Automatic Abdominal Organ Segmentation from CT images

In the recent years a great deal of research work has been devoted to the development of semi-automatic and automatic techniques for the analysis of abdominal CT images. Some of the current interests are the automatic diagnosis of liver, spleen, and kidney pathologies and the 3D volume rendering of the abdominal organs. The first and fundamental step in all these studies is the automatic organs segmentation, that is still an open problem. In this paper we propose our fully automatic system that employs a hierarchical gray level based framework to segment heart, bones (i.e. ribs and spine), liver and its blood vessels, kidneys, and spleen. The overall system has been evaluated on the data of 100 patients, obtaining a good assessment both by visual inspection by three experts, and by comparing the computed results to the boundaries manually traced by experts

The promises of large language models for protein design and modeling

The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the “language of proteins” invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design

Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding

We introduce a set of algorithms (Het-node2vec) that extend the original node2vec node-neighborhood sampling method to heterogeneous multigraphs, i.e. networks characterized by multiple types of nodes and edges. The resulting random walk samples capture both the structural characteristics of the graph and the semantics of the different types of nodes and edges. The proposed algorithms can focus their attention on specific node or edge types, allowing accurate representations also for underrepresented types of nodes/edges that are of interest for the prediction problem under investigation. These rich and well-focused representations can boost unsupervised and supervised learning on heterogeneous graphs.Comment: 20 pages, 5 figure

Use of the iliac-outlet and iliac-inlet combined views in percutaneous posterior column retrograde screw fixation

Posterior column fractures are common acetabular injuries. Although displaced fractures require open reduction and fixation, undisplaced patterns may benefit from percutaneous screw fixation. The combination of iliac oblique with inlet and outlet views offers an intuitive and panoramic rendering of the bony corridor into the posterior column; lateral cross table view completes the sequence of fluoroscopic projections. Herein we describe the use of outlet/inlet iliac views and a detailed procedure for percutaneous retrograde posterior column screw fixation

Endosteal plating for the treatment of malunions and nonunions of distal femur fractures

PurposeTo describe the surgical technique and the outcome of a case series of nonunion and malunion of distal femur fractures treated with an endosteal medial plate combined with a lateral locking plate and with autogenous bone grafting. MethodsWe retrospectively analyzed a series of patients with malunion or nonunion of the distal femur treated with a medial endosteal plate in combination with a lateral locking plate, in a period between January 2011 and December 2019, Database from chart review was obtained including all the clinical relevant available baseline data (demographics, type of fracture, mechanism of injury, time from injury to surgery, number of previous surgical procedures, type of bone graft, and type of lateral plate). Time to bone healing, limb alignment at follow-up and complications were documented. ResultsTen patients were included into the study: 7 male and 3 female with mean age of 48.3 years (range 21-67). The mechanism of trauma was in 8 cases a road traffic accident and in 2 cases a fall from height. According to AO/OTA classification 5 fractures were 33 A3, 3 were 33 C1, 1 was 33 C2 and 1 was 33 C3. The average follow up was 13.5 months. In all cases but one bony union was achieved. Bone healing was observed in average 3.3 months after surgery. No intraoperative or postoperative complications were reported. ConclusionA medial endosteal plate is a useful augmentation for lateral plate fixation in nonunion or malunion following distal femur fractures, particularly in cases of medial bone loss, severe comminution, or poor bone quality

The promises of large language models for protein design and modeling.

The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the language of proteins invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design

An expectation-maximization framework for comprehensive prediction of isoform-specific functions.

MOTIVATION: Advances in RNA sequencing technologies have achieved an unprecedented accuracy in the quantification of mRNA isoforms, but our knowledge of isoform-specific functions has lagged behind. There is a need to understand the functional consequences of differential splicing, which could be supported by the generation of accurate and comprehensive isoform-specific gene ontology annotations. RESULTS: We present isoform interpretation, a method that uses expectation-maximization to infer isoform-specific functions based on the relationship between sequence and functional isoform similarity. We predicted isoform-specific functional annotations for 85 617 isoforms of 17 900 protein-coding human genes spanning a range of 17 430 distinct gene ontology terms. Comparison with a gold-standard corpus of manually annotated human isoform functions showed that isoform interpretation significantly outperforms state-of-the-art competing methods. We provide experimental evidence that functionally related isoforms predicted by isoform interpretation show a higher degree of domain sharing and expression correlation than functionally related genes. We also show that isoform sequence similarity correlates better with inferred isoform function than with gene-level function. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, and resource files are freely available under a GNU3 license at https://github.com/TheJacksonLaboratory/isopretEM and https://zenodo.org/record/7594321

Intraoperative use of tranexamic acid to reduce transfusion rate in patients undergoing radical retropubic prostatectomy: double blind, randomised, placebo controlled trial

Objectives To determine the efficacy of intraoperative treatment with low dose tranexamic acid in reducing the rate of perioperative transfusions in patients undergoing radical retropubic prostatectomy

GraPE: fast and scalable Graph Processing and Embedding

Graph Representation Learning methods have enabled a wide range of learning problems to be addressed for data that can be represented in graph form. Nevertheless, several real world problems in economy, biology, medicine and other fields raised relevant scaling problems with existing methods and their software implementation, due to the size of real world graphs characterized by millions of nodes and billions of edges. We present GraPE, a software resource for graph processing and random walk based embedding, that can scale with large and high-degree graphs and significantly speed up-computation. GraPE comprises specialized data structures, algorithms, and a fast parallel implementation that displays everal orders of magnitude improvement in empirical space and time complexity compared to state of the art software resources, with a corresponding boost in the performance of machine learning methods for edge and node label prediction and for the unsupervised analysis of graphs.GraPE is designed to run on laptop and desktop computers, as well as on high performance computing cluster

GRAPE for fast and scalable graph processing and random-walk-based embedding

Graph representation learning methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are beyond the capabilities of current methods and software implementations. We present GRAPE (Graph Representation Learning, Prediction and Evaluation), a software resource for graph processing and embedding that is able to scale with big graphs by using specialized and smart data structures, algorithms, and a fast parallel implementation of random-walk-based methods. Compared with state-of-the-art software resources, GRAPE shows an improvement of orders of magnitude in empirical space and time complexity, as well as competitive edge- and node-label prediction performance. GRAPE comprises approximately 1.7 million well-documented lines of Python and Rust code and provides 69 node-embedding methods, 25 inference models, a collection of efficient graph-processing utilities, and over 80,000 graphs from the literature and other sources. Standardized interfaces allow a seamless integration of third- party libraries, while ready-to-use and modular pipelines permit an easy-to- use evaluation of graph-representation-learning methods, therefore also positioning GRAPE as a software resource that performs a fair comparison between methods and libraries for graph processing and embedding