In silico target prediction: identification of on- and off-targets for crop protection agents

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Räumliche Statistik zur Analyse Chemischer Datensätze zur Validierung von Techniken des Virtuellen Screenings

A common finding of many reports evaluating virtual screening methods is that validation results vary considerably with changing benchmark datasets. It is widely assumed that these effects are caused by the redundancy and cluster structure inherent to those datasets. These phenomena manifest themselves in descriptor space, which is termed the dataset topology. A methodology for the characterization of dataset topology based on spatial statistics is introduced. With this methodology it is possible to associate differences in virtual screening performance on different datasets with differences in dataset topology. Moreover, the better virtual screening performance of certain descriptors can be explained by their ability of representing the benchmark datasets by a more favorable topology. It is shown, that the composition of some benchmark datasets causes topologies that lead to over-optimistic validation results even in very "simple" descriptor spaces. Spatial statistics analysis as proposed here facilitates the detection of such biased datasets and provides a tool for the design of unbiased benchmark datasets. General principles for the design of benchmark datasets, which are not affected by topological bias, were developed. Refined Nearest Neighbor Analysis was used to design benchmark datasets based on PubChem bioactivity data. A workflow is devised that purges datasets of compounds active against pharmaceutically relevant targets from unselective hits. Topological optimization using experimental design strategies was applied to generate corresponding datasets of actives and decoys that are unbiased with regard to analogue bias and artificial enrichment. These datasets provide a tool for an Maximum Unbiased Validation (MUV) of virtual screening methods. The datasets and a MATLAB toolbox for spatial statistics are freely available on the enclosed CD-ROM or via the internet at http://www.pharmchem.tu-bs.de/lehre/baumann/MUV.html.Ein Ergebnis vieler Arbeiten zur Validierung von Methoden des Virtuellen Screenings ist, dass die Ergebnisse stark von den Validierdatensätzen abhängen. Es wird angenommen, dass diese Effekte durch die Redundanz und Clusterstruktur der Datensätze verursacht werden. Die Abbildung eines Datensatzes im Deskriptorraum, die ``Datensatztopologie'' , spiegelt diese Phänomene wider. Im Rahmen der Arbeit wird eine Methode aus dem Bereich der räumlichen Statistik zur Charakterisierung der Datensatztopologie eingeführt. Mit dieser Methode ist es möglich, Unterschiede in den Ergebnissen von Validierexperimenten mit Unterschieden in der Datensatztopologie zu erklären. Darüberhinaus kann das bessere Abschneiden einiger Deskriptoren mit deren Fähigkeit erklärt werden, günstigere Topologien zu erzeugen. Die Zusammensetzung mancher Validierdatensätze bedingt Topologien, die zu überoptimistischen Validierergebnissen führen. Die vorgestellte Methodik ermöglicht es, solche Datensätze vor der Validierung zu erkennen. Weiterhin kann die Methode verwendet werden, um zielgerichtet Datensätze zu konstruieren, die unverfälschte Validierergebnisse sicherstellen. Auf diesen Ergebnissen aufbauend werden generelle Kriterien für die Konstruktion von Validierdatensätzen entwickelt. Mit Hilfe von Methoden der ``Refined Nearest Neighbor Analysis” werden verzerrungsfreie Datesätze generiert. Als Basis dienen Datensätze von Substanzen mit Bioaktivität aus PubChem. Ein neu entwickeltes Verfahren ermöglicht es, Substanzen mit unspezifischer Bioaktivität aus diesen Datensätzen zu entfernen. Durch Optimierung der Datensatztopologie werden korrespondierende Datensätze von Aktiven und Inaktiven erstellt, die eine Maximal Unverfälschte Validierung (MUV) von Techniken des Virtuellen Screenings ermöglichen. Diese Datensätze und eine MATLAB Toolbox für räumliche Statistik sind auf der beiliegenden CD-ROM oder im Internet unter http://www.pharmchem.tu-bs.de/lehre/baumann/MUV.html frei verfügbar

Self-supervised learning methods for label-efficient dental caries classification

High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three selfsupervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist. This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency. In other words, the resulting models can be fine-tuned using few labels (annotations). Our results show that using as few as 18 annotations can produce ě45% sensitivity, which is comparable to human-level diagnostic performance. This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive

Eyetracking metrics reveal impaired spatial anticipation in behavioural variant frontotemporal dementia.

Eyetracking technology has had limited application in the dementia field to date, with most studies attempting to discriminate syndrome subgroups on the basis of basic oculomotor functions rather than higher-order cognitive abilities. Eyetracking-based tasks may also offer opportunities to reduce or ameliorate problems associated with standard paper-and-pencil cognitive tests such as the complexity and linguistic demands of verbal test instructions, and the problems of tiredness and attention associated with lengthy tasks that generate few data points at a slow rate. In the present paper we adapted the Brixton spatial anticipation test to a computerized instruction-less version where oculomotor metrics, rather than overt verbal responses, were taken into account as indicators of high level cognitive functions. Twelve bvFTD (in whom spatial anticipation deficits were expected), six SD patients (in whom deficits were predicted to be less frequent) and 38 healthy controls were presented with a 10 × 7 matrix of white circles. During each trial (N = 24) a black dot moved across seven positions on the screen, following 12 different patterns. Participants' eye movements were recorded. Frequentist statistical analysis of standard eye movement metrics were complemented by a Bayesian machine learning (ML) approach in which raw eyetracking time series datasets were examined to explore the ability to discriminate diagnostic group performance not only on the overall performance but also on individual trials. The original pen and paper Brixton test identified a spatial anticipation deficit in 7/12 (58%) of bvFTD and in 2/6 (33%) of SD patients. The eyetracking frequentist approach reported the deficit in 11/12 (92%) of bvFTD and in none (0%) of the SD patients. The machine learning approach had the main advantage of identifying significant differences from controls in 24/24 individual trials for bvFTD patients and in only 12/24 for SD patients. Results indicate that the fine grained rich datasets obtained from eyetracking metrics can inform us about high level cognitive functions in dementia, such as spatial anticipation. The ML approach can help identify conditions where subtle deficits are present and, potentially, contribute to test optimisation and the reduction of testing times. The absence of instructions also favoured a better distinction between different clinical groups of patients and can help provide valuable disease-specific markers

In silico target prediction for elucidating the mode of action of herbicides including prospective validation.

The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize $\textit{in silico}$ target prediction methods in the early discovery phase to suggest the MoA of a compound $\textit{via}$ data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model ("HerbiMod") is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of ≥80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of using applicability domains in the first place. However, performance better than 60% in balanced accuracy was achieved on the prospective testset, where all the compounds fell within the applicability domain, and which hence underlines the possibility of using target prediction also in the area of agrochemicals.BASF SE, Unilever, European Research Council (Starting Grant ERC-2013-StG-336159 MIXTURE

Investigation of potential interferences in the detection of atmospheric ROx_{x} radicals by laser-induced fluorescence under dark conditions

Direct detection of highly reactive, atmospheric hydroxyl radicals (OH) is widely accomplished by laser-induced fluorescence (LIF) instruments. The technique is also suitable for the indirect measurement of HO2 and RO2 peroxy radicals by chemical conversion to OH. It requires sampling of ambient air into a low pressure cell, where OH fluorescence is detected after excitation by 308 nm laser radiation. Although the residence time of air inside the fluorescence cell is typically only on the order of milliseconds, there is potential that additional OH is internally produced, which would artificially increase the measured OH concentration. Here, we present experimental studies investigating potential interferences in the detection of OH and peroxy radicals for the LIF instruments of Forschungszentrum Jülich for nighttime conditions. For laboratory experiments, the inlet of the instrument was overflown by excess synthetic air containing one or more reactants. In order to distinguish between OH produced by reactions upstream of the inlet and artificial signals produced inside the instrument, a chemical titration for OH was applied. Additional experiments were performed in the simulation chamber SAPHIR where simultaneous measurements by an open-path differential optical absorption spectrometer (DOAS) served as reference for OH to quantify potential artifacts in the LIF instrument. Experiments included the investigation of potential interferences related to the nitrate radical (NO3, N2O5), related to the ozonolysis of alkenes (ethene, propene, 1-butene, 2,3-dimethyl-2-butene, α-pinene, limonene, isoprene), and the laser photolysis of acetone. Experiments studying the laser photolysis of acetone yield OH signals in the fluorescence cell, which are equivalent to 0.05 × 106 cm−3 OH for a mixing ratio of 5 ppbv acetone. Under most atmospheric conditions, this interference is negligible. No significant interferences were found for atmospheric concentrations of reactants during ozonolysis experiments. Only for α-pinene, limonene, and isoprene at reactant concentrations which are orders of magnitude higher than in the atmosphere artificial OH could be detected. The value of the interference depends on the turnover rate of the ozonolysis reaction. For example, an apparent OH concentration of approximately 1 × 106 cm−3 is observed, if 5.8 ppbv limonene reacts with 600 ppbv ozone. Experiments with the nitrate radical NO3 reveal a small interference signal in the OH, HO2 and RO2 detection. Dependencies on experimental parameters point to artificial OH formation by surface reactions at the chamber walls or in molecular clusters in the gas expansion. The signal scales with the presence of NO3 giving equivalent radical concentrations of 1.1 × 105 cm−3 OH, 1 × 107 cm−3 HO2, and 1.7 × 107 cm−3 RO2 per 10 pptv NO3

MicroRNA-126-3p/5p and Aortic Stiffness in Patients with Turner Syndrome

Background: Turner Syndrome (TS) is a relatively rare X-chromosomal disease with increased cardiovascular morbidity and mortality. This study aimed to identify whether the circulating miR-126-3p/5p are involved in the pathophysiology of vascular dysfunction in TS. Methods: Using the RT-qPCR, the abundance levels of miR-126-3p and miR-126-5p were determined in 33 TS patients and 33 age-matched healthy volunteers (HVs). Vascular screening, including the assessment of blood pressure, pulse wave velocity, augmentation index, aortic deformation, arterial distensibility, and arterial elastance, was conducted in TS patients and HVs. Results: The abundance levels of miR-126-3p and miR-126-5p were significantly higher in TS patients compared to HVs (p < 0.0001). Within the TS cohort, miR-126-3p/5p correlated significantly with aortic deformation (r = 0.47, p = 0.01; r = 0.48, p < 0.01) and arterial distensibility (r = 0.55, p < 0.01; r = 0.48, p < 0.01). In addition, a significant negative correlation was demonstrated between miR-126-3p and arterial elastance (r = −0.48, p = 0.01). The receiver operating characteristic analysis showed that miR-126-3p and miR-126-5p separated the tested groups with high sensitivity and specificity. Conclusions: The abundance levels of miR-126-3p and miR-126-5p were significantly higher in TS patients compared to HVs. Within the TS cohort, a lower abundance level of miR-126-3p and miR-126-5p was linked with a significantly higher aortic stiffness

In silico target prediction for elucidating the mode of action of herbicides including prospective validation.

The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize in silico target prediction methods in the early discovery phase to suggest the MoA of a compound via data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model ("HerbiMod") is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of≥80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of using applicability domains in the first place. However, performance better than 60% in balanced accuracy was achieved on the prospective testset, where all the compounds fell within the applicability domain, and which hence underlines the possibility of using target prediction also in the area of agrochemicals.BASF SE, Unilever, European Research Council (Starting Grant ERC-2013-StG-336159 MIXTURE

Insights from circulating microRNAs in cardiovascular entities in turner syndrome patients

Background Turner syndrome (TS) is a chromosomal disorder, in which a female is partially or entirely missing one of the two X chromosomes, with a prevalence of 1:2500 live female births. The present study aims to identify a circulating microRNA (miRNA) signature for TS patients with and without congenital heart disease (CHD). Methods Microarray platform interrogating 2549 miRNAs were used to detect the miRNA abundance levels in the blood of 33 TS patients and 14 age-matched healthy volunteer controls (HVs). The differentially abundant miRNAs between the two groups were further validated by RT-qPCR. Results We identified 60 differentially abundant miRNA in the blood of TS patients compared to HVs, from which, 41 and 19 miRNAs showed a higher and a lower abundance levels in TS patients compared to HVs, respectively. RT-qPCR confirmed the significantly higher abundance levels of eight miRNAs namely miR-374b-5p, miR-199a-5p, miR-340-3p, miR-125b-5p, miR-30e-3p, miR-126-3p, miR-5695, and miR-26b-5p in TS patients as compared with the HVs. The abundance level of miR-5695 was higher in TS patients displaying CHD as compared to TS patients without CHD (p = 0.0265; log2-fold change 1.99); whereas, the abundance level of miR-126-3p was lower in TS patients with congenital aortic valve disease (AVD) compared to TS patients without BAV (p = 0.0139, log2-fold change 1.52). The clinical feature statistics revealed that miR-126-3p had a significant correlation with sinotubular junction Z-score (r = 0.42; p = 0.0154). Conclusion The identified circulating miRNAs signature for TS patients with manifestations associated with cardiovascular diseases provide new insights into the molecular mechanism of TS that may guide the development of novel diagnostic approaches