The polarimeter vector for τ→3πντ\tau \rightarrow 3 \pi\nu_{\tau} decays

The polarimeter vector of the $\tau$ represents an optimal observable for the measurement of the $\tau$ spin. In this paper we present an algorithm for the computation of the $\tau$ polarimeter vector for the decay channels $\tau^{-} \rightarrow \pi^{-}\pi^{+}\pi^{-}\nu_{\tau}$ and $\tau^{-} \rightarrow \pi^{-}\pi^{0}\pi^{0}\nu_{\tau}$. The algorithm is based on a model for the hadronic current in these decay channels, which was fitted to data recorded by the CLEO experiment.Comment: 7 page

Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics

When using machine learning (ML) techniques, users typically need to choose a plethora of algorithm-specific parameters, referred to as hyperparameters. In this paper, we compare the performance of two algorithms, particle swarm optimisation (PSO) and Bayesian optimisation (BO), for the autonomous determination of these hyperparameters in applications to different ML tasks typical for the field of high energy physics (HEP). Our evaluation of the performance includes a comparison of the capability of the PSO and BO algorithms to make efficient use of the highly parallel computing resources that are characteristic of contemporary HEP experiments.Comment: Accepted by Computer Physics Communications. Changes made compared to previous version: added references to other strategies, added Zenodo entry for the implemented software, added a brief description of PSO, added more explanations regarding the benchmark task

Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics

The analysis of vast amounts of data constitutes a major challenge in modern high energy physics experiments. Machine learning (ML) methods, typically trained on simulated data, are often employed to facilitate this task. Several choices need to be made by the user when training the ML algorithm. In addition to deciding which ML algorithm to use and choosing suitable observables as inputs, users typically need to choose among a plethora of algorithm-specific parameters. We refer to parameters that need to be chosen by the user as hyperparameters. These are to be distinguished from parameters that the ML algorithm learns autonomously during the training, without intervention by the user. The choice of hyperparameters is conventionally done manually by the user and often has a significant impact on the performance of the ML algorithm. In this paper, we explore two evolutionary algorithms: particle swarm optimization (PSO) and genetic algorithm (GA), for the purposes of performing the choice of optimal hyperparameter values in an autonomous manner. Both of these algorithms will be tested on different datasets and compared to alternative methods.Comment: Corrected typos. Removed a remark on page 2 regarding the similarity of minimization and maximization problem. Removed a remark on page 9 (Summary) regarding thee ANN, since this was not studied in the pape

AT II Receptor Blockade and Renal Denervation: Different Interventions with Comparable Renal Effects?

Background: Angiotensin II (Ang II) and the renal sympathetic nervous system exert a strong influence on renal sodium and water excretion. We tested the hypothesis that already low doses of an Ang II inhibitor (candesartan) will result in similar effects on tubular sodium and water reabsorption in congestive heart failure (CHF) as seen after renal denervation (DNX). Methods: Measurement of arterial blood pressure, heart rate (HR), renal sympathetic nerve activity (RSNA), glomerular filtration rate (GFR), renal plasma flow (RPF), urine volume, and urinary sodium. To assess neural control of volume homeostasis, 21 days after the induction of CHF via myocardial infarction rats underwent volume expansion (0.9% NaCL; 10% body weight) to decrease RSNA. CHF rat and controls with or without DNX or pretreated with the Ang II type-1 receptor antagonist candesartan (0.5 ug i.v.) were studied. Results: CHF rats excreted only 68 + 10.2% of the volume load (10% body weight) in 90 min. CHF rats pretreated with candesartan or after DNX excreted from 92 to 103% like controls. Decreases of RSNA induced by volume expansion were impaired in CHF rats but unaffected by candesartan pointing to an intrarenal drug effect. GFR and RPF were not significantly different in controls or CHF. Conclusion: The prominent function of increased RSNA – retaining salt and water – could no longer be observed after renal Ang II receptor blockade in CHF rats

Neurogenic tachykinin mechanisms in experimental nephritis of rats

Abstract We demonstrated earlier that renal afferent pathways combine very likely “classical” neural signal transduction to the central nervous system and a substance P (SP)–dependent mechanism to control sympathetic activity. SP content of afferent sensory neurons is known to mediate neurogenic inflammation upon release. We tested the hypothesis that alterations in SP-dependent mechanisms of renal innervation contribute to experimental nephritis. Nephritis was induced by OX-7 antibodies in rats, 6 days later instrumented for recording of blood pressure (BP), heart rate (HR), drug administration, and intrarenal administration (IRA) of the TRPV1 agonist capsaicin to stimulate afferent renal nerve pathways containing SP and electrodes for renal sympathetic nerve activity (RSNA). The presence of the SP receptor NK-1 on renal immune cells was assessed by FACS. IRA capsaicin decreased RSNA from 62.4 ± 5.1 to 21.6 ± 1.5 mV s (*p < 0.05) in controls, a response impaired in nephritis. Suppressed RSNA transiently but completely recovered after systemic administration of a neurokinin 1 (NK1-R) blocker. NK-1 receptors occurred mainly on CD11+ dendritic cells (DCs). An enhanced frequency of CD11c+NK1R+ cell, NK-1 receptor+ macrophages, and DCs was assessed in nephritis. Administration of the NK-1R antagonist aprepitant during nephritis reduced CD11c+NK1R+ cells, macrophage infiltration, renal expression of chemokines, and markers of sclerosis. Hence, SP promoted renal inflammation by weakening sympathoinhibitory mechanisms, while at the same time, substance SP released intrarenally from afferent nerve fibers aggravated immunological processes i.e. by the recruitment of DCs

Afferent neurons of the kidney with impaired firing pattern in inflammation – role of sodium currents?

Peripheral neurons with renal afferents exhibit a predominantly tonic firing pattern of higher frequency that is reduced to low frequencies (phasic firing pattern) in renal inflammation. We wanted to test the hypothesis that the reduction in firing activity during inflammation is due to high-activity tonic neurons switching from higher to low frequencies depending on altered sodium currents. We identified and cultivated afferent sensory neurons with renal projections from the dorsal root ganglia (Th11-L2). Cultivated neurons were incubated with the chemokine CXCL1 (1,5 nmol/ml) for 12 h. We characterized neurons as “tonic,” i.e., sustained action potential (AP) firing, or “phasic,” i.e., < 5 APs upon stimulation in the current clamp. Their membrane currents were investigated in a voltage clamp. Data analyzed: renal vs. non-renal and tonic vs. phasic neurons. Renal afferent neurons exposed to CXCL1 showed a decrease in tonic firing pattern (CXCL1: 35,6% vs. control: 57%, P  < 0.05). Na + and K + currents were not different between control renal and non-renal DRG neurons. Phasic neurons exhibited higher Na + and K + currents than tonic resulting in shorter APs (3.7 ± 0.3 vs. 6.1 ± 0.6 ms, P  < 0.01). In neurons incubated with CXCL1, Na + and K + peak current density increased in phasic (Na + : − 969 ± 47 vs. − 758 ± 47 nA/pF, P  < 0.01; K + : 707 ± 22 vs. 558 ± 31 nA/pF, P  < 0.01), but were unchanged in tonic neurons. Phasic neurons exposed to CXCL1 showed a broader range of Na + currents ([− 365– − 1429 nA] vs. [− 412– − 4273 nA]; P  < 0.05) similar to tonic neurons. After CXCL1 exposure, significant changes in phasic neurons were observed in sodium activation/inactivation as well as a wider distribution of Na + currents characteristic of tonic neurons. These findings indicate a subgroup of tonic neurons besides mere tonic or phasic neurons exists able to exhibit a phasic activity pattern under pathological conditions.Open Access funding enabled and organized by Projekt DEAL.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Friedrich-Alexander-Universität Erlangen-Nürnberg (1041

Neurogenic substance P—influences on action potential production in afferent neurons of the kidney?

We recently showed that a substance P (SP)–dependent sympatho-inhibitory mechanism via afferent renal nerves is impaired in mesangioproliferative nephritis. Therefore, we tested the hypothesis that SP released from renal afferents inhibits the action potential (AP) production in their dorsal root ganglion (DRG) neurons. Cultured DRG neurons (Th11-L2) were investigated in current clamp mode to assess AP generation during both TRPV1 stimulation by protons (pH 6) and current injections with and without exposure to SP (0.5 µmol) or CGRP (0.5 µmol). Neurons were classified as tonic (sustained AP generation) or phasic (≤ 4 APs) upon current injection; voltage clamp experiments were performed for the investigation of TRPV1-mediated inward currents due to proton stimulation. Superfusion of renal neurons with protons and SP increased the number of action potentials in tonic neurons (9.6 ± 5 APs/10 s vs. 16.9 ± 6.1 APs/10 s, P < 0.05, mean ± SD, n = 7), while current injections with SP decreased it (15.2 ± 6 APs/600 ms vs. 10.2 ± 8 APs/600 ms, P < 0.05, mean ± SD, n = 29). Addition of SP significantly reduced acid-induced TRPV1-mediated currents in renal tonic neurons (− 518 ± 743 pA due to pH 6 superfusion vs. − 82 ± 50 pA due to pH 6 with SP superfusion). In conclusion, SP increased action potential production via a TRPV1-dependent mechanism in acid-sensitive renal neurons. On the other hand, current injection in the presence of SP led to decreased action potential production. Thus, the peptide SP modulates signaling pathways in renal neurons in an unexpected manner leading to both stimulation and inhibition of renal neuronal activity in different (e.g., acidic) environmental contexts