Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge"in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets

Plasmodium-associated changes in human odor attract mosquitoes.

Malaria parasites (Plasmodium) can change the attractiveness of their vertebrate hosts to Anopheles vectors, leading to a greater number of vector-host contacts and increased transmission. Indeed, naturally Plasmodium-infected children have been shown to attract more mosquitoes than parasite-free children. Here, we demonstrate Plasmodium-induced increases in the attractiveness of skin odor in Kenyan children and reveal quantitative differences in the production of specific odor components in infected vs. parasite-free individuals. We found the aldehydes heptanal, octanal, and nonanal to be produced in greater amounts by infected individuals and detected by mosquito antennae. In behavioral experiments, we demonstrated that these, and other, Plasmodium-induced aldehydes enhanced the attractiveness of a synthetic odor blend mimicking "healthy" human odor. Heptanal alone increased the attractiveness of "parasite-free" natural human odor. Should the increased production of these aldehydes by Plasmodium-infected humans lead to increased mosquito biting in a natural setting, this would likely affect the transmission of malaria

Correction to: The Edinburgh Consensus: preparing for the advent of disease-modifying therapies for Alzheimer's disease.

Since the publication of this article [1], it has come to the attention of the authors that information for one of the authors was not included in the competing interests section. Craig Richie has declared potential competing interests with the following companies; Janssen, Eisai, Pfizer, Eli Lilly, Roche Diagnostics, Boeringher Ingleheim, Novartis, AC Immune, Ixico, Aridhia, Amgen, Berry Consultants, Lundbeck, Sanofi, Quintiles (IQVIA) and Takeda. The full competing interests section for this article can be found below

Blinded predictions and post-hoc analysis of the second solubility challenge data : exploring training data and feature set selection for machine and deep learning models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state-of-the-art, the American Chemical Society organised a “Second Solubility Challenge” in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019, but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms, and were trained on a relatively small dataset of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility datasets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge datasets, with the best model, a graph convolutional neural network, resulting in a RMSE of 0.86 log units. Critical analysis of the models reveal systematic di↵erences between the performance of models using certain feature sets and training datasets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy, but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modelling complex chemical spaces from sparse training datasets

A comprehensive model of factors associated with subjective perceptions of living well with dementia: findings from the IDEAL study

Background: The concept of ‘living well’ is increasingly used to indicate that it is, or should be, possible for a person living with dementia to experience a subjective sense of ‘comfort, function and contentment with life.’ We used a theoretically-derived conceptual framework to investigate capability to ‘live well’ with dementia through identifying the relative contribution of domains associated with the subjective experience of living well. Methods: We analysed data from 1550 community-dwelling individuals with mild to moderate dementia participating in the baseline wave of the Improving the experience of Dementia and Enhancing Active Life (IDEAL) cohort study. Subjective perceptions of ability to live well were obtained by generating a living well latent factor from responses on the Quality of Life in Alzheimer’s disease (QoL-AD), Satisfaction with Life and WHO-5 Well-being scales. Multivariate modelling and structural equation modelling was used to investigate variables potentially associated with living well. Variables were grouped into five domains, latent variables were constructed representing Social Location, Capitals, Assets and Resources, Psychological Characteristics and Psychological Health, Physical Fitness and Health, and Managing Everyday Life with Dementia, and associations with living well were examined. All models were adjusted for age, sex and dementia sub-type. Results: Considering the domains singly, the Psychological Characteristics and Psychological Health domain was most strongly associated with living well (3.56; 95% CI: 2.25, 4.88), followed by Physical Fitness and Physical Health (1.10, 95% CI: -2.26, 4.47). Effect sizes were smaller for Capitals, Assets and Resources (0.53; 95% CI: -0.66, 1.73), Managing Everyday Life with Dementia (0.34; 95% CI: 0.20, 0.87), and Social Location (-0.12; 95% CI: -5.72, 5.47). Following adjustment for the Psychological Characteristics and Psychological Health domain, other domains did not show independent associations with living well. Conclusions: Psychological resources are central to subjective perceptions of living well and offer important targets for immediate intervention. Availability of social and environmental resources, and physical fitness, underpin these positive psychological states, and also offer potential targets for interventions and initiatives aimed at improving the experience of living with dementia

A comprehensive model of factors associated with subjective perceptions of living well with dementia: findings from the IDEAL study

Background: The concept of ‘living well’ is increasingly used to indicate that it is, or should be, possible for a person living with dementia to experience a subjective sense of ‘comfort, function and contentment with life.’ We used a theoretically-derived conceptual framework to investigate capability to ‘live well’ with dementia through identifying the relative contribution of domains associated with the subjective experience of living well. Methods: We analysed data from 1550 community-dwelling individuals with mild to moderate dementia participating in the baseline wave of the Improving the experience of Dementia and Enhancing Active Life (IDEAL) cohort study. Subjective perceptions of ability to live well were obtained by generating a living well latent factor from responses on the Quality of Life in Alzheimer’s disease (QoL-AD), Satisfaction with Life and WHO-5 Well-being scales. Multivariate modelling and structural equation modelling was used to investigate variables potentially associated with living well. Variables were grouped into five domains, latent variables were constructed representing Social Location, Capitals, Assets and Resources, Psychological Characteristics and Psychological Health, Physical Fitness and Health, and Managing Everyday Life with Dementia, and associations with living well were examined. All models were adjusted for age, sex and dementia sub-type. Results: Considering the domains singly, the Psychological Characteristics and Psychological Health domain was most strongly associated with living well (3.56; 95% CI: 2.25, 4.88), followed by Physical Fitness and Physical Health (1.10, 95% CI: -2.26, 4.47). Effect sizes were smaller for Capitals, Assets and Resources (0.53; 95% CI: -0.66, 1.73), Managing Everyday Life with Dementia (0.34; 95% CI: 0.20, 0.87), and Social Location (-0.12; 95% CI: -5.72, 5.47). Following adjustment for the Psychological Characteristics and Psychological Health domain, other domains did not show independent associations with living well. Conclusions: Psychological resources are central to subjective perceptions of living well and offer important targets for immediate intervention. Availability of social and environmental resources, and physical fitness, underpin these positive psychological states, and also offer potential targets for interventions and initiatives aimed at improving the experience of living with dementia

Ancestry reported by white adults with cutaneous melanoma and control subjects in central Alabama

BACKGROUND: We sought to evaluate the hypothesis that the high incidence of cutaneous melanoma in white persons in central Alabama is associated with a predominance of Irish and Scots descent. METHODS: Frequencies of country of ancestry reports were tabulated. The reports were also converted to scores that reflect proportional countries of ancestry in individuals. Using the scores, we computed aggregate country of ancestry indices as estimates of group ancestry composition. HLA-DRB1*04 allele frequencies and relationships to countries of ancestry were compared in probands and controls. Results were compared to those of European populations with HLA-DRB1*04 frequencies. RESULTS: Ninety evaluable adult white cutaneous melanoma probands and 324 adult white controls reported countries of ancestry of their grandparents. The respective frequencies of Ireland, and Scotland and "British Isles" reported countries of ancestry were significantly greater in probands than in controls. The respective frequencies of Wales, France, Italy and Poland were significantly greater in controls. 16.7% of melanoma probands and 23.8% of controls reported "Native American" ancestry; the corresponding "Native American" country of ancestry index was not significantly different in probands and controls. The frequency of HLA-DRB1*04 was significantly greater in probands, but was not significantly associated with individual or aggregate countries of ancestry. The frequency of DRB1*04 observed in Alabama was compared to DRB1*04 frequencies reported from England, Wales, Ireland, Orkney Island, France, Germany, and Australia. CONCLUSION: White adults with cutaneous melanoma in central Alabama have a predominance of Irish, Scots, and "British Isles" ancestry and HLA-DRB1*04 that likely contributes to their high incidence of cutaneous melanoma

Infochemical-tritrophic Interactions of Soybean Aphids-host Plants-natural Enemies and Their Practical Applications in Pest Management

The soybean aphid, Aphis glycines Matsumura, is a newly invasive insect species that seriously threatens U.S. soybean production. This aphid pest has kept haunting many soybean growers by developing large colonies on soybeans in North America since 2000. Since its first appearance inWisconsin, it has spread to over half of US states and southern provinces in Canada. The heavy infestation of this pest whittles soybean growers’ profits and causes hundreds of million dollar losses. The present chapter will mainly describe efforts in studying aphid chemical ecology and sensory physiology for understanding how male aphids find their mates and host plants. It will also cover research efforts to understand host plant associated volatiles being used as cues for overwintering host plant location. In addition, findings on how soybean plant defensive system works against aphid infestation, as well as how those induced plant volatiles are used by aphid’s natural enemies for prey location will be presented. Finally, the use the basic understandings for developing useful tools for soybean aphid practical control will be discussed

Microarray scanner calibration curves: characteristics and implications

BACKGROUND: Microarray-based measurement of mRNA abundance assumes a linear relationship between the fluorescence intensity and the dye concentration. In reality, however, the calibration curve can be nonlinear. RESULTS: By scanning a microarray scanner calibration slide containing known concentrations of fluorescent dyes under 18 PMT gains, we were able to evaluate the differences in calibration characteristics of Cy5 and Cy3. First, the calibration curve for the same dye under the same PMT gain is nonlinear at both the high and low intensity ends. Second, the degree of nonlinearity of the calibration curve depends on the PMT gain. Third, the two PMTs (for Cy5 and Cy3) behave differently even under the same gain. Fourth, the background intensity for the Cy3 channel is higher than that for the Cy5 channel. The impact of such characteristics on the accuracy and reproducibility of measured mRNA abundance and the calculated ratios was demonstrated. Combined with simulation results, we provided explanations to the existence of ratio underestimation, intensity-dependence of ratio bias, and anti-correlation of ratios in dye-swap replicates. We further demonstrated that although Lowess normalization effectively eliminates the intensity-dependence of ratio bias, the systematic deviation from true ratios largely remained. A method of calculating ratios based on concentrations estimated from the calibration curves was proposed for correcting ratio bias. CONCLUSION: It is preferable to scan microarray slides at fixed, optimal gain settings under which the linearity between concentration and intensity is maximized. Although normalization methods improve reproducibility of microarray measurements, they appear less effective in improving accuracy