Binary upscaling on complex heterogeneities: The role of geometry and connectivity

The equivalent conductivity (Keq) of a binary medium is known to vary with the proportion of the two phases, but it also depends on the geometry and topology of the inclusions. In this paper, we analyze the role of connectivity and shape of the connected components through a correlation study between Keq and two topological and geometrical indicators: the Euler number and the Solidity indicator. We show that a local measure such as the Euler number is weakly correlated to Keq and therefore it is not suitable to quantify the influence of connectivity on the global flux; on the contrary the Solidity indicator, related to the convex hull of the connected components, presents a direct correlation with Keq. This result suggests that, in order to estimate Keq properly, one may consider the convex hull of each connected component as the area of influence of its spatial distribution on flow and make a correction of the proportion of the hydrofacies according to that. As a direct application of these principles, we propose a new method for the estimation of Keq using simple image analysis operations. In particular, we introduce a direct measure of the connected fraction and a non-parametric correction of the hydrofacies proportion to compensate for the influence of the connected components shape on flow. This model, tested on a large ensemble of isotropic media, provides a good Keq approximation even on complex heterogeneities without the need for calibration

Using data-driven algorithms for semi-automated geomorphological mapping

In this paper, we compare the performance of two data-driven algorithms to deal with an automatic classification problem in geomorphology: Direct Sampling (DS) and Random Forest (RF). The main goal is to provide a semi-automated procedure for the geomorphological mapping of alpine environments, using a manually mapped zone as training dataset and predictor variables to infer the classification of a target zone. The applicability of DS to geomorphological classification was never investigated before. Instead, RF based classification has already been applied in few studies, but only with a limited number of geomorphological classes. The outcomes of both approaches are validated by comparing the eight detected classes with a geomorphological map elaborated on the field and considered as ground truth. Both DS and RF give satisfactory results and provide similar performances in term of accuracy and Cohen’s Kappa values. The map obtained with RF presents a noisier spatial distribution of classes than when using DS, because DS takes into account the spatial dependence of the different classes. Results suggest that DS and RF are both suitable techniques for the semi-automated geomorphological mapping in alpine environments at regional scale, opening the way for further improvements

Quantifying temporal variability and spatial heterogeneity in rainfall recharge thresholds in a montane karst environment

Quantifying rainfall recharge thresholds, including their spatial and temporal heterogeneity, is of fundamental importance to better understand recharge processes and improving estimation of recharge rates. Caves provide a unique observatory into the percolation of water from the surface to the water table at the timescale of individual rainfall recharge events. Here, we monitor nine infiltration sites over six years at a montane cave site in south eastern Australia. Six of the drip hydrology time series have up to ~100 hydrograph responses to rainfall over the monitoring period, three sites do not respond to rainfall events. We use two approaches to quantify rainfall recharge thresholds. At an annual timescale, for all nine drip sites, the total annual percolation water volume was determined for each year of data. Daily rainfall recharge thresholds were then determined by maximising the correlation of annual percolation water volume and total precipitation above a variable daily threshold value. The annual recharge amount methodology produced rainfall recharge thresholds for seven sites, where high and significant correlations (rank correlations > 0.75) occur for daily precipitation thresholds between 6 mm and 38 mm/day. No rainfall recharge thresholds could be obtained from one site which had a low and constant annual drip amount, and from one site which exhibited ‘underflow’ behaviour. At an event timescale, for the six sites which had a hydrograph response to rainfall, the 7-day antecedent rainfall amounts were determined. Minimum 7-day precipitation amounts prior to a hydrograph response for specific drip sites were in the range 13–28 mm and 75% of all recharge events had a 7-day antecedent precipitation between 20.7 and 38.1 mm. Combining all drip water monitoring sites and analysing the data by month identifies a seasonal variability in the minimum 7-day antecedent precipitation necessary to generate potential recharge, from 15 to 25 mm in winter to >50 mm in February and March. We apply a simple water budget model, driven by P and ET and optimised to the observed potential recharge events, to infer a ‘whole cave’ soil and epikarst storage capacity. This storage capacity is between ~50 mm (using potential evapotranspiration, 92% of events simulated successfully) to ~60 mm (using actual evapotranspiration, 79% of events simulated successfully). Modelling of individual drip sites identifies spatial heterogeneity in soil and epikarst storage capacities. Our approach using multiple methodologies allows the comparison between both daily and weekly rainfall recharge thresholds and modelled soil and epikarst storage for the first time

High Risk of Secondary Infections Following Thrombotic Complications in Patients With COVID-19

Background. This study’s primary aim was to evaluate the impact of thrombotic complications on the development of secondary infections. The secondary aim was to compare the etiology of secondary infections in patients with and without thrombotic complications. Methods. This was a cohort study (NCT04318366) of coronavirus disease 2019 (COVID-19) patients hospitalized at IRCCS San Raffaele Hospital between February 25 and June 30, 2020. Incidence rates (IRs) were calculated by univariable Poisson regression as the number of cases per 1000 person-days of follow-up (PDFU) with 95% confidence intervals. The cumulative incidence functions of secondary infections according to thrombotic complications were compared with Gray’s method accounting for competing risk of death. A multivariable Fine-Gray model was applied to assess factors associated with risk of secondary infections. Results. Overall, 109/904 patients had 176 secondary infections (IR, 10.0; 95% CI, 8.8–11.5; per 1000-PDFU). The IRs of secondary infections among patients with or without thrombotic complications were 15.0 (95% CI, 10.7–21.0) and 9.3 (95% CI, 7.9–11.0) per 1000-PDFU, respectively (P = .017). At multivariable analysis, thrombotic complications were associated with the development of secondary infections (subdistribution hazard ratio, 1.788; 95% CI, 1.018–3.140; P = .043). The etiology of secondary infections was similar in patients with and without thrombotic complications. Conclusions. In patients with COVID-19, thrombotic complications were associated with a high risk of secondary infections

Off-label long acting injectable antipsychotics in real-world clinical practice: a cross-sectional analysis of prescriptive patterns from the STAR Network DEPOT study

Introduction Information on the off-label use of Long-Acting Injectable (LAI) antipsychotics in the real world is lacking. In this study, we aimed to identify the sociodemographic and clinical features of patients treated with on- vs off-label LAIs and predictors of off-label First- or Second-Generation Antipsychotic (FGA vs. SGA) LAI choice in everyday clinical practice. Method In a naturalistic national cohort of 449 patients who initiated LAI treatment in the STAR Network Depot Study, two groups were identified based on off- or on-label prescriptions. A multivariate logistic regression analysis was used to test several clinically relevant variables and identify those associated with the choice of FGA vs SGA prescription in the off-label group. Results SGA LAIs were more commonly prescribed in everyday practice, without significant differences in their on- and off-label use. Approximately 1 in 4 patients received an off-label prescription. In the off-label group, the most frequent diagnoses were bipolar disorder (67.5%) or any personality disorder (23.7%). FGA vs SGA LAI choice was significantly associated with BPRS thought disorder (OR = 1.22, CI95% 1.04 to 1.43, p = 0.015) and hostility/suspiciousness (OR = 0.83, CI95% 0.71 to 0.97, p = 0.017) dimensions. The likelihood of receiving an SGA LAI grew steadily with the increase of the BPRS thought disturbance score. Conversely, a preference towards prescribing an FGA was observed with higher scores at the BPRS hostility/suspiciousness subscale. Conclusion Our study is the first to identify predictors of FGA vs SGA choice in patients treated with off-label LAI antipsychotics. Demographic characteristics, i.e. age, sex, and substance/alcohol use co-morbidities did not appear to influence the choice towards FGAs or SGAs. Despite a lack of evidence, clinicians tend to favour FGA over SGA LAIs in bipolar or personality disorder patients with relevant hostility. Further research is needed to evaluate treatment adherence and clinical effectiveness of these prescriptive patterns

The Role of Attitudes Toward Medication and Treatment Adherence in the Clinical Response to LAIs: Findings From the STAR Network Depot Study

Background: Long-acting injectable (LAI) antipsychotics are efficacious in managing psychotic symptoms in people affected by severe mental disorders, such as schizophrenia and bipolar disorder. The present study aimed to investigate whether attitude toward treatment and treatment adherence represent predictors of symptoms changes over time. Methods: The STAR Network \u201cDepot Study\u201d was a naturalistic, multicenter, observational, prospective study that enrolled people initiating a LAI without restrictions on diagnosis, clinical severity or setting. Participants from 32 Italian centers were assessed at three time points: baseline, 6-month, and 12-month follow-up. Psychopathological symptoms, attitude toward medication and treatment adherence were measured using the Brief Psychiatric Rating Scale (BPRS), the Drug Attitude Inventory (DAI-10) and the Kemp's 7-point scale, respectively. Linear mixed-effects models were used to evaluate whether attitude toward medication and treatment adherence independently predicted symptoms changes over time. Analyses were conducted on the overall sample and then stratified according to the baseline severity (BPRS < 41 or BPRS 65 41). Results: We included 461 participants of which 276 were males. The majority of participants had received a primary diagnosis of a schizophrenia spectrum disorder (71.80%) and initiated a treatment with a second-generation LAI (69.63%). BPRS, DAI-10, and Kemp's scale scores improved over time. Six linear regressions\u2014conducted considering the outcome and predictors at baseline, 6-month, and 12-month follow-up independently\u2014showed that both DAI-10 and Kemp's scale negatively associated with BPRS scores at the three considered time points. Linear mixed-effects models conducted on the overall sample did not show any significant association between attitude toward medication or treatment adherence and changes in psychiatric symptoms over time. However, after stratification according to baseline severity, we found that both DAI-10 and Kemp's scale negatively predicted changes in BPRS scores at 12-month follow-up regardless of baseline severity. The association at 6-month follow-up was confirmed only in the group with moderate or severe symptoms at baseline. Conclusion: Our findings corroborate the importance of improving the quality of relationship between clinicians and patients. Shared decision making and thorough discussions about benefits and side effects may improve the outcome in patients with severe mental disorders