Best Practices in Knowledge Transfer: Insights from Top Universities

The impact of knowledge transfer induced by universities on economy, society, and culture is widely acknowledged; nevertheless, this aspect is often neglected by university rankings. Here, we considered three of the most popular global university rankings and specific knowledge transfer indicators by U-multirank, a European ranking system launched by the European Commission, in order to answer to the following research question: how do the world top universities, evaluated according to global university rankings, perform from a knowledge transfer point of view? To this aim, the top universities have been compared with the others through the calculation of a Global Performance Indicator in Knowledge Transfer (GPI KT), a hierarchical clustering, and an outlier analysis. The results show that the universities best rated by global rankings do not always perform as well from knowledge transfer point of view. By combining the obtained results, it is possible to state that only 5 universities (Berkeley, Stanford, MIT, Harvard, CALTEC), among the top in the world, exhibit a high-level performance in knowledge transfer activities. For a better understanding of the success factors and best practices in knowledge transfer, a brief description of the 5 cited universities, in terms of organization of technology transfer service, relationship with business, entrepreneurship programs, and, more generally, third mission activities, is provided. A joint reading of the results suggests that the most popular global university rankings probably fail to effectively photograph third mission activities because they can manifest in a variety of forms, due to the intrinsic and intangible nature of third mission variables, which are difficult to quantify with simple and few indicators

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

Multiplex Networks for Early Diagnosis of Alzheimer's Disease

Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm3 (“patches”), without any a priori segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies

The Nucleosome-Remodeling ATPase ISWI Is Regulated by Poly-ADP-Ribosylation

ATP-dependent nucleosome-remodeling enzymes and covalent modifiers of chromatin set the functional state of chromatin. However, how these enzymatic activities are coordinated in the nucleus is largely unknown. We found that the evolutionary conserved nucleosome-remodeling ATPase ISWI and the poly-ADP-ribose polymerase PARP genetically interact. We present evidence showing that ISWI is target of poly-ADP-ribosylation. Poly-ADP-ribosylation counteracts ISWI function in vitro and in vivo. Our work suggests that ISWI is a physiological target of PARP and that poly-ADP-ribosylation can be a new, important post-translational modification regulating the activity of ATP-dependent nucleosome remodelers

Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age

Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used

Intratumor Heterogeneity of ALK-Rearrangements and Homogeneity of EGFR-Mutations in Mixed Lung Adenocarcinoma

BACKGROUND: Non Small Cell Lung Cancer is a highly heterogeneous tumor. Histologic intratumor heterogeneity could be 'major', characterized by a single tumor showing two different histologic types, and 'minor', due to at least 2 different growth patterns in the same tumor. Therefore, a morphological heterogeneity could reflect an intratumor molecular heterogeneity. To date, few data are reported in literature about molecular features of the mixed adenocarcinoma. The aim of our study was to assess EGFR-mutations and ALK-rearrangements in different intratumor subtypes and/or growth patterns in a series of mixed adenocarcinomas and adenosquamous carcinomas. METHODS: 590 Non Small Cell Lung Carcinomas tumor samples were revised in order to select mixed adenocarcinomas with available tumor components. Finally, only 105 mixed adenocarcinomas and 17 adenosquamous carcinomas were included in the study for further analyses. Two TMAs were built selecting the different intratumor histotypes. ALK-rearrangements were detected through FISH and IHC, and EGFR-mutations were detected through IHC and confirmed by RT-PCR. RESULTS: 10/122 cases were ALK-rearranged and 7 from those 10 showing an intratumor heterogeneity of the rearrangements. 12/122 cases were EGFR-mutated, uniformly expressing the EGFR-mutated protein in all histologic components. CONCLUSION: Our data suggests that EGFR-mutations is generally homogeneously expressed. On the contrary, ALK-rearrangement showed an intratumor heterogeneity in both mixed adenocarcinomas and adenosquamous carcinomas. The intratumor heterogeneity of ALK-rearrangements could lead to a possible impact on the therapeutic responses and the disease outcomes

Genetic Identification of a Network of Factors that Functionally Interact with the Nucleosome Remodeling ATPase ISWI

Nucleosome remodeling and covalent modifications of histones play fundamental roles in chromatin structure and function. However, much remains to be learned about how the action of ATP-dependent chromatin remodeling factors and histone-modifying enzymes is coordinated to modulate chromatin organization and transcription. The evolutionarily conserved ATP-dependent chromatin-remodeling factor ISWI plays essential roles in chromosome organization, DNA replication, and transcription regulation. To gain insight into regulation and mechanism of action of ISWI, we conducted an unbiased genetic screen to identify factors with which it interacts in vivo. We found that ISWI interacts with a network of factors that escaped detection in previous biochemical analyses, including the Sin3A gene. The Sin3A protein and the histone deacetylase Rpd3 are part of a conserved histone deacetylase complex involved in transcriptional repression. ISWI and the Sin3A/Rpd3 complex co-localize at specific chromosome domains. Loss of ISWI activity causes a reduction in the binding of the Sin3A/Rpd3 complex to chromatin. Biochemical analysis showed that the ISWI physically interacts with the histone deacetylase activity of the Sin3A/Rpd3 complex. Consistent with these findings, the acetylation of histone H4 is altered when ISWI activity is perturbed in vivo. These findings suggest that ISWI associates with the Sin3A/Rpd3 complex to support its function in vivo

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe