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Institution

University of Modena and Reggio Emilia

EducationModena, Italy
About: University of Modena and Reggio Emilia is a education organization based out in Modena, Italy. It is known for research contribution in the topics: Population & Transplantation. The organization has 8179 authors who have published 22418 publications receiving 671337 citations. The organization is also known as: Università degli Studi di Modena e Reggio Emilia & Universita degli Studi di Modena e Reggio Emilia.


Papers
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Journal ArticleDOI
25 Jun 2010-Cell
TL;DR: These findings suggest a new pathway by which Dicer inhibition drifts epithelial cancer toward a less-differentiated, mesenchymal fate to foster metastasis, and attenuates miRNA biosynthesis by targeting Dicer, a key component of the miRNA processing machinery.

660 citations

Proceedings ArticleDOI
14 Jun 2020
TL;DR: The architecture improves both the image encoding and the language generation steps: it learns a multi-level representation of the relationships between image regions integrating learned a priori knowledge, and uses a mesh-like connectivity at decoding stage to exploit low- and high-level features.
Abstract: Transformer-based architectures represent the state of the art in sequence modeling tasks like machine translation and language understanding. Their applicability to multi-modal contexts like image captioning, however, is still largely under-explored. With the aim of filling this gap, we present M² - a Meshed Transformer with Memory for Image Captioning. The architecture improves both the image encoding and the language generation steps: it learns a multi-level representation of the relationships between image regions integrating learned a priori knowledge, and uses a mesh-like connectivity at decoding stage to exploit low- and high-level features. Experimentally, we investigate the performance of the M² Transformer and different fully-attentive models in comparison with recurrent ones. When tested on COCO, our proposal achieves a new state of the art in single-model and ensemble configurations on the "Karpathy" test split and on the online test server. We also assess its performances when describing objects unseen in the training set. Trained models and code for reproducing the experiments are publicly available at: https://github.com/aimagelab/meshed-memory-transformer.

660 citations

Journal ArticleDOI
TL;DR: Asthma is a global health problem affecting around 300 million individuals of all ages, ethnic groups and countries, and a common international approach is favored to define severe asthma, uncontrolled asthma, and when the 2 coincide, although adaptation may be required in accordance with local conditions.
Abstract: Asthma is a global health problem affecting around 300 million individuals of all ages, ethnic groups and countries. It is estimated that around 250,000 people die prematurely each year as a result of asthma. Concepts of asthma severity and control are important in evaluating patients and their response to treatment, as well as for public health, registries, and research (clinical trials, epidemiologic, genetic, and mechanistic studies), but the terminology applied is not standardized, and terms are often used interchangeably. A common international approach is favored to define severe asthma, uncontrolled asthma, and when the 2 coincide, although adaptation may be required in accordance with local conditions. A World Health Organization meeting was convened April 5-6, 2009, to propose a uniform definition of severe asthma. An article was written by a group of experts and reviewed by the Global Alliance against Chronic Respiratory Diseases review group. Severe asthma is defined by the level of current clinical control and risks as "Uncontrolled asthma which can result in risk of frequent severe exacerbations (or death) and/or adverse reactions to medications and/or chronic morbidity (including impaired lung function or reduced lung growth in children)." Severe asthma includes 3 groups, each carrying different public health messages and challenges: (1) untreated severe asthma, (2) difficult-to-treat severe asthma, and (3) treatment-resistant severe asthma. The last group includes asthma for which control is not achieved despite the highest level of recommended treatment and asthma for which control can be maintained only with the highest level of recommended treatment.

657 citations

Journal ArticleDOI
TL;DR: It is shown that malignant granular cell tumor is a high-grade sarcoma with a high rate of metastases and a short survival.
Abstract: Seventy-three cases of malignant, atypical, and multicentric granular cell tumors of soft tissue were studied to clarify criteria for malignancy and prognostic factors Six histologic criteria were assessed: necrosis, spindling, vesicular nuclei with large nucleoli, increased mitotic activity (> 2 mitoses/10 high-power fields at 200x magnification), high nuclear to cytoplasmic (N:C) ratio, and pleomorphism Neoplasms that met three or more of these criteria were classified as histologically malignant; those that met one or two criteria were classified as atypical; and those that displayed only focal pleomorphism but fulfilled none of the other criteria were classified as benign Hence, 46 cases were classified as histologically malignant, 21 as atypical (3 were multicentric), and 6 as benign (all were multicentric) The patients with benign multicentric and atypical granular cell tumors had no metastases and there were no tumor deaths In contrast, 11 of 28 patients (39%) with malignant granular cell tumor with follow-up information died of disease at a median interval of 3 years; 8 of 28 (29%) were alive with disease, and 9/28 (32%) were disease free (median intervals, 2 and 7 years, respectively) There were local recurrences in 9 of 28 malignant cases (32%) and metastases in 14 of 28 (50%) (median intervals, each 2 years) Forty-eight cases were studied immunohistochemically; 100% expressed vimentin, 98% S-100 protein, 98% neuron-specific enolase, 69% CD57, and 65% CD68 Alpha-smooth muscle actin, desmin, epithelial membrane antigen (EMA), cytokeratins (with CAM 52 and KL-1), chromogranin, and HMB45 were not detected The proliferative index with Ki67 (MIB 1) was 10-50% in 14 of 25 malignant tumors (56%), and immunostaining for p53 was detected in 50% or more of tumor cells in 17 of 25 (68%); both of these factors were statistically significant with regard to the histologic classification as benign, atypical, or malignant Ultrastructural examination of 13 benign, atypical, and malignant granular cell tumors showed engorgement of the cytoplasm with complex granules and lysosomes, as well as Schwannian features By flow cytometric DNA analysis, two of six malignant tumors were aneuploid, two were hyperdiploid, and two were diploid One atypical tumor was aneuploid and all 11 benign tumors were either diploid (9 cases) or hyperdiploid (2 cases) Statistically significant adverse prognostic factors with regard to survival included local recurrence, metastasis, larger tumor size, older patient age, histologic classification as malignant, presence of necrosis, increased mitotic activity, spindling of tumor cells, vesicular nuclei with large nucleoli, and Ki67 values greater [corrected] than 10% This study defines clinical and morphologic criteria for malignancy in granular cell tumors and shows that malignant granular cell tumor is a high-grade sarcoma with a high rate of metastases and a short survival

657 citations

Journal ArticleDOI
TL;DR: The quality of practice guidelines developed by specialty societies is unsatisfactory and explicit methodological criteria for the production of guidelines shared among public agencies, scientific societies, and patients' associations need to be set up.

643 citations


Authors

Showing all 8322 results

NameH-indexPapersCitations
Carlo M. Croce1981135189007
Gregory Y.H. Lip1693159171742
Geoffrey Burnstock141148899525
Peter M. Rothwell13477967382
Claudio Franceschi12085659868
Lorenzo Galluzzi11847771436
Leonardo M. Fabbri10956660838
David N. Reinhoudt107108248814
Stefano Pileri10063543369
Andrea Bizzeti99116846880
Brian K. Shoichet9828140313
Dante Gatteschi9772748729
Roberta Sessoli9542441458
Thomas A. Buchholz9349433409
Pier Luigi Zinzani9285735476
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202376
2022230
20212,354
20202,083
20191,633
20181,450