A Federated Learning Framework for Stenosis Detection

This study explores the use of Federated Learning (FL) for stenosis detection in coronary angiography images (CA). Two heterogeneous datasets from two institutions were considered: Dataset 1 includes 1219 images from 200 patients, which we acquired at the Ospedale Riuniti of Ancona (Italy); Dataset 2 includes 7492 sequential images from 90 patients from a previous study available in the literature. Stenosis detection was performed by using a Faster R-CNN model. In our FL framework, only the weights of the model backbone were shared among the two client institutions, using Federated Averaging (FedAvg) for weight aggregation. We assessed the performance of stenosis detection using Precision (P rec), Recall (Rec), and F1 score (F1). Our results showed that the FL framework does not substantially affects clients 2 performance, which already achieved good performance with local training; for client 1, instead, FL framework increases the performance with respect to local model of +3.76%, +17.21% and +10.80%, respectively, reaching P rec = 73.56, Rec = 67.01 and F1 = 70.13. With such results, we showed that FL may enable multicentric studies relevant to automatic stenosis detection in CA by addressing data heterogeneity from various institutions, while preserving patient privacy

Global, highly specific and fast filtering of alignment seeds

Background An important initial phase of arguably most homology search and alignment methods such as required for genome alignments is seed finding. The seed finding step is crucial to curb the runtime as potential alignments are restricted to and anchored at the sequence position pairs that constitute the seed. To identify seeds, it is good practice to use sets of spaced seed patterns, a method that locally compares two sequences and requires exact matches at certain positions only. Results We introduce a new method for filtering alignment seeds that we call geometric hashing. Geometric hashing achieves a high specificity by combining non-local information from different seeds using a simple hash function that only requires a constant and small amount of additional time per spaced seed. Geometric hashing was tested on the task of finding homologous positions in the coding regions of human and mouse genome sequences. Thereby, the number of false positives was decreased about million-fold over sets of spaced seeds while maintaining a very high sensitivity. Conclusions An additional geometric hashing filtering phase could improve the run-time, accuracy or both of programs for various homology-search-and-align tasks

The macronuclear genome of the Antarctic psychrophilic marine ciliate Euplotes focardii reveals new insights on molecular cold adaptation

The macronuclear (MAC) genomes of ciliates belonging to the genus Euplotes species are comprised of numerous small DNA molecules, nanochromosomes, each typically encoding a single gene. These genomes are responsible for all gene expression during vegetative cell growth. Here, we report the analysis of the MAC genome from the Antarctic psychrophile Euplotes focardii. Nanochromosomes containing bacterial sequences were not found, suggesting that phenomena of horizontal gene transfer did not occur recently, even though this ciliate species has a substantial associated bacterial consortium. As in other euplotid species, E. focardii MAC genes are characterized by a high frequency of translational frameshifting. Furthermore, in order to characterize differences that may be consequent to cold adaptation and defense to oxidative stress, the main constraints of the Antarctic marine microorganisms, we compared E. focardii MAC genome with those available from mesophilic Euplotes species. We focussed mainly on the comparison of tubulin, antioxidant enzymes and heat shock protein (HSP) 70 families, molecules which possess peculiar characteristic correlated with cold adaptation in E. focardii. We found that α-tubulin genes and those encoding SODs and CATs antioxidant enzymes are more numerous than in the mesophilic Euplotes species. Furthermore, the phylogenetic trees showed that these molecules are divergent in the Antarctic species. In contrast, there are fewer hsp70 genes in E. focardii compared to mesophilic Euplotes and these genes do not respond to thermal stress but only to oxidative stress. Our results suggest that molecular adaptation to cold and oxidative stress in the Antarctic environment may not only be due to particular amino acid substitutions but also due to duplication and divergence of paralogous genes

Occurrence and Characterisation of Colistin-Resistant <i>Escherichia coli </i>in Raw Meat in Southern Italy in 2018-2020

Colistin is a last-resort drug for the treatment of infections by carbapenem-resistant Enterobacteriaceae, and the emergence of colistin resistance poses a serious clinical challenge. The aim of this study was to investigate the occurrence of colistin-resistant Escherichia coli in retail meat in Southern Italy in 2018–2020. Of 570 samples, 147 contained E. coli. Two out of 147 (1.4%) E. coli showed a non-wild-type phenotype to colistin and harboured mcr-1. mcr-1 was also detected in a wild-type isolate, resulting in a 2% mcr prevalence. mcr-1-positive isolates originated from turkey meat collected in Apulia (n = 2) and Basilicata (n = 1). A whole-genome sequencing analysis confirmed mcr-1.2 and mcr-1.1 in two and one isolate, respectively. The strains were diverse, belonging to three multi-locus sequence types (ST354, ST410, SLV of ST10) and harbouring genes mediating resistance to antimicrobials in two, six and seven classes. mcr-1 was carried by IncX4 plasmids with high nucleotide similarity to IncX4 plasmids harbouring mcr-1.2 and mcr-1.1 in Enterobacterales from different sources and geographical regions. This is the first study reporting updates on E. coli non-wild-type to colistin from retail meat in Southern Italy, highlighting the importance of phenotypic and genotypic antimicrobial resistance surveillance to contain the dissemination of mcr among E. coli