OmicsVolcano: software for intuitive visualization and interactive exploration of high-throughput biological data

Advances in omics technologies have generated exponentially larger volumes of biological data; however, their analyses and interpretation are limited to computationally proficient scientists. We created OmicsVolcano, an interactive open-source software tool to enable visualization and exploration of high-throughput biological data, while highlighting features of interest using a volcano plot interface. In contrast to existing tools, our software and user-interface design allow it to be used without requiring any programming skills to generate high-quality and presentation-ready images

A Procedure for Sharper and Faster Characterization

Topological order in two-dimensional (2D) quantum matter can be determined by the topological contribution to the entanglement Rényi entropies. However, when close to a quantum phase transition, its calculation becomes cumbersome. Here, we show how topological phase transitions in 2D systems can be much better assessed by multipartite entanglement, as measured by the topological geometric entanglement of blocks. Specifically, we present an efficient tensor network algorithm based on projected entangled pair states to compute this quantity for a torus partitioned into cylinders and then use this method to find sharp evidence of topological phase transitions in 2D systems with a string-tension perturbation. When compared to tensor network methods for Rényi entropies, our approach produces almost perfect accuracies close to criticality and, additionally, is orders of magnitude faster. The method can be adapted to deal with any topological state of the system, including minimally entangled ground states. It also allows us to extract the critical exponent of the correlation length and shows that there is no continuous entanglement loss along renormalization group flows in topological phases

Participation Requests:A democratic innovation to unlock the door of public services?

Democracies are under pressure and public administrations must evolve to accommodate new forms of public participation. Participation processes may reproduce or disrupt existing power inequalities. Through a multi-method empirical study of "Participation Requests," a new legislative policy tool to open up public services in Scotland, this article addresses an empirical gap on governance-driven democratic innovations (DIs). We use Young's distinction of external and internal inclusion and find Participation Requests replicate the pitfalls of traditional forms of associative democracy. We contend that DIs should be co-produced between institutions and communities to bring a participatory and deliberative corrective to temper bureaucratic logics

Comparació entre els sistemes de classificació de grau tumoral WHO 1973 y WHO 2004 en el càncer vesical en relació a la seva associació a CISIS, recurrènncia i progressió en tumors TA

El nou sistema de classificació de grau de la WHO 2004 millora la variació interobservador entre patòlegs. L'associació a carcinoma in situ (CIS) és un factor pronòstic de recurrència i progressió en càncer no músculo- invasiu, i la incidència real del CIS en aquests tumors és desconeguda, ja que les biòpsies vesicals múltiples randomitzades no es fan rutinàriament en tots els tumors primaris. Globalment, el pronòstic dels tumors Ta és bona, però alguns són d'alt grau o estan associats a CIS. Evaluem l'associació dels tumors Ta a CIS, les taxes de recurrència i progressió comparant els sistemes de classificació WHO del 1973 amb els de 200

Artificial intelligence is revolutionizing everyday medical practice

Introduction This article discusses the impact of artificial intelligence (AI) on the medical field and its daily use in the practice of medicine. AI has applications in many stages of patient care, i.e.: prevention, diagnosis, personalising treatment plans, predicting disease progression and therapeutic outcomes or analysing medical images. GPs play a key role in patient care, but due to the complexity of medicine and the variety of symptoms, care and diagnosis can be time-consuming and difficult. Methods and materials The aim of this study is to explore and evaluate the potential of artificial intelligence in the process of diagnosing diseases by physicians and to provide practical suggestions and insights for its use in medical practice to improve the quality of healthcare. The methodology was based on material from PubMed and a review of the scientific literature on previous research and developments in AI in medicine.   State of knowledge Investment in artificial intelligence (AI) in medicine is growing rapidly. The role of GPs in patient care is highlighted and examples of the use of AI in everyday medical practice are given, including the role of Chatbots and the use of AI in specialised treatment.  Conclusions The conclusions of the article highlight the potential of AI in the area of physician-diagnosed diseases to reduce diagnosis time, increase accuracy of diagnoses and improve healthcare efficiency. Final diagnosis and therapy should still be determined by a qualified physician. There are areas where the doctor cannot be replaced by AI. AI cannot replace a doctor's diagnostic intelligence, empathy and rapport therefore doctors need to find a balance between these combinations to achieve better health outcomes with the highest possible care for patients.  

OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images

Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV-v2, a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions, and enables benchmarking of models for image classification, object detection, and 3D pose estimation. In addition to this novel dataset, we contribute extensive experiments using popular baseline methods, which reveal that: 1) Some nuisance factors have a much stronger negative effect on the performance compared to others, also depending on the vision task. 2) Current approaches to enhance robustness have only marginal effects, and can even reduce robustness. 3) We do not observe significant differences between convolutional and transformer architectures. We believe our dataset provides a rich test bed to study robustness and will help push forward research in this area. Our dataset can be accessed from https://bzhao.me/OOD-CV/Comment: arXiv admin note: substantial text overlap with arXiv:2111.1434

Segregation of nickel/iron bimetallic particles from lanthanum doped strontium titanates to improve sulfur stability of solid oxide fuel cell anodes

Perovskite derived Ni catalysts offer the remarkable benefit of regeneration after catalyst poisoning or Ni particle growth through the reversible segregation of Ni from the perovskite-type oxide host. Although this property allows for repeated catalyst regeneration, improving Ni catalyst stability towards sulfur poisoning by H2S is highly critical in solid oxide fuel cells. In this work Mn, Mo, Cr and Fe were combined with Ni at the B-site of La0.3Sr0.55TiO3±δ to explore possible benefits of segregation of two transition metals towards sulfur tolerance. Catalytic activity tests towards the water gas shift reaction were carried out to evaluate the effect of the additional metal on the catalytic activity and sulfur stability of the Ni catalyst. The addition of Fe to the Ni perovskite catalyst was found to increase sulfur tolerance. The simultaneous segregation of Fe and Ni from La0.3Sr0.55Ti0.95-xNi0.05FexO3±δ (x ≤ 0.05) was investigated by temperature programmed reduction, X-ray diffraction and X-ray absorption spectroscopy and catalytic tests after multiple redox cycles. It is shown that catalytic properties of the active phase were affected likely by the segregation of Ni/Fe alloy particles and that the reversible segregation of Ni persisted, while it was limited in the case of Fe under the same conditions