A phylogeny-aware approach reveals unexpected venom components in divergent lineages of cone snails

Marine gastropods of the genus Conus are renowned for their remarkable diversity and deadly venoms. While Conus venoms are increasingly well studied for their biomedical applications, we know surprisingly little about venom composition in other lineages of Conidae. We performed comprehensive venom transcriptomic profiling for Conasprella coriolisi and Pygmaeconus traillii , first time for both respective genera. We complemented reference-based transcriptome annotation by a de novo toxin prediction guided by phylogeny, which involved transcriptomic data on two additional ‘divergent’ cone snail lineages, Profundiconus , and Californiconus . We identified toxin clusters (SSCs) shared among all or some of the four analysed genera based on the identity of the signal region—a molecular tag present in toxins. In total, 116 and 98 putative toxins represent 29 and 28 toxin gene superfamilies in Conasprella and Pygmaeconus , respectively; about quarter of these only found by semi-manual annotation of the SSCs. Two rare gene superfamilies, originally identified from fish-hunting cone snails, were detected outside Conus rather unexpectedly, so we further investigated their distribution across Conidae radiation. We demonstrate that both these, in fact, are ubiquitous in Conidae, sometimes with extremely high expression. Our findings demonstrate how a phylogeny-aware approach circumvents methodological caveats of similarity-based transcriptome annotation.

Search for supersymmetry in events with four or more charged leptons in 139 fb−1 of $$ \sqrt{s} $$ = 13 TeV pp collisions with the ATLAS detector

A search for supersymmetry in events with four or more charged leptons (electrons, muons and τ-leptons) is presented. The analysis uses a data sample corresponding to 139 fb−1 of proton-proton collisions delivered by the Large Hadron Collider at $$ \sqrt{s} $$ s = 13 TeV and recorded by the ATLAS detector. Four-lepton signal regions with up to two hadronically decaying τ-leptons are designed to target several supersymmetric models, while a general five-lepton signal region targets any new physics phenomena leading to a final state with five charged leptons. Data yields are consistent with Standard Model expectations and results are used to set upper limits on contributions from processes beyond the Standard Model. Exclusion limits are set at the 95% confidence level in simplified models of general gauge-mediated supersymmetry, excluding higgsino masses up to 540 GeV. In R-parity-violating simplified models with decays of the lightest supersymmetric particle to charged leptons, lower limits of 1.6 TeV, 1.2 TeV, and 2.5 TeV are placed on wino, slepton and gluino masses, respectively.

Search for new light vector boson using J/Ψ at BESIII and Belle II

We investigate various search strategies for light vector boson X in $$ \mathcal{O} $$ O (10) MeV mass range using J/Ψ associated channels at BESIII and Belle II: (i) J/Ψ → ηcX with 1010J/Ψs at BESIII, (ii) J/Ψ(ηc + X) + $$ \mathrm{\ell}\overline{\mathrm{\ell}} $$ ℓ ℓ ¯ production at Belle II, and (iii) J/Ψ + X with the displaced vertex in X → e+e− decay are analyzed and the future sensitivities at Belle II with 50 ab−1 luminosity are comprehensively studied. By requiring the displaced vertex to be within the beam pipe, the third method results in nearly background-free analysis, and the vector boson-electron coupling and the vector boson mass can be probed in the unprecedented range, 10−4 ≤ |εe| ≤ 10−3 and 9 MeV ≤ mX ≤ 100MeV with 50 ab−1 at Belle II. This covers the favored signal region of 8Be* anomaly recently reported by Atomki experiment with mX ≃ 17 MeV.

ThickBrick: optimal event selection and categorization in high energy physics. Part I. Signal discovery

We provide a prescription called ThickBrick to train optimal machine-learning-based event selectors and categorizers that maximize the statistical significance of a potential signal excess in high energy physics (HEP) experiments, as quantified by any of six different performance measures. For analyses where the signal search is performed in the distribution of some event variables, our prescription ensures that only the information complementary to those event variables is used in event selection and categorization. This eliminates a major misalignment with the physics goals of the analysis (maximizing the significance of an excess) that exists in the training of typical ML-based event selectors and categorizers. In addition, this decorrelation of event selectors from the relevant event variables prevents the background distribution from becoming peaked in the signal region as a result of event selection, thereby ameliorating the challenges imposed on signal searches by systematic uncertainties. Our event selectors (categorizers) use the output of machine-learning-based classifiers as input and apply optimal selection cutoffs (categorization thresholds) that are functions of the event variables being analyzed, as opposed to flat cutoffs (thresholds). These optimal cutoffs and thresholds are learned iteratively, using a novel approach with connections to Lloyd’s k-means clustering algorithm. We provide a public, Python implementation of our prescription, also called ThickBrick, along with usage examples.

Implementation of the CMS-EXO-17-030 analysis in the MadAnalysis 5 framework (production of a pair of resonances decaying each into three jets; 35.9 fb−1)

We present the MadAnalysis 5 implementation and validation of the CMS-EXO-17-030 search. The search targets pair-produced resonances, each of which decaying into three jets. The results are interpreted within an [Formula: see text]-parity violating supersymmetric (RPV SUSY) model, that predicts that pair-produced gluinos decay into three jets. This leads to a six-jet event. For this study, proton–proton collision data which was collected with the CMS detector in 2016 at a center of energy of 13 TeV is used, with a corresponding luminosity of 35.9 fb[Formula: see text]. In the search, the resonance mass is expected to range from 200 GeV to 2000 GeV so that the analysis comprises four signal regions (SRs). To validate the results, we have selected four gluino benchmark masses of 200, 500, 900, and 1600 GeV, each of which being representative of a given signal region (that are denoted SR1, SR2, SR3, and SR4). We have simulated signal events and calculated the signal acceptance within the MadAnalysis 5 framework in each signal region. To validate the recast, our predicted acceptances have been compared with the official values for those benchmark scenarios. An agreement at the level of about 10% has been obtained.

Neural network approach to data-driven estimation of chemotactic sensitivity in the Keller-Segel model

<abstract><p>We consider the mathematical model of chemotaxis introduced by Patlak, Keller, and Segel. Aggregation and progression waves are present everywhere in the population dynamics of chemotactic cells. Aggregation originates from the chemotaxis of mobile cells, where cells are attracted to migrate to higher concentrations of the chemical signal region produced by themselves. The neural net can be used to find the approximate solution of the PDE. We proved that the error, the difference between the actual value and the predicted value, is bound to a constant multiple of the loss we are learning. Also, the Neural Net approximation can be easily applied to the inverse problem. It was confirmed that even when the coefficient of the PDE equation was unknown, prediction with high accuracy was achieved.</p></abstract>

Search for neutrinoless double-beta decays in Ge-76 in the LEGEND experiment

The search for neutrinoless double-beta decay is the most sensitive technique to establish the Majorana nature of neutrinos. Two operating experiments that look for such decays in Ge-76, GERDA and MAJORANA DEMONSTRATOR have achieved the lowest backgrounds and the best energy resolution in the signal region. These are two of the most important detector characteristics for sensitive searches of this undiscovered decay. The Large Enriched Germanium Experiment for Neutrinoless Double-Beta Decay (LEGEND) Collaboration has been formed to pursue a tonne-scale Ge-76 experiment that integrates the best technologies from these two experiments and others in the field. The Collaboration is developing a phased experimental program that uses existing resources as appropriate to expedite physics results, with the ultimate discovery potential at a decay half-life beyond 1028 years.

A Deep Convolutional Network for Multitype Signal Detection and Classification in Spectrogram

Wideband signal detection is an important problem in wireless communication. With the rapid development of deep learning (DL) technology, some DL-based methods are applied to wireless communication and have shown great potential. In this paper, we present a novel neural network for detecting signals and classifying signal types in wideband spectrograms. Our network utilizes the key point estimation to locate the rough centerline of the signal region and recognize its class. Then, several regressions are carried out to obtain properties, including the local offset and the border offsets of a bounding box, which are further synthesized for a more fine location. Experimental results demonstrate that our method performs more accurate than other DL-based object detection methods previously employed for the same task. In addition, our method runs obviously faster than existing methods, and it abandons the candidate anchors, which make it more favorable for real-time applications.

Weighted Constraint Iterative Algorithm for Phase Hologram Generation

A weighted constraint iterative algorithm is presented to calculate phase holograms with quality reconstruction. The image plane is partitioned into two regions where different constraint strategies are implemented during the iteration process. In the image plane, the signal region is constrained directly according to the amplitude distribution of the target image based on an adaptive strategy, whereas the non-signal region is constrained indirectly by total energy control of the hologram plane based on the energy conservation principle. The weighted constraint strategy can improve the reconstruction quality of the phase holograms by broadening the optimizing space of the iterative algorithm, leading to effective convergence of the iteration process. Finally, numerical and optical experiments have been performed to validate the feasibility of our method.

vRNA-vRNA interactions in influenza A virus HA vRNA packaging

The genome of the influenza A virus is composed of eight single-stranded negative-sense RNA segments (vRNAs). The eight different vRNAs are selectively packaged into progeny virions. This process likely involves specific interactions among vRNAs via segment-specific packaging signals located in the 3’ and 5’ terminal coding regions of vRNAs. To identify vRNA(s) that interact with hemagglutinin (HA) vRNA during genome packaging, we generated a mutant virus, HA 5m2, which possessed five silent mutations in the 5’ packaging signal region of HA vRNA. The HA 5m2 virus had a specific defect in HA vRNA incorporation, which reduced the viral replication efficiency. After serial passaging in cells, the virus acquired additional mutations in the 5’ terminal packaging signal regions of both HA and PB2 vRNAs. These mutations contributed to recovery of viral growth and packaging efficiency of HA vRNA. A direct RNA-RNA interaction between the 5’ ends of HA and PB2 vRNAs was confirmed in vitro. Our results indicate that direct interactions of HA vRNA with PB2 vRNA via their packaging signal regions are important for selective genome packaging and enhance our knowledge on the emergence of pandemic influenza viruses through genetic reassortment.