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Although it is important in hydrological cycles, catchment water storage dynamics is still not fully understood because it is affected by multiple drivers simultaneously and is difficult to be estimated using field hydrometric observations and hydrological models. Taking the headwater of Baiyang Lake, China as an example, this study employed a spectral approach to illustrate how catchment water storage was influenced by rainfall and vegetation, and how water storage modulated streamflow for the period of 1982–2015. The competence of the spectral approach in characterizing causality was verified and a more holistic understanding of hydrological cycles was gained. Results showed that under different climatic phases (wet/dry), catchment water storage dynamics were controlled by different factors and dominant streamflow generation mechanisms were not invariant. In the wet phase, catchment water storage dynamics was determined by rainfall. And groundwater flow was the most important part of streamflow, followed by subsurface flow and surface flow. Nevertheless, in the dry phase, catchment water storage dynamics was modulated by evapotranspiration. And the surface flow was the most important part of streamflow, followed by subsurface flow and groundwater flow. The land use change induced by human activities could alter the streamflow sensitivity to rainfall, but could not cause fundamental changes to hydrological cycles. We concluded that the spectral approach can be an effective supplement to the experimental methods and their integration can provide systematic insights into hydrological cycles in the study area and other watershed systems.

In the context of climate change and urbanization, urban floods have been one of the major issues around the world, causing significant impacts on the society and environment. To effectively handle these floods, an appropriate design of the urban drainage system (UDS) is highly important as its function can significantly influence the flooding severity and distribution. In recent years, evolutionary algorithms (EAs) have been increasingly used to design UDS due to their great ability in identifying optimal solutions. However, low computational efficiency and low solution practicality (i.e. the final solutions do not satisfy the design criteria) are major challenges for the majority of EA-based methods. To this end, this paper proposes an improved ant colony optimization (ACO, a typical type of EAs) based method to enhance the UDS design effectiveness, where the optimization efficiency is enhanced by initializing the ACO using an approximate design solution identified by the engineering design method, and the solution practicality is improved by explicitly accounting for the design criteria within the optimization using a proposed sampling method. The utility of the proposed method is demonstrated using two real-world UDSs with different system complexities. Results show that the proposed method can identify design solutions with significantly improved efficiency and solution practicality compared to the traditional design approach, with advantages being more prominent for larger UDS design problems. The proposed method can be used by researchers/ practitioners to explore and develop better understanding of the UDS design alternatives under various challenges of climate change and rapid urbanization.

The traditional methods of increasing the chlorine disinfectant dosage in the drinking water distribution system (DWDS) to control microorganisms and improve the safety of drinking water quality are subjected to several challenges. One noticeable problem is the unpleasant odor generated by chlorine and chloramines. However, the generally proposed chlorine dosage optimization model ignores the chloric odor distribution in the DWDS. This study proposes a comprehensive multi-parameter water quality model and aims to balance the trade-offs between: (i) minimize the flavor profile analysis (FPA) degree of the chloric odor produced by chlorine and chloramines in the DWDS, and (ii) minimize the economic investment (chlorine dosage and operation cost). EPANET and back propagation (BP) network integrated with the Borg algorithm were employed as innovative approaches to simulate the chlorine, chloramines, and chloric odor intensity in the DWDS. Moreover, the application of the multi-parameter model was demonstrated in a real-world DWDS case study. 0.5 mg-Cl

Bacterial transcription factors (TFs) are widely studied in Escherichia coli. Yet it remains unclear how individual genes in the underlying pathways of TF machinery operate together during environmental challenge. Here, we address this by applying an unbiased, quantitative synthetic genetic interaction (GI) approach to measure pairwise GIs among all TF genes in E. coli under auxotrophic (rich medium) and prototrophic (minimal medium) static growth conditions. The resulting static and differential GI networks reveal condition-dependent GIs, widespread changes among TF genes in metabolism, and new roles for uncharacterized TFs (yjdC, yneJ, ydiP) as regulators of cell division, putrescine utilization pathway, and cold shock adaptation. Pan-bacterial conservation suggests TF genes with GIs are co-conserved in evolution. Together, our results illuminate the global organization of E. coli TFs, and remodeling of genetic backup systems for TFs under environmental change, which is essential for controlling the bacterial transcriptional regulatory circuits.

Autosomal‐dominant polycystic kidney disease (ADPKD) is characterized by uncontrolled renal cyst formation, and few treatment options are available. There are many parallels between ADPKD and clear‐cell renal cell carcinoma (ccRCC); however, few studies have addressed the mechanisms linking them. In this study, we aimed to investigate their convergences and divergences based on bioinformatics and explore the potential of compounds commonly used in cancer research to be repurposed for ADPKD. We analysed gene expression datasets of ADPKD and ccRCC to identify the common and disease‐specific differentially expressed genes (DEGs). We then mapped them to the Connectivity Map database to identify small molecular compounds with therapeutic potential. A total of 117 significant DEGs were identified, and enrichment analyses results revealed that they are mainly enriched in arachidonic acid metabolism, p53 signalling pathway and metabolic pathways. In addition, 127 ccRCC‐specific up‐regulated genes were identified as related to the survival of patients with cancer. We focused on the compound NS398 as it targeted DEGs and found that it inhibited the proliferation of Pkd1 −/− and 786‐0 cells. Furthermore, its administration curbed cystogenesis in Pkd2 zebrafish and early‐onset Pkd1‐deficient mouse models. In conclusion, NS398 is a potential therapeutic agent for ADPKD.

Fleroxacin (FLE) is an emerging third-generation fluoroquinolone antibacterial agent (FQs) that has been frequently detected in aqueous environments. However, there is a lack of sufficient knowledge regarding the transformation mechanisms of FLE within drinking water distribution systems (DWDSs) when residual chlorine and ClO2 are present. To address this gap, this study makes the first attempt to explore the kinetics, transformation byproducts and toxicity variations during fleroxacin (FLE, an emergent pollutant) degradation by chlorine and ClO2 in a pilot-scale DWDSs. The obtained results show that (i) the FLE degradation rate by chlorine was higher than by ClO2 at pH 7.4 in the pilot-scale DWDSs; (ii) the degradation efficiency of FLE was significantly affected by pH, with FLE degradation by chlorine possessing the highest rate at neutral pH, and the degradation rate was positively correlated with pH (from 6.5 to 9) during the ClO2 disinfection process; (iii) pipe materials can appreciably affect the relative performance of the FLE degradation efficiency by chlorine and ClO2; (iv) seven and eight intermediates are identified during chlorination and ClO2 oxidation, respectively, and the cleavage of the piperazine group was the committed step and the main oxidation reaction, and (v) the toxicity assessment demonstrates that the toxicity of FLE chlorination and ClO2 are both higher than the blank experiment, and ClO2 disinfection can reduce the potential risk compared to the chlorine disinfection.

Chlorine is widely used as a drinking water disinfectant to ensure water security. However, the transformation mechanisms of its degradation of emerging pollutants within the water distribution system (WDS) is insufficiently understood. Thus, the kinetics, degradation byproducts, and toxicity of the chlorination of enoxacin (ENO, a type of emerging pollutant) were explored in a pilot-scale WDS for the first time. It was found that the chlorination rate of ENO was higher in deionized water (DW) than in the pilot-scale WDS, and the degradation followed second-order kinetics in DW. The degradation efficiency was found to be sensitive to pH, and was highest at a pH of 7.4. The chlorination rate of ENO increased with increasing temperature in both DW and WDS. For different pipe materials, the relative performance of ENO chlorination efficiency followed the order of steel pipe > ductile iron pipe > polyethylene (PE) pipe. Seven intermediates were identified during ENO chlorination, and the primary oxidation reaction involved the cleavage of the piperazine group. Finally, it was found that the potential for chlorine toxicity in treated drinking water in the presence of ENO is higher than it is without this pollutant.

Hydraulic modeling of a foul sewer system (FSS) enables a better understanding of the behavior of the system and its effective management. However, there is generally a lack of sufficient field measurement data for FSS model development due to the low number of in-situ sensors for data collection. To this end, this study proposes a new method to develop FSS models based on geotagged information and water consumption data from smart water meters that are readily available. Within the proposed method, each sewer manhole is firstly associated with a particular population whose size is estimated from geotagged data. Subsequently, a two-stage optimization framework is developed to identify daily time-series inflows for each manhole based on physical connections between manholes and population as well as sewer sensor observations. Finally, a new uncertainty analysis method is developed by mapping the probability distributions of water consumption captured by smart meters to the stochastic variations of wastewater discharges. Two real-world FSSs are used to demonstrate the effectiveness of the proposed method. Results show that the proposed method can significantly outperform the traditional FSS model development approach in accurately simulating the values and uncertainty ranges of FSS hydraulic variables (manhole water depths and sewer flows). The proposed method is promising due to the easy availability of geotagged information as well as water consumption data from smart water meters in near future.

Enoxacin (ENO) is widespread in water because it is commonly used as a human and veterinary antibiotic. However, little effort has been dedicated to revealing the transformation mechanisms of ENO destruction using ClO

This paper proposes a multistage method for burst leak localization through valve operations (VOs) and smart demand metering in district meter areas (DMAs) of water distribution systems (WDSs). Each stage includes partitioning of the DMA into two subregions using VOs and identification of potentially leaking pipes within the subregions through water balance analysis based on smart demand meters. Such a process is performed repeatedly (multiple stages) to narrow down the spatial range for pinpointing leak locations. To improve efficiency, a bisection optimization problem is formulated to localize the minimum leak areas using the lowest number of VOs, which is solved by a graph theory-based method. The utility of the proposed method is demonstrated using two DMAs (DMA1 and DMA2) of a real WDS with different topological properties. Results show that the proposed method can efficiently localize artificial burst leaks in DMA1 within 7–15% of the total pipe length, implying that the proposed method is theoretically effective in localizing pipe burst leaks. The real application to DMA2 has identified two leak regions with 2.3 and 4.2 km of pipe length (around 3–6% of the entire DMA2) using 18 VOs. These two burst leaks have been subsequently confirmed and pinpointed using listening rods by practitioners of the local water utility. These results indicate that the proposed multistage method is effective and efficient for burst leak localization, which can be promising for wide practical applications due to rapid developments of smart WDSs (e.g., smart demand meters or control valves).

Urban water distribution systems (WDSs) are often skeletonized to enable efficient system analysis and management. While different methods are available to account for transient dynamics wi...

This case study uses a long short-term memory (LSTM)-based model to predict short-term urban water demands for the Hefei City of China. The performance of the LSTM-based model is compared with the autoregressive integrated moving average (ARIMA) model, the support vector regression (SVR) model, and the random forests (RF) model based on data with time resolutions ranging from 15 min to 24 h. Additionally, this paper investigates the performance of the LSTM-based model in predicting multiple successive data points. Results show that the LSTM-based model can offer predictions with improved accuracy than the other models when dealing with data with high time resolutions, data points with abrupt changes, and data with a relatively high uncertainty level. It is also observed that the LSTM-based model exhibits the best performance in predicting multiple successive water demands with high time resolutions. In addition, the inclusion of external parameters (e.g., temperature) cannot enhance the performance of the LSTM-based model, but it can improve ARIMAX's prediction ability (ARIMAX is the ARIMA with variables). These observations provide additional and improved evaluations regarding the LSTM-based models used for short-term urban water demand forecasting, thereby enabling their wider adoption in practical applications.

Real-time hydraulic modelling can be used to address a wide range of issues in a foul sewer system and hence can help improve its daily operation and maintenance. However, the current bottleneck within real-time FSS modelling is the lack of spatio-temporal inflow data. To address the problem, this paper proposes a new method to develop real-time FSS models driven by water consumption data from associated water distribution systems (WDSs) as they often have a proportionally larger number of sensors. Within the proposed method, the relationship between FSS manholes and WDS water consumption nodes are determined based on their underlying physical connections. An optimization approach is subsequently proposed to identify the transfer factor k between nodal water consumption and FSS manhole inflows based on historical observations. These identified k values combined with the acquired real-time nodal water consumption data drive the FSS real-time modelling. The proposed method is applied to two real FSSs. The results obtained show that it can produce simulated sewer flows and manhole water depths matching well with observations at the monitoring locations. The proposed method achieved high R2, NSE and KGE (Kling-Gupta efficiency) values of 0.99, 0.88 and 0.92 respectively. It is anticipated that real-time models developed by the proposed method can be used for improved FSS management and operation.

With the possible climate change and increased pace of urbanization in the century, urban flooding has caused more and more attentions nowadays. Shallow water equations are widely used to reproduce the flow hydrodynamics of flooding around the urban areas, which have been proved a powerful tool for flood risk assessment and evacuation management, like river flow or flowing at drainage networks with irregular cross-sections at 1D scale. Over the last two decades, Godunov-type schemes have became popular for its robustness treating complex flow phenomenons. When tacking complex topography in the framework of Godunov-type scheme, sourer term needs to be treated property to preserve steady state, that flux gradient and sourer term are balanced. Capart et al. (2003) reconstructed the momentum flux by considering the balance of hydrostatic pressure with the approximated water surface level, which has the ability to tackle the irregular and non-prismatic channel flow with complex topography. This approximation is exact for two cases: 1) rectangular and prismatic channel; 2) water surface is horizontal. However, for other cases, approximation is employed to achieve the hydrostatic equilibrium, which has reduced the accuracy of the numerical solution and increased the complexity for the model implementation. In this work, we present a new well-balanced numerical scheme for simulating 1D frictional shallow water flow with irregular cross-sections over complex topography involving wetting and drying. The proposed scheme solves, in a finite volume Godunov-type framework, a set of pre-balanced shallow water equations derived by considering pressure balancing (Liang and Marche, 2009). HLL approximated Riemann solver is adopted for the flux calculation at the cell interface. Non-negative reconstruction of Riemann state (Audusse et al., 2004) and local bed modification (Liang, 2010) produce stable and well-balanced solutions to shallow water flow hydrodynamics. Bed slope source term can be approximated using central difference and no special treatment is needed for wet and dry bed. The friction source term is discretized using a splitting implicit scheme and limiting value of friction force is used to ensure stability for the dry bottom (Liang and Marche, 2009). The new numerical scheme is validated against two theoretical benchmark tests and then compared with the validated shallow water model with circular and trapezoid cross-sections over complex topography involving wetting and drying. This method is also possible to reproduce the mixed flow in the conduit or for the flow with non-prismatic channel like river flow in the near future.ReferencesAudusse, E., Bouchut, F., Bristeau, M. O., Klein, R., & Perthame, B. T. (2004). A fast and stable well-balanced scheme with hydrostatic reconstruction for shallow water flows. SIAM Journal on Scientific Computing, 25(6), 2050-2065.Capart, H, Eldho, TI, Huang, SY, Young, DL, and Zech, Yves, "Treatment of natural geometry in finite volume river flow computations", Journal of Hydraulic Engineering 129, 5 (2003), pp. 385--393.Liang, Qiuhua and Marche, Fabien, "Numerical resolution of well-balanced shallow water equations with complex source terms", Advances in water resources 32, 6 (2009), pp. 873--884.Liang, Qiuhua, "Flood simulation using a well-balanced shallow flow model", Journal of hydraulic engineering 136, 9 (2010), pp. 669--675.

Computational efficiency and accuracy of transient analysis for urban water distribution networks (WDN) become progressively important to the design and management of the system. In addition to the improvement of numerical model and computational capacity, which has been widely studied in the literature, efficient and accurate treatment of practical and complex WDN is another potential way to enhance the transient analysis. This paper aims to develop a zonal method for effective decomposition of WDN, which is mainly based on the transient sources and their influence regions in the system, in order to achieve efficient transient analysis. A concept of transient influence zone (TIZ) is firstly proposed and implemented to demonstrate the critical influence region of transient wave propagation in the system under specific design criteria. The obtained TIZ for each transient source is then mapped by introducing appropriate and equivalent boundaries so as to separate the TIZ from the entire WDN. To this end, the efficient Lagrangian model for prior-estimating pressure fluctuation extremes, the pressure fluctuation limitation for mapping TIZ borders and the quasi-reservoir condition for representing border boundaries are applied for characterizing the TIZs. A realistic network is adopted to demonstrate the applicability and accuracy of the proposed method. The application results and analysis indicate that the developed TIZ-based decomposition method provides a considerable efficiency improvement for transient analysis with sufficient modeling accuracy.

International audience; The paper presents the results of the Battle of Postdisaster Response and Restoration (BPDRR) presented in a special session at the first International water distribution systems analysis & computing and control in the water industry (WDSA/CCWI) Joint Conference, held in Kingston, Ontario, Canada, in July 2018. The BPDRR problem focused on how to respond and restore water service after the occurrence of five earthquake scenarios that cause structural damage in a water distribution system. Participants were required to propose a prioritization schedule to fix the damages of each scenario while following restrictions on visibility/nonvisibility of damages. Each team/approach was evaluated against six performance criteria: (1)time without supply for hospital/firefighting, (2)rapidity of recovery, (3)resilience loss, (4)average time of no user service, (5)number of users without service for eight consecutive hours, and (6)water loss. Three main types of approaches were identified from the submissions: (1)general-purpose metaheuristic algorithms, (2)greedy algorithms, and (3)ranking-based prioritizations. All three approaches showed potential to solve the challenge efficiently. The results of the participants showed that for this network, the impact of a large-diameter pipe failure on the network is more significant than several smaller pipes failures. The location of isolation valves and the size of hydraulic segments influenced the resilience of the system during emergencies. On average, the interruptions to water supply (hospitals and firefighting) varied considerably among solutions and emergency scenarios, highlighting the importance of private water storage for emergencies. The effects of damages and repair work were more noticeable during the peak demand periods (morning and noontime) than during the low-flow periods; and tank storage helped to preserve functionality of the network in the first few hours after a simulated event.

Water quality sensors are often spatially distributed in water distribution systems (WDSs) to detect contamination events and monitor quality parameters (e.g., chlorine residual levels), thereby ensuring safety of a WDS. The performance of a water quality sensor placement strategy (WQSPS) is not only affected by sensor spatial deployment that has been extensively analyzed in literature, but also by possible sensor failures that have been rarely explored so far. However, enumerating all possible sensor failure scenarios is computationally infeasible for a WQSPS with a large number of sensors. To this end, this paper proposes an evolutionary algorithm (EA) based method to systematically and efficiently investigate the WQSPS′ global resilience considering all likely sensor failures. First, new metrics are developed in the proposed method to assess the global resilience of a WQSPS. This is followed by a proposal of an efficient optimization approach based on an EA to identify the values of global resilience metrics. Finally, the sensors within the WQSPS are ranked to identify their relative importance in maintaining the WQSPS's detection performance. Two real-world WDSs with four WQSPSs for each case study are used to demonstrate the utility of the proposed method. Results show that: (i) compared to the traditional global resilience analysis method, the proposed EA-based approach identifies improved values of global resilience metrics, (ii) the WQSPSs that deploy sensors close to large demand users are overall more resilient in handling sensor failures relative to other design solutions, thus offering important insight to facilitate the selection of WQSPSs, and (iii) sensor rankings based on the global resilience can identify those sensors whose failure would significantly reduce the WQSPS's performance thereby providing guidance to enable effective water quality sensor management and maintenance.

Mitochondria (mt) are double-membraned, dynamic organelles that play an essential role in a large number of cellular processes, and impairments in mt function have emerged as a causative factor for a growing number of human disorders. Given that most biological functions are driven by physical associations between proteins, the first step towards understanding mt dysfunction is to map its protein-protein interaction (PPI) network in a comprehensive and systematic fashion. While mass-spectrometry (MS) based approaches possess the high sensitivity ideal for such an endeavor, it also requires stringent biochemical purification of bait proteins to avoid detecting spurious, non-specific PPIs. Here, we outline a tagging-based affinity purification coupled with mass spectrometry (AP-MS) workflow for discovering new mt protein associations and providing novel insights into their role in mt biology and human physiology/pathology. Because AP-MS relies on the creation of proteins fused with affinity tags, we employ a versatile-affinity (VA) tag, consisting of 3× FLAG, 6 × His, and Strep III epitopes. For efficient delivery of affinity-tagged open reading frames (ORF) into mammalian cells, the VA-tag is cloned onto a specific ORF using Gateway recombinant cloning, and the resulting expression vector is stably introduced in target cells using lentiviral transduction. In this chapter, we show a functional workflow for mapping the mt interactome that includes tagging, stable transduction, selection and expansion of mammalian cell lines, mt extraction, identification of interacting protein partners by AP-MS, and lastly, computational assessment of protein complexes/PPI networks.

Thiol–ene reactions have been used in a variety of applications that mostly involve an inter-molecular pathway. Herein, we report a facile method to construct thioether-tethered cyclic peptides via an intra-molecular thiol–ene reaction. This reaction is efficient, selective, and has good residue compatibility. Short peptides with thioether tethers were constructed and were used to construct longer cyclic peptides. This synthetic method may be useful for constructing bioactive peptides. Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd.

Mitochondria (mt) are double-membraned, dynamic organelles that play an essential role in a large number of cellular processes, and impairments in mt function have emerged as a causative factor for a growing number of human disorders. Given that most biological functions are driven by physical associations between proteins, the first step towards understanding mt dysfunction is to map its protein-protein interaction (PPI) network in a comprehensive and systematic fashion. While mass-spectrometry (MS) based approaches possess the high sensitivity ideal for such an endeavor, it also requires stringent biochemical purification of bait proteins to avoid detecting spurious, non-specific PPIs. Here, we outline a tagging-based affinity purification coupled with mass spectrometry (AP-MS) workflow for discovering new mt protein associations and providing novel insights into their role in mt biology and human physiology/pathology. Because AP-MS relies on the creation of proteins fused with affinity tags, we employ a versatile-affinity (VA) tag, consisting of 3× FLAG, 6 × His, and Strep III epitopes. For efficient delivery of affinity-tagged open reading frames (ORF) into mammalian cells, the VA-tag is cloned onto a specific ORF using Gateway recombinant cloning, and the resulting expression vector is stably introduced in target cells using lentiviral transduction. In this chapter, we show a functional workflow for mapping the mt interactome that includes tagging, stable transduction, selection and expansion of mammalian cell lines, mt extraction, identification of interacting protein partners by AP-MS, and lastly, computational assessment of protein complexes/PPI networks.

Summary Mitochondrial protein (MP) assemblies undergo alterations during neurogenesis, a complex process vital in brain homeostasis and disease. Yet which MP assemblies remodel during differentiation remains unclear. Here, using mass spectrometry-based co-fractionation profiles and phosphoproteomics, we generated mitochondrial interaction maps of human pluripotent embryonal carcinoma stem cells and differentiated neuronal-like cells, which presented as two discrete cell populations by single-cell RNA sequencing. The resulting networks, encompassing 6,442 high-quality associations among 600 MPs, revealed widespread changes in mitochondrial interactions and site-specific phosphorylation during neuronal differentiation. By leveraging the networks, we show the orphan C20orf24 as a respirasome assembly factor whose disruption markedly reduces respiratory chain activity in patients deficient in complex IV. We also find that a heme-containing neurotrophic factor, neuron-derived neurotrophic factor [NENF], couples with Parkinson disease-related proteins to promote neurotrophic activity. Our results provide insights into the dynamic reorganization of mitochondrial networks during neuronal differentiation and highlights mechanisms for MPs in respirasome, neuronal function, and mitochondrial diseases., Graphical Abstract, Highlights • Rewiring of mitochondrial (mt) protein interaction network in distinct cell states • Dramatic changes in site-specific phosphorylation during neuronal differentiation • C20orf24 is a respirasome assembly factor depleted in patients deficient in CIV • NENF binding with DJ-1/PINK1 promotes neurotrophic activity and neuronal survival, Biological Sciences; Developmental Neuroscience; Developmental Biology; Proteomics