Search for Neutrino-Induced Neutral-Current Δ Radiative Decay in MicroBooNE and a First Test of the MiniBooNE Low Energy Excess under a Single-Photon Hypothesis

We report results from a search for neutrino-induced neutral current (NC) resonant Δ(1232) baryon production followed by Δ radiative decay, with a ⟨0.8⟩  GeV neutrino beam. Data corresponding to MicroBooNE's first three years of operations (6.80×10^{20} protons on target) are used to select single-photon events with one or zero protons and without charged leptons in the final state (1γ1p and 1γ0p, respectively). The background is constrained via an in situ high-purity measurement of NC π^{0} events, made possible via dedicated 2γ1p and 2γ0p selections. A total of 16 and 153 events are observed for the 1γ1p and 1γ0p selections, respectively, compared to a constrained background prediction of 20.5±3.65(syst) and 145.1±13.8(syst) events. The data lead to a bound on an anomalous enhancement of the normalization of NC Δ radiative decay of less than 2.3 times the predicted nominal rate for this process at the 90% confidence level (C.L.). The measurement disfavors a candidate photon interpretation of the MiniBooNE low-energy excess as a factor of 3.18 times the nominal NC Δ radiative decay rate at the 94.8% C.L., in favor of the nominal prediction, and represents a greater than 50-fold improvement over the world's best limit on single-photon production in NC interactions in the sub-GeV neutrino energy range.

A tetranuclear nickel(II) complex, [Ni4(L)4](ClO4)4·C2H3N·2H2O, with an asymmetric Ni4O4 open-cubane-like core

A tetra­nuclear complex with an open-cubane like structure was synthesized from 2-meth­oxy-6-(pyridin-2-yl-hydrazonometh­yl)-phenol and characterized using micro-analytical and spectroscopic techniques, and single-crystal X-ray diffraction analysis.

A tetra­nuclear complex with an open-cubane-like core structure was synthesized from 2-meth­oxy-6-(pyridin-2-yl-hydrazonometh­yl)phenol (HL), namely, cyclo-tetra­kis­(μ-2-meth­oxy-6-{[2-(pyridin-2-yl)hydrazin-1-yl­idene]meth­yl}pheno­lato)tetra­nickel(II) tetra­kis­(perchlorate) aceto­nitrile monosolvate dihydrate, [Ni₄(C₁₃H₁₂N₃O₂)₄](ClO₄)₄·C₂H₃N·2H₂O, and characterized using micro-analytical and spectroscopic techniques. The crystal-structure determination reveals the formation of a distorted Ni₄O₄ cubane-like core architecture encapsulated by four hydrazone Schiff base (HL) mol­ecules. A open-cube tetra­nuclear architecture is created in which nickel(II) ions of the NiN₂O₃ unit are connected by μ₂-O anions of the phenolate moiety of HL. In this complex, each Ni centre has a slightly distorted square-pyramidal coordination environment. The supra­molecular architectures are stabilized via the presence of various inter­molecular hydrogen bonds and (ar­yl–aryl, ar­yl–chelate and chelate–chelate) stacking inter­actions.

Development, evaluation and validation of machine learning algorithms to detect atypical and asymptomatic presentations of Covid-19 in hospital practice

BACKGROUND

Diagnostic methods for Covid-19 have improved, both in speed and availability. Because of atypical and asymptomatic carriage of the virus and nosocomial spread within institutions, timely diagnosis remains a challenge. Machine learning models trained on blood test results have shown promise in identifying cases of Covid-19.

AIMS

To train and validate a machine learning model capable of differentiating Covid-19 positive from negative patients using routine blood tests and assess the model's accuracy against atypical and asymptomatic presentations.

DESIGN AND METHODS

We conducted a retrospective analysis of medical admissions to our institution during March and April 2020. Participants were categorized into Covid-19 positive or negative groups based on clinical, radiological features or nasopharyngeal swab. A machine learning model was trained on laboratory parameters and validated for accuracy, sensitivity and specificity and externally validated at an unconnected establishment.

RESULTS

An Ensemble Bagged Tree model was trained on data collected from 405 patients (212 Covid-19 positive) producing an accuracy of 81.79% (95% confidence interval (CI) 77.53-85.55%), the sensitivity of 85.85% (CI 80.42-90.24%) and specificity of 76.65% (CI 69.49-82.84%). Accuracy was preserved for atypical and asymptomatic subgroups. Using an external data set for 226 patients (141 Covid-19 positive) accuracy of 76.82% (CI 70.87-82.08%), sensitivity of 78.38% (CI 70.87-84.72%) and specificity of 74.12% (CI 63.48-83.01%) was achieved.

CONCLUSION

A machine learning model using routine laboratory parameters can detect atypical and asymptomatic presentations of Covid-19 and might be an adjunct to existing screening measures.