At least 20 different lacunar syndromes have been described and can be recognized by characteristic clinical features. Almost all occur in patients with hypertension. Small lacunes are usually due to lipohyalinosis, larger ones to atheromatous or embolic occlusion of a penetrating vessel. The concept of the "lacunar state" is examined in the light of recent knowledge with the conclusion that the clinical deficit is primarily related to unrecognized normal pressure hydrocephalus rather than to the presence of a few lacunes. The notion that lacunes occur haphazardly is criticized because the first or only lacune tends to be symptomatic. The incidence of cerebral lacunes has declined since the introduction of antihypertensive therapy, an indication that therapy is effective.

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python Jupyter notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists may be able to contribute.

RNAs fold into three-dimensional (3D) structures that subsequently undergo large, functionally important, conformational transitions in response to a variety of cellular signals. RNA structures are believed to encode spatially tuned flexibility that can direct transitions along specific conformational pathways. However, this hypothesis has proved difficult to examine directly because atomic movements in complex biomolecules cannot be visualized in 3D by using current experimental methods. Here we report the successful implementation of a strategy using NMR that has allowed us to visualize, with complete 3D rotational sensitivity, the dynamics between two RNA helices that are linked by a functionally important trinucleotide bulge over timescales extending up to milliseconds. The key to our approach is to anchor NMR frames of reference onto each helix and thereby directly measure their dynamics, one relative to the other, using 'relativistic' sets of residual dipolar couplings (RDCs). Using this approach, we uncovered super-large amplitude helix motions that trace out a surprisingly structured and spatially correlated 3D dynamic trajectory. The two helices twist around their individual axes by approximately 53 degrees and 110 degrees in a highly correlated manner (R = 0.97) while simultaneously (R = 0.81-0.92) bending by about 94 degrees. Remarkably, the 3D dynamic trajectory is dotted at various positions by seven distinct ligand-bound conformations of the RNA. Thus even partly unstructured RNAs can undergo structured dynamics that directs ligand-induced transitions along specific predefined conformational pathways.

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Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is now possible to follow the relative abundance of microbes in a community over time. These microbial communities exhibit rich ecological dynamics and an important goal of microbial ecology is to infer the ecological interactions between species directly from sequence data. Any algorithm for inferring ecological interactions must overcome three major obstacles: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units, bias inferences of species interactions due to a statistical problem called “errors-in-variables”. Here we introduce an approach, Learning Interactions from MIcrobial Time Series (LIMITS), that overcomes these obstacles. LIMITS uses sparse linear regression with boostrap aggregation to infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested LIMITS on synthetic data and showed that it could reliably infer the topology of the inter-species ecological interactions. We then used LIMITS to characterize the species interactions in the gut microbiomes of two individuals and found that the interaction networks varied significantly between individuals. Furthermore, we found that the interaction networks of the two individuals are dominated by distinct “keystone species”, Bacteroides fragilis and Bacteroided stercosis, that have a disproportionate influence on the structure of the gut microbiome even though they are only found in moderate abundance. Based on our results, we hypothesize that the abundances of certain keystone species may be responsible for individuality in the human gut microbiome.

The characterization of intrinsically disordered proteins is challenging because accurate models of these systems require a description of both their thermally accessible conformers and the associated relative stabilities or weights. These structures and weights are typically chosen such that calculated ensemble averages agree with some set of prespecified experimental measurements; however, the large number of degrees of freedom in these systems typically leads to multiple conformational ensembles that are degenerate with respect to any given set of experimental observables. In this work we demonstrate that estimates of the relative stabilities of conformers within an ensemble are often incorrect when one does not account for the underlying uncertainty in the estimates themselves. Therefore, we present a method for modeling the conformational properties of disordered proteins that estimates the uncertainty in the weights of each conformer. The Bayesian weighting (BW) formalism incorporates information from both experimental data and theoretical predictions to calculate a probability density over all possible ways of weighting the conformers in the ensemble. This probability density is then used to estimate the values of the weights. A unique and powerful feature of the approach is that it provides a built-in error measure that allows one to assess the accuracy of the ensemble. We validate the approach using reference ensembles constructed from the five-residue peptide met-enkephalin and then apply the BW method to construct an ensemble of the K18 isoform of the tau protein. Using this ensemble, we indentify a specific pattern of long-range contacts in K18 that correlates with the known aggregation properties of the sequence.

The relatively flat energy landscapes associated with intrinsically disordered proteins makes modeling these systems especially problematic. A comprehensive model for these proteins requires one to build an ensemble consisting of a finite collection of structures, and their corresponding relative stabilities, which adequately capture the range of accessible states of the protein. In this regard, methods that use computational techniques to interpret experimental data in terms of such ensembles are an essential part of the modeling process. In this review, we critically assess the advantages and limitations of current techniques and discuss new methods for the validation of these ensembles.

Vessel constriction followed by dilatation is a postulated mechanism for migraine, although there is no definitive evidence for this.2 Aside from these two conditions, reversible constriction of arteries is not a recognized cause of cerebrovascular disease.We describe the clinical and angiographic syndrome of four patients with reversible constriction of the cerebral vasculature. We also review some of the relevant literature and discuss the nosology of this only recently recognized condition. Case Reports Case IA 48-year-old woman was admitted to the hospital because of a severe headache that was associated with a sense of spinning, nausea, vomiting, and blurred vision. Headaches were unusual for her, and there was no personal or family history of migraine.Initially, her blood pressure was 200/100 mm Hg, but it quickly fell to the 120/65 mm Hg range without