The Synthesis and Antiviral Properties of 8- Amino-3- [(2 hydroxyethoxy)methyl]-1,2,4-triazolo- [4,3-a ]pyrazine

The preparation of 8-amino-3-[(2-hydroxyethoxy)methyl]-1,2,4- triazolo[4,3-a]pyrazine (IV) as an analogue of 9-[(2-hydroxyethoxy) methyl]guanine and 9-(S)-(2,3-dihydroxypropyl)adenine is described from the reaction of 3-chloro-2-hydrazinopyrazine (V) and ethyl 2-(2-acetoxyethoxy)thioacetimidate (IXg) followed by treatment with ammonia. Compound IV was found to lack antiviral properties towards herpes simplex I and II, vaccinia virus, vesicular stomatitis virus, measles, reovirus type 1, parainfluenza virus type 3, Sindbis virus, Coxsackie type B4 virus, and poliovirus type

Using State-of-the-art Emotion Detection Models in a Crisis Communication Context

Times of crisis are usually associated with highly emotional experiences, which often result in emotionally charged communication. This is especially the case on social media. Identifying the emotional climate on social media is imperative in the context of crisis communication, e.g., in view of shaping crisis response strategies. However, the sheer volume of social media data often makes manual oversight impossible. In this paper, we therefore investigate how automatic methods for emotion detection can aid research on crisis communication and social media. Concretely, we investigate two Dutch emotion detection models (a transformer model and a classical machine learning model based on dictionaries) and apply them to Dutch tweets about four different crisis cases. First, we perform a validation study to assess the performance of these models in the domain of crisis-related tweets. Secondly, we propose a framework for monitoring the emotional climate on social media, and assess whether emotion detection models can be used to address the steps in the framework

Time-dependent Hamiltonian estimation for Doppler velocimetry of trapped ions

The time evolution of a closed quantum system is connected to its Hamiltonian through Schroedinger's equation. The ability to estimate the Hamiltonian is critical to our understanding of quantum systems, and allows optimization of control. Though spectroscopic methods allow time-independent Hamiltonians to be recovered, for time-dependent Hamiltonians this task is more challenging. Here, using a single trapped ion, we experimentally demonstrate a method for estimating a time-dependent Hamiltonian of a single qubit. The method involves measuring the time evolution of the qubit in a fixed basis as a function of a time-independent offset term added to the Hamiltonian. In our system the initially unknown Hamiltonian arises from transporting an ion through a static, near-resonant laser beam. Hamiltonian estimation allows us to estimate the spatial dependence of the laser beam intensity and the ion's velocity as a function of time. This work is of direct value in optimizing transport operations and transport-based gates in scalable trapped ion quantum information processing, while the estimation technique is general enough that it can be applied to other quantum systems, aiding the pursuit of high operational fidelities in quantum control.Comment: 10 pages, 8 figure

Isolation and sequence of cDNA encoding the motilin precursor from monkey intestine. Demonstration of the motilin precursor in the monkey brain

AbstractThe motilin precursor cDNA has been isolated and sequenced from a cDNA library prepared from monkey small intestine. The sequence indicates a 345 bp open reading frame, a 63 bp 5′ untranslated region and a 154 bp 3′ untranslated region. The sequence encodes a 115 amino acid motilin precursor composed of a 25 amino acid signal peptide, the 22 amino acid motilin peptide and a 68 amino acid motilin associated peptide (MAP). Compared with the human motilin precursor cDNA, there are two amino acid substitutions in the signal peptide, one in motilin and four in the MAP. The presence of the motilin precursor in hypothalamus, hippocampus and cerebellum was demonstrated by RT-PCR

Mycophenolate mofetil inhibits the development of Coxsackie B3-virus-induced myocarditis in mice

BACKGROUND: Viral replication as well as an immunopathological component are assumed to be involved in the development of coxsackie B virus (CBV)-induced myocarditis. We observed that mycophenolic acid (MPA), the active metabolite of the immunosuppressive agent mycophenolate mofetil (MMF), inhibits coxsackie B3 virus (CBV3) replication in primary Human myocardial fibroblasts. We therefore studied whether MMF, which is thus endowed with a direct antiviral as well as immunosuppressive effect, may prevent CBV-induced myocarditis in a murine model. RESULTS: Four week old C3H-mice were infected with CBV3 and received twice daily, for 7 consecutive days (from one day before to 5 days post-virus inoculation) treatment with MMF via oral gavage. Treatment with MMF resulted in a significant reduction in the development of CBV-induced myocarditis as assessed by morphometric analysis, i.e. 78% reduction when MMF was administered at 300 mg/kg/day (p < 0.001), 65% reduction at 200 mg/kg/day (p < 0.001), and 52% reduction at 100 mg/kg/day (p = 0.001). The beneficial effect could not be ascribed to inhibition of viral replication since titers of infectious virus and viral RNA in heart tissue were increased in MMF-treated animals as compared to untreated animals. CONCLUSION: The immunosuppressive agent MMF results in an important reduction of CBV3-induced myocarditis in a murine model

Binary recurrences for which powers of two are discriminating moduli

Given a sequence of distinct positive integers $w_0 , w_1, w_2, \ldots$ and any positive integer $n$, we define the discriminator function $\mathcal{D}_{\bf w}(n)$ to be the smallest positive integer $m$ such that $w_0,\ldots, w_{n-1}$ are pairwise incongruent modulo $m$. In this paper, we classify all binary recurrent sequences $\{w_n\}_{n\geq 0}$ consisting of different integer terms such that $\mathcal{D}_{\bf w}(2^e)=2^e$ for every $e\geq 1.$ For all of these sequences it is expected that one can actually give a fairly simple description of $\mathcal{D}_{\bf w}(n)$ for every $n\ge 1.$ For two infinite families of such sequences this has been done already in 2019 by Faye, Luca and Moree, respectively Ciolan and Moree