urum wheat (DW), Triticum turgidum L. ssp. durum (Desf.) Husn., genome BBAA, is a cereal grain mainly used for pasta production and evolved from domesticated emmer wheat (DEW), T. turgidum ssp. dicoccum (Schrank ex Schübl.) Thell. DEW itself derived from wild emmer wheat (WEW), T. turgidum ssp. dicoccoides (Körn. ex Asch. & Graebn.

Abstract. A systematic study of black carbon (BC) vertical profiles measured at high-resolution over three Italian basin valleys (Terni Valley, Po Valley and Passiria Valley) is presented. BC vertical profiles are scarcely available in literature. The campaign lasted 45 days and resulted in 120 measured vertical profiles. Besides the BC mass concentration, measurements along the vertical profiles also included aerosol size distributions in the optical particle counter range, chemical analysis of filter samples and a full set of meteorological parameters. Using the collected experimental data, we performed calculations of aerosol optical properties along the vertical profiles. The results, validated with AERONET data, were used as inputs to a radiative transfer model (libRadtran). The latter allowed an estimation of vertical profiles of the aerosol direct radiative effect, the atmospheric absorption and the heating rate in the lower troposphere. The present measurements revealed some common behaviors over the studied basin valleys. Specifically, at the mixing height, marked concentration drops of both BC (range: from −48.4 ± 5.3 to −69.1 ± 5.5%) and aerosols (range: from −23.9 ± 4.3 to −46.5 ± 7.3%) were found. The measured percentage decrease of BC was higher than that of aerosols: therefore, the BC aerosol fraction decreased upwards. Correspondingly, both the absorption and scattering coefficients decreased strongly across the mixing layer (range: from −47.6 ± 2.5 to −71.3 ± 3.0% and from −23.5 ± 0.8 to −61.2 ± 3.1%, respectively) resulting in a single-scattering albedo increase along height (range: from +4.9 ± 2.2 to +7.4 ± 1.0%). This behavior influenced the vertical distribution of the aerosol direct radiative effect and of the heating rate. In this respect, the highest atmospheric absorption of radiation was predicted below the mixing height (~ 2–3 times larger than above it) resulting in a heating rate characterized by a vertical negative gradient (range: from −2.6 ± 0.2 to −8.3 ± 1.2 K day−1 km−1). In conclusion, the present results suggest that the BC below the mixing height has the potential to promote a negative feedback on the atmospheric stability over basin valleys, weakening the ground-based thermal inversions and increasing the dispersal conditions.

This study examines an innovative application of the aerosol deliquescence and crystallization determination, for corrosion prevention and energy-saving strategies in free-cooled data centers. Aerosol deliquescence and crystallization were investigated by combining standardized aerosol sampling techniques (i.e. EN-14907) with the assessment of the electrical effects of aerosol, while varying relative humidity within a specially designed aerosol exposure chamber. Aerosol samples collected in the Po Valley (Northern Italy) were analysed; a clearly defined hysteresis cycle (deliquescence and crystallization at 60.5 ± 0.8 and 47.9 ± 0.7 % of RH, respectively) was found. Results were applied to a data center designed for the Italian National Oil and Gas Company, making it possible to identify a critical area for direct free cooling at this data center. As a result, aerosol hydration was avoided (thus preventing aerosol from damaging electrical components) and a large amount of energy saved (using free cooling instead of air-conditioning); the potential energy saving achieved in this way was 79 % (compared to the energy consumption of a traditional air-conditioning system): 215 GWh of energy was saved, and 78 fewer kt of equivalent CO 2 was emitted per year. Moreover, in order to evaluate whether a real-time estimation of the aerosol hydration state within a data center could be performed, measured deliquescence and crystallization were compared through simulations performed using three different models: two thermodynamic models for deliquescence and a parametric model for crystallization. The results obtained tend to converge in terms of deliquescence, whereas in the case of crystallization, they failed to effectively simulate experimental aerosol behaviour.

Phosphodiesterase 11 (PDE11) is the latest isoform of the PDEs family to be identified, acting on both cyclic adenosine monophosphate and cyclic guanosine monophosphate. The initial reports of PDE11 found evidence for PDE11 expression in skeletal muscle, prostate, testis, and salivary glands; however, the tissue distribution of PDE11 still remains a topic of active study and some controversy. Given the sequence similarity between PDE11 and PDE5, several PDE5 inhibitors have been shown to cross-react with PDE11. Accordingly, many non-selective inhibitors, such as IBMX, zaprinast, sildenafil, and dipyridamole, have been documented to inhibit PDE11. Only recently, a series of dihydrothieno[3,2-d]pyrimidin-4(3H)-one derivatives proved to be selective toward the PDE11 isoform. In the absence of experimental data about PDE11 X-ray structures, we found interesting to gain a better understanding of the enzyme-inhibitor interactions using in silico simulations. In this work, we describe a computational approach based on homology modeling, docking, and molecular dynamics simulation to derive a predictive 3D model of PDE11. Using a Graphical Processing Unit architecture, it is possible to perform long simulations, find stable interactions involved in the complex, and finally to suggest guideline for the identification and synthesis of potent and selective inhibitors.

The energy demands of data centers (DCs) worldwide are rapidly increasing, as are their environmental and economic costs. This paper presents a study conducted at Sannazzaro de' Burgondi (Po Valley), Italy, specifically aimed at optimizing the operating conditions of a DC designed for the Italian Oil and Gas Company (Eni) (5200 m(2) of Information Technology installed, 30 MW) and based on a direct free cooling (DFC) system. The aim of the study was to save the largest possible quantity of energy, while at the same time preventing aerosol corrosion. The aerosol properties (number size distribution, chemical composition, deliquescence relative humidity (DRH), acidity) and meteorological parameters were monitored and utilized to determine the potential levels of aerosol entering the DC (equivalent ISO class), together with its DRH. These data enabled us both to select the DC's filtering system (MERV13 filters) and to optimize the cooling cycle through calculation of the most reliable humidity cycle (60% of maximum allowed RH) applicable to the DFC. A potential energy saving of 81%, compared to a traditional air conditioning cooling system, was estimated: in one year, for 1 kW of installed information technology, the estimated energy saving is 7.4 MWh, resulting in 2.7 fewer tons of CO2 being emitted, and a financial saving of € 1100.

BackgroundNowadays, the increasing availability of omics data, due to both the advancements in the acquisition of molecular biology results and in systems biology simulation technologies, provides the bases for precision medicine. Success in precision medicine depends on the access to healthcare and biomedical data. To this end, the digitization of all clinical exams and medical records is becoming a standard in hospitals. The digitization is essential to collect, share, and aggregate large volumes of heterogeneous data to support the discovery of hidden patterns with the aim to define predictive models for biomedical purposes. Patients’ data sharing is a critical process. In fact, it raises ethical, social, legal, and technological issues that must be properly addressed.ResultsIn this work, we present an infrastructure devised to deal with the integration of large volumes of heterogeneous biological data. The infrastructure was applied to the data collected between 2010–2016 in one of the major diagnostic analysis laboratories in Italy. Data from three different platforms were collected (i.e., laboratory exams, pathological anatomy exams, biopsy exams). The infrastructure has been designed to allow the extraction and aggregation of both unstructured and semi-structured data. Data are properly treated to ensure data security and privacy. Specialized algorithms have also been implemented to process the aggregated information with the aim to obtain a precise historical analysis of the clinical activities of one or more patients. Moreover, three Bayesian classifiers have been developed to analyze examinations reported as free text. Experimental results show that the classifiers exhibit a good accuracy when used to analyze sentences related to the sample location, diseases presence and status of the illnesses.ConclusionsThe infrastructure allows the integration of multiple and heterogeneous sources of anonymized data from the different clinical platforms. Both unstructured and semi-structured data are processed to obtain a precise historical analysis of the clinical activities of one or more patients. Data aggregation allows to perform a series of statistical assessments required to answer complex questions that can be used in a variety of fields, such as predictive and precision medicine. In particular, studying the clinical history of patients that have developed similar pathologies can help to predict or individuate markers able to allow an early diagnosis of possible illnesses.

Autism spectrum disorder (ASD) is marked by a strong genetic heterogeneity, which is underlined by the low overlap between ASD risk gene lists proposed in different studies. In this context, molecular networks can be used to analyze the results of several genome-wide studies in order to underline those network regions harboring genetic variations associated with ASD, the so-called “disease modules.” In this work, we used a recent network diffusion-based approach to jointly analyze multiple ASD risk gene lists. We defined genome-scale prioritizations of human genes in relation to ASD genes from multiple studies, found significantly connected gene modules associated with ASD and predicted genes functionally related to ASD risk genes. Most of them play a role in synapsis and neuronal development and function; many are related to syndromes that can be in comorbidity with ASD and the remaining are involved in epigenetics, cell cycle, cell adhesion and cancer.

Spinal neurofibromatosis (SNF), a phenotypic subclass of neurofibromatosis 1 (NF1), is characterized by bilateral neurofibromas involving all spinal roots. In order to deepen the understanding of SNF’s clinical and genetic features, we identified 81 patients with SNF, 55 from unrelated families, and 26 belonging to 19 families with at least 1 member affected by SNF, and 106 NF1 patients aged >30 years without spinal tumors. A comprehensive NF1 mutation screening was performed using NGS panels, including NF1 and several RAS pathway genes. The main features of the SNF subjects were a higher number of internal neurofibromas (p < 0.001), nerve root swelling (p < 0.001), and subcutaneous neurofibromas (p = 0.03), while hyperpigmentation signs were significantly less frequent compared with the classical NF1-affected cohorts (p = 0.012). Fifteen patients underwent neurosurgical intervention. The histological findings revealed neurofibromas in 13 patients and ganglioneuromas in 2 patients. Phenotypic variability within SNF families was observed. The proportion of missense mutations was higher in the SNF cases than in the classical NF1 group (21.40% vs. 7.5%, p = 0.007), conferring an odds ratio (OR) of 3.34 (CI = 1.33–10.78). Two unrelated familial SNF cases harbored in trans double NF1 mutations that seemed to have a subclinical worsening effect on the clinical phenotype. Our study, with the largest series of SNF patients reported to date, better defines the clinical and genetic features of SNF, which could improve the management and genetic counseling of NF1.