Measurements of positive ions and air-earth current density at Maitri, Antarctica

Simultaneous measurements of the small-, intermediate- and large- positive ions and air earth current density made at a coastal station, Maitri at Antarctica during January to February 2005, are reported. Although, small and large positive ion concentrations do not show any systematic diurnal variations, variations in them are almost similar to each other. On the other hand, variations in intermediate positive ion concentrations are independent of variations in the small/large positive ions and exhibit a diurnal variation which is similar to that in atmospheric temperature on fair weather days with a maximum during the day and minimum during the night hours. No such diurnal variation in intermediate positive ion concentration is observed on cloudy days when variations in them are also similar to those insmall/large positive ion concentrations. Magnitude of diurnal variation in intermediate positive ion concentration on fair weather days increases with the lowering of atmospheric temperature in this season. Scavenging of ions by snowfall and trapping of Alha - rays from the ground radioactivity by a thin layer of snow on ground, is demonstrated from observations. Variations in intermediate positive ion concentration are explained on the basis of the formation of new particles by the photolytic nucleation process.Comment: 38 pages, 11 figure and 2 tabl

Multiple Acetylation of Pentaphenylferrocene – Synthesis and Asymmetric Reduction of 1‐Acetyl‐1′,2′,3′,4′,5′‐penta(para‐acetylphenyl)ferrocene

The Friedel–Crafts acetylation of pentaphenylferrocene has been revisited using 1.1 equivalents of AcCl/AlCl3 in CH2Cl2 at room temperature leading to the synthesis of 1‐acetyl‐1′,2′,3′,4′,5′‐pentaphenylferrocene (78 % yield). Increased quantities of reagents and longer reaction times resulted in acetylation of the phenyl groups exclusively at the para‐position, this methodology culminating in the synthesis of 1‐acetyl‐1′,2′,3′,4′,5′‐penta(para‐acetylphenyl)ferrocene (32 % for a two step process). Subsequent asymmetric reduction of all six ketone functionalities with BH3·SMe2 catalysed by 60 mol‐% (S)‐CBS proceeded in 81 % yield to give (R,R,R,R,R,R)‐1‐(α‐hydroxyethyl)‐1′,2′,3′,4′,5′‐penta[para‐(α‐hydroxyethyl)phenyl]ferrocene, a highly functionalised enantiopure building block for the synthesis of ligands and materials

Deep Learning-Based Conformal Prediction of Toxicity

Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately quantified. With recent studies showing great promise for deep learning-based models also for toxicity predictions, we investigate the combination of deep learning-based predictors with the conformal prediction framework to generate highly predictive models with well-defined uncertainties. We use a range of deep feedforward neural networks and graph neural networks in a conformal prediction setting and evaluate their performance on data from the Tox21 challenge. We also compare the results from the conformal predictors to those of the underlying machine learning models. The results indicate that highly predictive models can be obtained that result in very efficient conformal predictors even at high confidence levels. Taken together, our results highlight the utility of conformal predictors as a convenient way to deliver toxicity predictions with confidence, adding both statistical guarantees on the model performance as well as better predictions of the minority class compared to the underlying models

Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning

Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate a recently introduced version of conformal prediction, synergy conformal prediction, focusing on the predictive performance when applied to bioactivity data. We compare the performance to other variants of conformal predictors for multiple partitioned datasets and demonstrate the utility of synergy conformal predictors for federated learning where data cannot be pooled in one location. Our results show that synergy conformal predictors based on training data randomly sampled with replacement can compete with other conformal setups, while using completely separate training sets often results in worse performance. However, in a federated setup where no method has access to all the data, synergy conformal prediction is shown to give promising results. Based on our study, we conclude that synergy conformal predictors are a valuable addition to the conformal prediction toolbox

Incorporating physiologically relevant mobile phases in micellar liquid chromatography for the prediction of human intestinal absorption

Micellar liquid chromatography (MLC) is a popular method used in the determination of a compounds lipophilicity. This study describes the use of the obtained micelle/water partition coefficient (log Pmw) by such a method in the prediction of human intestinal absorption (HIA). As a result of the close resemblance of the novel composition of the micellar mobile phase to that of physiological intestinal fluid, prediction was deemed to be highly successful. The unique micellar mobile phase consisted of a mixed micellar mixture of lecithin and six bile salts, i.e. a composition matching that found in the human intestinal environment, prepared in ratios resembling those in the intestine. This is considered to be the first method to use a physiological mixture of biosurfactants in the prediction of HIA. As a result, a mathematical model with high predictive ability (R2PRED= 81 %) was obtained using multiple linear regression. The micelle/water partition coefficient (log Pmw) obtained from MLC was found to be a successful tool for prediction where the final optimum model included (log Pmw) and polar surface area (PSA) as key descriptors with high statistical significance for the prediction of HIA. This can be attributed to the nature of the mobile phase used in this study which contains the lecithin-bile salt complex, thus forming a bilayer system therefore mimicking absorption across the intestinal membrane

ESBL in horses in Europe

”Extended-spectrum β-lactamases” (ESBL) är idag ett stort problem inom humanmedicinen men börjar även skapa problem inom veterinärmedicinen. ESBL-produktion hos bakterier medför resistens mot cefalosporiner av 3:e generationen, som är viktiga antibiotika mot infektioner med gramnegativa bakterier. Resistensen är överförbar mellan bakterier, och spridning från djur till människor kan ske. Ofta är bakterier med ESBL-produktion samtidigt resistenta mot flera andra grupper av antibiotika. Multiresistensen hotar vår förmåga att kunna behandla bakterieinfektioner, speciellt hos hästar där valet av antibiotika redan är begränsat. Svårbehandlade infektioner med ESBL-producerande bakterier har i flera fall beskrivits med allvarliga och ibland dödliga konsekvenser hos hästar. Problemen med ESBL-producerande bakterier hos häst i Sverige är ännu små, endast ett fåtal ESBL-positiva kliniska prover har påvisats de senaste åren. I andra europeiska länder finns det dock studier gjorda på förekomst och potentiella riskfaktorer. I dessa studier är en typ av ESBL överlägset vanligast konstaterad i både kliniska prover och faecesprover från friska hästar, och den tillhör samma grupp som den typ som är vanligast förekommande hos människor. Escherichia coli i träckprover används ofta som en indikator på resistensläget i tarmfloran och karakteriserade riskfaktorer associerade med ESBL-förekomst och multiresistens hos dessa bakterier varierar mellan studierna. Riskfaktorerna går ofta inte att särskilja på grund av för litet urval, men har ofta ett samband med klinikvistelse eller antibiotikabehandling av den provtagna hästen eller hästar i dess närmiljö.The threat from antimicrobial resistance is an important and growing problem. There is cause for concern regarding the ability to treat important bacterial infections. Production of ”Extended-spectrum β-lactamases” (ESBL) in bacteria confers transmissible resistance against β-lactam antibiotics including third generation cephalosporins, an important group of antibiotics in both human and veterinary medicine. In addition bacteria with ESBL often show resistance to a wide number of antibiotic classes, further limiting the options for effective treatments. ESBL is spread among bacteria in humans all over the world, and lately emerging in animal bacteria as well. Treatment failure affects animal welfare and is one consequence of antimicrobial resistance in bacteria from animals. The antibiotic treatment options in horses are already limited, and more than one case of lethal complications caused by ESBL-producing bacteria have been reported. ESBL has been demonstrated in a few isolates from clinical infections in horses in Sweden. Only a few ESBL-types have been detected, in clinical samples and bacteria from the gut flora of healthy horses. One type predominates in all of the studies, and although it is not the same as the one most commonly found in humans, they belong to the same family. A few studies have characterized risk factors for faecal carriage of multidrug resistant and ESBL-producing bacteria in horses. The studies are usually too small to distinguish all factors, but most risk factors are associated with staying in a clinic or antibiotic treatment, of the sampled horse or horses in the same environment

LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity – Application to the Tox21 and Mutagenicity Datasets

Machine learning algorithms have attained widespread use in assessing the potential toxicities of pharmaceuticals and industrial chemicals because of their faster-speed and lower-cost compared to experimental bioassays. Gradient boosting is an effective algorithm that often achieves high predictivity, but historically the relative long computational time limited its applications in predicting large compound libraries or developing in silico predictive models that require frequent retraining. LightGBM, a recent improvement of the gradient boosting algorithm inherited its high predictivity but resolved its scalability and long computational time by adopting leaf-wise tree growth strategy and introducing novel techniques. In this study, we compared the predictive performance and the computational time of LightGBM to deep neural networks, random forests, support vector machines, and XGBoost. All algorithms were rigorously evaluated on publicly available Tox21 and mutagenicity datasets using a Bayesian optimization integrated nested 10-fold cross-validation scheme that performs hyperparameter optimization while examining model generalizability and transferability to new data. The evaluation results demonstrated that LightGBM is an effective and highly scalable algorithm offering the best predictive performance while consuming significantly shorter computational time than the other investigated algorithms across all Tox21 and mutagenicity datasets. We recommend LightGBM for applications in in silico safety assessment and also in other areas of cheminformatics to fulfill the ever-growing demand for accurate and rapid prediction of various toxicity or activity related endpoints of large compound libraries present in the pharmaceutical and chemical industry