Multisite Model Research Articles

Microbiome Transmission During Sexual Intercourse Appears Stochastic and Supports the Red Queen Hypothesis.

Microbes inhabit virtually everywhere on and/or in our bodies, including the seminal and vaginal fluids. They have significant importance in maintaining reproductive health and protecting hosts from diseases. The exchange of microbes during sexual intercourse is one of the most direct and significant microbial transmissions between men and women. Nevertheless, the mechanism of this microbial transmission was little known. Is the transmission mode stochastic, passive diffusion similar to the random walk of particles, or driven by some deterministic forces? What is the microbial transmission probability? What are the possible evolutionary implications, particularly from the perspective of sexual reproduction (selection)? We tackle these intriguing questions by leveraging the power of Hubbell’s unified neutral theory of biodiversity, specifically implemented as the HDP-MSN (hierarchical Dirichlet process approximated multi-site neutral model), which allows for constructing truly multi-site metacommunity models, simultaneously including vaginal and semen microbiomes. By reanalyzing the microbiome datasets of seminal and vaginal fluids from 23 couples both before and after sexual intercourses originally reported by Mändar and colleagues, we found that the microbial transmission between seminal and vaginal fluids is a stochastic, passive diffusion similar to the random walk of particles in physics, rather than driven by deterministic forces. The transmission probability through sexual intercourse seems to be approximately 0.05. Inspired by the results from the HDP-MSN model, we further conjecture that the stochastic drifts of microbiome transmissions during sexual intercourses can be responsible for the homogeneity between semen and vaginal microbiomes first identified in a previous study, which should be helpful for sexual reproduction by facilitating the sperm movement/survival and/or egg fertilization. This inference seems to be consistent with the classic Red Queen hypothesis, which, when extended to the co-evolutionary interactions between humans and their symbiotic microbiomes, would predict that the reproductive system microbiomes should support sexual reproduction.

Multi-site calibration of hydrological model and the response of water balance components to land use land cover change in a rift valley Lake Basin in Ethiopia

This study aims to perform multi-site calibration and validation of SWAT model using streamflow data, and investigating the responses of water balance components to land use land cover (LULC) change in the Gidabo river sub-basin of rift valley lake basin in southern part of Ethiopia. SUFI-2 algorithm embedded in the SWAT-CUP was employed for sensitivity analysis, calibration, and validation on a monthly basis. The 17 years (1990–2006) streamflow data for three (Aposto, Bedessa, and Measso) gauging stations were used for calibrating the model while 8 years (2007–2014) streamflow data was used for validating the model. The calibrated and validated SWAT model was then used to investigate the response of water balance components to LULC change for three periods (1990, 2005, and 2019) which were performed using ERDAS Imagine 2014 with a maximum likelihood classifier. The most common statistical model performance evaluation indices namely Coefficient of Determination (R2), Nash-Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS) were used to evaluate performance of the SWAT model in simulating sub-basin hydrology; in addition to physical inspection of observed and simulated streamflow hydrograph. The findings of the multi-site model performance evaluation indicated that the values of R2 ranged from 0.80 to 0.64 and 0.74 to 0.72 during calibration and validation periods respectively. The values of NSE ranged from 0.74 to 0.61 and 0.71 to 0.65 during calibration and validation periods respectively whereas PBIAS ranged from 19.70 to -3.20 and 18.10 to 0.80 during calibration and validation periods respectively. The calibration and validation results indicated that the SWAT model would simulate fairly well the historical streamflow at three gauging stations. The mean annual streamflow response to LULC change for the periods 1990–2005 and 2005–2019 was observed to increase by 2.13% (1.16m3/s) and 3.62% (2.04m3/s), respectively and the mean seasonal streamflow was obtained to increase during wet season (April-September) while decreasing trend was observed during dry season (October-March) in all three gauged stations. Results also revealed that there were significant spatiotemporal variations of surface runoff, groundwater flow, lateral flow, and evapotranspiration in the sub-basin. The multi-site calibration and validation together with uncertainty analysis detects spatial variability and simulates the water balance components under changing LULC, which is paramount importance for planning and formulating appropriate integrated land and water resources management and development strategies in the rift valley lake basin of Gidabo river sub-basin in Ethiopia.

Stochastic neutral drifts seem prevalent in driving human virome assembly: Neutral, near-neutral and non-neutral theoretic analyses

It is estimated that human body is inhabited by approximately 380 trillions of viruses, which exist in the form of viral communities and are collectively termed as human virome. How virome is assembled and what kind of forces maintain the composition and diversity of viral communities is still an open question. The question is of obvious importance because of its implications to human health and diseases. Here we address the question by harnessing the power of Hubbell’s unified neutral theory of biodiversity (UNTB) in terms of three neutral models including standard Hubbell’s neutral model (HNM), Sloan’s near-neutral model (SNM) and Harris et al. (2017) multi-site neutral model (MSN), further augmented by Ning et al. (2019) normalized stochasticity ratio (NSR) and Hammal et al. (2015) power analysis for the neutral test (PNT). With the five models applied to 179 virome samples, we aim to obtain robust findings given both Type-I and Type-II errors are addressed and possible alternative, non-neutral processes are detected. It was found that stochastic neutral drifts seem prevalent: approximately 65–92% at metacommunity/landscape scales and 67–80% at virus species scale. The non-neutral selection is approximately 26–28% at community scale and 23% at species scale. The false negative rate is about 2–3%, which suggested rather limited confounding effects of non-neutral process on neutrality tests. We postulate that prevalence of neutrality in human virome is likely due to extremely simple structure of viruses (stands of DNA/RNA) and their inter-species homogeneities, forming the foundation of species equivalence—the hallmark of neutral theory.

A Novel Mobility-Aware Offloading Management Scheme in Sustainable Multi-Access Edge Computing

The concept of Multi-access Edge Computing (MEC) extends the provisioning of computing and storage capabilities from remote Cloud Data Centers (DC) to the proximity of end users via heterogeneous networks. By augmenting User Equipment (UE) with external computing power under the local coverage, Cloudlet-based offloading performs as a critical enabler to boost application execution performance and to prolong battery lifespan in the mobile devices. However, the mobility of UEs introduces intra-Cloudlet intermittent connections and inter-Cloudlet unbalanced load distributions in the MEC environment, which consequently leads to offloading failures and service downgrading. In this paper, we propose a novel MEC-based mobility-aware offloading model to solve the intra-Cloudlet offloading scheduling issue and inter-Cloudlet load-aware heterogeneous resource allocation issue in terms of concerning the offloading execution efficiency, task processing time constraints, and energy efficiency. A priority-based queue model is designed to formulate the intra-Cloudlet mobility-aware offloading scheduling problem, resolved by the adoption of the Particle Swarm heuristic. The energy-aware inter-Cloudlet resource selection procedure is formalized in a mobility-aware multi-site resource allocation model, which is further solved by lightweight dynamic load balancing. The results of the experiment indicate that the proposed framework can effectively improve the overall offloading service provisioning quality in the intra-Cloudlet and inter-Cloudlet offloading scenarios, compared to the current works.

Distributional Effects of Entry Fees and Taxation for Financing Public Beaches

We use a multisite general population demand model to assess welfare and distributional effects of entrance pricing and taxation to finance Great Lakes beach management. We compare revenue resulting from uniform entry (i.e., gate) fees across sites to additional state income tax generating equivalent revenues. We present empirical demand elasticities with respect to total prices, including entry fees and elasticities with respect only to fees. We find that demand is price elastic for total trips and individual sites, with individual sites being significantly more elastic. Over a broad range of entry fees, total trip and site demands are fee elastic.

Single-Walled Carbon Nanotube Probes for the Characterization of Biofilm-Degrading Enzymes Demonstrated against Pseudomonas aeruginosa Extracellular Matrices.

Hydrolase co-therapies that degrade biofilm extracellular polymeric substances (EPS) allow for a better diffusion of antibiotics and more effective treatment; current methods for quantitatively measuring the enzymatic degradation of EPS are not amendable to high-throughput screening. Herein, we present biofilm EPS-functionalized single-walled carbon nanotube (SWCNT) probes for rapid screening of hydrolytic enzyme selectivity and activity on EPS. The extent of biofilm EPS degradation is quantified by monitoring the quenching of the SWCNT fluorescence. We used this platform to screen 16 hydrolases with varying bond breaking selectivity against a panel of wild-type Pseudomonas aeruginosa and mutants deficient or altered in one or more EPS. Next, we performed concentration-dependent studies of six enzymes on two common strains found in cystic fibrosis (CF) environments and, for each enzyme, extracted three first-order rate constants and their relative contributions by fitting a parallel, multi-site degradation model, with a good model fit (R2 from 0.65 to 0.97). Reaction rates (turnover rates) are dependent on the enzyme concentration and range from 6.67 × 10-11 to 2.80 × 10-3 *s-1 per mg/mL of enzymes. Lastly, we confirmed findings from this new assay using an established crystal-violet staining assay for a subset of hydrolase panels. In summary, our work shows that this modular sensor is amendable to the high-throughput screening of EPS degradation, thereby improving the rate of discovery and development of novel hydrolases.

Modelling digital and manual contact tracing for COVID-19. Are low uptakes and missed contacts deal-breakers?

Comprehensive testing schemes, followed by adequate contact tracing and isolation, represent the best public health interventions we can employ to reduce the impact of an ongoing epidemic when no or limited vaccine supplies are available and the implications of a full lockdown are to be avoided. However, the process of tracing can prove feckless for highly-contagious viruses such as SARS-CoV-2. The interview-based approaches often miss contacts and involve significant delays, while digital solutions can suffer from insufficient adoption rates or inadequate usage patterns. Here we present a novel way of modelling different contact tracing strategies, using a generalized multi-site mean-field model, which can naturally assess the impact of manual and digital approaches alike. Our methodology can readily be applied to any compartmental formulation, thus enabling the study of more complex pathogen dynamics. We use this technique to simulate a newly-defined epidemiological model, SEIR-T, and show that, given the right conditions, tracing in a COVID-19 epidemic can be effective even when digital uptakes are sub-optimal or interviewers miss a fair proportion of the contacts.

Competitive Carboxylate-Silicate Binding at Iron Oxyhydroxide Surfaces.

Dissolved silicate ions in wet and dry soils can determine the fate of organic contaminants via competitive binding. While fundamental surface science studies have advanced knowledge of binding in competitive systems, little is still known about the ranges of solution conditions, the time dependence, and the molecular processes controlling competitive silicate–organic binding on minerals. Here we address these issues by describing the competitive adsorption of dissolved silicate and of phthalic acid (PA), a model carboxylate-bearing organic contaminant, onto goethite, a representative natural iron oxyhydroxide nanomineral. Using surface complexation thermodynamic modeling of batch adsorption data and chemometric analyses of vibrational spectra, we find that silicate concentrations representative of natural waters (50–1000 μM) can displace PA bound at goethite surfaces. Below pH ∼8, where PA binds, every bound Si atom removes ∼0.3 PA molecule by competing with reactive singly coordinated hydroxo groups (−OH) on goethite. Long-term (30 days) reaction time and a high silicate concentration (1000 μM) favored silicate polymer formation, and increased silicate while decreasing PA loadings. The multisite complexation model predicted PA and silicate binding in terms of the competition for −OH groups without involving PA/silicate interactions, and in terms of a lowering of outer-Helmholtz potentials of the goethite surface by these anions. The model predicted that silicate binding lowered loadings of PA species, and whose two carboxylate groups are hydrogen- (HB) and metal-bonded (MB) with goethite. Vibrational spectra of dried samples revealed that the loss of water favored greater proportions of MB over HB species, and these coexisted with predominantly monomeric silicate species. These findings underscored the need to develop models for a wider range of organic contaminants in soils exposed to silicate species and undergoing wet–dry cycles.

Adsorption behavior of Cs(I) on natural soils: Batch experiments and model-based quantification of different adsorption sites

Understanding the adsorption behavior of radiocesium (RCs) in natural soils is crucial for remediation and evaluation of radioactive contaminated sites. In this study, we investigated the adsorption behavior of Cs(I) onto natural soils collected in Beijing by batch adsorption experiments and sequential extraction. A multi-site adsorption model was built to quantitatively analyze the adsorption capacities of soil clay minerals and predict of Cs(I) adsorption ratio of different adsorption sites. Linear programming calculations show that illite/smectite (I/S) mixture and illite(I) are the mainly clay mineral composition. Batch adsorption experiment results show that soils adsorption of Cesium ions is an exothermic process, and the order of influence of competitive cations on the competitive adsorption strength of Cs(I) is:K+>Mg2+≈Ca2+>Na+. HA (Humic Acid)has little effect on soil adsorption. SEM-EDS analysis shows that Cs+ is mainly distributed on the surface (PS) of soil particles. Based on the above results, the adsorption of Cs(I) onto clay minerals in soils is well predicts in both linear programming calculations and a multi-site adsorption model. The multi-site adsorption model can quantitatively describe and predict the adsorption behavior of Cs(I) on different clay sites in the soils. Frayed edge sites (FES) in the soil can effectively fix trace RCs. The higher concentration of cesium ions is mainly adsorbed on the PS and TIIS. Sequential extraction experiment further proved the adsorption form of cesium in soil under trace and high concentration conditions.

Cs selectivity and adsorption reversibility on Ca-illite and Ca-vermiculite

For understanding and predicting the Cs behavior in soils and groundwater, Cs adsorption properties of illite and vermiculite were investigated under various pH conditions and Cs concentrations. Cs adsorption and desorption experiments have been conducted with Ca-homoionic illite and Ca-vermiculite in CaCl2 solution with an ionic strength of 0.03 and of 0.06 mol.L−1 respectively, by focusing on cation exchanges between Cs, proton and calcium at thermodynamic equilibrium. Ca-illite displayed more affinity for Cs than Ca-vermiculite. Cs adsorption was non-linear for both clay minerals and a multi-site exchange model approach was adopted to interpret and model adsorption isotherms. Each mineral reactivity was described by their sorption site properties expressed by their exchange capacity and ionic selectivity. High-selective and low-capacity sites were shown to control Cs uptake at concentrations below 10−8-10−7 mol.L−1 for both Ca-illite and Ca-vermiculite. Three high-capacity sites dominated Cs adsorption at higher concentrations. Cs adsorption reversibility was demonstrated for illite at Ca concentrations ranging from 5 10−2 to 10−9 mol.L−1. The partial irreversibility of Cs adsorption onto vermiculite at Cs concentrations greater than 10−5 mol.L−1 was related to interlayer collapse. Reversible adsorption may occur as long as the interlayer space stays open. The irreversible adsorbed fraction was quantified and taken into account in the modeling approach to calculate the selectivity coefficient of each site.

On the use of a multi-site ion-exchange model to predictively simulate the adsorption behaviour of strontium and caesium onto French agricultural soils

In case of nuclear accident, 90Sr and 134,137Cs are major radionuclides to account. In previous works (Appl. Geochem. 87, 167; ibid 93, 167), a database of ion-exchange parameters allowing the description of the Sr2+ and Cs + adsorption on purified illite and smectite was developed for a multi-site ion-exchange (MSIE) model. In this study, the adsorption behaviours of Sr2+ and Cs+ were obtained with <150 μm fractions of French soil samples: a cambisol fluvic, a calcosol, and a cambisol typic. The <2 μm fractions of the soil samples were analysed by X-ray diffraction to estimate their clay minerals proportions that were then approximated to an illite/smectite mixture, in consistency with the CEC of the <150 μm fractions. The database was implemented with K-illite and -smectite parameters to account for the amendment of K+ in agricultural soils. The isotherms of Sr2+ and Cs+ on the three soils — at 0.033 mol kgw−1 CaCl2 (I = 0.1 mol kgw−1) and at the pH value of the water equilibrated with the soils — were then compared with simulations obtained using ion-exchange parameters from the database for the MSIE model. This simulation approach, based on the additive adsorption properties between several reactive phases, allowed to describe satisfactorily the adsorption of Sr2+ and Cs+ in most cases. In order to highlight the limiting parameters of the modelling predictive ability, different treatments were made on soil samples. The removal of the natural organic matter did not change significantly the adsorption behaviour of either Sr2+ or Cs+. The removal of the exchangeable aluminium from the cambisol typic allowed a better simulation of the adsorption isotherm in the case of Sr2+. Finally, in the case of the calcosol, the satisfactory modelling of the decrease in adsorption of Sr and Cs using a synthetic CaCl2 pore water with increasing concentrations of KNO3 allowed to verify the robustness of the MSIE model and exchange parameters from the database.

A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic Generation

Currently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model for several solar PV power plants in various regions of South Korea to establish stable supply-and-demand power grid systems. To reflect the spatial and temporal characteristics of solar PV generation, data extracted from satellite images and numerical text data were combined and used. Experiments were conducted on solar PV power plants in Incheon, Busan, and Yeongam, and various machine learning algorithms were applied, including the SARIMAX, which is a traditional statistical time-series analysis method. Furthermore, for developing a precise solar PV generation prediction model, the SARIMAX-LSTM model was applied using a stacking ensemble technique that created one prediction model by combining the advantages of several prediction models. Consequently, an advanced multisite hybrid spatio-temporal solar PV generation prediction model with superior performance was proposed using information that could not be learned in the existing single-site solar PV generation prediction model.

Reactive transport of strontium in two laboratory-scale columns: Experiments and modelling

To support the environmental monitoring of nuclear sites, reactive transport models used to predict the migration of contaminants such as strontium-90 (90Sr) in soils, sediments and aquifers are developed, continuously tested and improved. This study aims at assessing the adequacy of the multi-site ion exchanger model (MSIE) based on a component “additivity approach” and coupled to the advection-dispersion equation (ADE) to simulate Sr transport in a clayey sandstone and a Bt soil horizon. We have also compared the performance of the modelling approach with simulation results obtained by considering a Kd approach (constant Kd). Transport experiments were performed in centimetre- and decimetre-scale columns in order to test the model sensitivities to the mineral abundance and the specificities of their reactive parameters. Non-reactive transport experiments with conservative tracers allowed us to determine the transport parameters, such as porosity and dispersivity. In this paper, we have compared the Sr transport simulation results with Sr experimental breakthrough curves acquired at various flow velocities. The simulations results show that the Kd approach can reproduce experimental data in the case of the clayey sandstone when a certain amount of uncertainty is accepted, whereas the additivity approach better fits the Sr reactive transport in both columns (especially the maximum value) without it being necessary to adjust the parameters. These results advocate for more complex retention models than Kd to better understand and improve the robustness of Sr transport predictions. The clay content, the relative abundance of illite and smectite, and the clay mineral specificity, are all sensitive parameters when it comes to defining the reactive system involved in Sr transport simulation. The results highlight the influence of illite in the spreading of the Sr breakthrough curve, especially through its low-capacity and high-selectivity site. This implies having access to a robust and extensive set of retention parameters acquired on reference minerals. In this study, the results obtained for the clayey sandstone confirm the robustness of our selected parameters when clay minerals have similar reactivity levels as the reference minerals. This set of parameters appears more limited in the case of the Bt soil containing weathered or evolved minerals. The choice of modelling approach is therefore crucial for accurately modelling and predicting Sr transport behaviour in porous media, as is the representativeness of the minerals in the database.

Data driven robust optimization for handling uncertainty in supply chain planning models

While addressing supply chain planning under uncertainty, Robust Optimization (RO) is regarded as an efficient and tractable method. As RO involves calculation of several statistical moments or maximum / minimum values involving the objective functions under realizations of these uncertain parameters, the accuracy of this method significantly depends on the efficient techniques to sample the uncertainty parameter space with limited amount of data. Conventional sampling techniques, e.g. box/budget/ellipsoidal, work by sampling the uncertain parameter space inefficiently, often leading to inaccuracies in such estimations. This paper proposes a methodology to amalgamate machine learning and data analytics with RO, thereby making it data-driven. A novel neuro fuzzy clustering mechanism is implemented to cluster the uncertain space such that the exact regions of uncertainty are optimally identified. Subsequently, local density based boundary point detection and Delaunay triangulation based boundary construction enable intelligent Sobol based sampling to sample the uncertain parameter space more accurately. The proposed technique is utilized to explore the merits of RO towards addressing the uncertainty issues of product demand, machine uptime and production cost associated with a multiproduct, and multisite supply chain planning model. The uncertainty in supply chain model is thoroughly analysed by carefully constructing examples and its case studies leading to large scale mixed integer linear and nonlinear programming problems which were efficiently solved in GAMS® framework. Demonstration of efficacy of the proposed method over the box, budget and ellipsoidal sampling method through comprehensive analysis adds to other highlights of the current work.

A new framework for a multi-site stochastic daily rainfall model: Coupling a univariate Markov chain model with a multi-site rainfall event model

Multi-site rainfall models are useful tools to provide synthetic realizations of spatially-correlated rainfall at multiple stations, which are of great importance for flood and drought risk assessment and climate change impact analysis. Therefore, a good preservation of various observed rainfall characteristics including rainfall time-series statistics and rainfall event characteristics at individual stations and the inter-site correlations of these rainfall characteristics is very crucial. To achieve this purpose, this study aims to develop a multi-site stochastic daily rainfall model by coupling a univariate Markov chain with a multi-site rainfall event model (MSDRM-MCREM), based on our previously-developed single-site SDRM-MCREM. The univariate Markov chain model in MSDRM-MCREM is used to generate spatially-correlated multi-site rainfall occurrence time series and extract simulated rainfall events for individual stations based on continuous wet days. The multi-site rainfall event model is then constructed using Vine copulas to simulate spatially-correlated rainfall event characteristics of those simulated rainfall events that occur simultaneously at multiple stations, including rainfall durations, rainfall depths and temporal patterns. Subsequently, this model was applied to the Changshangang River basin in Zhejiang Province, East China and its performance in reproducing rainfall characteristics and spatial correlations was evaluated for three cases, i.e. simulations for two, three and four stations. Results show that except for overestimation of light rainfall, MSDRM-MCREM can simultaneously well preserve rainfall time-series statistics (i.e. different rainfall percentiles, mean monthly rainfall, standard deviations and probabilities and mean values of wet days), extreme rainfall (i.e. exceedance probabilities of annual maximum 1-day, 3-day and 5-day rainfall) and rainfall event characteristics (i.e. cumulative probabilities of wet spell, dry spell and rainfall depth, temporal patterns and occurrence probabilities of rainfall types for different depth-based event classes) at individual stations. In addition, the spatial correlations of rainfall characteristics have also been well maintained, including rainfall occurrence time series and rainfall event characteristics in different groups, with the inter-site correlations of rainfall time series being slightly underestimated.

A new multi-site multi-variable stochastic model with inter-site and inter-variable correlations, low frequency attributes and stochasticity: A case study in the lower Yellow River basin

• A stochastic model that can reproduce spatial and temporal correlation, as well as stochasticity is proposed. • A new indicator is proposed to measure the reproduction of natural stochasticity. • The new stochastic model is evaluated and compared to two other stochastic models.

Reaction Path Analysis of CO2 Reduction to Methanol through Multisite Microkinetic Modelling over Cu/ZnO/Al2O3 Catalysts

The changing hierarchical structure of the applied heterogeneous Cu/ZnO/Al2O3 material during methanol synthesis reactions hinders an efficient engineered process condition optimization, causing sub-optimal functional performance. A robust literature comparison is conducted to determine that activity is tightly coupled with Cu–Zn interactions. In order to investigate this physical behaviour further, characteristic experimental data is acquired through the catalytic reactor tests with an activated commercial catalyst, aged at different input measurements, monitored and characterized by the Brunauer–Emmett–Teller (BET), X-ray diffraction (XRD), scanning transmission electron microscopy (STEM) with energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), H2 transient adsorption (TA) and N2O pulsed surface oxidation (PSO) methodologies. It is shown that apparent rate law, exponents and activation energies do not vary significantly by increasing the ZnOX coverage from 7% to 23 %, while not all of ZnOX over-layer is catalytically active. For Cu/ZnO/Al2O3 with ZnOX over 7%, a highly-dispersed Al2O3 decreases the measured intrinsic kinetics of the Cu–Zn site, implying a steric hindrance effect. Finally, building on unveiled chemical relations, a thorough multisite system micro-kinetic model, based on systematic contribution analysis, mechanisms and quantitative density functional theory (DFT) constants is developed. Values were optimized using the sequential screening results for an industrially relevant application (the temperatures of 160–260 °C, 50 bar pressure, 12,000–200,000 h–1 gas hourly space velocity (GHSV) flow and relative feed compositions).Designed mathematical relationships can therefore be utilised to accurately predict the turnover, selectivity and stability/deactivation in correspondence to ZnOX over Cu.

Multisite Model of Allosterism for the Adenosine A1 Receptor.

Despite being a target for about one-third of approved drugs, G protein-coupled receptors (GPCRs) still represent a tremendous reservoir for therapeutic strategies against diseases. For example, several cardiovascular and central nervous system conditions could benefit from clinical agents that activate the adenosine 1 receptor (A1R); however, the pursuit of A1R agonists for clinical use is usually impeded by both on- and off-target side effects. One of the possible strategies to overcome this issue is the development of positive allosteric modulators (PAMs) capable of selectively enhancing the effect of a specific receptor subtype and triggering functional selectivity (a phenomenon also referred to as bias). Intriguingly, besides enforcing the effect of agonists upon binding to an allosteric site, most of the A1R PAMs display intrinsic partial agonism and orthosteric competition with antagonists. To rationalize this behavior, we simulated the binding of the prototypical PAMs PD81723 and VCP171, the full-agonist NECA, the antagonist 13B, and the bitopic agonist VCP746. We propose that a single PAM can bind several A1R sites rather than a unique allosteric pocket, reconciling the structure-activity relationship and the mutagenesis results.

Modelling streamflow using the SWAT model and multi-site calibration utilizing SUFI-2 of SWAT-CUP model for high altitude catchments, NW Himalaya's

Modelling streamflow is important in developing effective long-term management, soil conservation planning, and utilization of water resource management strategies. The current study aims to establish a stable hydrological model simulating streamflow with minimal uncertainty amongst the calibration parameters. The SWAT 2012 model is utilized to simulate the Lidder catchment streamflow located in the Southeastern part of the Kashmir valley for the (2007–2015) periods. SWAT- CUP’s algorithm called Sequential Uncertainty Fitting 2 (SUFI-2) is employed in the study for multi-site model calibration and validation for monthly time steps. The calibration results for monthly simulation for the (2009–2012) period displayed an excellent model performance for flow rates with R2 of 0.89, 0.85, and 0.89, and NSE values obtained are 0.73, 0.72, and 0.83. However, the validation results for monthly simulation for the (2013–2015) period also displayed good model performance for flow rates with R2 values obtained as 0.86, 0.81, and 0.80, and NSE values of 0.76, 0.52, and 0.56. Following calibration, the combined impact of each used parameter is ranked by the SWAT-CUP global sensitivity function. A total of twenty parameters are optimized in the calibration. From the study, we find that the most sensitive parameters were Snowfall temperature (SMTMP), Temperature lapse rate (TLAPS), melt factor for snow (SFTMP), and initial SCS curve number value (CN2). Therefore, it is concluded that while developing a reliable hydrologic model, complete knowledge about the hydrological processes occurring within the river basins and awareness of an appropriate and meaningful set of parameters is important.

Spinal Dermoid and Epidermoid Cyst: An Institutional Experience and Clinical Insight into the Neural Tube Closure Models.

Objectives The spinal dermoid and epidermoid cysts (SDECs) are rare entities comprising less than 1% of pediatric intraspinal tumors. The present study aims to extrapolate the clinicoradiological data, in order to identify the most plausible neural tube closure model in human and provide a retrospective representation from our clinical experience. Materials and Methods We collected the details of all histologically proven, newly diagnosed primary SDECs who underwent excision over the past 20 years. Secondary or recurrent lesions and other spinal cord tumors were excluded. Surgical and follow-up details of these patients as well as those with associated spinal dysraphism were reviewed. Clinical and radiological follow-up revealed the recurrence in these inborn spinal cord disorders. Results A total of 73 patients were included retrospectively, having a mean age of 22.4 ± 13.3 years, and 41 (56.2%) cases fell in the first two decades of life. Twenty-four (32.9%) dermoid and 49 (67.1%) epidermoid cysts comprised the study population and 20 of them had associated spinal dysraphism. The distribution of SDECs was the most common in lumbosacral region ( n = 30) which was 10 times more common than in the sacral region ( n = 3). Bladder dysfunction 50 (68.5%) and pain 48 (65.7%) were the most common presenting complaints. During follow-up visits, 40/48 (83.3%) cases showed sensory improvement while 11/16 (68.7%) regained normal bowel function. There was no surgical mortality with recurrence seen in eight till the last follow-up. Conclusions The protracted clinical course of the spinal inclusion cysts mandates a long-term follow-up. The results of our study support the multisite closure model and attempt to provide a retrospective reflection of neural tube closure model in humans by using SDECs as the surrogate marker of neural tube closure defect.