During March, 2020, most European countries implemented lockdowns to restrict the transmission of SARS-CoV-2, the virus which causes COVID-19 through their populations. These restrictions had positive impacts for air quality due to a dramatic reduction of economic activity and emissions. In this work, a machine learning approach was designed and implemented to analyze local air quality improvements during the COVID-19 lockdown in Graz, Austria. The machine learning approach was used as a robust alternative to simple, historical measurement comparisons for various individual pollutants. Concentrations of NO2 (nitrogen dioxide), PM10, O3 (ozone) and Ox (total oxidant) were selected from five measurement sites in Graz and were set as target variables for random forest regression models to predict their expected values during the city's lockdown period. The true vs. expected difference is presented here as an indicator of true pollution during the lockdown. The machine learning models showed a high level of generalization for predicting the concentrations. Therefore, the approach was suitable for analyzing reductions in pollution concentrations. Results on the validation set showed very good performance for Ox and NO2 when compared to PM10 and O3. The analysis indicated that the city's average concentration reductions for the lockdown period were:-36.9 to-41.6%, and-6.6 to-14.2% for NO2 and PM10, respectively. However, an increase of 11.6 to 33.8% for O3 was estimated. The reduction in pollutant concentration, especially NO2 can be explained by significant drops in traffic-flows during the lockdown period (-51.6 to-43.9%). The results presented give a real-world example of what pollutant concentration reductions can be achieved by reducing traffic-flows and other economic activities.

We present a collection of publicly available intrinsic aqueous solubility data of 829 drug-like compounds. Four different machine learning algorithms (random forests [RF], LightGBM, partial least squares, and least absolute shrinkage and selection operator [LASSO]) coupled with multistage permutation importance for feature selection and Bayesian hyperparameter optimization were used for the prediction of solubility based on chemical structural information. Our results show that LASSO yielded the best predictive ability on an external test set with a root mean square error (RMSE) (test) of 0.70 log points, an R 2 (test) of 0.80, and 105 features. Taking into account the number of descriptors as well, an RF model achieves the best balance between complexity and predictive ability with an RMSE(test) of 0.72 log points, an R 2 (test) of 0.78, and with only 17 features. On a more aggressive test set (principal component analysis [PCA]-based split), better generalization was observed for the RFmodel. We propose a ranking score for choosing the best model, as test set performance is only one of the factors in creating an applicable model. The ranking score is a weighted combination of generalization, number of features, and test performance. Out of the two best learners, a consensus model was built exhibiting the best predictive ability and generalization with RMSE(test) of 0.67 log points and a R 2 (test) of 0.81.

Supplementary data are available at Bioinformatics online.

Malignant ureteral obstruction can result in renal dysfunction or urosepsis and can limit the physician's ability to treat the underlying cancer. There are multiple methods to deal with ureteral obstruction including regular polymeric double J stents (DJS), tandem DJS, nephrostomy tubes, and then more specialized products such as solid metal stents (e.g., Resonance Stent, Cook Medical) and polyurethane stents reinforced with nickel-titanium (e.g., UVENTA stents, TaeWoong Medical). In patients who require long-term stenting, a nephrostomy tube could be transformed subcutaneously into an extra-anatomic stent that is then inserted into the bladder subcutaneously. We outline the most recent developments published since 2012 and report on identifiable risk factors that predict for failure of urinary drainage. These failures are typically a sign of cancer progression and the natural history of the disease rather than the individual type of drainage device. Factors that were identified to predict drainage failure included low serum albumin, bilateral hydronephrosis, elevated C-reactive protein, and the presence of pleural effusion. Head-to-head studies show that metal stents are superior to polymeric DJS in terms of maintaining patency. Discussions with the patient should take into consideration the frequency that exchanges will be needed, the need for externalized hardware (with nephrostomy tubes), or severe urinary symptoms in the case of internal DJS. This review will highlight the current state of diversions in the setting of malignant ureteral obstruction.

<div>Here, we present a collection of publicly available<br>intrinsic aqueous solubility data of 829 drug-like<br>compounds. Four different machine learning algorithms<br>(random forest, light GBM, partial least squares and<br>LASSO) coupled with multi-stage permutation<br>importance for feature selection and Bayesian hyperparameter optimization were employed for the<br>prediction of solubility based on chemical structural<br>information. Our results have shown that LASSO<br>yielded the best predictive ability on an external test set<br>with and RMSE(test) of 0.70 log points and 105 features<br>in the model. Taking into account the number of<br>descriptors as well, an RF model achieved the best<br>balance between complexity and predictive ability with<br>an RMSE(test) of 0.72 with only 17 features. We<br>propose a ranking score for choosing the best model, as<br>test set performance is only one of the factors in creating<br>an applicable model. The ranking score is a weighted<br>combination of generalization, number of features<br>involved and test set performance <br></div><div><br></div><div><br></div><div><br></div><div>The data related to this paper can be downloaded from 10.5281/zenodo.3968754</div>

Genome editing technologies not only provide unprecedented opportunities to study basic cellular system functionality but also improve the outcomes of several clinical applications. In this review, we analyze various gene editing techniques used to finetune immune systems from a basic research and clinical perspective. We discuss recent advances in the development of programmable nucleases, such as zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeat (CRISPR)-Cas-associated nucleases. We also discuss the use of programmable nucleases and their derivative reagents such as base editing tools to engineer immune cells via gene disruption, insertion, and rewriting of T cells and other immune components, such natural killers (NKs) and hematopoietic stem and progenitor cells (HSPCs). In addition, with regard to chimeric antigen receptors (CARs), we describe how different gene editing tools enable healthy donor cells to be used in CAR T therapy instead of autologous cells without risking graft-versus-host disease or rejection, leading to reduced adoptive cell therapy costs and instant treatment availability for patients. We pay particular attention to the delivery of therapeutic transgenes, such as CARs, to endogenous loci which prevents collateral damage and increases therapeutic effectiveness. Finally, we review creative innovations, including immune system repurposing, that facilitate safe and efficient genome surgery within the framework of clinical cancer immunotherapies.

Asthma in children is a heterogeneous disease manifested by various phenotypes and endotypes. The level of disease control, as well as the effectiveness of anti-inflammatory treatment, is variable and inadequate in a significant portion of patients. By applying machine learning algorithms, we aimed to predict the treatment success in a pediatric asthma cohort and to identify the key variables for understanding the underlying mechanisms. We predicted the treatment outcomes in children with mild to severe asthma (N = 365), according to changes in asthma control, lung function (FEV1 and MEF50) and FENO values after 6 months of controller medication use, using Random Forest and AdaBoost classifiers. The highest prediction power is achieved for control- and, to a lower extent, for FENO-related treatment outcomes, especially in younger children. The most predictive variables for asthma control are related to asthma severity and the total IgE, which were also predictive for FENO-based outcomes. MEF50-related treatment outcomes were better predicted than the FEV1-based response, and one of the best predictive variables for this response was hsCRP, emphasizing the involvement of the distal airways in childhood asthma. Our results suggest that asthma control- and FENO-based outcomes can be more accurately predicted using machine learning than the outcomes according to FEV1 and MEF50. This supports the symptom control-based asthma management approach and its complementary FENO-guided tool in children. T2-high asthma seemed to respond best to the anti-inflammatory treatment. The results of this study in predicting the treatment success will help to enable treatment optimization and to implement the concept of precision medicine in pediatric asthma treatment.

During March, 2020, most European countries implemented lockdowns to restrict the transmission of SARS-CoV-2, the virus which causes COVID-19 through their populations. These restrictions had positive impacts for air quality due to a dramatic reduction of economic activity and emissions. In this work, a machine learning approach was designed and implemented to analyze local air quality improvements during the COVID-19 lockdown in Graz, Austria. The machine learning approach was used as a robust alternative to simple, historical measurement comparisons for various individual pollutants. Concentrations of NO2 (nitrogen dioxide), PM10, O3 (ozone) and Ox (total oxidant) were selected from five measurement sites in Graz and were set as target variables for random forest regression models to predict their expected values during the city's lockdown period. The true vs. expected difference is presented here as an indicator of true pollution during the lockdown. The machine learning models showed a high level of generalization for predicting the concentrations. Therefore, the approach was suitable for analyzing reductions in pollution concentrations. Results on the validation set showed very good performance for Ox and NO2 when compared to PM10 and O3. The analysis indicated that the city’s average concentration reductions for the lockdown period were: -36.9 to -41.6%, and -6.6 to -14.2% for NO2 and PM10, respectively. However, an increase of 11.6 to 33.8% for O3 was estimated. The reduction in pollutant concentration, especially NO2 can be explained by significant drops in traffic-flows during the lockdown period (-51.6 to -43.9%). The results presented give a real-world example of what pollutant concentration reductions can be achieved by reducing traffic-flows and other economic activities.