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Since it was detected in 1980, Jupiter's 8-μm CH₄ north polar hot spot (8CNPHS), ∼20 K warmer than the surrounding polar stratosphere, has been observed for four decades. Unlike normal auroral ovals (i.e., the bright rings of auroral emissions), it is usually filled with bright emission. The causes of both its shape and longevity are not understood, although several mechanisms have been proposed to explain its existence. In order to investigate the deriving mechanism of the 8CNPHS, we have observed the north polar regions near 3 μm, where line emission from another CH₄ band as well as a C₂H₆ fundamental band occur. Using Gemini North/GNIRS in 2013, 2020, 2021, and 2022, we have detected transient 3-μm CH₄ and/or C₂H₆ bright spots within the 8CNPHS, and occasionally no apparent bright spots. By comparing the emission from CH₄ with that from C₂H₆, we demonstrate that the origin of the 8CNPHS must be due to transient and energetic magnetospheric particles, which can penetrate down to the hydrocarbon layers, heating the homopause (∼1 μbar level) and the stratosphere (∼1 mbar level) and energize the 8CNPHS. Based on our observations and analysis, we propose the following three mechanisms for maintaining and containing the decades long warmth of the 8CNPHS: 1) energetic and transient auroral particle precipitations warming the stratosphere of the 8CNPHS, 2) a longer radiative cooling time in the 8CNPHS stratosphere (∼6 months) compared to less than or equal to one month at the homopause, and 3) recently detected polar stratospheric jets likely associated with polar fronts, which resist the free flow of warm gas in the 8CNPHS to the surrounding polar regions. We show that other heating mechanisms proposed so far in the literature, such as Joule heating, polar haze heated by sunlight, etc., are only the secondary mechanisms that follow atmospheric ionization caused by energetic particle bombardment. Our finding of the magnetospheric-ionospheric-stratospheric coupling in the Jovian polar regions open the possibility of quantitatively refining 3-D global circulation models for the atmospheres of giant planets and exoplanets.

Background: Neuroleptic malignant syndrome (NMS), a rare but serious adverse reaction of antipsychotics, however, the little evidence risk and epidemiology of NMS is available. We aimed to determine the risk of NMS associated with exposure to any antipsychotic, and four antipsychotics commonly implicated in cases of NMS: haloperidol, olanzapine, quetiapine, and risperidone. To characterize the incidence and case fatality risk of NMS in patients taking antipsychotics. Methods: We conducted a population-based cohort analyzing health records of 297 647 antipsychotic users identified from January 1st, 2004 to December 31st, 2016 and 328 in patients with incident diagnosis of NMS during the same period, to measure the incidence and case fatality risk of NMS. We further extended the NMS identification period to November 30th, 2017 and conducted a case-crossover study analyzing data for a total of 336 eligible patients to estimate the risk of NMS associated with any antipsychotic, haloperidol, olanzapine, quetiapine, and risperidone, by comparing the use of antipsychotics during day 1 to 30 (case period) and day 91 to 120 (reference period) preceding the diagnosis of NMS. Findings: In the case-crossover study, 336 patients were diagnosed with NMS (210 males [62.5%]; mean age, 49.7 years; case fatality risk 6.0%). After adjustment for time trends in exposure, concurrent medications, and medical conditions, diagnosis of NMS was associated with exposure to any antipsychotic (OR, 4.77; 95% CI, 1.95-11.66), haloperidol (OR, 4.40; 95% CI 1.97-9.81), and quetiapine (OR, 4.32; 95% CI, 1.70-10.97), while there was no observed association for risperidone (OR, 2.00; 95% CI, 0.93-4.32) or olanzapine (OR, 1.23; 95% CI, 0.47-3.18). In the cohort of patients exposed to antipsychotics, NMS occurred with an incidence of 1.10 per 1000 persons. Interpretation: Antipsychotics are associated with an acutely increased risk of NMS. Patients had statistically significant increased odds of exposure to, haloperidol and quetiapine but not risperidone or olanzapine, during the 30 days prior to the diagnosis of NMS, as compared with the earlier reference period. Clinicians who prescribe antipsychotics should weigh the acutely increased risk of NMS with the potential benefits of antipsychotic therapy, especially for haloperidol and quetiapine. Funding: Early Career Scheme, Research Grants Council, Hong Kong. Declaration of Interest: This study was partially funded by Early Career Scheme, Research Grants Council, Hong Kong (project reference 789813) received by Esther W Chan. All authors declare that no other support has been received from any organization for the submitted work; no other financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted study. Ethical Approval: Research ethics approval was obtained from the Institutional Review Board of The University of Hong Kong/Hospital Authority HKWC (Reference Number: UW 15-619).

a b s t r a c t The true slime mold Physarum polycephalum, a single-celled amoeboid organism, is capable of efficiently allocating a constant amount of intracellular resource to its pseudopod-like branches that best fit the environment where dynamic light stimuli are applied. Inspired by the resource allocation process, the authors formulated a concurrent search algorithm, called the Tug-of-War (TOW) model, for maximiz- ing the profit in the multi-armed Bandit Problem (BP). A player (gambler) of the BP should decide as quickly and accurately as possible which slot machine to invest in out of the N machines and faces an "exploration-exploitation dilemma." The dilemma is a trade-off between the speed and accuracy of the decision making that are conflicted objectives. The TOW model maintains a constant intracellular resource volume while collecting environmental information by concurrently expanding and shrinking its branches. The conservation law entails a nonlocal correlation among the branches, i.e., volume incre- ment in one branch is immediately compensated by volume decrement(s) in the other branch(es). Owing to this nonlocal correlation, the TOW model can efficiently manage the dilemma. In this study, we extend the TOW model to apply it to a stretched variant of BP, the Extended Bandit Problem (EBP), which is a problem of selecting the best M-tuple of the N machines. We demonstrate that the extended TOW model exhibits better performances for 2-tuple-3-machine and 2-tuple-4-machine instances of EBP compared with the extended versions of well-known algorithms for BP, the � -Greedy and SoftMax algorithms, par- ticularly in terms of its short-term decision-making capability that is essential for the survival of the amoeba in a hostile environment.

We propose a model – the “tug-of-war (TOW) model” – to conduct unique parallel searches using many nonlocally-correlated search agents. The model is based on the property of a single-celled amoeba, the true slime mold Physarum, which maintains a constant intracellular resource volume while collecting environmental information by concurrently expanding and shrinking its branches. The conservation law entails a “nonlocal correlation” among the branches, i.e., volume increment in one branch is immediately compensated by volume decrement(s) in the other branch(es). This nonlocal correlation was shown to be useful for decision making in the case of a dilemma. The multi-armed bandit problem is to determine the optimal strategy for maximizing the total reward sum with incompatible demands, by either exploiting the rewards obtained using the already collected information or exploring new information for acquiring higher payoffs involving risks. Our model can efficiently manage the “exploration–exploitation dilemma” and exhibits good performances. The average accuracy rate of our model is higher than those of well-known algorithms such as the modified ϵ -greedy algorithm and modified softmax algorithm, especially, for solving relatively difficult problems. Moreover, our model flexibly adapts to changing environments, a property essential for living organisms surviving in uncertain environments.