The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics.

Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment.

CODD: A benchmark dataset for the automated sorting of construction and demolition waste.

This study presents the Construction and Demolition Waste Object Detection Dataset (CODD), a benchmark dataset specifically curated for the training of object detection models and the full-scale implementation of automated sorting of Construction and Demolition Waste (CDW). The CODD encompasses a comprehensive range of CDW scenarios, capturing a diverse array of debris and waste materials frequently encountered in real-world construction and demolition sites. A noteworthy feature of the presented study is the ongoing collaborative nature of the dataset, which invites contributions from the scientific community, ensuring its perpetual improvement and adaptability to emerging research and practical requirements.

Reconciling a national methane emission inventory with in-situ measurements.

Reconciling top-down and bottom-up country-level greenhouse gas emission estimates remains a key challenge in the MRV (Monitoring, Reporting, Verification) paradigm. Here we propose to independently quantify cumulative emissions from a significant number of methane (CH) emitters at national level and derive robust constraints for the national inventory. Methane emissions in Cyprus, an insular country, stem primarily from waste and agricultural activities.

Real-time construction demolition waste detection using state-of-the-art deep learning methods; single-stage vs two-stage detectors.

Central to the development of a successful waste sorting robot lies an accurate and fast object detection system. This study assesses the performance of the most representative deep-learning models for the real-time localisation and classification of Construction and Demolition Waste (CDW). For the investigation, both single-stage (SSD, YOLO) and two-stage (Faster-RCNN) detector architectures coupled with various backbone feature extractors (ResNet, MobileNetV2, efficientDet) were considered.

Multi-Omics and Management of Follicular Carcinoma of the Thyroid.

Follicular thyroid carcinoma (FTC) is the second most common cancer of the thyroid gland, accounting for up to 20% of all primary malignant tumors in iodine-replete areas. The diagnostic work-up, staging, risk stratification, management, and follow-up strategies in patients who have FTC are modeled after those of papillary thyroid carcinoma (PTC), even though FTC is more aggressive. FTC has a greater propensity for haematogenous metastasis than PTC.