Particle Density Estimation with Grid-Projected Adaptive Kernels

The reconstruction of smooth density fields from scattered data points is a procedure that has multiple applications in a variety of disciplines, including Lagrangian (particle-based) models of solute transport in fluids. In random walk particle tracking (RWPT) simulations, particle density is directly linked to solute concentrations, which is normally the main variable of interest, not just for visualization and post-processing of the results, but also for the computation of non-linear processes, such as chemical reactions. Previous works have shown the superiority of kernel density estimation (KDE) over other methods such as binning, in terms of its ability to accurately estimate the "true" particle density relying on a limited amount of information. Here, we develop a grid-projected KDE methodology to determine particle densities by applying kernel smoothing on a pilot binning; this may be seen as a "hybrid" approach between binning and KDE. The kernel bandwidth is optimized locally. Through simple implementation examples, we elucidate several appealing aspects of the proposed approach, including its computational efficiency and the possibility to account for typical boundary conditions, which would otherwise be cumbersome in conventional KDE

ChatGPT in Hydrology and Earth Sciences: Opportunities, Prospects, and Concerns

The emergence of large language models (LLMs), such as ChatGPT, has garnered significant attention, particularly in academic and scientific circles. Researchers, scientists, and instructors hold varying perspectives on the advantages and disadvantages of using ChatGPT for research and teaching purposes. ChatGPT will be used by many scientists going forward for creating content and driving scientific progress. This commentary offers a brief explanation of the fundamental principles behind ChatGPT and how it can be applied in the fields of hydrology and other Earth sciences. The article examines the primary applications of this open artificial intelligence tool within these fields, specifically its ability to assist with writing and coding tasks, and highlights both the advantages and concerns associated with using such a model. Moreover, the study brings up some other limitations of the model, and the dangers of potential miss-uses. Finally, we suggest that the academic community adapts its regulations and policies to harness the potential benefits of LLMs while mitigating its pitfalls, including establishing a structure for utilizing LLMs and presenting clear regulations for their implementation. We also outline some specific steps on how to accomplish this structure

On Multi‐Model Assessment of Complex Degradation Paths: The Fate of Diclofenac and Its Transformation Products

We present a methodology to quantify the impact of model structure and parametric uncertainty on formulations targeting biotransformation processes of Emerging Contaminants in subsurface water resources. The study is motivated by recognizing that modeling of bio-mediated reactions of recalcitrant compounds in soil and aquifers is plagued by uncertainty. At the same time, process-based models often require the parameterization of complex physico-chemical processes, a situation which is exacerbated by the paucity of direct observations. Thus, assessment and formulation of modeling tools capable to balance complexity and reliability is a key challenge. The modeling strategy proposed here aims at pairing and applying a suite of quantitative tools starting from a prior diagnosis of multiple uncertainty sources and leading to parameter estimation and model selection in the presence of a limited number of observations. The methodology is illustrated through application to a multi-step, reactive scenario involving biotransformation of the pharmaceutical diclofenac (DCF) in groundwater. Our framework includes four plausible models. These are obtained through successive simplifications of a recently developed highly complex model. Such simplifications are accomplished consistent with the results of a comprehensive Multi-Model Global Sensitivity Analysis. The latter allows ranking the levels of influence of system processes on model outputs by incorporating the effects of model formulation and parametric uncertainties. The kinetic of the loop-initiating process (DCF nitrosation, linked to the temporal evolution of N-cycle components) is documented as dominating in explaining the variability of model outputs of environmental interest. Model discrimination criteria suggest that a simplified counterpart of the reference model is favored to interpret available data. Our modeling approach can assist interpretation and prototyping of a wide range of contaminant biotransformation models. The latter is a key objective also for the purpose of developing credible (environmental) risk assessment tools and designing experimental sampling campaigns

On the formation of multiple local peaks in breakthrough curves

The analysis of breakthrough curves (BTCs) is of interest in hydrogeology as a way to parameterize and explain processes related to anomalous transport. Classical BTCs assume the presence of a single peak in the curve, where the location and size of the peak and the slope of the receding limb has been of particular interest. As more information is incorporated into BTCs (for example, with high-frequency data collection, supercomputing efforts), it is likely that classical definitions of BTC shapes will no longer be adequate descriptors for contaminant transport problems. We contend that individual BTCs may display multiple local peaks depending on the hydrogeologic conditions and the solute travel distance. In such cases, classical definitions should be reconsidered. In this work, the presence of local peaks in BTCs is quantified from high-resolution numerical simulations in synthetic fields with a particle tracking technique and a kernel density estimator to avoid either overly jagged or smoothed curves that could mask the results. Individual BTCs from three-dimensional heterogeneous hydraulic conductivity fields with varying combinations of statistical anisotropy, heterogeneity models, and local dispersivity are assessed as a function of travel distance. The number of local peaks, their corresponding slopes, and a transport connectivity index are shown to strongly depend on statistical anisotropy and travel distance. Results show that the choice of heterogeneity model also affects the frequency of local peaks, but the slope is less sensitive to model selection. We also discuss how solute shearing and rerouting can be determined from local peak quantification.Peer ReviewedPostprint (published version

Flexible Graphene Solution-Gated Field-Effect Transistors : Efficient Transducers for Micro-Electrocorticography

Brain-computer interfaces and neural prostheses based on the detection of electrocorticography (ECoG) signals are rapidly growing fields of research. Several technologies are currently competing to be the first to reach the market; however, none of them fulfill yet all the requirements of the ideal interface with neurons. Thanks to its biocompatibility, low dimensionality, mechanical flexibility, and electronic properties, graphene is one of the most promising material candidates for neural interfacing. After discussing the operation of graphene solution-gated field-effect transistors (SGFET) and characterizing their performance in saline solution, it is reported here that this technology is suitable for μ-ECoG recordings through studies of spontaneous slow-wave activity, sensory-evoked responses on the visual and auditory cortices, and synchronous activity in a rat model of epilepsy. An in-depth comparison of the signal-to-noise ratio of graphene SGFETs with that of platinum black electrodes confirms that graphene SGFET technology is approaching the performance of state-of-the art neural technologies

Probiotic Sonicates Selectively Induce Mucosal Immune Cells Apoptosis through Ceramide Generation via Neutral Sphingomyelinase

This is an open-access article distributed under the terms of the Creative Commons Attribution License.-- et al.[Background]: Probiotics appear to be beneficial in inflammatory bowel disease, but their mechanism of action is incompletely understood. We investigated whether probiotic-derived sphingomyelinase mediates this beneficial effect. [Methodology/Principal Findings]: Neutral sphingomyelinase (NSMase) activity was measured in sonicates of the probiotic L. brevis (LB) and S. thermophilus (ST) and the non-probiotic E. coli (EC) and E. faecalis (EF). Lamina propria mononuclear cells (LPMC) were obtained from patients with Crohn's disease (CD) and Ulcerative Colitis (UC), and peripheral blood mononuclear cells (PBMC) from healthy volunteers, analysing LPMC and PBMC apoptosis susceptibility, reactive oxygen species (ROS) generation and JNK activation. In some experiments, sonicates were preincubated with GSH or GW4869, a specific NSMase inhibitor. NSMase activity of LB and ST was 10-fold that of EC and EF sonicates. LB and ST sonicates induced significantly more apoptosis of CD and UC than control LPMC, whereas EC and EF sonicates failed to induce apoptosis. Pre-stimulation with anti-CD3/CD28 induced a significant and time-dependent increase in LB-induced apoptosis of LPMC and PBMC. Exposure to LB sonicates resulted in JNK activation and ROS production by LPMC. NSMase activity of LB sonicates was completely abrogated by GW4869, causing a dose-dependent reduction of LB-induced apoptosis. LB and ST selectively induced immune cell apoptosis, an effect dependent on the degree of cell activation and mediated by bacterial NSMase. [Conclusions]: These results suggest that induction of immune cell apoptosis is a mechanism of action of some probiotics, and that NSMase-mediated ceramide generation contributes to the therapeutic effects of probiotics.The funding sources included grants from Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Ministerio de Ciencia e Innovación (SAF2005-00280 and SAF2008-03676 to MS, FIS2009-00056 to AM, SAF2009-11417 to JCF), Fundación Ramón Areces (to MS), the National Institutes of Health (DK30399 and DK50984 to CF) and the Research Center for Liver and Pancreatic Diseases funded by the United States National Institute for Alcohol Abuse and Alcoholism (P50 AA 11999 to JCF).Peer reviewe

Famílies botàniques de plantes medicinals

Facultat de Farmàcia, Universitat de Barcelona. Ensenyament: Grau de Farmàcia, Assignatura: Botànica Farmacèutica, Curs: 2013-2014, Coordinadors: Joan Simon, Cèsar Blanché i Maria Bosch.Els materials que aquí es presenten són els recull de 175 treballs d’una família botànica d’interès medicinal realitzats de manera individual. Els treballs han estat realitzat per la totalitat dels estudiants dels grups M-2 i M-3 de l’assignatura Botànica Farmacèutica durant els mesos d’abril i maig del curs 2013-14. Tots els treballs s’han dut a terme a través de la plataforma de GoogleDocs i han estat tutoritzats pel professor de l’assignatura i revisats i finalment co-avaluats entre els propis estudiants. L’objectiu principal de l’activitat ha estat fomentar l’aprenentatge autònom i col·laboratiu en Botànica farmacèutica