Moving beyond the transmon: Noise-protected superconducting quantum circuits

Artificial atoms realized by superconducting circuits offer unique opportunities to store and process quantum information with high fidelity. Among them, implementations of circuits that harness intrinsic noise protection have been rapidly developed in recent years. These noise-protected devices constitute a new class of qubits in which the computational states are largely decoupled from local noise channels. The main challenges in engineering such systems are simultaneously guarding against both bit- and phase-flip errors, and also ensuring high-fidelity qubit control. Although partial noise protection is possible in superconducting circuits relying on a single quantum degree of freedom, the promise of complete protection can only be fulfilled by implementing multimode or hybrid circuits. This Perspective reviews the theoretical principles at the heart of these new qubits, describes recent experiments, and highlights the potential of robust encoding of quantum information in superconducting qubits

Nucleocytoplasmic Shuttling of Endocytic Proteins

Many cellular processes rely on the ordered assembly of macromolecular structures. Here, we uncover an unexpected link between two such processes, endocytosis and transcription. Many endocytic proteins, including eps15, epsin1, the clathrin assembly lymphoid myeloid leukemia (CALM), and α-adaptin, accumulate in the nucleus when nuclear export is inhibited. Endocytosis and nucleocytoplasmic shuttling of endocytic proteins are apparently independent processes, since inhibition of endocytosis did not appreciably alter nuclear translocation of endocytic proteins, and blockade of nuclear export did not change the initial rate of endocytosis. In the nucleus, eps15 and CALM acted as positive modulators of transcription in a GAL4-based transactivation assay, thus raising the intriguing possibility that some endocytic proteins play a direct or indirect role in transcriptional regulation

Tongue cancer following hematopoietic cell transplantation for Fanconi anemia

Objectives: The aim of this retrospective study was to determine the incidence and the clinical outcome of tongue cancer (TC) in patients affected by Fanconi anemia (FA) who received an allogeneic hematopoietic cell transplantation (HCT). Materials and methods: The patient database from the Bone Marrow Transplant Center of Pescara was reviewed to enroll FA patients. Patients', donors', HCT's, and screening's data were collected as well to look for the incidence and the treatment of TC. Results: Twelve patients affected by FA were identified. Three patients died for transplant-related causes. Five of nine surviving patients were diagnosed with TC at a median of 21.7 years since transplantation and at a median age of 32.10 years. Interestingly, no patient manifested graft-versus-host-disease (GvHD). The 28-year cumulative incidence function of TC was 46.9% (95% CI, 36.9-56.9%). Two patients were treated with chemotherapy alone, two patients were treated with surgery alone, and one with surgery followed by chemotherapy. Overall, 4 patients with TC showed a clinical course characterized by a marked aggressiveness of the tumor disease which led to death due to cancer progression between 2 and 13 months. One patient is surviving 8 months after diagnosis of TC. Conclusions: Our study confirms the high incidence of tumors and in particular tongue tumors in allotransplanted FA patients. A careful screening has to be life-long maintained. Clinical relevance: Considering the rarity of FA and the frailty of FA patients, this study may add important information for the cancer management of these patients

Deep Segmentation of the Mandibular Canal: a New 3D Annotated Dataset of CBCT Volumes

Inferior Alveolar Nerve (IAN) canal detection has been the focus of multiple recent works in dentistry and maxillofacial imaging. Deep learning-based techniques have reached interesting results in this research field, although the small size of 3D maxillofacial datasets has strongly limited the performance of these algorithms. Researchers have been forced to build their own private datasets, thus precluding any opportunity for reproducing results and fairly comparing proposals. This work describes a novel, large, and publicly available mandibular Cone Beam Computed Tomography (CBCT) dataset, with 2D and 3D manual annotations, provided by expert clinicians. Leveraging this dataset and employing deep learning techniques, we are able to improve the state of the art on the 3D mandibular canal segmentation. The source code which allows to exactly reproduce all the reported experiments is released as an open-source project, along with this article