Time-lapse monitoring of root water uptake using electrical resistivity tomography and mise-à-la-masse: a vineyard infiltration experiment

Abstract. This paper presents a time-lapse application of electrical methods (electrical resistivity tomography, ERT; and mise-à-la-masse, MALM) for monitoring plant roots and their activity (root water uptake) during a controlled infiltration experiment. The use of non-invasive geophysical monitoring is of increasing interest as these techniques provide time-lapse imaging of processes that otherwise can only be measured at few specific spatial locations. The experiment here described was conducted in a vineyard in Bordeaux (France) and was focused on the behaviour of two neighbouring grapevines. The joint application of ERT and MALM has several advantages. While ERT in time-lapse mode is sensitive to changes in soil electrical resistivity and thus to the factors controlling it (mainly soil water content, in this context), MALM uses DC current injected into a tree stem to image where the plant root system is in effective electrical contact with the soil at locations that are likely to be the same where root water uptake (RWU) takes place. Thus, ERT and MALM provide complementary information about the root structure and activity. The experiment shows that the region of likely electrical current sources produced by MALM does not change significantly during the infiltration time in spite of the strong changes of electrical resistivity caused by changes in soil water content. Ultimately, the interpretation of the current source distribution strengthened the hypothesis of using current as a proxy for root detection. This fact, together with the evidence that current injection in the soil and in the stem produces totally different voltage patterns, corroborates the idea that this application of MALM highlights the active root density in the soil. When considering the electrical resistivity changes (as measured by ERT) inside the stationary volume of active roots delineated by MALM, the overall tendency is towards a resistivity increase during irrigation time, which can be linked to a decrease in soil water content caused by root water uptake. On the contrary, when considering the soil volume outside the MALM-derived root water uptake region, the electrical resistivity tends to decrease as an effect of soil water content increase caused by the infiltration. The use of a simplified infiltration model confirms at least qualitatively this behaviour. The monitoring results are particularly promising, and the method can be applied to a variety of scales including the laboratory scale where direct evidence of root structure and root water uptake can help corroborate the approach. Once fully validated, the joint use of MALM and ERT can be used as a valuable tool to study the activity of roots under a wide variety of field conditions

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Heterogeneity matters: aggregation bias of reach-wise gas transfer velocity vs. energy dissipation rate relationships - DATASET

This dataset contains slope data for more than 200 reaches of the stream network of the Rio Valfredda (Veneto, Italy), originally used to estimate the aggregation bias of gas transfer velocity when estimated at the reach scale

Heterogeneity Matters: Aggregation Bias of Gas Transfer Velocity Versus Energy Dissipation Rate Relations in Streams

The gas transfer velocity, k, modulates gas fluxes across air-water interfaces in rivers. While the theory postulates a local scaling law between k and the turbulent kinetic energy dissipation rate e, empirical studies usually interpret this relation at the reach-scale. Here, we investigate how local k(e) laws can be integrated along heterogeneous reaches exploiting a simple hydrodynamic model, which links stage and velocity to the local slope. The model is used to quantify the relative difference between the gas transfer velocity of a heterogeneous stream and that of an equivalent homogeneous system. We show that this aggregation bias depends on the exponent of the local scaling law, b, and internal slope variations. In high-energy streams, where b>1, spatial heterogeneity of e significantly enhances reach-scale values of k as compared to homogeneous settings. We conclude that small-scale hydro-morphological traits bear a profound impact on gas evasion from inland waters

Dataset: unraveling the dynamics of gas evasion in a step-and-pool configuration

This collection contains measures related to a flume experiment and a numerical simulation of a step-and-pool configuration

Steps dominate gas evasion from a mountain headwater stream

Dataset of hydraulic properties, dissolved carbon dioxide concentrations and estimation of the damping factors and the dominance ratios relative to the article "Steps dominate gas evasion from a mountain headwater stream.

DIRECTions: Design and Specification of an IR Evaluation Infrastructure

Abstract. Information Retrieval (IR) experimental evaluation is an es-sential part of the research on and development of information access methods and tools. Shared data sets and evaluation scenarios allow for comparing methods and systems, understanding their behaviour, and tracking performances and progress over the time. On the other hand, experimental evaluation is an expensive activity in terms of human effort, time, and costs required to carry it out. Software and hardware infrastructures that support experimental evaluation operation as well as management, enrichment, and exploita-tion of the produced scientific data provide a key contribution in reduc-ing such effort and costs and carrying out systematic and throughout analysis and comparison of systems and methods, overall acting as en-ablers of scientific and technical advancement in the field. This paper describes the specification for an IR evaluation infrastructure by con-ceptually modeling the entities involved in IR experimental evaluation and their relationships and by defining the architecture of the proposed evaluation infrastructure and the APIs for accessing it.