On the Dissipation of Ideal Hamiltonian Monte Carlo Sampler

We report on what seems to be an intriguing connection between variable integration time and partial velocity refreshment of Ideal Hamiltonian Monte Carlo samplers, both of which can be used for reducing the dissipative behavior of the dynamics. More concretely, we show that on quadratic potentials, efficiency can be improved through these means by a $\sqrt{\kappa}$ factor in Wasserstein-2 distance, compared to classical constant integration time, fully refreshed HMC. We additionally explore the benefit of randomized integrators for simulating the Hamiltonian dynamics under higher order regularity conditions

Direct Conversion of Fibroblasts to Neurons by Reprogramming PTB-Regulated MicroRNA Circuits

SummaryThe induction of pluripotency or trans-differentiation of one cell type to another can be accomplished with cell-lineage-specific transcription factors. Here, we report that repression of a single RNA binding polypyrimidine-tract-binding (PTB) protein, which occurs during normal brain development via the action of miR-124, is sufficient to induce trans-differentiation of fibroblasts into functional neurons. Besides its traditional role in regulated splicing, we show that PTB has a previously undocumented function in the regulation of microRNA functions, suppressing or enhancing microRNA targeting by competitive binding on target mRNA or altering local RNA secondary structure. A key event during neuronal induction is the relief of PTB-mediated blockage of microRNA action on multiple components of the REST complex, thereby derepressing a large array of neuronal genes, including miR-124 and multiple neuronal-specific transcription factors, in nonneuronal cells. This converts a negative feedback loop to a positive one to elicit cellular reprogramming to the neuronal lineage

Langerhans cell histiocytosis of the skull in 23 children

Abstract Objective To explore the clinical features, diagnosis, treatment and prognosis of Langerhans cell histiocytosis (LCH) of the skull in children. Methods This study retrospectively summarized the clinical manifestations, treatment methods and follow-up status of children with skull LCH who were admitted to the Department of Neurosurgery of Shanghai Children’s Hospital from January 2014 to June 2021. Results A total of 23 patients confirmed by histology as LCH received hospitalization treatment, including 14 males and 9 females, aged (5.76 ± 3.86) years old. The clinical manifestations were mostly incidentally discovered head masses that gradually enlarged (19 cases, 82.61%). Only 2 cases are affected by multiple systems, while the rest are affected by single systems. 9 patients were involved in multiple skull lesions, and 14 patients had local skull lesions. All patients underwent surgical intervention, with 17 patients undergoing total resection and 6 patients undergoing biopsy. 21 patients received chemotherapy after surgery. The median follow-up was 2.46 years (range 0.33–6.83 years). 21 patients had their symptoms and signs under control or even resolved, and 2 patients experienced recurrence during follow-up. The overall control rate reached 91.30%. Conclusion Personalized treatment plans according to different clinical types. Regular outpatient follow-up is crucial to monitor disease recurrence and late effects

NEMo: An Evolutionary Model with Modularity for PPI Networks

Modelling the evolution of biological networks is a major challenge. Biological networks are usually represented as graphs; evolutionary events include addition and removal of vertices and edges, but also duplication of vertices and their associated edges. Since duplication is viewed as a primary driver of genomic evolution, recent work has focused on duplication-based models. Missing from these models is any embodiment of modularity, a widely accepted attribute of biological networks. Some models spontaneously generate modular structures, but none is known to maintain and evolve them. We describe NEMo (Network Evolution with Modularity), a new model that embodies modularity. NEMo allows modules to emerge and vanish, to fission and merge, all driven by the underlying edge-level events using a duplication-based process. We introduce measures to compare biological networks in terms of their modular structure and use them to compare NEMo and existing duplication-based models and to compare both generated and published networks