Rationale

Acute cocaine administration produces an initial rewarding state followed by a dysphoric/anxiogenic "crash."

Objective

The objective of this study was to determine whether individual differences in the relative value of cocaine's positive and negative effects would account for variations in subsequent drug self-administration.

Methods

The dual actions of cocaine were assessed using a conditioned place test (where animals formed preferences for environments paired with the immediate rewarding effects of 1.0mg/kg i.v. cocaine or aversions of environments associated with the anxiogenic effects present 15-min postinjection) and a runway test (where animals developed approach-avoidance "retreat" behaviors about entering a goal box associated with cocaine delivery). Ranked scores from these two tests were then correlated with each other and with the escalation in the operant responding of the same subjects observed over 10 days of 1- or 6-h/day access to i.v. (0.4 mg/inj) cocaine self-administration.

Results

Larger place preferences were associated with faster runway start latencies (r s = -0.64), but not with retreat frequency or run times; larger place aversions predicted slower runway start times (r s = 0.62), increased run times (r s = 0.65), and increased retreats (r s = 0.62); response escalation was observed in both the 1- and 6-h self-administration groups and was associated with increased CPPs (r s = 0.58) but not CPAs, as well as with faster run times (r s = -0.60).

Conclusions

Together, these data suggest that animals exhibiting a greater positive than negative response to acute (single daily injections of) cocaine are at the greatest risk for subsequent escalated cocaine self-administration, a presumed indicator of cocaine addiction.

© 2015 Vossel et al.Axonal transport deficits in Alzheimer's disease (AD) are attributed to amyloid β (Aβ) peptides and pathological forms of the microtubuleassociated protein tau. Genetic ablation of tau prevents neuronal overexcitation and axonal transpo

Abstract: This study compares projected changes of precipitation characteristics in the U.S. Northeast in two analog-based climate downscaling products, Multivariate Adaptive Constructed Analogs (MACA) and Localized Constructed Analogs (LOCA). The level of similarity or differences between the two products varies with the type of precipitation metrics. For the total precipitation amount, the two products project significant annual increases that are similar in magnitude, spatial pattern, and seasonal distribution, with the largest increases in winter and spring. For the overall precipitation intensity or temporal aggregation of heavy precipitation (e.g., number of days with more than one inch of precipitation, the simple intensity index, and the fraction of annual precipitation accounted for by heavy events), both products project significant increases across the region with strong model consensus; the magnitude of absolute increases are similar between the two products, but the relative increases are larger in LOCA due to an underestimation of heavy precipitation in LOCA’s training data. For precipitation extremes such as the annual maximum 1-day precipitation, both products project significant increases in the long-term mean, but the magnitude of both the absolute and relative changes are much smaller in LOCA than in MACA, indicating that the extreme precipitation differences in the training data are amplified in future projections as a result of the analog-based downscaling algorithms. The two products differ the most in the intensity and frequency of rare extremes (e.g., 1-in-20-years events) for which MACA projects significant increases while the LOCA-projected changes are inconclusive over much of the study area.

The first measurement of lepton-jet momentum imbalance and azimuthal correlation in lepton-proton scattering at high momentum transfer is presented. These data, taken with the H1 detector at HERA, are corrected for detector effects using an unbinned machine learning algorithm (multifold), which considers eight observables simultaneously in this first application. The unfolded cross sections are compared with calculations performed within the context of collinear or transverse-momentum-dependent factorization in quantum chromodynamics as well as Monte Carlo event generators.

We report measurements of the charged daughter fraction of 218Po as a result of the 222Rn alpha decay, and the mobility of 218Po+ ions, using radon-polonium coincidences from the 238U chain identified in 532 live-days of DarkSide-50 WIMP-search data. The fraction of 218Po that is charged is found to be 0.37 ± 0.03 and the mobility of 218Po+ is (8.6 ± 0.1) × 10−4 cmVs2.

The radiation pattern within high energy quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force as well as an environment for optimizing event generators with numerous applications in high energy particle and nuclear physics. Looking at electron-proton collisions is of particular interest as many of the complications present at hadron colliders are absent. A detailed study of modern jet substructure observables, jet angularities, in electron-proton collisions is presented using data recorded using the H1 detector at HERA. The measurement is unbinned and multi-dimensional, using machine learning to correct for detector effects. All of the available reconstructed object information of the respective jets is interpreted by a graph neural network, achieving superior precision on a selected set of jet angularities. Training these networks was enabled by the use of a large number of GPUs in the Perlmutter supercomputer at Berkeley Lab. The particle jets are reconstructed in the laboratory frame, using the $k_{\mathrm{T}}$ jet clustering algorithm. Results are reported at high transverse momentum transfer $Q^2>150$ GeV${}^2$, and inelasticity $0.2 < y < 0.7$. The analysis is also performed in sub-regions of $Q^2$, thus probing scale dependencies of the substructure variables. The data are compared with a variety of predictions and point towards possible improvements of such models.

The radiation pattern within high energy quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force as well as an environment for optimizing event generators with numerous applications in high energy particle and nuclear physics. Looking at electron-proton collisions is of particular interest as many of the complications present at hadron colliders are absent. A detailed study of modern jet substructure observables, jet angularities, in electron-proton collisions is presented using data recorded using the H1 detector at HERA. The measurement is unbinned and multi-dimensional, using machine learning to correct for detector effects. All of the available reconstructed object information of the respective jets is interpreted by a graph neural network, achieving superior precision on a selected set of jet angularities. Training these networks was enabled by the use of a large number of GPUs in the Perlmutter supercomputer at Berkeley Lab. The particle jets are reconstructed in the laboratory frame, using the kT jet clustering algorithm. Results are reported at high transverse momentum transfer Q2>150GeV2, and inelasticity 0.2

The HERAPDF2.0 ensemble of parton distribution functions (PDFs) was introduced in 2015. The final stage is presented, a next-to-next-to-leading-order (NNLO) analysis of the HERA data on inclusive deep inelastic ep scattering together with jet data as published by the H1 and ZEUS collaborations. A perturbative QCD fit, simultaneously of αs(MZ2) and the PDFs, was performed with the result αs(MZ2)=0.1156±0.0011(exp)-0.0002+0.0001(model+parameterisation)±0.0029(scale). The PDF sets of HERAPDF2.0Jets NNLO were determined with separate fits using two fixed values of αs(MZ2), αs(MZ2)=0.1155 and 0.118, since the latter value was already chosen for the published HERAPDF2.0 NNLO analysis based on HERA inclusive DIS data only. The different sets of PDFs are presented, evaluated and compared. The consistency of the PDFs determined with and without the jet data demonstrates the consistency of HERA inclusive and jet-production cross-section data. The inclusion of the jet data reduced the uncertainty on the gluon PDF. Predictions based on the PDFs of HERAPDF2.0Jets NNLO give an excellent description of the jet-production data used as input.