A case report of duodenal arteriovenous malformation: usefulness of intraoperative indocyanine green angiography for precise identification of the lesion

Background Arteriovenous malformation (AVM) of the gastrointestinal (GI) tract can cause bleeding. The treatment choice for GI tract AVM is surgical resection of the involved bowel segment with complete resection of the nidus. The AVM formed in the duodenum or pancreatic head could also cause gastrointestinal bleeding, and there are several reports of pancreaticoduodenectomy as its treatment. However, if the area of AVM can be accurately identified during surgery, it may be possible to completely resect the AVM while preserving the organ. We report a case of duodenal AVM in a patient successfully treated with a subtotal stomach-preserving duodenal bulb resection using intraoperative indocyanine green (ICG) angiography technique. Case presentation An 18-year-old man was diagnosed with duodenal AVM after several examinations for anemia and was referred to our hospital for further treatment. Preoperative imaging studies showed that the inflow vessels of this duodenal AVM were the inferior pyloric artery and the superior duodenal artery, and the AVM was localized to the duodenal bulb. Thereafter, stomach-preserving duodenal bulb resection preceded by ligation of the inflow vessels was performed. During the surgery, ICG angiography clearly demonstrated the area, where the nidus was distributed, and a duodenal bulb resection with complete resection of the AVM was successfully performed. There was no recurrence at the 6-month follow-up. Conclusions Intraoperative ICG angiography was a useful procedure for precise identification of the AVM of the GI tract.

Higgs and top physics reconstruction challenges and opportunities at FCC-ee

The Higgs bosons and the top quark decay into rich and diverse final states, containing both light and heavy quarks, gluons, photons as well as W and Z bosons. This article reviews the challenges involved in reconstructing Higgs and top events at the FCC-ee and identifies the areas where novel developments are needed. The precise identification and reconstruction of these final states at the FCC-ee rely on the capability of the detector to provide excellent flavour tagging, jet energy and angular resolution, and global kinematic event reconstruction. Excellent flavour tagging performance requires low-material vertex and tracking detectors, and advanced machine learning techniques as successfully employed in LHC experiments. In addition, the Z pole run will provide abundant samples of heavy flavour partons that can be used for calibration of the tagging algorithms. For the reconstruction of jets, leptons, and missing energy, particle-flow algorithms are crucial to explore the full potential of the highly granular tracking and calorimeter systems, and give access to excellent energy–momentum resolution and precise identification of heavy bosons in their hadronic decays. This enables, among many other key elements, the reconstruction of Higgsstrahlung processes with leptonically and hadronically decaying Z bosons, and an almost background-free identification of top quark pair events. Exploiting the full available kinematic constraints together with exclusive jet clustering algorithms will allow for the optimisation of global event reconstruction with kinematic fitting techniques.

Precise identification of cancer cells from allelic imbalances in single cell transcriptomes

A fundamental step of tumour single cell mRNA analysis is separating cancer and non-cancer cells. We show that the common approach to separation, using shifts in average expression, can lead to erroneous biological conclusions. By contrast, allelic imbalances representing copy number changes directly detect the cancer genotype and accurately separate cancer from non-cancer cells. Our findings provide a definitive approach to identifying cancer cells from single cell mRNA sequencing data.

High-Resolution Boundary Refined Convolutional Neural Network for Automatic Agricultural Greenhouses Extraction from GaoFen-2 Satellite Imageries

Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs.

Demonstration of a Rocking Prosthetic Mitral Valve Using 3-Dimentional Echocardiography and Live Surgical Imaging

Prosthetic valve endocarditis is a complication of bacteremia which can cause damage to the prosthetic valve or the tissue to which it was sewn. Extensive tissue damage can result in a loss of anchoring and allow for abnormal valvular motion. Dehiscence can lead to excessive motion of the valve which is termed rocking. Through advances in imaging technology, live 3-dimentional (3-D) transesophageal echocardiography can allow for precise identification of the location of, and amount of dehiscence. We present a 37-year old male with a rocking prosthetic valve demonstrated on 3-D echocardiography and correlated to surgical manipulation.

„Alles ist in Gott“ – Überlegungen zur bestimmenden theologischen Denkform des Corpus Hermeticum

Given the Corpus Hermeticum’s history of formation, it has prompted the attempt to separate layers or groups of writings within the collection of treatises. This process of division, which was for the most part undertaken on criteria of content (dualism, pantheism, etc.), has been viewed rather negatively by the more recent research, on grounds of method. Given the discovery of numerous doctrinal contents that remain constant across different treatises, increased efforts are being made to reconstruct the Corpus’s moments of unity. The present paper aims, in this spirit, to provide a more precise identification of the overarching forms of theological thought in the Hermetic writings. The reflections that follow aim to make it plausible that the defining form of theological thought in the Corpus Hermeticum can be classified as panentheism. In addition, the distinctive form of this influential theological paradigm in the Hermetic writings will be considered.