Solubility prediction remains a critical challenge in drug development, synthetic route and chemical process design, extraction and crystallisation. Here we report a successful approach to solubility ...prediction in organic solvents and water using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry. Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions which are independent of the applied machine learning method. These models gave significantly more accurate predictions compared to benchmarked open-access and commercial tools, achieving accuracy close to the expected level of noise in training data (LogS ± 0.7). Finally, they reproduced physicochemical relationship between solubility and molecular properties in different solvents, which led to rational approaches to improve the accuracy of each models.
An interactive tool has been developed to facilitate solvent selection, allowing consideration of chemical functionality, physical properties, regulatory concerns, and safety/health/environmental ...(SHE) impact. Appropriate solvents can be identified prior to screening experiments, and less desirable solvents can be replaced in established processes. Once a shortlist has been identified, the data can define experimental programs or else be exported to a molecular properties prediction tool to assess suitability through, e.g., solubility and partitioning.
Background Phenotypes of childhood-onset asthma are characterized by distinct trajectories and functional features. For atopy, definition of phenotypes during childhood is less clear. Objective We ...sought to define phenotypes of atopic sensitization over the first 6 years of life using a latent class analysis (LCA) integrating 3 dimensions of atopy: allergen specificity, time course, and levels of specific IgE (sIgE). Methods Phenotypes were defined by means of LCA in 680 children of the Multizentrische Allergiestudie (MAS) and 766 children of the Protection against allergy: Study in Rural Environments (PASTURE) birth cohorts and compared with classical nondisjunctive definitions of seasonal, perennial, and food sensitization with respect to atopic diseases and lung function. Cytokine levels were measured in the PASTURE cohort. Results The LCA classified predominantly by type and multiplicity of sensitization (food vs inhalant), allergen combinations, and sIgE levels. Latent classes were related to atopic disease manifestations with higher sensitivity and specificity than the classical definitions. LCA detected consistently in both cohorts a distinct group of children with severe atopy characterized by high seasonal sIgE levels and a strong propensity for asthma; hay fever; eczema; and impaired lung function, also in children without an established asthma diagnosis. Severe atopy was associated with an increased IL-5/IFN-γ ratio. A path analysis among sensitized children revealed that among all features of severe atopy, only excessive sIgE production early in life affected asthma risk. Conclusions LCA revealed a set of benign, symptomatic, and severe atopy phenotypes. The severe phenotype emerged as a latent condition with signs of a dysbalanced immune response. It determined high asthma risk through excessive sIgE production and directly affected impaired lung function.
We have expanded the ligand knowledge base for monodentate P-donor ligands (LKB-P, Chem. Eur. J. 2006, 12, 291−302) by 287 ligands and added descriptors derived from computational results on a gold ...complex AuClL. This expansion to 348 ligands captures known ligand space for this class of monodentate two-electron donor ligands well, and we have used principal component analysis (PCA) of the descriptors to derive an improved map of ligand space. Potential applications of this map, including the visualization of ligand similarities/differences and trends in experimental data, as well as the design of ligand test sets for high-throughput screening and the identification of ligands for reaction optimization, are discussed. Descriptors of ligand properties can also be used in regression models for the interpretation and prediction of available response data, and here we explore such models for both experimental and calculated data, highlighting the advantages of large training sets that sample ligand space well.
Background
The highly consistent association of growing up on a farm with a reduced asthma risk has so far been attributed to direct farm exposure. In contrast, geographic determinants of the larger ...environment have never been assessed. In this study, the effects of proximity to farms and environmental variables in relation to the residential address on asthma and atopy were assessed.
Methods
Addresses of 2265 children of the Bavarian arm of the GABRIELA study were converted into geocodes. Proximity to the nearest cow farm was calculated, and environmental characteristics were derived from satellite data or terrestrial monitoring. Bacterial diversity in mattress dust samples was assessed in 501 children by sequencing of the 16S rRNA amplicons. Logistic regression models were used to calculate associations between outcomes and exposure variables.
Results
Asthma and atopy were inversely associated with the presence of a farm within a radius of maximum 100 m. The environmental variables greenness, tree cover, soil sealing, altitude, air pollution differed not only between farm and non‐farm children but also between farm children with and without another farm nearby. The latter distinction revealed strong associations with characteristics of traditional farms including a broader diversity of microbial exposure, which mainly contributed to the protective effect on asthma. In non‐farm children, the protective effect of a farm nearby was completely explained by consumption of farm milk.
Conclusions
Clustering of farms within a neighborhood of 100 m is strongly associated with the protective effect on asthma and may represent a more traditional style of farming with broader microbial exposure.
Presented here is the design and performance of a coalescing liquid–liquid filter, based on low-cost and readily available meltblown nonwoven substrates for separation of immiscible phases. The ...performance of the coalescer was determined across three broad classes of fluid mixtures: (i) immiscible organic/aqueous systems, (ii) a surfactant laden organic/aqueous system with modification of the type of emulsion and interfacial surface tension through the addition of sodium chloride, and (iii) a water–acetone/toluene system. The first two classes demonstrated good performance of the equipment in effecting separation, including the separation of a complex emulsion system for which a membrane separator, operating through transport of a preferentially wetting fluid through the membrane, failed entirely. The third system was used to demonstrate the performance of the separator within a multistage liquid–liquid counterflow extraction system. The performance, robust nature, and scalability of coalescing filters should mean that this approach is routinely considered for liquid–liquid separations and extractions within the fine chemical and pharmaceutical industry.
Solvent-dependent reactivity is a key aspect of synthetic science, which controls reaction selectivity. The contemporary focus on new, sustainable solvents highlights a need for reactivity ...predictions in different solvents. Herein, we report the excellent machine learning prediction of the nucleophilicity parameter N in the four most-common solvents for nucleophiles in the Mayr’s reactivity parameter database (R 2 = 0.93 and 81.6% of predictions within ±2.0 of the experimental values with Extra Trees algorithm). A Causal Structure Property Relationship (CSPR) approach was utilized, with focus on the physicochemical relationships between the descriptors and the predicted parameters, and on rational improvements of the prediction models. The nucleophiles were represented with a series of electronic and steric descriptors and the solvents were represented with principal component analysis (PCA) descriptors based on the ACS Solvent Tool. The models indicated that steric factors do not contribute significantly, because of bias in the experimental database. The most important descriptors are solvent-dependent HOMO energy and Hirshfeld charge of the nucleophilic atom. Replacing DFT descriptors with Parameterization Method 6 (PM6) descriptors for the nucleophiles led to an 8.7-fold decrease in computational time, and an ∼10% decrease in the percentage of predictions within ±2.0 and ±1.0 of the experimental values.
A higher diversity of food items introduced in the first year of life has been inversely related to subsequent development of asthma. In the current analysis, we applied latent class analysis (LCA) ...to systematically assess feeding patterns and to relate them to asthma risk at school age. PASTURE (N=1133) and LUKAS2 (N=228) are prospective birth cohort studies designed to evaluate protective and risk factors for atopic diseases, including dietary patterns. Feeding practices were reported by parents in monthly diaries between the 4
and 12
month of life. For 17 common food items parents indicated frequency of feeding during the last 4 weeks in 4 categories. The resulting 153 ordinal variables were entered in a LCA. The intestinal microbiome was assessed at the age of 12 months by 16S rRNA sequencing. Data on feeding practice with at least one reported time point was available in 1042 of the 1133 recruited children. Best LCA model fit was achieved by the 4-class solution. One class showed an elevated risk of asthma at age 6 as compared to the other classes (adjusted odds ratio (aOR): 8.47, 95% CI 2.52-28.56, p = 0.001) and was characterized by daily meat consumption and rare consumption of milk and yoghurt. A refined LCA restricted to meat, milk, and yoghurt confirmed the asthma risk effect of a particular class in PASTURE and independently in LUKAS2, which we thus termed unbalanced meat consumption (UMC). The effect of UMC was particularly strong for non-atopic asthma and asthma irrespectively of early bronchitis (aOR: 17.0, 95% CI 5.2-56.1, p < 0.001). UMC fostered growth of iron scavenging bacteria such as Acinetobacter (aOR: 1.28, 95% CI 1.00-1.63, p = 0.048), which was also related to asthma (aOR: 1.55, 95% CI 1.18-2.03, p = 0.001). When reconstructing bacterial metabolic pathways from 16S rRNA sequencing data, biosynthesis of siderophore group nonribosomal peptides emerged as top hit (aOR: 1.58, 95% CI 1.13-2.19, p = 0.007). By a data-driven approach we found a pattern of overly meat consumption at the expense of other protein sources to confer risk of asthma. Microbiome analysis of fecal samples pointed towards overgrowth of iron-dependent bacteria and bacterial iron metabolism as a potential explanation.
A test of silicon photomultipliers as readout for PET Otte, A.N.; Barral, J.; Dolgoshein, B. ...
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
06/2005, Volume:
545, Issue:
3
Journal Article
Peer reviewed
The silicon photomultiplier (SiPM) is a novel photon detector based on Geiger mode operating avalanche photodiodes. In this paper, we present results from a test, demonstrating the feasibility of ...SiPM as readout elements in scintillator-based positron emission tomography (PET). As scintillator we use the newly developed LYSO crystals having similar characteristics as LSO. With our setup we measure an energy resolution of about 22% and a time resolution of a single crystal element of
(
1.51
±
0.07
)
ns
, both full-width at half-maximum. A significant improvement in time resolution could be achieved by triggering on the first photoelectron in the signal. We also present the coincidence rate of two detector channels vs. the position of a small point-like
22Na positron source.
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