Integrated simulation-surrogate-optimization modeling framework for multiple tradeoffs among socioeconomic and ecological targets in reservoir operations.

Integrated reservoir water quantity and quality management is significant for water supply security and river ecosystem health. However, the spatiotemporal heterogeneity of water quality and the nonuniform response of multiple indicators to operation changes make it difficult to determine optimal operation schedules. This study proposes a coupled simulation-surrogate-optimization modeling approach for compromising multiple water quantity and quality targets in reservoir operations. The Environmental Fluid Dynamics Code (EFDC) was used to simulate spatiotemporal reservoir water quality dynamics. Subsequently, an ecological damage assessment method was established, accounting for the spatiotemporal heterogeneity of multiple water quality indicators and the nonlinear relationship between the water quality deterioration and ecological damage. To quickly simulate the ecological damage, a surrogate model was developed using the nonlinear autoregressive network with exogenous inputs (NARX). Finally, the surrogate model was integrated into a reservoir operation optimization model for compromising socioeconomic and ecological targets. By applying the methods to China's Danjiangkou Reservoir as a case, it was shown that more even nutrient distribution in the reservoir increased water eutrophication area while reducing concentration peak values, which helped decrease the ecological damage. Operation changes could lead to opposite effects on in-reservoir and downstream ecological targets, increasing operation optimization complexity. Both ecological and socioeconomic benefits significantly increased (by 9.4%-16.4%) during dry years under the optimized operation scheme, implying that synergies were obtained. This study offers implications and a management tool for reservoir operations to address the multiple tradeoffs among socioeconomic and ecological benefits.

Identification of male schizophrenia patients using brain morphology based on machine learning algorithms.

BACKGROUND: Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task. AIM: To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls. METHODS: The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886. CONCLUSION: Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.

Investigation of peripheral inflammatory biomarkers in association with violence in schizophrenia.

BACKGROUND: Violent behavior carried out by patients with schizophrenia (SCZ) is a public health issue of increasing importance that may involve inflammation. Peripheral inflammatory biomarkers, such as the systemic immune inflammation index (SII), the neutrophil lymphocyte ratio (NLR), the platelet-lymphocyte ratio (PLR) and the monocyte lymphocyte ratio (MLR) are objective, easily accessible and cost-effective measures of inflammation. However, there are sparse studies investigating the role of peripheral inflammatory biomarkers in violence of patients with SCZ. METHODS: 160 inpatients diagnosed with SCZ between January and December 2022 were recruited into this study. Violent behavior and positive symptoms of all participants were evaluated using Modified Overt Aggression Scale (MOAS) and Positive and Negative Syndrome Scale (PANSS), respectively. The partial correlation analysis was performed to examine the relationship of inflammatory indices and positive symptoms. Based on machine learning (ML) algorithms, these different inflammatory indices between groups were used to develop predictive models for violence in SCZ patients. RESULTS: After controlling for age, SII, NLR, MLR and PANSS positive scores were found to be increased in SCZ patients with violence, compared to patients without violence. SII, NLR and MLR were positively related to positive symptoms in all participants. Positive symptoms partially mediated the effects of peripheral inflammatory indices on violent behavior in SCZ. Among seven ML algorithms, penalized discriminant analysis (pda) had the best performance, with its an area under the receiver operator characteristic curve (AUC) being 0.7082. Subsequently, with the use of pda, we developed predictive models using four inflammatory indices, respectively. SII had the best performance and its AUC was 0.6613. CONCLUSIONS: These findings suggest that inflammation is involved in violent behavior of SCZ patients and positive symptoms partially mediate this association. The models built by peripheral inflammatory indices have a good median performance in predicting violent behavior in SCZ patients.

Heterogeneous distribution of carbofuran shaped by Pseudomonas stutzeri biofilms enhances biodegradation of agrochemicals.

Biodegradation, harnessing the metabolic versatility of microorganisms to reduce agrochemical contaminations, is commonly studied with enriched planktonic cells but overlooking the dominant lifestyle of microorganisms is to form biofilms, which compromises the efficiency of biodegradation in natural environment. Here, we employed a carbofuran-degrading bacterium Pseudomonas stutzeri PS21 to investigate how the bacterial biofilms formed and responded to agrochemicals. First, the PS21 biofilms formed with a core of bacterial cells enclosing with extracellular polymeric substances (EPSs), and the biofilms were active and resilient when exposed to carbofuran (up to 50 mg L-1). The formation was regulated by the second messenger bis-(3'-5')-cyclic di-guanosine monophosphate signaling, which strengthened the structural resistance and metabolic basis of biofilms to remain the degrading efficiency as comparable as the planktonic cells. Second, carbofuran distributed heterogeneously in the near-biofilm microenvironment via the covalent adsorption of biofilms, which provided a spontaneous force that enhanced the combination of carbofuran with biofilms to maintain high degrading activity. Additionally, we elucidated the biodegradation was driven by the integrated metabolic system of biofilms involving the extracellular enzymes located in the EPSs. This study exhibited the structural and metabolic advantages of microbial biofilms, highlighting the attractive potentials of exploring biofilm-based strategies to facilitate the in-situ bioremediation of organic contaminations.

Prediction of violence in male schizophrenia using sMRI, based on machine learning algorithms.

BACKGROUND: Violent behavior in patients with schizophrenia (SCZ) is a major social problem. The early identification of SCZ patients with violence can facilitate implementation of targeted intervention. METHODS: A total of 57 male SCZ patients were recruited into this study. The general linear model was utilized to compare differences in structural magnetic resonance imaging (sMRI) including gray matter volume, cortical surface area, and cortical thickness between 30 SCZ patients who had exhibited violence and 27 SCZ patients without a history of violence. Based on machine learning algorithms, the different sMRI features between groups were integrated into the models for prediction of violence in SCZ patients. RESULTS: After controlling for the whole brain volume and age, the general linear model showed significant reductions in right bankssts thickness, inferior parietal thickness as well as left frontal pole volume in the patients with SCZ and violence relative to those without violence. Among seven machine learning algorithms, Support Vector Machine (SVM) have better performance in differentiating patients with violence from those without violence, with its balanced accuracy and area under curve (AUC) reaching 0.8231 and 0.841, respectively. CONCLUSIONS: Patients with SCZ who had a history of violence displayed reduced cortical thickness and volume in several brain regions. Based on machine learning algorithms, structural MRI features are useful to improve predictive ability of SCZ patients at particular risk of violence.

The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms.

Background: Early to identify male schizophrenia patients with violence is important for the performance of targeted measures and closer monitoring, but it is difficult to use conventional risk factors. This study is aimed to employ machine learning (ML) algorithms combined with routine data to predict violent behavior among male schizophrenia patients. Moreover, the identified best model might be utilized to calculate the probability of an individual committing violence. Method: We enrolled a total of 397 male schizophrenia patients and randomly stratified them into the training set and the testing set, in a 7:3 ratio. We used eight ML algorithms to develop the predictive models. The main variables as input features selected by the least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were integrated into prediction models for violence among male schizophrenia patients. In the training set, 10 × 10-fold cross-validation was conducted to adjust the parameters. In the testing set, we evaluated and compared the predictive performance of eight ML algorithms in terms of area under the curve (AUC) for the receiver operating characteristic curve. Result: Our results showed the prevalence of violence among male schizophrenia patients was 36.8%. The LASSO and LR identified main risk factors for violent behavior in patients with schizophrenia integrated into the predictive models, including lower education level [0.556 (0.378-0.816)], having cigarette smoking [2.121 (1.191-3.779)], higher positive syndrome [1.016 (1.002-1.031)] and higher social disability screening schedule (SDSS) [1.081 (1.026-1.139)]. The Neural Net (nnet) with an AUC of 0.6673 (0.5599-0.7748) had better prediction ability than that of other algorithms. Conclusion: ML algorithms are useful in early identifying male schizophrenia patients with violence and helping clinicians take preventive measures.

Correction: Trail Resistance Induces Epithelial-Mesenchymal Transition and Enhances Invasiveness by Suppressing PTEN via miR-221 in Breast Cancer.

[This corrects the article DOI: 10.1371/journal.pone.0099067.].

Radioactive Iodine Therapy in Patients with Differentiated Thyroid Cancer: Study of External Dose Rate Attenuation Law and Individualized Patient Management.

BACKGROUND: Therapy with radioactive iodine (131I) is a well established treatment method for postsurgical differentiated thyroid carcinoma (DTC). A fixed discharge time is generally set, regardless of individual differences in residual body radioactivity (RBA). This study aimed to investigate the RBA of each patient to find the attenuation law and to identify underlying factors in order to predict the time point for a safe, scientifically sound discharge plan. METHODS: A total of 231 DTC patients undergoing 131I treatment were all treated with 3.7 GBq (100 mCi) of 131I. RBA was estimated by measuring the external body dose rate (EDR) at a distance of 1 m from the body surface between 0 and 72 hours after oral administration of 131I. Data from each patient were used to establish a time-EDR value (h-µSv/h) curve. Software was developed to predict the time when a patient's dose equivalent meets the national safety standard by including six time points between 40 and 60 hours. Several factors that might affect that time were analyzed. RESULTS: The EDR attenuation law in patients could be described with a double exponential decay model, and the cutoff value was set as 23.3 µSv/h, upon which the predictive software was developed. Student's t-test showed there was no statistical difference between predicted values and the actual measured values (p > 0.05). Correlation analysis found that serum thyroglobulin, total triiodothyronine, total thyroxine, free triiodothyronine, free thyroxine, thyrotropin, 2- and 24-hour iodine uptake rate of the thyroid, scores of 99mTc-pertechnetate thyroid scan, scores of 131I whole-body scan, scores of ultrasound scan, and gastrointestinal residues were associated with attenuation speed. A further multiple linear regression analysis found that 24-hour iodine uptake (X1), residual thyroid grading by 131I whole-body scan (X2), blood free triiodothyronine (X3) and free thyroxine (X4) predominantly influenced the decline of the EDR. The regression equation was Y = 2.091X1 + 6.370X2 + 4.529X3 + 2.466X4 - 8.614 (F = 44.03, p < 0.01). CONCLUSIONS: An effective and convenient method was created to measure and predict the individual safety time for discharge. This could play a significant role not only for scientific hospital discharge planning, rational use of medical resources, and better individualized management, but also in public radiation protection.

Clinical significance of FAP-α on microvessel and lymphatic vessel density in lung squamous cell carcinoma.

AIMS: We aimed to determine whether cancer-associated fibroblasts (CAFs) are associated with microvessel density (MVD) and lymphatic vessel density (LVD) in lung squamous cell carcinoma, as well as their clinical significance in predicting survival. METHODS: 122 patients were enrolled in the study. Samples were obtained on resection at the Department of Thoracic Surgery of the Qingdao Municipal Hospital between January 2011 and December 2014. Immunohistochemistry was used to determine vessel and lymphatic vessel density, and CAF abundance (fibroblast activation protein α (FAP-α) positivity). Statistical analyses were performed on 85 patients to test for correlation of CAF density and other clinicopathological variables with 3-year overall survival (OS) and disease-free survival (DFS). RESULTS: High stromal CAF abundance significantly correlated with increased MVD and LVD in lung squamous cell carcinoma (p<0.05). χ2 test revealed a significant association of CAF density with lymph node metastasis. Cox proportional hazards model showed that both higher CAF density and lymph node metastasis negatively correlate with survival. CAF density or lymph node status can be used as an independent prognostic factor to predict 3-year OS and DFS. CONCLUSIONS: CAF density, identified by FAP-α staining pattern, should be considered as a novel biomarker for disease prognosis in patients with lung squamous cell carcinoma.

Trail resistance induces epithelial-mesenchymal transition and enhances invasiveness by suppressing PTEN via miR-221 in breast cancer.

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) can selectively induce apoptosis of cancer cells and is verified effective to various cancers. However, a variety of breast cancer cell lines are resistant to TRAIL and the mechanisms of resistance are largely unknown. In our present experiment, we successfully utilized breast cancer cell line MDA-MB-231 to establish TRAIL-resistant cell line. We found resistance to TRAIL could induce epithelial-mesenchymal transition (EMT) and enhance invasiveness. We further demonstrated PTEN was down-regulated in TRAIL-resistant cells. Silencing miR-221, PTEN expression was up-regulated, the process of EMT could be reversed, and the ability of migration and invasion were correspondingly weakened. We also demonstrated knockdown of miR-221 could reverse resistance to TRAIL partially by targeting PTEN. Our findings suggest that resistance to TRAIL could induce EMT and enhance invasiveness by suppressing PTEN via miR-221. Re-expression of miR-221 or targeting PTEN might serve as potential therapeutic approaches for the treatment of Trail-resistant breast cancer.

MTDH mediates estrogen-independent growth and tamoxifen resistance by down-regulating PTEN in MCF-7 breast cancer cells.

BACKGROUND: About 70% of human breast cancers express estrogen receptor α (ERα) and in this kind of breast cancer estrogen plays an important role. Estrogen independent growth has been reported to promote resistance to one of the selective estrogen receptor modulators (SERMs) tamoxifen which is clinically the first line treatment for patients with ERα-positive breast cancer. The resistance of tamoxifen is a major problem in the clinical management of breast cancer. METHODS: We used MCF-7 cells with ectopic expression of MDTH in this study. MTT, clone formation and tumor formation in nude mice methods were utilized to confirm the role of MTDH in estrogen-independent growth and tamoxifen resistance. Flow cytometry, western blot and siRNA were used to study the detailed mechanisms. RESULTS: We found that MTDH could mediate estrogen-independent growth and induce resistance to tamoxifen in ERα-positive breast cancer cells. MTDH could reduce the expression of PTEN, up-regulate AKT and BCL2 and inhibit the apoptosis induced by tamoxifen. CONCLUSION: Our study indicated that MTDH was a candidate marker to predict the clinical efficacy of tamoxifen and targeting MTDH would overcome the resistance to tamoxifen in breast cancer cells.