harmful cyanobacterial blooms frequently occur during summer. However, the proportion imbalance on all alert level data as the output variable leads to biased training of the data-driven model and degradation of model prediction performance. Therefore, the synthetic data generated by an adaptive synthetic (ADASYN) sampling method were used to resolve the imbalance of minority class data in the original data and to improve the prediction performance of the models. The results showed that the overall prediction performance yielded by the caution level (L1) and warning level (L2) in the models constructed using a combination of original and synthetic data was higher than the models constructed using original data only. In particular, the optimal ANN and SVM constructed using a combination of original and synthetic data during both training (including validation) and test generated distinctively improved recall and precision values of L1, which is a very critical alert level as it indicates a transition status from normalcy to bloom formation. In addition, both optimal models constructed using synthetic-added data exhibited improvement in recall and precision by more than 33.7% while predicting L-1 and L-2 during the test. Therefore, the application of synthetic data can improve detection performance of machine learning models by solving the imbalance of observed data. Reliable prediction by the improved models can be used to aid the design of management practices to mitigate algal blooms within a reservoir.

Lymphedema in Endometrial Cancer Survivor: A Nationwide Cohort Study.

BACKGROUND: Endometrial cancer is the most common gynecological cancer in developed countries. Treatment-related lymphedema negatively affects the quality of life and function of patients. This study investigated the cumulative incidence and risk factors of, and utilization of health care resources for, lymphedema in patients with endometrial cancer. METHODS: We conducted a nationwide, retrospective cohort study of women with endometrial cancer who underwent cancer-direct treatment using the Korean National Health Insurance Service (NHIS) database. Patients were categorized by age, region, income, and treatment modality. Cox proportional hazards regression models were used to analyze the incidence and risk factors of lymphedema. We also analyzed utilization of health care resources for lymphedema using diagnostic and treatment claim codes. RESULTS: A total of 19,027 patients with endometrial cancer were evaluated between January 2004 and December 2017. Among them, 2493 (13.1%) developed lymphedema. Age (<40 years, adjusted odds ratio [aOR] = 1 vs. 40-59 years, aOR = 1.413; 95% confidence interval (CI) 1.203-1.66 vs. 60+ years, aOR = 1.472; 95% CI 1.239-1.748) and multimodal treatment (surgery only, aOR = 1 vs. surgery + radiation + chemotherapy, aOR = 2.571; 95% CI 2.27-2.912) are considered to be possible risk factors for lymphedema in patients with endometrial cancer (p < 0.001). The utilization of health care resources for the treatment of lymphedema has increased over the years. CONCLUSIONS: Lymphedema is a common complication affecting women with endometrial cancer and leads to an increase in national healthcare costs. Post-treatment surveillance of lymphedema, especially in high-risk groups, is needed.

Primary signet ring cell carcinoma of the uterine cervix: A case report.

RATIONALE: Primary signet ring cell carcinoma of the uterine cervix is extremely rare and the clinical characteristics and prognosis are not well known and there are no specific guidelines for treatment. PATIENT CONCERNS: A 43-year-old woman was referred to our hospital for abnormal uterine bleeding lasting 1 month. DIAGNOSES: Histological examination revealed a signet ring cell carcinoma of the uterine cervix. After evaluation of extragenital origin, the patient was diagnosed International Federation of Gynecology and Obstetrics stage IIIC1 primary signet ring cell carcinoma or the uterine cervix. INTERVENTION: The patient was prescribed concomitant chemo-radiation followed by intracavitary brachytherapy. OUTCOMES: She showed no evidence of disease after treatment but, it recurred after 7 months of last treatment. LESSONS: Different approaches to diagnosis and treatment of this rare disease are needed and molecular pathological studies related to the onset of the disease are required.

Prediction of lymphovascular space invasion in patients with endometrial cancer.

Objective: Predict the presence of lymphovascular space invasion (LVSI), using uterine factors such as tumor diameter (TD), grade, and depth of myometrial invasion (MMI). Develop a predictive model that could serve as a marker of LVSI in women with endometrial cancer (EC). Methods: Data from 888 patients with endometrioid EC who were treated between January 2009 and December 2018 were reviewed. The patients' data were retrieved from six institutions. We assessed the differences in the clinicopathological characteristics between patients with and without LVSI. We performed logistic regression analysis to determine which clinicopathological characteristics were the risk factors for positive LVSI status and to estimate the odds ratio (OR) for each covariate. Using the risk factors and OR identified through this process, we created a model that could predict LVSI and analyzed it further using receiver operating characteristic curve analysis. Results: In multivariate logistic regression analysis, tumor size (P = 0.027), percentage of MMI (P < 0.001), and presence of cervical stromal invasion (P = 0.002) were identified as the risk factors for LVSI. Based on the results of multivariate logistic regression analysis, we developed a simplified LVSI prediction model for clinical use. We defined the "LVSI index" as "TD×%MMI×tumor grade×cervical stromal involvement." The area under curve was 0.839 (95% CI= 0.809-0.869; sensitivity, 74.1%; specificity, 80.5%; negative predictive value, 47.3%; positive predictive value, 8.6%; P < 0.001), and the optimal cut-off value was 200. Conclusion: Using the modified risk index of LVSI, it is possible to predict the presence of LVSI in women with endometrioid endometrial cancer. Our prediction model may be an appropriate tool for integration into the clinical decision-making process when assessed either preoperatively or intraoperatively.

A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir.

Understanding the dynamics of harmful algal blooms is important to protect the aquatic ecosystem in regulated rivers and secure human health. In this study, artificial neural network (ANN) and support vector machine (SVM) models were used to predict algae alert levels for the early warning of blooms in a freshwater reservoir. Intensive water-quality, hydrodynamic, and meteorological data were used to train and validate both ANN and SVM models. The Latin-hypercube one-factor-at-a-time (LH-OAT) method and a pattern search algorithm were applied to perform sensitivity analyses for the input variables and to optimize the parameters of the models, respectively. The results indicated that the two models well reproduced the algae alert level based on the time-lag input and output data. In particular, the ANN model showed a better performance than the SVM model, displaying a higher performance value in both training and validation steps. Furthermore, a sampling frequency of 6- and 7-day were determined as efficient early-warning intervals for the freshwater reservoir. Therefore, this study presents an effective early-warning prediction method for algae alert level, which can improve the eutrophication management schemes for freshwater reservoirs.

Pharmacological inhibition of catalase induces peroxisome leakage and suppression of LPS induced inflammatory response in Raw 264.7 cell.

Peroxisomes are metabolically active organelles which are known to exert anti-inflammatory effects especially associated with the synthesis of mediators of inflammation resolution. However, the role of catalase and effects of peroxisome derived reactive oxygen species (ROS) caused by lipid peroxidation through 4-hydroxy-2-nonenal (4-HNE) on lipopolysaccharide (LPS) mediated inflammatory pathway are largely unknown. Here, we show that inhibition of catalase by 3-aminotriazole (3-AT) results in the generation of peroxisomal ROS, which contribute to leaky peroxisomes in RAW264.7 cells. Leaky peroxisomes cause the release of matrix proteins to the cytosol, which are degraded by ubiquitin proteasome system. Furthermore, 3-AT promotes the formation of 4HNE-IκBα adduct which directly interferes with LPS induced NF-κB activation. Even though, a selective degradation of peroxisome matrix proteins and formation of 4HNE- IκBα adduct are not directly related with each other, both of them are could be the consequences of lipid peroxidation occurring at the peroxisome membrane.

Dimethyloxaloylglycine induces pexophagy in a HIF-2α dependent manner involving autophagy receptor p62.

Peroxisomes are metabolically active oxygen demanding organelles with a high abundance of oxidases making it vulnerable to low oxygen levels such as hypoxic conditions. However, the exact mechanism of peroxisome degradation in hypoxic condition remains elusive. In order to study the mechanism of peroxisome degradation in hypoxic condition, we use Dimethyloxaloylglycine (DMOG), a cell-permeable prolyl-4-hydroxylase inhibitor, which mimics hypoxic condition by stabilizing hypoxia-inducible factors. Here we report that DMOG degraded peroxisomes by selectively activating pexophagy in a HIF-2α dependent manner involving autophagy receptor p62. Furthermore, DMOG not only increased peroxisome turnover by pexophagy but also reduced HIF-2α dependent peroxisome proliferation at the transcriptional level. Taken together, our data suggest that hypoxic condition is a negative regulator for peroxisome abundance through increasing pexophagy and decreasing peroxisome proliferation in HIF-2α dependent manner.

Associating the spatial properties of a watershed with downstream Chl-a concentration using spatial analysis and generalized additive models.

We examined the relationship between downstream algal growth potential and the spatial environmental factors of both upland areas and stream buffer zones using spatial analysis and generalized additive models (GAMs). The models employed site-representative concentrations of chlorophyll a (Chl-a) from a total of 688 national water quality monitoring stations and the spatial factors of the corresponding 688 watersheds. The spatial environmental factors included topography, climate, land use class, soil type, and proximity of the monitoring station to the weir downstream and wastewater treatment plants (WWTPs). The explanatory power (adjusted R2 or Radj2) of the models was used to compare different spatial influential scales defined by stream buffers and upstream circular buffers. The spatial environmental factors of the entire watershed area better explained the inter-station variation in Chl-a than did those of the stream buffer and/or upstream circular buffer areas. However, the spatial environmental factors of watershed areas more than 25 km upstream circular buffer zones had only minor influence on the explainability of the models with regards to the inter-station variation in Chl-a levels. Generally, land use patterns were more strongly related to the inter-station Chl-a variation than were point sources of pollutants such as WWTPs. The two most influencing land uses on the inter-station Chl-a variation were urban and agricultural land uses, with varying relative contributions depending on the spatial influential scale: In general relative contribution of urban land use was larger at a larger spatial influential scale while that of agricultural land use showed an opposite trend. In addition, the proximity to the weir downstream explained high Chl-a concentrations in the stream water. Relative importance and causal effects of the spatial environmental variables to instream Chl-a were established based on this national scale correlative analysis, leading to decision-making with the goal of controlling instream algal growth.

Use of coliphages to investigate norovirus contamination in a shellfish growing area in Republic of Korea.

A number of severe norovirus outbreaks due to the consumption of contaminated shellfish have been reported recently. In this study, we evaluated the distribution of coliphage densities to determine their efficacy as fecal indicators of enteric viruses, including noroviruses, in water samples collected from a shellfish growing area in Republic of Korea over a period of approximately 1 year. Male-specific and somatic coliphages in water samples were analyzed using the single agar layer method, and norovirus genogroups I and II, which infect mainly humans, were analyzed using duplex reverse transcription quantitative PCR. Male-specific and somatic coliphages were detected widely throughout the study area. Several environmental parameters, including salinity, precipitation, temperature, and wind speed were significantly correlated with coliphage concentrations (P < 0.05). Moreover, the concentrations of male-specific coliphages were positively correlated with the presence of human noroviruses (r = 0.443; P < 0.01). The geospatial analysis with coliphage concentrations using a geographic information system revealed that densely populated residential areas were the major source of fecal contamination. Our results indicate that coliphage monitoring in water could be a useful approach to prevent norovirus contamination in shellfish.

Co-infection Of Ureaplasma urealyticum And Human Papilloma Virus In Asymptomatic Sexually Active Individuals.

This study aimed to determine the role of asymptomatic bacterial sexually transmitted infections (STIs), such as Chlamydia trachomatis (Ct), Mycoplasma genitalium (Mg), Mycoplasma hominis (Mh), and Ureaplasma urealyticum (Uu) in human papillomavirus (HPV) infection. In total, 264 asymptomatic outpatients aged between 21 and 80 years were prospectively enrolled in this study during routine gynecological screening tests. Specimens collected with a Cervex Brush were routinely analyzed with the Hybrid Capture 2 assay for HPV. Simultaneously, a specimen obtained with an endocervical swab was used to detect Ct and Mg with a monoplex real-time polymerase chain reaction (PCR) and to confirm Mh and Uu with a Mycoplasma IST 2 kit. The detection rates (%) of HPV, Ct, Mg, Mh, and Uu were 82/264 (31.1), 6/264 (2.3), 5/264 (1.9), 16/264 (6.1), and 95/264 (36.0), respectively. Of 95 Uu, 32 (33.7%) showed high density colonization (HDC, ≥104 color-changing units/mL). HDC-Uu was significantly associated with HPV infection (p=0.014, chi-square test). Mg infection and Mh infection were not associated with HPV infection (p=0.981 and p=0.931, chi-square test). Age was not associated with HPV infection or bacterial infection. Our data suggested that asymptomatic HDC-Uu was closely associated with HPV infection. Therefore, simultaneous evaluation for Uu and HPV should be performed during gynecological screening, even in asymptomatic individuals.

Paper-Based Surface-Enhanced Raman Spectroscopy for Diagnosing Prenatal Diseases in Women.

We report the development of a surface-enhanced Raman spectroscopy sensor chip by decorating gold nanoparticles (AuNPs) on ZnO nanorod (ZnO NR) arrays vertically grown on cellulose paper (C). We show that these chips can enhance the Raman signal by 1.25 × 107 with an excellent reproducibility of <6%. We show that we can measure trace amounts of human amniotic fluids of patients with subclinical intra-amniotic infection (IAI) and preterm delivery (PTD) using the chip in combination with a multivariate statistics-derived machine-learning-trained bioclassification method. We can detect the presence of prenatal diseases and identify the types of diseases from amniotic fluids with >92% clinical sensitivity and specificity. Our technology has the potential to be used for the early detection of prenatal diseases and can be adapted for point-of-care applications.

Host-Specific Bacteroides Markers-Based Microbial Source Tracking in Aquaculture Areas.

Various waterborne pathogens originate from human or animal feces and may cause severe gastroenteric outbreaks. Bacteroides spp. that exhibit strong host- or group-specificities are promising markers for identifying fecal sources and their origins. In the present study, 240 water samples were collected from two major aquaculture areas in Republic of Korea over a period of approximately 1 year, and the concentrations and occurrences of four host-specific Bacteroides markers (human, poultry, pig, and ruminant) were evaluated in the study areas. Host-specific Bacteroides markers were detected widely in the study areas, among which the poultry-specific Bacteroides marker was detected at the highest concentration (1.0-1.2 log10 copies L-1). During the sampling period, high concentrations of host-specific Bacteroides markers were detected between September and December 2015. The host-specific Bacteroides marker-combined geospatial map revealed the up-to-downstream gradient of fecal contamination, as well as the effects of land-use patterns on host-specific Bacteroides marker concentrations. In contrast to traditional bacterial indicators, the human-specific Bacteroides marker correlated with human specific pathogens, such as noroviruses (r=0.337; P<0.001). The present results indicate that host-specific Bacteroides genetic markers with an advanced geospatial analysis are useful for tracking fecal sources and associated pathogens in aquaculture areas.

Parity and increased risk of insulin resistance in postmenopausal women: the 2010 Korean National Health and Nutrition Examination Survey.

OBJECTIVE: The objective of this study was to assess the association between parity and insulin resistance in nondiabetic, postmenopausal women. METHODS: This cross-sectional study was conducted using data from the 2010 Korean National Health and Nutrition Examination Survey administered by the Korean Ministry of Health and Welfare. A total of 1,243 nondiabetic postmenopausal women were included in this study and subdivided into three groups according to parity (1-2, 3-4, and ≥5 live births). Insulin resistance was measured using the homeostasis model assessment of insulin resistance (HOMA-IR) index. The relationship between parity and insulin resistance was investigated using analysis of covariance. RESULTS: HOMA-IR showed a positive relationship with parity. Mean HOMA-IR (geometric mean and 95% CI) increased according to increasing parity group (1-2, 3-4, and ≥5 live births) after adjustment for age, smoking, alcohol consumption, exercise, education, income, and body mass index as follows: 2.1 (2.0-2.2) < 2.2 (2.1-2.3) < 2.5 (2.2-2.8) (P = 0.040 and P for trend = 0.012). In addition, this positive association was more apparent when insulin resistance was accompanied by obesity. The mean parity of the obese and insulin-resistant group was significantly higher than that of the nonobese insulin-sensitive group (3.6 ±â€Š0.1 vs 3.2 ±â€Š0.1, P = 0.047). CONCLUSIONS: Our study provides the first evidence that parity is significantly associated with insulin resistance in nondiabetic postmenopausal women. Further prospective longitudinal studies are needed to confirm the impact of parity on insulin resistance.

Potential use of ionic species for identifying source land-uses of stormwater runoff.

Identifying critical land-uses or source areas is important to prioritize resources for cost-effective stormwater management. This study investigated the use of information on ionic composition as a fingerprint to identify the source land-use of stormwater runoff. We used 12 ionic species in stormwater runoff monitored for a total of 20 storm events at five sites with different land-use compositions during the 2012-2014 wet seasons. A stepwise forward discriminant function analysis (DFA) with the jack-knifed cross validation approach was used to select ionic species that better discriminate the land-use of its source. Of the 12 ionic species, 9 species (K+, Mg2+, Na+, NH4+, Br-, Cl-, F-, NO2-, and SO42-) were selected for better performance of the DFA. The DFA successfully differentiated stormwater samples from urban, rural, and construction sites using concentrations of the ionic species (70%, 95%, and 91% of correct classification, respectively). Over 80% of the new data cases were correctly classified by the trained DFA model. When applied to data cases from a mixed land-use catchment and downstream, the DFA model showed the greater impact of urban areas and rural areas respectively in the earlier and later parts of a storm event.

Zinc induces apoptosis on cervical carcinoma cells by p53-dependent and -independent pathway.

OBJECTIVE: There is evidence that the mineral zinc is involved in the apoptotic cell death of various carcinoma cells. In this study, we aim to determine whether zinc in the form of CIZAR induces apoptosis in cervical carcinoma cells by increasing intracellular zinc concentration. STUDY DESIGN: CaSki and HeLa cervical carcinoma cells and HPV-16 DNA-transformed keratinocyte (CRL2404) were treated with different concentrations of CIZAR. The cell viability test was carried out, the intracellular level of zinc was determined, and apoptosis was confirmed by flow cytometry after propidium iodide (PI) staining and fluorescence microscopy under DAPI staining. The expression of cell-cycle regulators was analyzed by Western blot, including the knock down of p53 and expression of HPV E6 and E7 genes by RT-PCR. RESULTS: Intracellular zinc accumulation induced the down-regulation of E6/E7 proteins through targeting of the specific transcriptional factors in the upstream regulatory region. p53 was induced after CIZAR treatment and p53-dependent apoptosis did not occur after knock down by p53 siRNA. In cervical carcinoma cells, regardless of HPV-infection, CIZAR induces apoptosis by the activation of the p53-independent pathways through the up-regulation of p21waf1, the down-regulation of c-Myc, and by decreasing the Bcl-2/Bax ratio. CONCLUSIONS: CIZAR induces apoptosis not only through the restoration of p53/Rb-dependent pathways in HPV-positive cells, but also through the activation of p53/Rb-independent pathways and the mitochondrial death-signal pathway in cervical carcinoma cells regardless of HPV-infection.