Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C–N couplings, as well as Pauson–Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model

Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning

Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C–N couplings, as well as Pauson–Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model

Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.Comment: 23 pages, 10 tables, 13 figures, to appear in the ICML 2020 Workshop on Graph Representation Learning and Beyond (GRLB

Combinations of β-lactam or aminoglycoside antibiotics with plectasin are synergistic against methicillin-sensitive and methicillin-resistant Staphylococcus aureus.

Bacterial infections remain the leading killer worldwide which is worsened by the continuous emergence of antibiotic resistance. In particular, methicillin-sensitive (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) are prevalent and the latter can be difficult to treat. The traditional strategy of novel therapeutic drug development inevitably leads to emergence of resistant strains, rendering the new drugs ineffective. Therefore, rejuvenating the therapeutic potentials of existing antibiotics offers an attractive novel strategy. Plectasin, a defensin antimicrobial peptide, potentiates the activities of other antibiotics such as β-lactams, aminoglycosides and glycopeptides against MSSA and MRSA. We performed in vitro and in vivo investigations to test against genetically diverse clinical isolates of MSSA (n = 101) and MRSA (n = 115). Minimum inhibitory concentrations (MIC) were determined by the broth microdilution method. The effects of combining plectasin with β-lactams, aminoglycosides and glycopeptides were examined using the chequerboard method and time kill curves. A murine neutropenic thigh model and a murine peritoneal infection model were used to test the effect of combination in vivo. Determined by factional inhibitory concentration index (FICI), plectasin in combination with aminoglycosides (gentamicin, neomycin or amikacin) displayed synergistic effects in 76-78% of MSSA and MRSA. A similar synergistic response was observed when plectasin was combined with β-lactams (penicillin, amoxicillin or flucloxacillin) in 87-89% of MSSA and MRSA. Interestingly, no such interaction was observed when plectasin was paired with vancomycin. Time kill analysis also demonstrated significant synergistic activities when plectasin was combined with amoxicillin, gentamicin or neomycin. In the murine models, plectasin at doses as low as 8 mg/kg augmented the activities of amoxicillin and gentamicin in successful treatment of MSSA and MRSA infections. We demonstrated that plectasin strongly rejuvenates the therapeutic potencies of existing antibiotics in vitro and in vivo. This is a novel strategy that can have major clinical implications in our fight against bacterial infections

Measurement of the Inclusive Charm Cross Section at 4.03 GeV and 4.14 GeV

The cross section for charmed meson production at $\sqrt{s} = 4.03$ and 4.14 GeV has been measured with the Beijing Spectrometer. The measurement was made using 22.3 $pb^{-1}$ of $e^+e^-$ data collected at 4.03 GeV and 1.5 $pb^{-1}$ of $e^+e^-$ data collected at 4.14 GeV. Inclusive observed cross sections for the production of charged and neutral D mesons and momentum spectra are presented. Observed cross sections were radiatively corrected to obtain tree level cross sections. Measurements of the total hadronic cross section are obtained from the charmed meson cross section and an extrapolation of results from below the charm threshold.Comment: 11 pages, 13 figures. The top level tex file is paper.tex. It builds the paper from other tex files in this .tar and the .eps file

First Measurement of the Branching Fraction of the Decay psi(2S) --> tau tau

The branching fraction of the psi(2S) decay into tau pair has been measured for the first time using the BES detector at the Beijing Electron-Positron Collider. The result is $B_{\tau\tau}=(2.71\pm 0.43 \pm 0.55) \times 10^{-3}$, where the first error is statistical and the second is systematic. This value, along with those for the branching fractions into e+e- and mu+mu of this resonance, satisfy well the relation predicted by the sequential lepton hypothesis. Combining all these values with the leptonic width of the resonance the total width of the psi(2S) is determined to be $(252 \pm 37)$ keV.Comment: 9 pages, 2 figure

Ready ... Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals

Study of J/ψ→ppˉJ/\psi\to p\bar{p} and J/ψ→nnˉJ/\psi\to n\bar{n}

The decays $J/\psi\to p\bar{p}$ and $J/\psi\to n\bar{n}$ have been investigated with a sample of 225.2 million $J/\psi$ events collected with the BESIII detector at the BEPCII $e^+e^-$ collider. The branching fractions are determined to be $\mathcal{B}(J/\psi\to p\bar{p})=(2.112\pm0.004\pm0.031)\times10^{-3}$ and $\mathcal{B}(J/\psi\to n\bar{n})=(2.07\pm0.01\pm0.17)\times10^{-3}$. Distributions of the angle $\theta$ between the proton or anti-neutron and the beam direction are well described by the form $1+\alpha\cos^2\theta$, and we find $\alpha=0.595\pm0.012\pm0.015$ for $J/\psi\to p\bar{p}$ and $\alpha=0.50\pm0.04\pm0.21$ for $J/\psi\to n\bar{n}$. Our branching-fraction results suggest a large phase angle between the strong and electromagnetic amplitudes describing the $J/\psi\to N\bar{N}$ decay.Comment: 16 pages, 13 figures, the 2nd version, submitted to PR

Head Position in Stroke Trial (HeadPoST)- sitting-up vs lying-flat positioning of patients with acute stroke: study protocol for a cluster randomised controlled trial

Background Positioning a patient lying-flat in the acute phase of ischaemic stroke may improve recovery and reduce disability, but such a possibility has not been formally tested in a randomised trial. We therefore initiated the Head Position in Stroke Trial (HeadPoST) to determine the effects of lying-flat (0°) compared with sitting-up (≥30°) head positioning in the first 24 hours of hospital admission for patients with acute stroke. Methods/Design We plan to conduct an international, cluster randomised, crossover, open, blinded outcome-assessed clinical trial involving 140 study hospitals (clusters) with established acute stroke care programs. Each hospital will be randomly assigned to sequential policies of lying-flat (0°) or sitting-up (≥30°) head position as a ‘business as usual’ stroke care policy during the first 24 hours of admittance. Each hospital is required to recruit 60 consecutive patients with acute ischaemic stroke (AIS), and all patients with acute intracerebral haemorrhage (ICH) (an estimated average of 10), in the first randomised head position policy before crossing over to the second head position policy with a similar recruitment target. After collection of in-hospital clinical and management data and 7-day outcomes, central trained blinded assessors will conduct a telephone disability assessment with the modified Rankin Scale at 90 days. The primary outcome for analysis is a shift (defined as improvement) in death or disability on this scale. For a cluster size of 60 patients with AIS per intervention and with various assumptions including an intracluster correlation coefficient of 0.03, a sample size of 16,800 patients at 140 centres will provide 90 % power (α 0.05) to detect at least a 16 % relative improvement (shift) in an ordinal logistic regression analysis of the primary outcome. The treatment effect will also be assessed in all patients with ICH who are recruited during each treatment study period. Discussion HeadPoST is a large international clinical trial in which we will rigorously evaluate the effects of different head positioning in patients with acute stroke. Trial registration ClinicalTrials.gov identifier: NCT02162017 (date of registration: 27 April 2014); ANZCTR identifier: ACTRN12614000483651 (date of registration: 9 May 2014). Protocol version and date: version 2.2, 19 June 2014