Mechanical Systems with Symmetry, Variational Principles, and Integration Algorithms

This paper studies variational principles for mechanical systems with symmetry and their applications to integration algorithms. We recall some general features of how to reduce variational principles in the presence of a symmetry group along with general features of integration algorithms for mechanical systems. Then we describe some integration algorithms based directly on variational principles using a discretization technique of Veselov. The general idea for these variational integrators is to directly discretize Hamilton’s principle rather than the equations of motion in a way that preserves the original systems invariants, notably the symplectic form and, via a discrete version of Noether’s theorem, the momentum map. The resulting mechanical integrators are second-order accurate, implicit, symplectic-momentum algorithms. We apply these integrators to the rigid body and the double spherical pendulum to show that the techniques are competitive with existing integrators

Supplier-induced demand for psychiatric admissions in Northern New England

The development of hospital service areas (HSAs) using small area analysis has been useful in examining variation in medical and surgical care; however, the techniques of small area analysis are underdeveloped in understanding psychiatric admission rates. We sought to develop these techniques in order to understand the relationship between psychiatric bed supply and admission rates in Northern New England. Our primary hypotheses were that there would be substantial variation in psychiatric admission across geographic settings and that bed availability would be positively correlated with admission rates, reflecting a supplier-induced demand phenomenon. Our secondary hypothesis was that the construction of psychiatric HSAs (PHSAs) would yield more meaningful results than the use of existing general medical hospital service areas

The mass distribution of the unusual merging cluster Abell 2146 from strong lensing

Abell 2146 consists of two galaxy clusters that have recently collided close to the plane of the sky, and it is unique in showing two large shocks on $\textit{Chandra X-ray Observatory}$ images. With an early stage merger, shortly after first core passage, one would expect the cluster galaxies and the dark matter to be leading the X-ray emitting plasma. In this regard, the cluster Abell 2146-A is very unusual in that the X-ray cool core appears to lead, rather than lag, the brightest cluster galaxy (BCG) in their trajectories. Here we present a strong-lensing analysis of multiple-image systems identified on $\textit{Hubble Space Telescope}$ images. In particular, we focus on the distribution of mass in Abell 2146-A in order to determine the centroid of the dark matter halo. We use object colours and morphologies to identify multiple-image systems; very conservatively, four of these systems are used as constraints on a lens mass model. We find that the centroid of the dark matter halo, constrained using the strongly lensed features, is coincident with the BCG, with an offset of ≈2 kpc between the centres of the dark matter halo and the BCG. Thus from the strong-lensing model, the X-ray cool core also leads the centroid of the dark matter in Abell 2146-A, with an offset of ≈30 kpc.JEC acknowledges support from The University of Texas at Dallas, and NASA through a Fellowship of the Texas Space Grant Consortium. Based on observations made with the NASA/ESA HST, obtained through programme 12871 through the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. Additional funding supporting JEC, LJK, and DIC came from a grant from the Space Telescope Science Institute under the same programme 12871. Additional funding supporting JEC and LJK came from a grant from the National Science Foundation, number 1517954. This work was supported in part by World Premier International Research Center Initiative (WPI Initiative), MEXT, Japan, and JSPS KAKENHI Grant Number 26800093 and 15H05892

DHODH modulates transcriptional elongation in the neural crest and melanoma

Melanoma is a tumour of transformed melanocytes, which are originally derived from the embryonic neural crest. It is unknown to what extent the programs that regulate neural crest development interact with mutations in the BRAF oncogene, which is the most commonly mutated gene in human melanoma1. We have used zebrafish embryos to identify the initiating transcriptional events that occur on activation of human BRAF(V600E) (which encodes an amino acid substitution mutant of BRAF) in the neural crest lineage. Zebrafish embryos that are transgenic for mitfa:BRAF(V600E) and lack p53 (also known as tp53) have a gene signature that is enriched for markers of multipotent neural crest cells, and neural crest progenitors from these embryos fail to terminally differentiate. To determine whether these early transcriptional events are important for melanoma pathogenesis, we performed a chemical genetic screen to identify small-molecule suppressors of the neural crest lineage, which were then tested for their effects on melanoma. One class of compound, inhibitors of dihydroorotate dehydrogenase (DHODH), for example leflunomide, led to an almost complete abrogation of neural crest development in zebrafish and to a reduction in the self-renewal of mammalian neural crest stem cells. Leflunomide exerts these effects by inhibiting the transcriptional elongation of genes that are required for neural crest development and melanoma growth. When used alone or in combination with a specific inhibitor of the BRAF(V600E) oncogene, DHODH inhibition led to a marked decrease in melanoma growth both in vitro and in mouse xenograft studies. Taken together, these studies highlight developmental pathways in neural crest cells that have a direct bearing on melanoma formation

Morphology, fluid Motion and Predation by the Scyphomedusa Aurelia Aurita

Although medusan predators play demonstrably important roles in a variety of marine ecosystems, the mechanics of prey capture and, hence, prey selection, have remained poorly defined. A review of the literature describing the commonly studied medusa Aurelia aurita (Linnaeus 1758) reveals no distinct patterns of prey selectivity and suggests that A. aurita is a generalist and feeds unselectively upon available zooplankton. We examined the mechanics of prey capture by A. aurita using video methods to record body and fluid motions. Medusae were collected between February and June in 1990 and 1991 from Woods Hole, Massachusetts and Narragansett Bay, Rhode Island, USA. Tentaculate A. aurita create fluid motions during swimming which entrain prey and bring them into contact with tentacles. We suggest that this mechanism dominates prey selection by A. aurita. In this case, we predict that medusae of a specific diameter will positively select prey with escape speeds slower than the flow velocities at their bell margins. Negatively selected prey escape faster than the medusan flow velocity draws them to capture surfaces. Faster prey will be captured by larger medusac because flow field velocity is a function of bell diameter. On the basis of prey escape velocities and flow field velocities of A. aurita with diameters of 0.8 to 7.1 cm, we predict that A. aurita will select zooplankton such as barnacle nauplii and some slow swimming hydromedusae, while faster copepods will be negatively selected

Depression and sickness behavior are Janus-faced responses to shared inflammatory pathways

It is of considerable translational importance whether depression is a form or a consequence of sickness behavior. Sickness behavior is a behavioral complex induced by infections and immune trauma and mediated by pro-inflammatory cytokines. It is an adaptive response that enhances recovery by conserving energy to combat acute inflammation. There are considerable phenomenological similarities between sickness behavior and depression, for example, behavioral inhibition, anorexia and weight loss, and melancholic (anhedonia), physio-somatic (fatigue, hyperalgesia, malaise), anxiety and neurocognitive symptoms. In clinical depression, however, a transition occurs to sensitization of immuno-inflammatory pathways, progressive damage by oxidative and nitrosative stress to lipids, proteins, and DNA, and autoimmune responses directed against self-epitopes. The latter mechanisms are the substrate of a neuroprogressive process, whereby multiple depressive episodes cause neural tissue damage and consequent functional and cognitive sequelae. Thus, shared immuno-inflammatory pathways underpin the physiology of sickness behavior and the pathophysiology of clinical depression explaining their partially overlapping phenomenology. Inflammation may provoke a Janus-faced response with a good, acute side, generating protective inflammation through sickness behavior and a bad, chronic side, for example, clinical depression, a lifelong disorder with positive feedback loops between (neuro)inflammation and (neuro)degenerative processes following less well defined triggers

Simulations of events for the LUX-ZEPLIN (LZ) dark matter experiment

The LUX-ZEPLIN dark matter search aims to achieve a sensitivity to the WIMP-nucleon spin-independent cross-section down to (1–2)×10−12 pb at a WIMP mass of 40 GeV/c2. This paper describes the simulations framework that, along with radioactivity measurements, was used to support this projection, and also to provide mock data for validating reconstruction and analysis software. Of particular note are the event generators, which allow us to model the background radiation, and the detector response physics used in the production of raw signals, which can be converted into digitized waveforms similar to data from the operational detector. Inclusion of the detector response allows us to process simulated data using the same analysis routines as developed to process the experimental data

Projected sensitivity of the LUX-ZEPLIN experiment to the 0νββ decay of Xe 136

The LUX-ZEPLIN (LZ) experiment will enable a neutrinoless double β decay search in parallel to the main science goal of discovering dark matter particle interactions. We report the expected LZ sensitivity to Xe136 neutrinoless double β decay, taking advantage of the significant (>600 kg) Xe136 mass contained within the active volume of LZ without isotopic enrichment. After 1000 live-days, the median exclusion sensitivity to the half-life of Xe136 is projected to be 1.06×1026 years (90% confidence level), similar to existing constraints. We also report the expected sensitivity of a possible subsequent dedicated exposure using 90% enrichment with Xe136 at 1.06×1027 years

Use of structure-activity landscape index curves and curve integrals to evaluate the performance of multiple machine learning prediction models

<p>Abstract</p> <p>Background</p> <p>Standard approaches to address the performance of predictive models that used common statistical measurements for the entire data set provide an overview of the average performance of the models across the entire predictive space, but give little insight into applicability of the model across the prediction space. Guha and Van Drie recently proposed the use of structure-activity landscape index (SALI) curves via the SALI curve integral (SCI) as a means to map the predictive power of computational models within the predictive space. This approach evaluates model performance by assessing the accuracy of pairwise predictions, comparing compound pairs in a manner similar to that done by medicinal chemists.</p> <p>Results</p> <p>The SALI approach was used to evaluate the performance of continuous prediction models for MDR1-MDCK <it>in vitro </it>efflux potential. Efflux models were built with ADMET Predictor neural net, support vector machine, kernel partial least squares, and multiple linear regression engines, as well as SIMCA-P+ partial least squares, and random forest from Pipeline Pilot as implemented by AstraZeneca, using molecular descriptors from <it>SimulationsPlus </it>and AstraZeneca.</p> <p>Conclusion</p> <p>The results indicate that the choice of training sets used to build the prediction models is of great importance in the resulting model quality and that the SCI values calculated for these models were very similar to their Kendall τ values, leading to our suggestion of an approach to use this SALI/SCI paradigm to evaluate predictive model performance that will allow more informed decisions regarding model utility. The use of SALI graphs and curves provides an additional level of quality assessment for predictive models.</p