Analyzing Collective Motion with Machine Learning and Topology

We use topological data analysis and machine learning to study a seminal model of collective motion in biology [D'Orsogna et al., Phys. Rev. Lett. 96 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based in topology that summarize the time-varying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the one that is based on traditional order parameters.Comment: Published in Chaos 29, 123125 (2019), DOI: 10.1063/1.512549

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Coupled ATP and potassium efflux from intercalated cells

Increased flow in the distal nephron induces K secretion through the large-conductance, calcium-activated K channel (BK), which is primarily expressed in intercalated cells (IC). Since flow also increases ATP release from IC, we hypothesized that purinergic signaling has a role in shear stress (τ; 10 dynes/cm2) -induced, BK-dependent, K efflux. We found that 10 μM ATP led to increased IC Ca concentration, which was significantly reduced in the presence of the P2 receptor blocker suramin or calcium-free buffer. ATP also produced BK-dependent K efflux, and IC volume decrease. Suramin inhibited τ-induced K efflux, suggesting that K efflux is at least partially dependent on purinergic signaling. BK-β4 small interfering (si) RNA, but not nontarget siRNA, decreased ATP secretion and both ATP-dependent and τ-induced K efflux. Similarly, carbenoxolone (25 μM), which blocks connexins, putative ATP pathways, blocked τ-induced K efflux and ATP secretion. Compared with BK-β4−/− mice, wild-type mice with high distal flows exhibited significantly more urinary ATP excretion. These data demonstrate coupled electrochemical efflux between K and ATP as part of the mechanism for τ-induced ATP release in IC

Quantitation of motexafin lutetium in human plasma by liquid chromatography-tandem mass spectrometry and inductively coupled plasma-atomic emission spectroscopy

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) and inductively coupled plasma-atomic emission spectroscopy (ICP-AES) methods were developed and validated for the evaluation of motexafin lutetium (MLu, lutetium texaphyrin, PCI-0123) pharmacokinetics in human plasma. The LC-MS/MS method was specific for MLu, whereas the ICP-AES method measured total elemental lutetium. Both methods were fast, simple, precise, and accurate. For the LC-MS/MS method, a closely related analogue (PCI-0353) was used as the internal standard (IS). MLu and the IS were extracted from plasma by protein precipitation and injected onto and LC-MS/MS system configured with a C18 column and an electrospray interface. The lower limit of quantitation was 0.05 μg MLu mL−1, with a signal-to-noise ratio of 15∶1. The response was linear from 0.05 to 5.0 μg MLu mL−1. For the ICP-AES method, indium was used as the IS. The sample was digested with nitric acid, diluted, filtered, and then injected onto the ICP-AES system. Two standard curve ranges were validated to meet the expected range of sample concentrations: 0.5 to 50, and 0.1 to 10 μg Lu mL−1. The LC-MS/MS and ICP-AES methods were validated to establish accuracy, precision, analyte stability, and assay robustness. Interday precision and accuracy of quality control samples were ≤6.3% coefficient of variation (CV) and within 2.2% relative error (RE) for the LC-MS/MS method, and ≤8.7% CV and within 4.9% RE for the ICP-AES method. Plasma samples from a subset of patients in a clinical study were analyzed using both methods. For a representative patient, over 90% of the elemental lutetium in plasma could be ascribed to intact MLu at early time points. This percentage decreased to 59% at 48 hours after dosing, suggesting that some degradation and/or metabolism of the drug may have occurred