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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.


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Journal ArticleDOI
TL;DR: It is suggested that roflumilast reduces exacerbations and hospital admissions in patients with severe chronic obstructive pulmonary disease and chronic bronchitis who are at risk of frequent and severe exacerbations despite inhaled corticosteroid and longacting β2 agonist therapy, even in combination with tiotropium.

299 citations

Journal ArticleDOI
TL;DR: The authors tested 9 interventions (8 real and 1 sham) to reduce implicit racial preferences over time and found that none were effective after a delay of several hours to several days, and also found that these interventions did not change explicit racial preferences and were not reliably moderated by motivations to respond without prejudice.
Abstract: Implicit preferences are malleable, but does that change last? We tested 9 interventions (8 real and 1 sham) to reduce implicit racial preferences over time. In 2 studies with a total of 6,321 participants, all 9 interventions immediately reduced implicit preferences. However, none were effective after a delay of several hours to several days. We also found that these interventions did not change explicit racial preferences and were not reliably moderated by motivations to respond without prejudice. Short-term malleability in implicit preferences does not necessarily lead to long-term change, raising new questions about the flexibility and stability of implicit preferences. (PsycINFO Database Record

298 citations

Journal ArticleDOI
TL;DR: Mechanistic insight is provided into how milk glycans enrich specific beneficial bacterial populations in infants and clues for enhancing enrichment of bifidobacterial populations in at risk populations - such as premature infants are revealed.
Abstract: Individuals with inactive alleles of the fucosyltransferase 2 gene (FUT2; termed the ‘secretor’ gene) are common in many populations. Some members of the genus Bifidobacterium, common infant gut commensals, are known to consume 2′-fucosylated glycans found in the breast milk of secretor mothers. We investigated the effects of maternal secretor status on the developing infant microbiota with a special emphasis on bifidobacterial species abundance. On average, bifidobacteria were established earlier and more often in infants fed by secretor mothers than in infants fed by non-secretor mothers. In secretor-fed infants, the relative abundance of the Bifidobacterium longum group was most strongly correlated with high percentages of the order Bifidobacteriales. Conversely, in non-secretor-fed infants, Bifidobacterium breve was positively correlated with Bifidobacteriales, while the B. longum group was negatively correlated. A higher percentage of bifidobacteria isolated from secretor-fed infants consumed 2′-fucosyllactose. Infant feces with high levels of bifidobacteria had lower milk oligosaccharide levels in the feces and higher amounts of lactate. Furthermore, feces containing different bifidobacterial species possessed differing amounts of oligosaccharides, suggesting differential consumption in situ. Infants fed by non-secretor mothers are delayed in the establishment of a bifidobacteria-laden microbiota. This delay may be due to difficulties in the infant acquiring a species of bifidobacteria able to consume the specific milk oligosaccharides delivered by the mother. This work provides mechanistic insight into how milk glycans enrich specific beneficial bacterial populations in infants and reveals clues for enhancing enrichment of bifidobacterial populations in at risk populations - such as premature infants.

297 citations

Journal ArticleDOI
TL;DR: State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice.
Abstract: Summary Background Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions. Methods For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-image batches from a test set of 1511 images. The diagnoses from human readers were compared with those of 139 algorithms created by 77 machine-learning labs, who participated in the International Skin Imaging Collaboration 2018 challenge and received a training set of 10 015 images in advance. The ground truth of each lesion fell into one of seven predefined disease categories: intraepithelial carcinoma including actinic keratoses and Bowen's disease; basal cell carcinoma; benign keratinocytic lesions including solar lentigo, seborrheic keratosis and lichen planus-like keratosis; dermatofibroma; melanoma; melanocytic nevus; and vascular lesions. The two main outcomes were the differences in the number of correct specific diagnoses per batch between all human readers and the top three algorithms, and between human experts and the top three algorithms. Findings Between Aug 4, 2018, and Sept 30, 2018, 511 human readers from 63 countries had at least one attempt in the reader study. 283 (55·4%) of 511 human readers were board-certified dermatologists, 118 (23·1%) were dermatology residents, and 83 (16·2%) were general practitioners. When comparing all human readers with all machine-learning algorithms, the algorithms achieved a mean of 2·01 (95% CI 1·97 to 2·04; p Interpretation State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice. However, a possible limitation of these algorithms is their decreased performance for out-of-distribution images, which should be addressed in future research. Funding None.

297 citations

Journal ArticleDOI
TL;DR: The survey aims to tackle all the issues and challenging aspects of people reidentification while simultaneously describing the previously proposed solutions for the encountered problems, including the first attempts of holistic descriptors and progresses to the more recently adopted 2D and 3D model-based approaches.
Abstract: The field of surveillance and forensics research is currently shifting focus and is now showing an ever increasing interest in the task of people reidentification. This is the task of assigning the same identifier to all instances of a particular individual captured in a series of images or videos, even after the occurrence of significant gaps over time or space. People reidentification can be a useful tool for people analysis in security as a data association method for long-term tracking in surveillance. However, current identification techniques being utilized present many difficulties and shortcomings. For instance, they rely solely on the exploitation of visual cues such as color, texture, and the object’s shape. Despite the many advances in this field, reidentification is still an open problem. This survey aims to tackle all the issues and challenging aspects of people reidentification while simultaneously describing the previously proposed solutions for the encountered problems. This begins with the first attempts of holistic descriptors and progresses to the more recently adopted 2D and 3D model-based approaches. The survey also includes an exhaustive treatise of all the aspects of people reidentification, including available datasets, evaluation metrics, and benchmarking.

296 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