Swarm Reinforcement Learning For Adaptive Mesh Refinement

Adaptive Mesh Refinement (AMR) enhances the Finite Element Method, an important technique for simulating complex problems in engineering, by dynamically refining mesh regions, enabling a favorable trade-off between computational speed and simulation accuracy. Classical methods for AMR depend on heuristics or expensive error estimators, hindering their use for complex simulations. Recent learning-based AMR methods tackle these issues, but so far scale only to simple toy examples. We formulate AMR as a novel Adaptive Swarm Markov Decision Process in which a mesh is modeled as a system of simple collaborating agents that may split into multiple new agents. This framework allows for a spatial reward formulation that simplifies the credit assignment problem, which we combine with Message Passing Networks to propagate information between neighboring mesh elements. We experimentally validate our approach, Adaptive Swarm Mesh Refinement (ASMR), on challenging refinement tasks. Our approach learns reliable and efficient refinement strategies that can robustly generalize to different domains during inference. Additionally, it achieves a speedup of up to $2$ orders of magnitude compared to uniform refinements in more demanding simulations. We outperform learned baselines and heuristics, achieving a refinement quality that is on par with costly error-based oracle AMR strategies

Swarm Reinforcement Learning For Adaptive Mesh Refinement

The Finite Element Method, an important technique in engineering, is aided by Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a favorable trade-off between computational speed and simulation accuracy. Classical methods for AMR depend on task-specific heuristics or expensive error estimators, hindering their use for complex simulations. Recent learned AMR methods tackle these problems, but so far scale only to simple toy examples. We formulate AMR as a novel Adaptive Swarm Markov Decision Process in which a mesh is modeled as a system of simple collaborating agents that may split into multiple new agents. This framework allows for a spatial reward formulation that simplifies the credit assignment problem, which we combine with Message Passing Networks to propagate information between neighboring mesh elements. We experimentally validate the effectiveness of our approach, Adaptive Swarm Mesh Refinement (ASMR), showing that it learns reliable, scalable, and efficient refinement strategies on a set of challenging problems. Our approach significantly speeds up computation, achieving up to 30-fold improvement compared to uniform refinements in complex simulations. Additionally, we outperform learned baselines and achieve a refinement quality that is on par with a traditional error-based AMR strategy without expensive oracle information about the error signal.Comment: Version 1 of this paper is a preliminary workshop version that was accepted as a workshop paper in the ICLR 2023 Workshop on Physics for Machine Learnin

Combining 16S sequencing and qPCR quantification reveals Staphylococcus aureus driven bacterial overgrowth in the skin of severe atopic dermatitis patients

Atopic dermatitis (AD) is an inflammatory skin disease with a microbiome dysbiosis towards a high relative abundance of Staphylococcus aureus. However, information is missing on the actual bacterial load on AD skin, which may affect the cell number driven release of pathogenic factors. Here, we combined the relative abundance results obtained by next-generation sequencing (NGS, 16S V1-V3) with bacterial quantification by targeted qPCR (total bacterial load = 16S, S. aureus = nuc gene). Skin swabs were sampled cross-sectionally (n = 135 AD patients; n = 20 healthy) and longitudinally (n = 6 AD patients; n = 6 healthy). NGS and qPCR yielded highly inter-correlated S. aureus relative abundances and S. aureus cell numbers. Additionally, intra-individual differences between body sides, skin status, and consecutive timepoints were also observed. Interestingly, a significantly higher total bacterial load, in addition to higher S. aureus relative abundance and cell numbers, was observed in AD patients in both lesional and non-lesional skin, as compared to healthy controls. Moreover, in the lesional skin of AD patients, higher S. aureus cell numbers significantly correlated with the higher total bacterial load. Furthermore, significantly more severe AD patients presented with higher S. aureus cell number and total bacterial load compared to patients with mild or moderate AD. Our results indicate that severe AD patients exhibit S. aureus driven increased bacterial skin colonization. Overall, bacterial quantification gives important insights in addition to microbiome composition by sequencing

Матеріали інформаційно-методичного забезпечення дисципліни "Правоохоронне право (Прокуратура України)"

Завдання вивчення курсу "Правоохоронне право (Прокуратура України)" полягає в тому, щоб студенти отримали знання про дисципліну, повноваження, систему, організацію та діяльність прокуратури. Крім того, завданням є надання знань про головні установи, які повинні забезпечити реалізацію правових прин- ципів, здійснювати захист прав та інтересів громадян і юридичних осіб, що рег- ламентовано Конституцією та іншими законодавчими актами. Головне завдання вивчення курсу "Правоохоронне право (Прокуратура України)" полягає в точній орієнтації в системі органів прокуратури, її органі- зації та діяльності. Прокуратура є єдиним органом суспільного призначення, який створюється спеціально саме для здійснення контрольно-наглядових фун- кцій у самому прямому розумінні.Становлення України як правової держави передбачає якісно новий рі- вень підготовки спеціалістів з вищою юридичною освітою. Цьому у великій мі- рі сприяє вивчення такої дисципліни як "Правоохоронне право (Прокуратура України)", яка охоплює роботу системи органів прокуратури, розкриває завдан- ня, які покладені на них у зв'язку зі здійсненням нагляду за додержанням зако- нів в Україні

Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data

Background Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls. Results We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (108–103 cells, 0.4–80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 106 cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden’s index. In the skin dataset, the Decontam prevalence filter and MicrobIEM’s ratio filter effectively reduced common contaminants while keeping skin-associated genera. Conclusions MicrobIEM’s ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience

Oxaliplatin-Induced Leukocytoclastic Vasculitis under Adjuvant Chemotherapy for Colorectal Cancer: Two Cases of a Rare Adverse Event

Leukocytoclastic vasculitis is a multicausal systemic inflammatory disease of the small vessels, histologically characterized by inflammation and deposition of both nuclear debris and fibrin in dermal postcapillary venules. The clinical picture typically involves palpable purpura of the lower legs and may be associated with general symptoms such as fatigue, arthralgia and fever. Involvement of the internal organs, most notably the kidneys, the central nervous system or the eyes, is possible and determines the prognosis. Oxaliplatin-induced leukocytoclastic vasculitis is a very rare event that limits treatment options in affected patients. We report 2 patients who developed the condition under chemotherapy for advanced rectal and metastatic colon carcinoma, respectively; a termination of the therapy was therefore necessary. While current therapies for colorectal cancer include the combination of multimodal treatment with new and targeted agents, rare and unusual side effects elicited by established agents also need to be taken into account for the clinical management