Observer-oriented approach improves species distribution models from citizen science data

Citizen science platforms are increasingly growing, and, storing a huge amount of data on species locations, they provide researchers with essential information to develop sound strategies for species conservation. However, the lack of information on surveyed sites (i.e., where the observers did not record the target species) and sampling effort (e.g., the number of surveys at a given site, by how many observers, and for how much time) strongly limit the use of citizen science data. Thus, we examined the advantage of using an observer-oriented approach (i.e., considering occurrences of species other than the target species collected by the observers of the target species as pseudo-absences and additional predictors relative to the total number of observations, observers, and days in which locations were collected in a given sampling unit, as proxies of sampling effort) to develop species distribution models. Specifically, we considered 15 mammal species occurring in Italy and compared the predictive accuracy of the ensemble predictions of nine species distribution models carried out considering random pseudo-absences versus observer-oriented approach. Through cross-validations, we found that the observer-oriented approach improved species distribution models, providing a higher predictive accuracy than random pseudo-absences. Our results showed that species distribution modeling developed using pseudo-absences derived citizen science data outperform those carried out using random pseudo-absences and thus improve the capacity of species distribution models to accurately predict the geographic range of species when deriving robust surrogate of sampling effort

A distributed approach for parameter estimation in Systems Biology models

Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology Mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an environment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better parameter estimation of systems biology models

ochratoxin a as possible factor trigging autism and its male prevalence via epigenetic mechanism

The role of dysbiosis causing leaky gut with xenobiotic production and absorption is increasingly demonstrated in autism spectrum disorder (ASD) pathogenesis. Among xenobiotics, we focused on ochratoxin A (one of the major food contaminating mycotoxin), that in vitro and in vivo exerts a male-specific neurotoxicity probably via microRNA modulation of a specific target gene. Among possible targets, we focused on neuroligin4X. Interestingly, this gene carries some single nucleotide polymorphisms (SNPs) already correlated with the disease and with illegitimate microRNA binding sites and, being located on X-chromosome, could explain the male prevalence. In conclusion, we propose a possible gene–environment interaction triggering ASD explaining the epigenetic neurotoxic mechanism activated by ochratoxin A in genetically predisposed children. This mechanism offers a clue for male prevalence of the disease and may have an important impact on prevention and cure of ASD

Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach

In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation. In this perspective the collective behavior of a set of molecular alterations on a gene network is a particularly adapt scenario for a first application of the proposed method, since most diseases are neither related to a single mutation nor to an established set of molecular alterations. Therefore, after characterizing the method numerically, we applied as a proof of principle the pME approach to breast cancer (BC) somatic mutation data downloaded from Cancer Genome Atlas (TCGA) database. For each patient we measured the network frailness of over 90 significant subnetworks of the protein-protein interaction network, where each perturbation was defined by patient-specific somatic mutations. Interestingly the frailness measures depend on the position of the alterations on the gene network more than on their amount, unlike most traditional enrichment scores. In particular low-degree mutations play an important role in causing high frailness measures. The potential applicability of the proposed method is wide and suggests future development in the control theory context

Large-scale modelling of neuronal systems

The brain is, without any doubt, the most complex system of the human body. Its complexity is also due to the extremely high number of neurons, as well as the huge number of synapses connecting them. Each neuron is capable to perform complex tasks, like learning and memorizing a large class of patterns. The simulation of large neuronal systems is challenging for both technological and computational reasons, and can open new perspectives for the comprehension of brain functioning. A well-known and widely accepted model of bidirectional synaptic plasticity, the BCM model, is stated by a differential equation approach based on bistability and selectivity properties. We have modified the BCM model extending it from a single-neuron to a whole-network model. This new model is capable to generate interesting network topologies starting from a small number of local parameters, describing the interaction between incoming and outgoing links from each neuron. We have characterized this model in terms of complex network theory, showing how this learning rule can be a support for network generation

Decreased apoptosis in fatty livers submitted to subnormothermic machine-perfusion respect to cold storage

Machine perfusion at subnormothermic temperature (20°C), MP20, was developed by Vairetti et al. and showed to afford a better preservation of fatty livers respect to traditional cold storage (CS) in terms of enzyme release into the perfusate, bile production, glycogen stores, energy charge and oxidative stress. Here we investigated whether it also caused decreased cell death by apoptosis. Fatty and lean Zucker rats were submitted to MP20 or CS for 6 h and reperfused normothermically for 2 h. Apoptotic cells were revealed by immunohistochemistry of activated caspase-3 and M30 (new epitope on CK18 degraded by caspase-3) and by the TUNEL assay. Portal pressure was also determined. A statistically significant reduction of hepatocyte apoptosis, but especially of sinusoidal cells was determined for fatty livers submitted to MP20 respect to CS. Portal pressure was significantly lower after MP20 respect to CS. The reduction of sinusoidal cell death by apoptosis without need for anti-apoptotic therapies appears particularly positive since apoptotic sinusoidal cells hinder microcirculation in the sinusoids and are thrombogenic. These results further confirm the potential of MP20 for preserving fatty livers that would be otherwise discarded as grafts, and thus for increasing the donor pool for liver transplantation