Un modello computazionale per le dinamiche di popolazioni di rana verde (Pelophylax esculentus)

La presente tesi propone un simulatore per modellare teorie sulle dinamiche delle popolazioni della rana ibrida Pelophylax esculentus e le sue due specie genitrici Pelophylax lessona e Pelophylax ridibundus, vengono inoltre mostrate alcune simulazioni notevoli sulla diffusione di mutazioni e la variazione di composizioni di questi sistemi simpatrici sotto differenti condizioni, parametri ed assunzioni teoriche

Methodologies & formalisms for modeling macroscopic biological problems

This work presents a new computational approach, based on the formalism of P systems, for modelling and running simulations of animal population dynamics phenomena. The three formalisms proposed are: MPP systems (Minimal Probabilistic P systems), APP systems (Attributed Probabilistic P systems) and MAPP systems (Multilevel Attributed Probabilistic P systems). All of them are formally defined by providing their syntax notations and formal semantics as inference rules. Case study are provided with examples for all three formalism

Involvement of the sigma factor sigma H in the regulation of a small heat shock protein gene in Lactobacillus plantarum WCFS1

The relative expression of the heat shock protein hsp18.55 gene was monitored in Lactobacillus plantarum ΔctsR and ΔftsH mutant strains. Transcription of the hsp18.55 gene was drastically repressed in the ΔctsR mutant of L. plantarum; conversely, significant transcriptional induction of the hsp18.55 gene was noted in the L. plantarum ΔftsH strain. Overall, these results suggest a possible regulation of the hsp18.55 gene by the alternative sigma H factor in L. plantarum. We also noted a similarity with the small heat shock gene (shs) locus of Lactobacillus brevis, which might indicate a possible evolutionary relationship

Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling

International scientific fishery survey programmes systematically collect samples of target stocks’ biomass and abundance and use them as the basis to estimate stock status in the framework of stock assessment models. The research surveys can also inform decision makers about Essential Fish Habitat conservation and help define harvest control rules based on direct observation of biomass at the sea. However, missed survey locations over the survey years are common in long-term programme data. Currently, modelling approaches to filling gaps in spatiotemporal survey data range from quickly applicable solutions to complex modelling. Most models require setting prior statistical assumptions on spatial distributions, assuming short-term temporal dependency between the data, and scarcely considering the environmental aspects that might have influenced stock presence in the missed locations. This paper proposes a statistical and machine learning based model to fill spatiotemporal gaps in survey data and produce robust estimates for stock assessment experts, decision makers, and regional fisheries management organizations. We apply our model to the SoleMon survey data in North-Central Adriatic Sea (Mediterranean Sea) for 4 stocks: Sepia officinalis, Solea solea, Squilla mantis, and Pecten jacobaeus. We reconstruct the biomass-index (i.e., biomass over the swept area) of 10 locations missed in 2020 (out of the 67 planned) because of several factors, including COVID-19 pandemic related restrictions. We evaluate model performance on 2019 data with respect to an alternative index that assumes biomass proportion consistency over time. Our model’s novelty is that it combines three complementary components. A spatial component estimates stock biomass-index in the missed locations in one year, given the surveyed location’s biomass-index distribution in the same year. A temporal component forecasts, for each missed survey location, biomass-index given the data history of that haul. An environmental component estimates a biomass-index weighting factor based on the environmental suitability of the haul area to species presence. Combining these components allows understanding the interplay between environmental-change drivers, stock presence, and fisheries. Our model formulation is general enough to be applied to other survey data with lower spatial homogeneity and more temporal gaps than the SoleMon dataset

Dissolution test for risk assessment of nanoparticles: a pilot study

Worldwide efforts are currently trying to produce effective risk assessment models for orally ingested nanoparticles. These tests should provide quantitative information on the bioaccessibility and bioavailability of products of biotransformation, such as dissolved ionic species and/or aggregates. In vitro dissolution tests might be useful for nanoparticle risk assessment, because of their potential to quantitatively monitor the changes of specific properties (e.g., dissolution, agglomeration, etc.), which are critical factors linked to bioaccessibility/bioavailability. Unfortunately, the technological advancement of such tools is currently hampered by the complexity and evolving nature of nanoparticle properties that are strongly influenced by the environment and are often difficult to trace in a standardized manner. Hence, the test's success depends on its ability to quantify such properties using standardized experimental conditions to mimic reality as closely as possible. Here we applied an in vitro dissolution test to quantify the dissolution of silver nanoparticles under dynamic conditions, which likely occur in human digestion, providing a clear description of the bioaccessible ionic species (free and matrix bound ions or soluble silver organic or inorganic complexes) occurring during the different digestion phases. We demonstrated the test feasibility using a multi-technique approach and following pre-standardized operational procedures to allow for a comprehensive description of the process as a whole. Moreover, this can favour data reliability for benchmarking. Finally, we showed how the estimated values of the bioaccessible ionic species relate to absorption and excretion parameters, as measured in vivo. The outcomes presented in this work highlight the potential regulatory role of the dissolution test for orally ingested nanoparticles and, although preliminary, experimentally demonstrate the regulatory oriented "read-across" principle

Seeing the wood through the trees. Combining shape information from different landmark configurations

The geometric morphometric (GM) analysis of complex anatomical structures is an ever more powerful tool to study biological variability, adaptation and evolution. Here, we propose a new method (combinland), developed in R, meant to combine the morphological information contained in different landmark coordinate sets into a single dataset, under a GM context. combinland builds a common ordination space taking into account the entire shape information encoded in the starting configurations. We applied combinland to a Primate case study including 133 skulls belonging to 14 species. On each specimen, we simulated photo acquisitions converting the 3D landmark sets into six 2D configurations along standard anatomical views. The application of combinland shows statistically negligible differences in the ordination space compared to that of the original 3D objects, in contrast to a previous method meant to address the same issue. Hence, we argue combinland allows to correctly retrieve 3D-quality statistical information from 2D landmark configurations. This makes combinland a viable alternative when the extraction of 3D models is not possible, recommended, or too expensive, and to make full use of disparate sources (and views) of morphological information regarding the same specimens. The code and examples for the application of combinland are available in the Arothron R package

Estimating hidden fishing activity hotspots from vessel transmitted data

Monitoring fishery activity is essential for resource planning and guaranteeing fisheries sustainability. Large fishing vessels constantly and continuously communicate their positions via Automatic Identification System (AIS) or Vessel Monitoring Systems (VMSs). These systems can use radio or Global Positioning System (GPS) devices to transmit data. Processing and integrating these big data with other fisheries data allows for exploring the relations between socio-economic and ecosystem assets in marine areas, which is fundamental in fishery monitoring. In this context, estimating actual fishing activity from time series of AIS and VMS data would enhance the correct identification of fishing activity patterns and help assess regulations' effectiveness. However, these data might contain gaps because of technical issues such as limited coverage of the terrestrial receivers or saturated transmission bands. Other sources of data gaps are adverse meteorological conditions and voluntary switch-offs. Gaps may also include hidden (unreported) fishing activity whose quantification would improve actual fishing activity estimation. This paper presents a workflow for AIS/VMS big-data analysis that estimates potential unreported fishing activity hotspots in a marine area. The workflow uses a statistical spatial analysis over vessel speeds and coordinates and a multi-source data integration approach that can work on multiple areas and multiple analysis scales. Specifically, it (i) estimates fishing activity locations and rebuilds data gaps, (ii) estimates the potential unreported fishing hour distribution and the unreported-over-total ratio of fishing hours at a 0.01° spatial resolution, (iii) identifies potential unreported fishing activity hotspots, (iv) extracts the stocks involved in these hotspots (using global-scale repositories of stock and species observation data) and raises an alert about their possible endangered, threatened, and protected (ETP) status. The workflow is also a free-to-use Web Service running on an open science-compliant cloud computing platform with a Web Processing Service (WPS) standard interface, allowing efficient big data processing. As a study case, we focussed on the Adriatic Sea. We reconstructed the monthly reported and potential unreported trawling activity in 2019, using terrestrial AIS data with a 5-min sampling period, containing ~50 million records transmitted by ~1,600 vessels. The results highlight that the unreported fishing activity hotspots especially impacted Italian coasts and some forbidden and protected areas. The potential unreported activity involved 33 stocks, four of which were ETP species in the basin. The extracted information agreed with expert studies, and the estimated trawling patterns agreed with those produced by the Global Fishing Watch

Changes in renal function after nephroureterectomy for upper urinary tract carcinoma: analysis of a large multicenter cohort (Radical Nephroureterectomy Outcomes (RaNeO) Research Consortium)

Purpose To investigate prevalence and predictors of renal function variation in a multicenter cohort treated with radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC). Methods Patients from 17 tertiary centers were included. Renal function variation was evaluated at postoperative day (POD)-1, 6 and 12 months. Timepoints differences were Delta 1 = POD-1 eGFR - baseline eGFR; Delta 2 = 6 months eGFR - POD-1 eGFR; Delta 3 = 12 months eGFR - 6 months eGFR. We defined POD-1 acute kidney injury (AKI) as an increase in serum creatinine by >= 0.3 mg/dl or a 1.5 1.9-fold from baseline. Additionally, a cutoff of 60 ml/min in eGFR was considered to define renal function decline at 6 and 12 months. Logistic regression (LR) and linear mixed (LM) models were used to evaluate the association between clinical factors and eGFR decline and their interaction with follow-up. Results A total of 576 were included, of these 409(71.0%) and 403(70.0%) had an eGFR < 60 ml/min at 6 and 12 months, respectively, and 239(41.5%) developed POD-1 AKI. In multivariable LR analysis, age (Odds Ratio, OR 1.05, p < 0.001), male gender (OR 0.44, p = 0.003), POD-1 AKI (OR 2.88, p < 0.001) and preoperative eGFR < 60 ml/min (OR 7.58, p < 0.001) were predictors of renal function decline at 6 months. Age (OR 1.06, p < 0.001), coronary artery disease (OR 2.68, p = 0.007), POD-1 AKI (OR 1.83, p = 0.02), and preoperative eGFR < 60 ml/min (OR 7.80, p < 0.001) were predictors of renal function decline at 12 months. In LM models, age (p = 0.019), hydronephrosis (p < 0.001), POD-1 AKI (p < 0.001) and pT-stage (p = 0.001) influenced renal function variation (ss 9.2 +/- 0.7, p < 0.001) during follow-up. Conclusion Age, preoperative eGFR and POD-1 AKI are independent predictors of 6 and 12 months renal function decline after RNU for UTUC

Diagnosis of prostate cancer with magnetic resonance imaging in men treated with 5-alpha-reductase inhibitors

Purpose The primary aim of this study was to evaluate if exposure to 5-alpha-reductase inhibitors (5-ARIs) modifies the effect of MRI for the diagnosis of clinically significant Prostate Cancer (csPCa) (ISUP Gleason grade &gt;= 2).Methods This study is a multicenter cohort study including patients undergoing prostate biopsy and MRI at 24 institutions between 2013 and 2022. Multivariable analysis predicting csPCa with an interaction term between 5-ARIs and PIRADS score was performed. Sensitivity, specificity, and negative (NPV) and positive (PPV) predictive values of MRI were compared in treated and untreated patients.Results 705 patients (9%) were treated with 5-ARIs [median age 69 years, Interquartile range (IQR): 65, 73; median PSA 6.3 ng/ml, IQR 4.0, 9.0; median prostate volume 53 ml, IQR 40, 72] and 6913 were 5-ARIs naive (age 66 years, IQR 60, 71; PSA 6.5 ng/ml, IQR 4.8, 9.0; prostate volume 50 ml, IQR 37, 65). MRI showed PIRADS 1-2, 3, 4, and 5 lesions in 141 (20%), 158 (22%), 258 (37%), and 148 (21%) patients treated with 5-ARIs, and 878 (13%), 1764 (25%), 2948 (43%), and 1323 (19%) of untreated patients (p &lt; 0.0001). No difference was found in csPCa detection rates, but diagnosis of high-grade PCa (ISUP GG &gt;= 3) was higher in treated patients (23% vs 19%, p = 0.013). We did not find any evidence of interaction between PIRADS score and 5-ARIs exposure in predicting csPCa. Sensitivity, specificity, PPV, and NPV of PIRADS &gt;= 3 were 94%, 29%, 46%, and 88% in treated patients and 96%, 18%, 43%, and 88% in untreated patients, respectively.Conclusions Exposure to 5-ARIs does not affect the association of PIRADS score with csPCa. Higher rates of high-grade PCa were detected in treated patients, but most were clearly visible on MRI as PIRADS 4 and 5 lesions.Trial registration The present study was registered at ClinicalTrials.gov number: NCT05078359