Dynamical Criticality in Gene Regulatory Networks

A well-known hypothesis, with far-reaching implications, is that biological evolution should preferentially lead to states that are dynamically critical. In previous papers, we showed that a well-known model of genetic regulatory networks, namely, that of random Boolean networks, allows one to study in depth the relationship between the dynamical regime of a living being's gene network and its response to permanent perturbations. In this paper, we analyze a huge set of new experimental data on single gene knockouts in S. cerevisiae, laying down a statistical framework to determine its dynamical regime. We find that the S. cerevisiae network appears to be slightly ordered, but close to the critical region. Since our analysis relies on dichotomizing continuous data, we carefully consider the issue of an optimal threshold choice

Statistical moments of power spectrum: a fast tool for the classification of seismic events recorded on volcanoes

Abstract. Spectral analysis has been applied to almost thousand seismic events recorded at Vesuvius volcano (Naples, southern Italy) in 2018 with the aim to test a new tool for a fast event classification. We computed two spectral parameters, central frequency and shape factor, from the spectral moments of order 0, 1, and 2, for each event at seven seismic stations taking the mean among the three components of ground motion. The analyzed events consist of volcano-tectonic earthquakes, low frequency events and unclassified events (landslides, rockfall, thunders, quarry blasts, etc.). Most of them are of low magnitude, and/or low maximum signal amplitude, therefore the signal to noise ratio is very different between the low noise summit stations and the higher noise stations installed at low elevation around the volcano. The results of our analysis show that volcano-tectonic earthquakes and low frequency events are easily distinguishable through the spectral moments values, particularly at seismic stations closer to the epicenter. On the contrary, unclassified events show the spectral parameters values distributed in a broad range which overlap both the volcano-tectonic earthquakes and the low frequency events. Since the computation of spectral parameters is extremely easy and fast for a detected event, it may become an effective tool for event classification in observatory practice

Clinical characteristics and outcome of patients with autoimmune hemolytic anemia (AIHA) uniformly defined as primary by a diagnostic work-up

Primary autoimmune hemolytic anemia (P-AIHA) is a relatively uncommon and hetereogeneous disease characterized by the destruction of red blood cells due to anti-erythrocyte autoantibodies (AeAbs) in the absence of an associated disease [1–3]. Secondary AHIA is frequently associated with lymphoproliferative diseases (LD) in particular, chronic lymphocytic leukemia, aggressive or indolent lymphomas, autoimmune disorders, malignancies other than lymphoid, and infections [1,2,4]. On the hypothetical assumption that in a significant proportion of cases defined as P-AIHA the clinical heterogeneity could be due to an ignored associated disease, we retrospectively analyzed the clinical characteristics and outcome of patients with a diagnosis of P-AIHA based on a diagnostic work-up aimed at excluding or identifying an associated disease. ..

The GENIUS Grid Portal and robot certificates: a new tool for e-Science

<p>Abstract</p> <p>Background</p> <p>Grid technology is the computing model which allows users to share a wide <it>pletora </it>of distributed computational resources regardless of their geographical location. Up to now, the high security policy requested in order to access distributed computing resources has been a rather big limiting factor when trying to broaden the usage of Grids into a wide community of users. Grid security is indeed based on the Public Key Infrastructure (PKI) of X.509 certificates and the procedure to get and manage those certificates is unfortunately not straightforward. A first step to make Grids more appealing for new users has recently been achieved with the adoption of robot certificates.</p> <p>Methods</p> <p>Robot certificates have recently been introduced to perform automated tasks on Grids on behalf of users. They are extremely useful for instance to automate grid service monitoring, data processing production, distributed data collection systems. Basically these certificates can be used to identify a person responsible for an unattended service or process acting as client and/or server. Robot certificates can be installed on a smart card and used behind a portal by everyone interested in running the related applications in a Grid environment using a user-friendly graphic interface. In this work, the GENIUS Grid Portal, powered by EnginFrame, has been extended in order to support the new authentication based on the adoption of these robot certificates.</p> <p>Results</p> <p>The work carried out and reported in this manuscript is particularly relevant for all users who are not familiar with personal digital certificates and the technical aspects of the Grid Security Infrastructure (GSI). The valuable benefits introduced by robot certificates in e-Science can so be extended to users belonging to several scientific domains, providing an asset in raising Grid awareness to a wide number of potential users.</p> <p>Conclusion</p> <p>The adoption of Grid portals extended with robot certificates, can really contribute to creating transparent access to computational resources of Grid Infrastructures, enhancing the spread of this new paradigm in researchers' working life to address new global scientific challenges. The evaluated solution can of course be extended to other portals, applications and scientific communities.</p

Partial cystectomy in young male for a urachal tumor masquerading a bladder leiomyoma

Leiomyoma of the bladder is a very rare disorder that accounts for 0.43% of all bladder neoplasms. Although the pathophysiology of the bladder leiomyoma is unknown, there are some theories on it. The patients can be asymptomatic; when present, clinical symptoms (lower urinary tract symptoms and\or hematuria), are associated with tumor size and location. For diagnosis, imaging plays an important role: ultrasound, computed tomography (CT) scan and magnetic resonance imaging (MRI) are the examinations most frequently performed. Treatment consists of surgical removal of the tumor, and the prognosis is excellent

Scattering and absorption imaging of a highly fractured fluid-filled seismogenetic volume in a region of slow deformation

Regions of slow strain often produce swarm-like sequences, characterized by the lack of a clear mainshock-aftershock pattern. The comprehension of their underlying physical mechanisms is challenging and still debated. We used seismic recordings from the last Pollino swarm (2010–2014) and nearby to separate and map seismic scattering (from P peak-delays) and absorption (from late-time coda-wave attenuation) at different frequencies in the Pollino range and surroundings. High-scattering and high-absorption anomalies are markers of a fluid-filled fracture volume extending from SE to NW (1.5–6 Hz) across the range. With increasing frequency, these anomalies approximately cover the area where the strongest earthquakes occurred from the sixteenth century until 1998. In our interpretation, the NW fracture propagation ends where carbonates of the Lucanian Apennines begin, as marked by a high-scattering and low-absorption area. At the highest frequency (12 Hz) the anomalies widen southward in the middle of the range, consistently marking the faults active during the recent Pollino swarm. Our results suggest that fracture healing has closed small-scale fractures across the SE faults that were active in the past centuries, and that the propagation of fluids may have played a crucial role in triggering the 2010–2014 Pollino swarm. Assuming that the fluid propagation ended at the carbonates barrier in the NW direction, fractures opened new paths to the South, favoring the nucleation of the last Pollino swarm. Indeed, the recently active faults in the middle of the seismogenic volume are marked by a high-scattering and high-absorption footprints. Our work provides evidence that attenuation parameters may track shape and dynamics of fluid-filled fracture networks in fault areas. Keywords: Pollino, Seismic attenuation, Scattering, Fluids, Fractures, Healin

Social Support Mediates the Relationship between Body Image Distress and Depressive Symptoms in Prostate Cancer Patients

Treatments for prostate cancer (PCa), the second most common cancer in men, may affect the body image (BI) of patients, increasing the risk of negative mental health outcomes. However, an enabling social support network may be a protective factor against the effects of BI distress on health. Therefore, the present study examined the mediating role of social support in the relationship between BI distress and depressive symptoms. Data were retrospectively collected from 197 PCa patients aged from 48 to 79 years (M = 67.19; SD = 6.83). The statistical package for the social sciences with PROCESS Macro was used to assess the direct and mediating effects with bias-corrected bootstrapping (10,000 samples). Results showed that BI distress was positively associated with depressive symptoms and that social support partially mediated this relationship. Moreover, among the different sources of social support, only friend support significantly mediated the association between BI distress and depressive symptoms. This study sheds light on the crucial role of social support as a dimension that can promote health in PCa patients

Predicting brain age with complex networks: From adolescence to adulthood.

In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging