Ga^+ beam lithography for nanoscale silicon reactive ion etching

By using a dry etch chemistry which relies on the highly preferential etching of silicon, over that of gallium (Ga), we show resist-free fabrication of precision, high aspect ratio nanostructures and microstructures in silicon using a focused ion beam (FIB) and an inductively coupled plasma reactive ion etcher (ICP-RIE). Silicon etch masks are patterned via Ga^+ ion implantation in a FIB and then anisotropically etched in an ICP-RIE using fluorinated etch chemistries. We determine the critical areal density of the implanted Ga layer in silicon required to achieve a desired etch depth for both a Pseudo Bosch (SF_6/C_4F_8) and cryogenic fluorine (SF_6/O_2) silicon etching. High fidelity nanoscale structures down to 30 nm and high aspect ratio structures of 17:1 are demonstrated. Since etch masks may be patterned on uneven surfaces, we utilize this lithography to create multilayer structures in silicon. The linear selectivity versus implanted Ga density enables grayscale lithography. Limits on the ultimate resolution and selectivity of Ga lithography are also discussed

Frequent Cross-Species Transmission of Parvoviruses among Diverse Carnivore Hosts

Although parvoviruses are commonly described in domestic carnivores, little is known about their biodiversity in nondomestic species. A phylogenetic analysis of VP2 gene sequences from puma, coyote, gray wolf, bobcat, raccoon, and striped skunk revealed two major groups related to either feline panleukopenia virus (“FPV-like”) or canine parvovirus (“CPV-like”). Crossspecies transmission was commonplace, with multiple introductions into each host species but, with the exception of raccoons, relatively little evidence for onward transmission in nondomestic species

Assessment of post-infarct ventricular septal defects through 3D printing and statistical shape analysis: Supplementary table

Table 1: Examples of feedback from clinicians in relation to dominant themes from analysis of model evaluation.Background: Post-infarct ventricular septal defect (PIVSD) is a serious complication of myocardial infarction. We evaluated 3D-printing models in PIVSD clinical assessment and the feasibility of statistical shape modeling for morphological analysis of the defects. Methods: Models (n = 15) reconstructed from computed tomography data were evaluated by clinicians (n = 8). Statistical shape modeling was performed on 3D meshes to calculate the mean morphological configuration of the defects. Results: Clinicians’ evaluation highlighted the models’ utility in displaying defects for interventional/surgical planning, education/training and device development. However, models lack dynamic representation. Morphological analysis was feasible and revealed oval-shaped (n = 12) and complex channel-like (n = 3) defects. Conclusion: 3D-PIVSD models can complement imaging data for teaching and procedural planning. Statistical shape modeling is feasible in this scenario.The authors gratefully acknowledge the support of the British Heart Foundation (CH/17/1/32804), the Bristol BHF Accelerator Award (AA/18/1/34219), The Grand Appeal (Bristol Children’s Hospital Charity), and the Bristol National Institute for Health Research (NIHR) Biomedical Research Centre (BRC). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.Peer reviewe

Assessment of post-infarct ventricular septal defects through 3D printing and statistical shape analysis

© 2023 Giovanni Biglino. This work is licensed under the Creative Commons Attribution 4.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/Background: Post-infarct ventricular septal defect (PIVSD) is a serious complication of myocardial infarction. We evaluated 3D-printing models in PIVSD clinical assessment and the feasibility of statistical shape modeling for morphological analysis of the defects. Methods: Models (n = 15) reconstructed from computed tomography data were evaluated by clinicians (n = 8). Statistical shape modeling was performed on 3D meshes to calculate the mean morphological configuration of the defects. Results: Clinicians’ evaluation highlighted the models’ utility in displaying defects for interventional/surgical planning, education/training and device development. However, models lack dynamic representation. Morphological analysis was feasible and revealed oval-shaped (n = 12) and complex channel-like (n = 3) defects. Conclusion: 3D-PIVSD models can complement imaging data for teaching and procedural planning. Statistical shape modeling is feasible in this scenario.The authors gratefully acknowledge the support of the British Heart Foundation (CH/17/1/32804), the Bristol BHF Accelerator Award (AA/18/1/34219), The Grand Appeal (Bristol Children’s Hospital Charity), and the Bristol National Institute for Health Research (NIHR) Biomedical Research Centre (BRC).Peer reviewe

Discovering cancer genes by integrating network and functional properties

<p>Abstract</p> <p>Background</p> <p>Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI) data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO) annotations, to facilitate the identification of cancer genes.</p> <p>Methods</p> <p>Topological features of the PPI network, as well as protein domain compositions, enrichment of gene ontology categories, sequence and evolutionary conservation features were extracted and compared between cancer genes and other genes. The predictive power of various classifiers for identification of cancer genes was evaluated by cross validation. Experimental validation of a subset of the prediction results was conducted using siRNA knockdown and viability assays in human colon cancer cell line DLD-1.</p> <p>Results</p> <p>Cross validation demonstrated advantageous performance of classifiers based on support vector machines (SVMs) with the inclusion of the topological features from the PPI network, protein domain compositions and GO annotations. We then applied the trained SVM classifier to human genes to prioritize putative cancer genes. siRNA knock-down of several SVM predicted cancer genes displayed greatly reduced cell viability in human colon cancer cell line DLD-1.</p> <p>Conclusion</p> <p>Topological features of PPI networks, protein domain compositions and GO annotations are good predictors of cancer genes. The SVM classifier integrates multiple features and as such is useful for prioritizing candidate cancer genes for experimental validations.</p

Phase transition in Random Circuit Sampling

Quantum computers hold the promise of executing tasks beyond the capability of classical computers. Noise competes with coherent evolution and destroys long-range correlations, making it an outstanding challenge to fully leverage the computation power of near-term quantum processors. We report Random Circuit Sampling (RCS) experiments where we identify distinct phases driven by the interplay between quantum dynamics and noise. Using cross-entropy benchmarking, we observe phase boundaries which can define the computational complexity of noisy quantum evolution. We conclude by presenting an RCS experiment with 70 qubits at 24 cycles. We estimate the computational cost against improved classical methods and demonstrate that our experiment is beyond the capabilities of existing classical supercomputers