High-Speed imaging reveals opposing effects of chronic stress and antidepressants on neuronal activity propagation through the hippocampal trisynaptic circuit

Antidepressants (ADs) are used as first-line treatment for most stress-related psychiatric disorders. The alterations in brain circuit dynamics that can arise from stress exposure and underlie therapeutic actions of ADs remain, however, poorly understood. Here, enabled by a recently developed voltage-sensitive dye imaging assay in mouse brain slices, we examined the impact of chronic stress and concentration-dependent effects of eight clinically used ADs (belonging to different chemical/functional classes) on evoked neuronal activity propagations through the hippocampal trisynaptic circuitry (HTC: perforant path - dentate gyrus - area CA3 - area CA1). Exposure of mice to chronic social defeat stress led to markedly weakened activity propagations (HTC-Waves). In contrast, at concentrations in the low micromolar range, all ADs, which were bath applied to slices, caused an amplification of HTC-Waves in CA regions (invariably in area CA1). The fast-acting antidepressant ketamine, the mood stabilizer lithium, and brain-derived neurotrophic factor (BDNF) exerted comparable enhancing effects, whereas the antipsychotic haloperidol and the anxiolytic diazepam attenuated HTC-Waves. Collectively, we provide direct experimental evidence that chronic stress can depress neuronal signal flow through the HTC and demonstrate shared opposing effects of ADs. Thus, our study points to a circuit-level mechanism of ADs to counteract stress-induced impairment of hippocampal network function. However, the observed effects of ADs are impossible to depend on enhanced neurogenesis

Measurement of charged particle spectra in deep-inelastic ep scattering at HERA

Charged particle production in deep-inelastic ep scattering is measured with the H1 detector at HERA. The kinematic range of the analysis covers low photon virtualities, 5 LT Q(2) LT 100 GeV2, and small values of Bjorken-x, 10(-4) LT x LT 10(-2). The analysis is performed in the hadronic centre-of-mass system. The charged particle densities are measured as a function of pseudorapidity (n(*)) and transverse momentum (p(T)(*)) in the range 0 LT n(*) LT 5 and 0 LT p(T)(*) LT 10 GeV in bins of x and Q(2). The data are compared to predictions from different Monte Carlo generators implementing various options for hadronisation and parton evolutions

Diffractive Dijet Production with a Leading Proton in ep Collisions at HERA

The cross section of the diffractive process e^+p -> e^+Xp is measured at a centre-of-mass energy of 318 GeV, where the system X contains at least two jets and the leading final state proton p is detected in the H1 Very Forward Proton Spectrometer. The measurement is performed in photoproduction with photon virtualities Q^2 <2 GeV^2 and in deep-inelastic scattering with 4 GeV^2<Q^2<80 GeV^2. The results are compared to next-to-leading order QCD calculations based on diffractive parton distribution functions as extracted from measurements of inclusive cross sections in diffractive deep-inelastic scattering

Erratum to: Measurement of jet production cross sections in deep-inelastic ep scattering at HERA

The measurement of the jet cross sections by the H1 collaboration had been compared to various predictions including the next-to-next-to-leading order (NNLO) QCD calculations which are corrected in this erratum for an implementation error in one of the components of the NNLO calculations. The jet data and the other predictions remain unchanged. Eight figures, one table and conclusions are adapted accordingly, exhibiting even better agreement between the corrected NNLO predictions and the jet data

Measurement of lepton-jet correlation in deep-inelastic scattering with the H1 detector using machine learning for unfolding

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 OmniFold, which considers eight observables simultaneously in this first application. The unfolded cross sections are compared to calculations performed within the context of collinear or transverse-momentum-dependent (TMD) factorization in Quantum Chromodynamics (QCD) as well as Monte Carlo event generators. The measurement probes a wide range of QCD phenomena, including TMD parton distribution functions and their evolution with energy in so far unexplored kinematic regions