Automated Self-Optimisation of Multi-Step Reaction and Separation Processes Using Machine Learning

There has been an increasing interest in the use of automated self-optimising continuous flow platforms for the development and manufacture in synthesis in recent years. Such processes include multiple reactive and work-up steps, which need to be efficiently optimised. Here, we report the combination of multi-objective optimisation based on machine learning methods (TSEMO algorithm) with self-optimising platforms for the optimisation of multi-step continuous reaction processes. This is demonstrated for a pharmaceutically relevant Sonogashira reaction. We demonstrate how optimum reaction conditions are re-evaluated with the changing downstream work-up specifications in the active learning process. Furthermore, a Claisen-Schmidt condensation reaction with subsequent liquid-liquid separation was optimised with respect to three-objectives. This approach provides the ability to simultaneously optimise multi-step processes with respect to multiple objectives, and thus has the potential to make substantial savings in time and resources

From Antenna to Antenna: Lateral Shift of Olfactory Memory Recall by Honeybees

Honeybees, Apis mellifera, readily learn to associate odours with sugar rewards and we show here that recall of the olfactory memory, as demonstrated by the bee extending its proboscis when presented with the trained odour, involves first the right and then the left antenna. At 1–2 hour after training using both antennae, recall is possible mainly when the bee uses its right antenna but by 6 hours after training a lateral shift has occurred and the memory can now be recalled mainly when the left antenna is in use. Long-term memory one day after training is also accessed mainly via the left antenna. This time-dependent shift from right to left antenna is also seen as side biases in responding to odour presented to the bee's left or right side. Hence, not only are the cellular events of memory formation similar in bees and vertebrate species but also the lateralized networks involved may be similar. These findings therefore seem to call for remarkable parallel evolution and suggest that the proper functioning of memory formation in a bilateral animal, either vertebrate or invertebrate, requires lateralization of processing

Crowding: risk factor or protective factor for lower respiratory disease in young children?

BACKGROUND: To study the effects of household crowding upon the respiratory health of young children living in the city of São Paulo, Brazil. METHODS: Case-control study with children aged from 2 to 59 months living within the boundaries of the city of São Paulo. Cases were children recruited from 5 public hospitals in central São Paulo with an acute episode of lower respiratory disease. Children were classified into the following diagnostic categories: acute bronchitis, acute bronchiolitis, pneumonia, asthma, post-bronchiolitis wheezing and wheezing of uncertain aetiology. One control, crudely matched to each case with regard to age (<2, 2 years old or more), was selected among healthy children living in the neighborhood of the case. All buildings were surveyed for the presence of environmental contaminants, type of construction and building material. Plans of all homes, including measurements of floor area, height of walls, windows and solar orientation, was performed. Data were analysed using conditional logistic regression. RESULTS: A total of 313 pairs of children were studied. Over 70% of the cases had a primary or an associated diagnosis of a wheezing illness. Compared with controls, cases tended to live in smaller houses with less adequate sewage disposal. Cases and controls were similar with respect to the number of people and the number of children under five living in the household, as well the number of people sharing the child's bedroom. After controlling for potential confounders, no evidence of an association between number of persons sharing the child's bedroom and lower respiratory disease was identified when all cases were compared with their controls. However, when two categories of cases were distinguished (infections, asthma) and each category compared separately with their controls, crowding appeared to be associated with a 60% reduction in the incidence of asthma but with 2 1/2-fold increase in the incidence of lower respiratory tract infections (p = 0.001). CONCLUSION: Our findings suggest that household crowding places young children at risk of acute lower respiratory infection but may protect against asthma. This result is consistent with the hygiene hypothesis

Identification of Type 1 Diabetes-Associated DNA Methylation Variable Positions That Precede Disease Diagnosis

Monozygotic (MZ) twin pair discordance for childhood-onset Type 1 Diabetes (T1D) is similar to 50%, implicating roles for genetic and non-genetic factors in the aetiology of this complex autoimmune disease. Although significant progress has been made in elucidating the genetics of T1D in recent years, the non-genetic component has remained poorly defined. We hypothesized that epigenetic variation could underlie some of the non-genetic component of T1D aetiology and, thus, performed an epigenome-wide association study (EWAS) for this disease. We generated genome-wide DNA methylation profiles of purified CD14(+) monocytes (an immune effector cell type relevant to T1D pathogenesis) from 15 T1D-discordant MZ twin pairs. This identified 132 different CpG sites at which the direction of the intra-MZ pair DNA methylation difference significantly correlated with the diabetic state, i.e. T1D-associated methylation variable positions (T1D-MVPs). We confirmed these T1D-MVPs display statistically significant intra-MZ pair DNA methylation differences in the expected direction in an independent set of T1D-discordant MZ pairs (P = 0.035). Then, to establish the temporal origins of the T1D-MVPs, we generated two further genome-wide datasets and established that, when compared with controls, T1D-MVPs are enriched in singletons both before (P = 0.001) and at (P = 0.015) disease diagnosis, and also in singletons positive for diabetes-associated autoantibodies but disease-free even after 12 years follow-up (P = 0.0023). Combined, these results suggest that T1D-MVPs arise very early in the etiological process that leads to overt T1D. Our EWAS of T1D represents an important contribution toward understanding the etiological role of epigenetic variation in type 1 diabetes, and it is also the first systematic analysis of the temporal origins of disease-associated epigenetic variation for any human complex disease

Significant discharge of CO2 from hydrothermalism associated with the submarine volcano of El Hierro Island

The residual hydrothermalism associated with submarine volcanoes, following an eruption event, plays an important role in the supply of CO2 to the ocean. The emitted CO2 increases the acidity of seawater. The submarine volcano of El Hierro, in its degasification stage, provided an excellent opportunity to study the effect of volcanic CO2 on the seawater carbonate system, the global carbon flux, and local ocean acidification. A detailed survey of the volcanic edifice was carried out using seven CTD-pH-ORP tow-yo studies, localizing the redox and acidic changes, which were used to obtain surface maps of anomalies. In order to investigate the temporal variability of the system, two CTD-pH-ORP yo-yo studies were conducted that included discrete sampling for carbonate system parameters. Meridional tow-yos were used to calculate the amount of volcanic CO2 added to the water column for each surveyed section. The inputs of CO2 along multiple sections combined with measurements of oceanic currents produced an estimated volcanic CO2 flux = 6.0 105 ± 1.1 105 kg d−1 which is ~0.1% of global volcanic CO2 flux. Finally, the CO2 emitted by El Hierro increases the acidity above the volcano by ~20%.En prens

Trypanosoma brucei PUF9 Regulates mRNAs for Proteins Involved in Replicative Processes over the Cell Cycle

Many genes that are required at specific points in the cell cycle exhibit cell cycle–dependent expression. In the early-diverging model eukaryote and important human pathogen Trypanosoma brucei, regulation of gene expression in the cell cycle and other processes is almost entirely post-transcriptional. Here, we show that the T. brucei RNA-binding protein PUF9 stabilizes certain transcripts during S-phase. Target transcripts of PUF9—LIGKA, PNT1 and PNT2—were identified by affinity purification with TAP-tagged PUF9. RNAi against PUF9 caused an accumulation of cells in G2/M phase and unexpectedly destabilized the PUF9 target mRNAs, despite the fact that most known Puf-domain proteins promote degradation of their target mRNAs. The levels of the PUF9-regulated transcripts were cell cycle dependent, peaking in mid- to late- S-phase, and this effect was abolished when PUF9 was targeted by RNAi. The sequence UUGUACC was over-represented in the 3′ UTRs of PUF9 targets; a point mutation in this motif abolished PUF9-dependent stabilization of a reporter transcript carrying the PNT1 3′ UTR. LIGKA is involved in replication of the kinetoplast, and here we show that PNT1 is also kinetoplast-associated and its over-expression causes kinetoplast-related defects, while PNT2 is localized to the nucleus in G1 phase and redistributes to the mitotic spindle during mitosis. PUF9 targets may constitute a post-transcriptional regulon, encoding proteins involved in temporally coordinated replicative processes in early G2 phase

Theory of Low-Mass Stars and Substellar Objects

Since the discovery of the first bona-fide brown dwarfs and extra-solar planets in 1995, the field of low mass stars and substellar objects has considerably progressed, both from theoretical and observational viewpoints.Recent developments in the physics entering the modeling of these objects have led to significant improvements in the theory and to a better understanding of their mechanical and thermal properties. This theory can now be confronted with observations directly in various observational diagrams (color-color, color-magnitude, mass-magnitude, mass-spectral type), a stringent and unavoidable constraint which became possible only recently, with the generation of synthetic spectra. In this paper, we present the current state-of-the-art general theory of low-mass stars and sub-stellar objects, from one solar mass to one Jupiter mass, regarding primarily their interior structure and evolution. This review is a natural complement to the previous review on the atmosphere of low-mass stars and brown dwarfs (Allard et al 1997). Special attention is devoted to the comparison of the theory with various available observations. The contribution of low-mass stellar and sub-stellar objects to the Galactic mass budget is also analysed.Comment: 81 pages, Latex file, uses aasms4.sty, review for Annual Review of Astronomy and Astrophysics, vol. 38 (2000

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survivalof out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrilla-tors (AED). AED algorithms for VF-detection are customarily assessed using Holter record-ings from public electrocardiogram (ECG) databases, which may be different from the ECGseen during OHCA events. This study evaluates VF-detection using data from both OHCApatients and public Holter recordings. ECG-segments of 4-s and 8-s duration were ana-lyzed. For each segment 30 features were computed and fed to state of the art machinelearning (ML) algorithms. ML-algorithms with built-in feature selection capabilities wereused to determine the optimal feature subsets for both databases. Patient-wise bootstraptechniques were used to evaluate algorithm performance in terms of sensitivity (Se), speci-ficity (Sp) and balanced error rate (BER). Performance was significantly better for publicdata with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times morefeatures than the data from public databases for an accurate detection (6 vs 3). No signifi-cant differences in performance were found for different segment lengths, the BER differ-ences were below 0.5-points in all cases. Our results show that VF-detection is morechallenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s

Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research

The replicability of research results has been a cause of increasing concern to the scientific community. The long-held belief that experimental standardization begets replicability has also been recently challenged, with the observation that the reduction of variability within studies can lead to idiosyncratic, lab-specific results that cannot be replicated. An alternative approach is to, instead, deliberately introduce heterogeneity, known as "heterogenization" of experimental design. Here, we explore a novel perspective in the heterogenization program in a meta-analysis of variability in observed phenotypic outcomes in both control and experimental animal models of ischemic stroke. First, by quantifying interindividual variability across control groups, we illustrate that the amount of heterogeneity in disease state (infarct volume) differs according to methodological approach, for example, in disease induction methods and disease models. We argue that such methods may improve replicability by creating diverse and representative distribution of baseline disease state in the reference group, against which treatment efficacy is assessed. Second, we illustrate how meta-analysis can be used to simultaneously assess efficacy and stability (i.e., mean effect and among-individual variability). We identify treatments that have efficacy and are generalizable to the population level (i.e., low interindividual variability), as well as those where there is high interindividual variability in response; for these, latter treatments translation to a clinical setting may require nuance. We argue that by embracing rather than seeking to minimize variability in phenotypic outcomes, we can motivate the shift toward heterogenization and improve both the replicability and generalizability of preclinical research