A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation

Rodent hippocampal population codes represent important spatial information about the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and propose a nonparametric Bayesian approach to infer rat hippocampal population codes during spatial navigation. To tackle the model selection problem, we leverage a nonparametric Bayesian model. Specifically, to analyze rat hippocampal ensemble spiking activity, we apply a hierarchical Dirichlet process-hidden Markov model (HDP-HMM) using two Bayesian inference methods, one based on Markov chain Monte Carlo (MCMC) and the other based on variational Bayes (VB). We demonstrate the effectiveness of our Bayesian approaches on recordings from a freely-behaving rat navigating in an open field environment. We find that MCMC-based inference with Hamiltonian Monte Carlo (HMC) hyperparameter sampling is flexible and efficient, and outperforms VB and MCMC approaches with hyperparameters set by empirical Bayes

Quality Uncertainty and Grain Merchandising Risk: Vomitoxin in Spring Wheat

Marketing, Risk and Uncertainty,

Recombinase polymerase amplification for fast, selective, DNA‐based detection of faecal indicator Escherichia coli

The bacterium Escherichia coli is commonly associated with the presence of faecal contamination in environmental samples, and is therefore subject to statutory surveillance. This is normally done using a culture‐based methodology, which can be slow and laborious. Nucleic acid amplification for the detection of E. coli DNA sequences is a significantly more rapid approach, suited for applications in the field such as a point of sample analysis, and to provide an early warning of contamination. An existing, high integrity qPCR method to detect the E. coli ybbW gene, which requires almost an hour to detect low quantities of the target, was compared with a novel, isothermal RPA method, targeting the same sequence but achieving the result within a few minutes. The RPA technique demonstrated equivalent inclusivity and selectivity, and was able to detect DNA extracted from 100% of 99 E. coli strains, and exclude 100% of 30 non‐target bacterial species. The limit of detection of the RPA assay was at least 100 target sequence copies. The high speed, and simple, isothermal amplification chemistry may indicate that RPA is a more suitable methodology for on‐site E. coli monitoring than an existing qPCR technique

Big Data, Social Physics, and Spatial Analysis: The Early Years

This paper examines one of the historical antecedents of Big Data, the social physics movement. Its origins are in the scientific revolution of the 17th century in Western Europe. But it is not named as such until the middle of the 19th century, and not formally institutionalized until another hundred years later when it is associated with work by George Zipf and John Stewart. Social physics is marked by the belief that large-scale statistical measurement of social variables reveals underlying relational patterns that can be explained by theories and laws found in natural science, and physics in particular. This larger epistemological position is known as monism, the idea that there is only one set of principles that applies to the explanation of both natural and social worlds. Social physics entered geography through the work of the mid-20th-century geographer William Warntz, who developed his own spatial version called ‘‘macrogeography.’’ It involved the computation of large data sets, made ever easier with the contemporaneous development of the computer, joined with the gravitational potential model. Our argument is that Warntz’s concerns with numeracy, large data sets, machine-based computing power, relatively simple mathematical formulas drawn from natural science, and an isomorphism between natural and social worlds became grounds on which Big Data later staked its claim to knowledge; it is a past that has not yet passed

The Promise of Research to Advance Smart Decarceration

This paper describes the concept of "Smart Decarceration" and introduces the special issue of Criminal Justice and Behavior entitled Research to Advance Smart Decarceration Policies, Programs, and Interventions. The concept of Smart Decarceration originated nearly a decade ago as the United States reached a tipping point in mass incarceration, and it focuses on three interrelated outcomes: substantially reducing the use of incarceration and other forms of punishment; reversing racial disparities and other inequities in the criminal justice system; and promoting safety and well-being, particularly for communities that have been most impacted by mass incarceration. Ultimately, Smart Decarceration efforts should prioritize reducing the overall footprint of the criminal justice system, while building capacity outside of the system to support safety, health, and well-being. Research plays a critical role in advancing Smart Decarceration, as new forms of knowledge and evidence must be developed to replace ineffective and unjust policies and practices associated with mass incarceration. The paper discusses approaches to research that move beyond typical criminal justice outcomes and focus on the multifaceted goals of Smart Decarceration. The six articles in this special issue are introduced, highlighting their foci across ecological levels and the breadth of the criminal justice continuum, centering populations most impacted by incarceration, and identifying practice and policy innovations

Methods for the Detection, Study, and Dynamic Profiling of O-GlcNAc Glycosylation

The addition of O-linked β-N-acetylglucosamine (O-GlcNAc) to serine/threonine residues of proteins is a ubiquitous posttranslational modification found in all multicellular organisms. Like phosphorylation, O-GlcNAc glycosylation (O-GlcNAcylation) is inducible and regulates a myriad of physiological and pathological processes. However, understanding the diverse functions of O-GlcNAcylation is often challenging due to the difficulty of detecting and quantifying the modification. Thus, robust methods to study O-GlcNAcylation are essential to elucidate its key roles in the regulation of individual proteins, complex cellular processes, and disease. In this chapter, we describe a set of chemoenzymatic labeling methods to (1) detect O-GlcNAcylation on proteins of interest, (2) monitor changes in both the total levels of O-GlcNAcylation and its stoichiometry on proteins of interest, and (3) enable mapping of O-GlcNAc to specific serine/threonine residues within proteins to facilitate functional studies. First, we outline a procedure for the expression and purification of a multiuse mutant galactosyltransferase enzyme (Y289L GalT). We then describe the use of Y289L GalT to modify O-GlcNAc residues with a functional handle, N-azidoacetylgalactosamine (GalNAz). Finally, we discuss several applications of the copper-catalyzed azide-alkyne cycloaddition “click” reaction to attach various alkyne-containing chemical probes to GalNAz and demonstrate how this functionalization of O-GlcNAc-modified proteins can be used to realize (1)–(3) above. Overall, these methods, which utilize commercially available reagents and standard protein analytical tools, will serve to advance our understanding of the diverse and important functions of O-GlcNAcylation

The Importance of Formative Assessment in Science and Engineering Ethics Education: Some Evidence and Practical Advice

Recent research in ethics education shows a potentially problematic variation in content, curricular materials, and instruction. While ethics instruction is now widespread, studies have identified significant variation in both the goals and methods of ethics education, leaving researchers to conclude that many approaches may be inappropriately paired with goals that are unachievable. This paper speaks to these concerns by demonstrating the importance of aligning classroom-based assessments to clear ethical learning objectives in order to help students and instructors track their progress toward meeting those objectives. Two studies at two different universities demonstrate the usefulness of classroom-based, formative assessments for improving the quality of students’ case responses in computational modeling and research ethics

Analytical and Experimental Evaluation of Aerodynamic Thrust Vectoring on an Aerospike Nozzle

Results from numerical and cold-flow experimental investigations of aerodynamic thrust vectoring on a small-scale aerospike thruster are presented. Thrust vectoring was created by the injection of a secondary fluid into the primary flow field normal to the nozzle axis. The experimental aerospike nozzle was truncated at 57% of its full theoretical length. Data derived from cold-flow thrust vectoring tests with carbon dioxide as the working fluid are presented. Injection points near the end of the truncated spike produced the highest force amplification factors. Explanations are given for this phenomenon. For secondary injection near the end of the aerospike, side force amplification factors up to approximately 1.4 and side force specific impulses up to approximately 55 s with main flow specific impulses clustering around 38 s were demonstrated. These forces crisply reproduce input pulses with a high degree of fidelity. The side force levels are approximately 2.7% of the total thrust level at maximum effectiveness. Higher side forces on the order of 4.7% of axial thrust were also achieved at reduced efficiency. The side force amplification factors were independent of operating nozzle pressure ratio for the range of chamber pressures used in this test series

Dynamical instabilities of Bose-Einstein condensates at the band-edge in one-dimensional optical lattices

We report on experiments that demonstrate dynamical instability in a Bose-Einstein condensate at the band-edge of a one-dimensional optical lattice. The instability manifests as rapid depletion of the condensate and conversion to a thermal cloud. We consider the collisional processes that can occur in such a system, and perform numerical modeling of the experiments using both a mean-field and beyond mean-field approach. We compare our numerical results to the experimental data, and find that the Gross-Pitaevskii equation is not able to describe this experiment. Our beyond mean-field approach, known as the truncated Wigner method, allows us to make quantitative predictions for the processes of parametric growth and thermalization that are observed in the laboratory, and we find good agreement with the experimental results.Comment: v2: Added several reference