Despite the consequences of neonatal distress and agitation, preterm infants undergo stress owing to weighing procedures. The objective of this study was to enable very low birth weight infants to maintain adequate self‐regulation during weighing.This prospective crossover study utilizes a within‐subjects design, where intervention days were compared to control days.Infants were exposed to both swaddled and unswaddled weighing in an intensive care nursery setting. Nineteen very low birth weight infants were weighed on two consecutive days. Variables of heart rate, respiratory rate and ALPS‐Neo score were recorded.Stress score decreased significantly from 1.65 (pre‐weight) to 0.23 (weight measurement) in swaddled‐intervention periods; conversely, it increased significantly from 1.26 (pre‐weight) to 4.97 (weight measurement) in control periods. During weight measurement, heart and respiratory rate were significantly lower for swaddled‐intervention days when compared to control days. Given the significant impact of swaddled weighing in reducing stress, this method can be used as an appropriate weighing procedure in intensive care. This research has no patient or public contribution. Agitation in neonatal life impacts on multiple regions of the developing brain  and exhibit less motor stress than when unswaddled in the NICU  was selected for the study. The same panel of VLBW infants was surveyed repeatedly over time. Twenty‐three eligible infants were admitted to the NICU during the data‐collection period. One infant was not fed human milk, one infant was under deep sedation and two sets of parents declined to participate. These four infants did not differ from the sample in illness severity or birth weight.Based on the expected respiratory rate (RR) mean value differences (51.8 vs. 41.5 breaths/min), derived from the only previous study on physiologic and behavioural distress of preterm infants during swaddled versus unswaddled weighing  on even days (Day 2). This sequence was applied inversely to babies in Group 2. Conditions for both groups were low‐intensity light, minimal noise, gentle movements during the weighing procedure, and no handling from 20 min before to 60 min after weighing. The swaddling device device was weighed in isolation, before each intervention, and has always weighed between 20 and 30 grams. During any period of evaluation, any unnecessary interventions were avoided.This prospective crossover study utilizes a within‐subjects design, where intervention days were compared to control days. Participants were randomized into two groups (Group 1 and Group 2) to receive a different intervention on each of 2 days (Day 1 and Day 2). Half of the infants (Group 1) started with routine care (control days), while the other half started with swaddling care (Group 2). Group 1 babies were weighed according to the hospital standard procedures, without any intervention on Day 1; subsequently, a swaddling device was used during weighing care sessions on intervention day (Day 2). Group 1 infants were weighed conventionally on odd days (Day 1) and weighed swaddled in a not marketed swaddling device (SWADDY) was wrapped around the infant so as to cover his/her upper trunk and limit his/her arms movements. The cap of the garment allows head‐trunk alignment, thereby ensuring that the airway remains unobstructed  using the continuous display on a General Electric MD15T monitor. HR and respirations were recorded five times every 4 min before the weighing to establish a baseline average for vital signs comparison. The nurses continued the physiologic data recording throughout the weighing procedure and additionally 60 min after the weighing to assess the degree of organization after this procedure.Close monitoring and strict measurement of vital signs was part of the work of specialist nurses in the intensive care unit. Two research nurses, not routinely involved in neonatal care, analysed ALPS‐Neo score before, during, and after weighing care, with and without ALPS‐Neo scale was scored 20 min before handling, immediately after weighing, and 60 min after weighing. ALPS‐Neo is a five‐item scale for the continuous evaluation of preterm infants' behavioural responses to pain and stress  were examined by independent samples 2.7This study was conducted with the approval of the University Hospital Clinical Research Ethics Committee. The committee gave its favourable opinion on 22 August 2016, which was registered under the name: IIBSP‐EST‐2015‐97. The authors certify that they have obtained all appropriate patient consent forms. In each case, a member of the nursing staff of the neonatal unit purposely contacted the parent of the newborn who was a candidate for participation in the study, explained orally the procedures and objectives of the study, and requested that, if the family felt it appropriate, they give their consent by signing the written document given to them containing all the details of the intervention.33.1We screened 23 preterm babies for eligibility, and 19 consecutive subjects were enrolled . The main reasons for exclusion were: withheld consent (10%), deep sedation (5%) and formula feeding (5%). The profile of the study subjects is shown in Table SWADDY‐days. Thereafter, post‐weighing RR and ALPS‐Neo scores were significantly lower in SWADDY‐days than in control‐days.Table Analysis of within‐group variability in unswaddled infants shows that there is an increase of vital signs and ALPS‐Neo score during weighing, whereas there is a decrease of all variables during post‐weight care  from the Nurse and Society Foundation  to the first author.All authors declare no actual or potential conflicts of interests.This study was conducted with the approval of the Clinical Research Ethics Committee, Sant Pau Hospital (Barcelona). The authors certify that they have obtained all appropriate patient consent forms.


The single‐cell RNA sequencing (scRNA‐seq) quantifies the gene expression of individual cells, while the bulk RNA sequencing (bulk RNA‐seq) characterizes the mixed transcriptome of cells. The inference of drug sensitivities for individual cells can provide new insights to understand the mechanism of anti‐cancer response heterogeneity and drug resistance at the cellular resolution. However, pharmacogenomic information related to their corresponding scRNA‐Seq is often limited. Therefore, a transfer learning model is proposed to infer the drug sensitivities at single‐cell level. This framework learns bulk transcriptome profiles and pharmacogenomics information from population cell lines in a large public dataset and transfers the knowledge to infer drug efficacy of individual cells. The results suggest that it is suitable to learn knowledge from pre‐clinical cell lines to infer pre‐existing cell subpopulations with different drug sensitivities prior to drug exposure. In addition, the model offers a new perspective on drug combinations. It is observed that drug‐resistant subpopulation can be sensitive to other drugs ; such finding corroborates the previously reported drug combination (Gefitinib + Vorinostat) strategy in several cancer types. The identified drug sensitivity biomarkers reveal insights into the tumor heterogeneity and treatment at cellular resolution. A transfer learning framework for single cell drug response prediction. This framework integrates domain adaptation to learn cell line bulk pharmacogenomics and transfers the knowledge to infer drug sensitivities at single cells before treatment. This ranking‐based framework for drug sensitivity inference provides new strategies to account for intratumoral heterogeneity; providing new perspectives for biomarker discovery and drug combination applications. The intratumoral heterogeneity is caused by many factors, such as genetic heterogeneity of cells and the existence of multiple independent cell subpopulations within a tumor. The intratumor heterogeneity is a well‐known issue leading to treatment failure. Currently, there are past studies which have evaluated the selective pressure after drug treatment using single cell data; for instance, genomics and single‐cell phosphoprotomics analysis on patient‐derived epidermal growth factor receptor (EGFR)‐mutated in vivo mTOR kinase inhibitor (mTORki) resistance glioblastoma (GBM) model have been proposed. Researchers identified promising drug combinations by resolving the related protein signal networks. Moreover, the drug combination efficacy was validated with the complete and lasting tumor suppression on GBM39 implanted mice model. Another research uses single‐cell proteomics to study the drug‐induced (anti‐EGFR therapy) resistance and tumor phenotypic switch  in triple‐negative breast cancer (TNBC). EGFR is highly expressed in TNBC and was suggested as a therapeutic target. However, anti‐EGFR monotherapies for TNBC are normally failed in clinical. Researchers demonstrated that distinct cell subpopulations could independently grow in response to different anti‐cancer treatments. Such kind of anti‐cancer progression may be due to the expansion of previously untargeted (no tailored accurately) or undetected cell subclones. In addition, the treatment that does not target estrogen receptor (ER) may result in incomplete suppression of cancer cells and lead to a phenotype switch from TNBC to ER‐positive enrichment. In contrast, anti‐EGFR therapy may benefit when combined with anti‐ER treatment; it is suggested by the patient‐specific signaling signature (PaSSS) based predictions and was experimentally validated in two TNBC cell lines.The personalized anti‐cancer therapies have achieved successes in several malignancies.1 Howe more studies are mainly based on the tumor data profiled in bulk RNA‐seq. Among them, cancer cell lines are still the most widely applied laboratory models for oncology study and anti‐cancer drug discovery. In contrast to the limited drug sensitivity information in single cells, there are numerous drug efficacy profiling and studies in cancer cell lines. For example, in the Genomics of Drug Sensitivity in Cancer database (GDSC), over 1000 human cancer cell lines were screened with a variety of anti‐cancer compounds including both cytotoxic chemotherapeutics and targeted agents that were clinically approved or undergoing clinical development, allowing the identification of transcriptome‐based (bulk RNA‐seq) drug efficacy predictors and biomarkers. Other related perturbational databases such as Connectivity Map (CMAP), the Library of Integrated Network‐Based Cellular Signatures (LINCS‐L1000), and Cancer Therapeutics Response Portal (CTRP) also include numerous compound sensitivity characteristics of cell lines for drug perturbation analysis. Those large‐scale pharmacogenomic datasets offer researchers an opportunity to better characterize the heterogeneity of perturbational biological features through machine learning technologies. For instance, Peilin et al. developed a deep learning model to impute drug responses in both tumor cell lines and cancer patients, demonstrating the clinical translational potential of the deep learning (DL) method for drug response biomarker discoveries. Hostallero et al. built a tumor tissue‐informed normalization deep learning (TINDL) model to impute the compound efficacy on tumor patients, which integrated transcriptome profiles of both the GDSC database and TCGA primary samples. To alleviate the discrepancies between cell lines and tumor tissues, TINDL integrates tumor type information of test samples to enhance the performance of drug efficacy prediction on patients. Similarly, Hossein et al. proposed an adversarial inductive transfer learning model for the drug efficacy prediction on four anti‐cancer compounds. The adversarial learning is an approach to improve data recognition despite the existence of dataset bias and was applied to bridge the gap between cell lines and clinical datasets  to improve model generalization.However, unlike studies that were currently limited to a few cancer types and a small subset of drugs at the single‐cell level,5 more It was suggested that a multitude of cancer cells with distinct molecular characteristics could coexist in the same tissue. In addition, a small proportion of pre‐existing or acquired resistant cells may evolve and gradually weaken the original medical strategy, and ultimately cause treatment failure. These therapeutic resistances due to tumor heterogeneity suggest that those “one‐size‐fits‐all” evidence‐based medicine approaches, which is informed by bulk RNA‐seq analysis, may provide inappropriate medical solutions for outlier patients.We noticed that the above models were constructed based on bulk RNA‐seq data and many therapeutic strategies of patients are derived from mean recommendations informed by meta‐analysis. The major drawback of bulk RNA‐seq is that it only gives the average and a mixture profile of heterogeneous cells. Its limited capability conceals the uniqueness as well as the actual diversity of individual cells.18 It  metastatic colorectal cancer, and non‐small cell lung cancer. New diagnostics such as molecular imaging and gene expression quantification, as well as novel clinical trial designs, resulting in a reduction of treatment failure and a decreased morbidity rate. Scientists are now calling for more precise methodologies for personalized treatments. The emergence and development of single‐cell sequencing technology offers us an opportunity to decipher previously undiscovered heterogeneity of cancer, facilitating the identification of previously hidden cell subpopulations with distinct characteristics. Recently, a variety of machine learning (ML) and statistical methods have been developed for different single‐cell transcriptomics tasks, such as subtype clustering and cell type distinction. Nevertheless, due to the limited amount of scRNA‐seq data with both sequencing and drug efficacy information , there are few ML models focused on single‐cell drug efficacy prediction, especially those cells sequenced prior to drug treatment. The scRNA‐seq is currently more expensive than bulk sequencing, prolonging its application in large‐scale pharmacogenomic research and clinical studies. Although the latest released version of Single Cell Portal from Broad Institute includes over four hundred single‐cell studies, few of them contain drug sensitivity data. Based on the fact that multiple ML models have elevated the drug efficacy prediction performance on tumor patients by integrating transcriptome information from large cell line pharmacogenomic databases, we hypothesized that the technical shortcoming of scRNA‐seq could be alleviated by integrating the plentiful transcriptome profiles from cell lines of large pharmacogenomic databases while keeping the microscopic resolution features of scRNA‐seq data. In summary, the drug perturbation effect information could be learned and migrated to the scRNA‐seq drug sensitivity prediction task and biomarker discovery.It is noteworthy that this “one‐size‐fits‐all” treatment situation is now shifting to an era of precision medicine in many tumors such as breast cancer,22 metFigure  Therefore, we proposed the single cell drug response prediction framework by integrating adversarial domain adaptation (SCAD) to eliminate the domain shift problem. The model adopts the idea of adversarial discriminative domain adaptation (ADDA) composed of both adversarial learning and domain adaptation. The adversarial learning pits two networks against each other. In our SCAD framework, the feature extractor and domain (datasets) discriminator are trained in an adversarial manner. In the end, the feature extractor can extract domain‐invariant feature representations that the domain discriminator cannot distinguish whether the features come from the source domain or the target domain. Moreover, the adversarial learning is an approach to improve data recognition despite the existence of dataset bias. Domain adaptation is a method to alleviate the unfavorable effects of dataset bias, reducing the differences between the source and target domain distributions to enhance the model generalization. The domain‐invariant feature representations could guarantee the transferability. Based on this benefit, we proposed and adopted ADDA to minimize the domain discrepancy between cell lines (source domain) and scRNA‐seq dataset (target domain). In addition, we considered transfer learning as two independent tasks for the cells with different tolerant origins based on the mechanism that drug tolerance may occur due to pre‐existing cell populations before treatment or resistance‐acquired cells after drug exposure. The details of the source domain and target domain datasets with shared compounds, which are proposed to train a model for drug sensitivity prediction at cells after drug exposure, are provided in Table Table In this study, we leveraged transfer learning to learn the pharmacogenomic characteristics of cell lines in the GDSC database to infer the drug sensitivities of samples at the single cell level Figure 1. The s2 In this study, we evaluated the prediction performance of our SCAD model in two scenarios independently. Due to the unbalanced ratio of sensitive and resistant cell lines in the source domain, we conducted both weight sampling and smote sampling in model training processes . In addition, we considered three different strategies to explore the impact of performance in the feature selection step before training, namely “all” , “tp4k” , and “PPI” .It was suggested that the resistance to drug treatment can occur due to the growth of the acquired drug‐tolerant cells or the expansion of pre‐existing subclones.19 In In this study, we relied on average AUC and average AUPR scores as the performance metrics. We also applied a ranking‐based strategy to sort single cells from the inferred most positive (drug‐sensitive) cells to the most negative (drug‐resistant) one for cell stratification and biomarker exploration. Interestingly, we observed that drug‐resistant cell subpopulation to a specific compound may be sensitive to other drugs. This phenomenon offers us an opportunity to explore and identify promising drug pairs for drug‐combination.2.1 Therefore, parental cells (sequenced from drug untreated cell lines) are regarded as sensitive cells, while drug‐tolerant cells that survived after drug exposures are selected and considered as resistant cells. We conducted transfer learning on two GSE datasets with both scRNA‐seq profiles and drug response experiments from three different cell lines . The means and standard deviations (SD) of the AUC and AUPR scores for each drug are summarized in Table Tables  In addition, they clustered the gene expression profiles of 451Lu_BR (BRAFi resistant), 451Lu_Par , A375_BR (BRAFi resistant), and A375_Par  cells to observe the expression patterns among those BRAFi treated and untreated cells. They found that the untreated 451Lu cells are clustered with BRAFi‐resistant 451Lu cells. Similarly, the untreated A375 cells are clustered with BRAFi‐resistant A375 cells. The clustering result suggested that the cell type diversity could be more prominent than the drug perturbational effect  scale. Among them, there are around 1000 single cells prior to drug treatment that sequenced from two different cell lines , based on the expression level of proteins‐coding genes Claudin4 and AXL. By performing the in vitro cell viability assay on the FACS sorted cells with different EpiSen program levels in two head and neck squamous cell carcinoma (HNSCC) model cell lines (JHU006 and SCC47), the authors demonstrated that the EpiSen‐high and EpiSen‐low cells derived from same cell lines show different responses to a dozen of drugs. The authors generated a method to computationally define the EpiSen program status for every single cell, where cells could be classified into EpiSen‐high , EpiSen‐low , and a third neutral reference population . Inspired by the above work, the scRNA‐seq profiles sequenced from the JHU006 cell line and the SCC47 cell line are treated as target domains in our SCAD transfer learning task. GDSC cell lines with both bulk RNA‐seq profiles and drug sensitivity labels from the in vitro experiments were selected to evaluate whether the transcriptome feature and pharmacogenomic knowledge from the source domain could be learned and transferred to help better identify the distinct single cell subpopulations with diverse drug sensitivities prior to treatment. The details of the source domain and the target domain are tabulated in Table In the discovery of above SCP542 study, researchers identified an epithelial senescence‐related (EpiSen) program that can reflect co‐existing distinct subsets of cells observed within cell lines and tumors.20 TheTables As demonstrated in  In this way the drug response predictor trained in the source domain could well predict the drug sensitivities in the target domain. As an example of understanding and visualizing the domain adaption process, we conducted the t‐SNE technique, which is a dimensional reduction method to visualize high dimensional data in 2D or 3D, to visualize the representations learned from feature extractor  in drug AR‐42 with the highest average AUC value under Weight_all mode , considering all genes shared by the source domain and the target domain obtained higher five‐fold average AUC and AUPR scores than merely considering tp4k highly variable genes or PPI genes  while excluding hematopoietic cell lines (blood cancer) derived from leukemia, lymphoma, and myeloma cancer. The sample sizes of GDSC source domain cell lines for each compound with different tumor type origins are listed in Table https://www.cancerrxgene.org/). We conducted a fisher exact test for the frequency distribution of drug‐sensitive cell lines and drug‐resistant cell lines between solid tumor origin and hematopoietic origin in the source domain for each compound. Among all seven drugs, six of them have a significantly different (p <  0.01) frequency distribution between solid tumor origin cell lines and hematopoietic ones . It was supported with clinical evidence that blood cancers are especially responsive to HDAC inhibitors, and Vorinostat is one of the promising options for blood cancers with manageable drug‐related adverse events. Moreover, Vorinostat was approved by US Food and Drug Administration (FDA) to treat advanced cutaneous T‐cell lymphoma (CTCL). Similarly, there is a different proportion of Sorafenib‐sensitive cell lines between solid cancer and blood cancer . Removing the blood cancer cell lines of Sorafenib results in decreased prediction results on single cell data. Sorafenib, a first‐generation type‐II multi‐kinase inhibitor, could interact with multiple cell surface kinases , and VEGFR‐3). It was reported in a recent clinical trial  that Sorafenib could led to a significant prolongation of event‐free survival (EFS) and relapse‐free survival (RFS) in patients with newly diagnosed acute myeloid leukaemia . Based on the above results, the exclusion of hematopoietic cell lines for drugs Vorinostat and Sorafenib might result in an insufficient number of sensitive cell lines for the model to learn, resulting in reduced prediction performances of SCAD in single cell data. Therefore, the tumor cell line origin information can enhance drug efficacy inference, due to the MoA differences between solid cancer and blood cancer.The above results can be attributed to the underlying mechanisms of actions (MoAs) of the drugs  less than 5.6 months (short‐PFS group), and 26 patients obtained long PFS exceeding 12 months (long‐PFS group) after following Cetuximab treatment together with a first line platinum‐based chemotherapy. This GSE65021 dataset can be regarded as an independent clinical cohort to explore the prognostic value of our SCAD model. For a comprehensive comparison with biomarkers identified by bulk RNA‐seq as the evidence‐based support and to evaluate the prognostic prediction value of gene biomarkers that independently identified from scRNA‐seq data, we also selected the drug Cetuximab as our case study.Although we have constructed and evaluated the SCAD model for drug efficacy inference, genes that contribute most to the drug sensitivity prediction are still unknown. However, it is vital to find out those biomarkers to help us better understand the biological mechanism of drug response heterogeneity and the potential for clinical application. Fortunately, a recent functional genomics study includes the pre‐treatment gene expression profiles of 40 head and neck squamous cell cancer (HNSCC) patients (GSE65021).40 Amo method to infer the subset of genes that contributes the most to drug sensitivity prediction . The IntegratedGradients is a method to infer how much an independent variable affects the value of the prediction output from the model, which is one of the most widely used ML model explainer to interpret the association between input data and machine learning model outputs. A total of 70 genes that over‐expressed in predicted Cetuximab‐sensitive cells are identified  results suggested that overexpressed genes in predicted sensitive cells (sc_sens) are enriched in several biological processes , we could not directly compared to bulk RNA‐seq data since there are no EpiSen‐related over‐expression genes that were reported in short‐PFS patients of Cetuximab  or drugs that have not been evaluated in cell viability assay is not available  are presented in Figure To evaluate whether our SCAD model could identify drug‐sensitive cells and drug‐resistant ones within tumors, we selected drug Cetuximab, which obtained the highest AUC and AUPR scores under Weight_all strategy among drugs in the SCC47 cell line Tables  and 6. T20 TheNext, to evaluate the prediction results of the SCAD model in all SCC47 cells  and predicted high resistant (rank_Res) cells are relatively distributed at distinct locations in the PCA plots for most cells Figure , suggestp = 0.0034, Figure To explore the possible gene expression heterogeneity between predicted Cetuximab‐sensitive cells and Cetuximab‐resistant cells (based on our ranking result), we conducted a similar biomarker exploration process that we introduced in Figure 2.5 In addition, the efficacy of this combination is independent on the HPV status, a strong prognosis biomarker in HNC cancer. Gefitinib is an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) while Vorinostat is a Histone deacetylase inhibitor (HDACi). The synergistic efficacy of dual EGFRi and HDACi in overcoming resistance to monotherapy was also found in many other cancer types, such as non‐small‐cell lung cancer (NSCLC) and mucoepidermoid carcinoma. Interestingly, a similar opposite drug response status was found between drug Gefitinib and drug AR‐42  of cancer cell lines from large pharmacogenomic databases to infer the drug sensitivities of other cell lines or patient samples. However, most of them are based on bulk RNA‐seq data, which only give an average profile of cell mixtures, masking cellular heterogeneity. Fortunately, the growth of scRNA‐seq data offers an opportunity to elucidate the intratumoral heterogeneity at cellular resolution. There are currently some studies that evaluate the selective pressure after drug treatment using single‐cell proteomics/phosphoprotomics data to study the drug‐induced acquired drug tolerance mechanism and identify possible therapeutic target to address post‐treatment resistance. These results suggest the possibility and necessity of conducting additional explorations of drug response heterogeneity in single‐cell data.It is necessary to infer the drug sensitivities of cells and explore the tumor heterogeneity at single‐cell resolution. Previous studies suggested that the anti‐cancer treatment may fail due to the pre‐existing resistant cells prior to treatment or the acquired tolerant ones after drug exposure.49 To  while the disadvantage is that the resolution is not comparable to single‐cell sequencing. On the contrary, scRNA‐seq drug perturbation data were currently limited, hindering the study of machine learning in single‐cell drug sensitivity prediction. Based on the successful application of transfer learning in many real‐world problems, we proposed a transfer learning framework to learn pharmacogenomics information from cell lines in large public dataset GDSC and transfers the learned model to infer drug efficacy at the single cell resolution.The relatively scarce drug sensitivity information at single cells can be alleviated by transfer learning. Currently, high‐throughput pharmacogenomic databases such as GDSC and CCLE harbor numerous transcriptome profiles and drug response sensitivity data,8, 9 which may not be suitable to infer the drug sensitivities of single cells after the treatment of anti‐cancer agents ideally, since the expression of genes may change a lot before and after drug exposure. As a comparison, we observed stable prediction performance  among a total of seven anti‐cancer drugs when evaluating the drug sensitivities in single cells that were sequenced from two cell lines prior to anti‐cancer therapy. These results suggest that pre‐clinical cell lines may be more suitable for transfer learning analysis in pre‐clinical single cells, where both the source domain and the target domain were sequenced before drug exposures.In this analysis, we explored and demonstrated the feasibility of predicting drug sensitivities of single cells by transfer learning. In the SCP542 project, researchers found that the EpiSen‐high and EpiSen‐low cells sequenced from two independent cell lines are featured with distinct drug response sensitivities to several compounds, this phenomenon was validated by the in vitro cell viability assay experiments. We integrated this information and the corresponding single‐cell sequencing profiles into a dataset to investigate the feasibility of transfer learning on single‐cell expression profiles. We conducted several experiments to infer the drug sensitivities of cells with distinct EpiSen program status by transfer learning. Our SCAD model attempts to integrate and transfer the pharmacogenomic knowledge from cell lines to infer the drug efficacy at scRNA‐seq data. In addition, we formulated our transfer learning work as two independent tasks. Our results showed that the model's drug sensitivity prediction performance was improved with the help of domain adaptation (ADDA) compared to two non‐ADDA baselines. Surprisingly, we also found that the prediction performance can be fluctuating when predicting the drug sensitivities between parental cells and resistant cells after drug exposures. One possible reason is that the source domain GDSC cell lines are sequenced at the pre‐clinical stage (prior to the exposure to different drugs)9 whicDrug resistance ranking identify promising prognostication biomarkers and drug combination. It is vital to uncover the black‐box mechanism of machine learning model for model interpretation. We applied IntegratedGradients to explore the relationships between input and output of SCAD model. For Cetuximab, we observed a high overlap of genes between the overexpressed genes in long PFS patients and the ones with high IntegratedGradient attribution scores in predicted sensitive cells. For the genes identified that may contribute to drug resistance, we also found evidence from the published literature that supported our gene oncology (GO) pathway enrichment analysis results  that contain five‐thousand (5K) single cells (SC). We found that our model obtained slightly increased magnitudes in both AUC and AUPR. Moreover, we also found that the variances of AUC and AUPR in ensemble mode (SCAD (SC‐5K non‐adjusted model plus SCAD SC‐5K adjusted model) are smaller than SCAD (SC‐5K non‐adjusted model), suggesting that the performance of ensemble mode will be more stable than the counterparts  were selected as the target domain for the evaluation of pre‐existing drug‐sensitive and drug‐resistant cells. Drug response sensitivities for each compound in the corresponding cell lines are listed in Table m Etoposide for 3 days) and GSE108383 (scRNA‐seq for A375 cell line and 451Lu cell line) datasets were selected as target domain for the evaluation of acquired resistant after the exposure of drug Etoposide and PLX4720, respectively. The details of drug names, cell lines, and sample size of drug resistant and sensitive cells for source and target domain are shown in Table As previously described, the prediction for pre‐existing drug resistant cells prior to treatment and acquired resistant cells after compound exposure was considered as two independent tasks, due to different drug tolerant mechanisms of tumor heterogeneity were treated.19 As https://ibm.ent.box.com/v/paccmann‐pytoda‐data/folder/91702227426. Genes were standardized into z‐score by scikit‐learn StandardScaler function. Next, cell lines with both RMA normalized bulk RNA‐seq expression profiles and LOBICO binarized drug response IC50 values were kept for further analysis.The GDSC RMA normalized bulk RNA‐seq data was downloaded as the source domain data, which was processed and described in 02227426.34 Gen More specifically, the quality control pre‐processing steps were conducted using the Scanpy package based on the profiling feature of each dataset , considering the diversity of technical and sequencing depth among different target datasets , log‐transformed and standardized as z‐score. In addition, only genes in both the source domain and the target domain were retained for model training and testing.In the SCAD model, scRNA‐seq datasets were regarded as the target domain. Several data quality control (QC) steps described in ref.  were fol36 Morts Table  2. In ad the SCAD model consisted of a feature extractor to learn the representations of the source domain and target domain, a drug response predictor to learn the informative features for drug sensitivity inference, and a domain discriminator to make the feature extractor to learn domain invariant features from both domains . The details of each component of SCAD framework are described as follows and in Figure The source domain data were bulk RNA‐seq of cell lines from the GDSC database with binarized IC50 labels.51 Theh_dim was set to adjust the number of nodes of the feature extractor. The source and target data was denoted as XS, XT, and the source and target representation mapping asThe source domain and target domain shared the same feature extractor, enabling weight sharing and symmetric mapping of data from both domains. Similar to ref. , the feaz_dim was set to customize the number of nodes of the hidden layer. A sigmoid function was appended after the five hidden layers of predictor to obtain the drug sensitivity probability. The sigmoid output was denoted asThe drug response predictor was trained solely based on source domain data, and target domain drug sensitivity labels were unseen until final testing. The drug response predictor was a five‐layer MLP with dropout rate of 0.5. The number of nodes in each layer remained the same. A hyperparameter D) was trained to force the feature extractor to learn domain invariant features from both the source domain and target domain. The domain adaptation could help to reduce the difference between the source domain (GDSC bulk RNA‐seq datasets) and the target domain (scRNA‐seq dataset) distributions to enhance the performance of drug response predictor. In this end of training, the feature extractor might be able to extract feature representations (domain‐invariant features) that the domain discriminator was unable to distinguish whether the coming features were extracted from the source domain or the target domain. The domain discriminator is a five layer MLP with dropout rate of 0.5. The number of nodes in each layer was the same. A hyperparameter h_dim (hyperparameter that is shared with the feature extractor) was set to customize the number of nodes of the hidden layer. A sigmoid function was appended after the five hidden layers. The domain discriminator outputs for source data and target data were denoted asA domain discriminator  to the shared feature space and directly classified by the communal drug response predictor that was learned from the source domain. Solid lines in Figure The performance of the feature extractor and drug predictor were evaluated based on a binary‐cross entropy (BCE) lossThe adversarial loss for discriminator isReferred to ref. , a weighA five‐fold cross‐validation (CV) strategy was conducted to reduce data splitting bias and evaluate the performance of the SCAD model. In each splitting, four‐fold of the source domain sample were selected as the training set to train the source feature extractor, domain discriminator, and drug response predictor, while another fold of the source domain data was treated as the validation set for hyper‐parameter selection (The final selected hyper‐parameters were tabulated in Table rank_genes_groups_df function was then conducted in Scanpy to filter out the inferred statistical significance drug‐resistance and drug‐sensitive related gene.For each fold of the five‐fold cross‐validation, the attribution score for each gene in the testing set of the target domain was calculated and recorded. The attribution scores of each gene were then averaged to get the final mean attribution score, which helped to reduce the bias of single‐fold splitting. Next, genes with the top 5% high or bottom 5% low contribution scores were selected as candidate genes that are related to drug response heterogeneity Figure . Differein Scanpy33 to The authors declare no conflict of interest.Supporting InformationClick here for additional data file.Supplemental Tables S1‐S7Click here for additional data file.


We report a 47‐year‐old man who presented with right‐sided heart failure. Transthoracic echocardiography revealed a tunnel‐shaped communication  between the left ventricle and the right ventricle with a significant left‐to‐right shunt. The VSD is connected to the lateral wall of the right ventricle by a large tunnel.  The patient had a history of ischemic cerebrovascular accident (CVA) and deep vein thrombosis 1 year before the current admission. No history of any cardiac surgery was reported by the patient. Initially, right‐sided heart failure was diagnosed in the patient. He was afebrile and normotensive, and oxygen saturation  findings were atrial fibrillation with a rapid ventricular response, significant electrical alternans, and low voltages in limb leads.Chest computed tomography (CT) scan demonstrated cardiomegaly, bilateral pleural effusion, and evidence of pulmonary venous congestion.Initial transthoracic echocardiography revealed severe LV enlargement with moderate LV systolic dysfunction (LVEF: 35%–40%), mild LVH, severe right ventricular enlargement with severe systolic dysfunction, severe biatrial enlargement, flail AMVL with severe mitral regurgitation, torrential tricuspid regurgitation, and mild‐ to moderate‐pulmonary and aortic regurgitation.During the echocardiography, an abnormal turbulent systolic flow directed to the lateral wall of the right ventricle from the lateral side of the tricuspid valve annulus drew our attention. By the clockwise tilting of the probe, a strange finding was detected. There was a defect in the perimembranous part of the interventricular septum which is directed to the lateral wall of RV by a large complete tunnel with a significant left‐to‐right shunt (PPG = 56 mmHg). The VSD flow was entered to RV just near the lateral wall . The length of the tunnel was about 40 mm, but the diameter of its exit from the left ventricle was about 9.5 mm and the diameter of the entrance to the right ventricle was about 6.5 mm. Transesophageal echocardiography was done and confirmed TTE findings Figures .Full heart failure treatment by guideline‐directed medical therapy (GDMT) was done including B‐blocker, diuretics, angiotensin‐converting enzyme (ACE) inhibitors, and mineralocorticoid receptor antagonists. Consequently, the patient underwent right heart catheterization. It was confirmed that a significant shunt from the left to right ventricle exists.1Ventricular septal defect (VSD) or interventricular communication is the most common congenital heart defect.VSD may be caused mostly by one of these malformations: deficient development of proximal conus, failure of the fusion of muscular portion to conus septum, and failure of endocardial cushions to fuse.VSD is sub‐categorized as perimembranous, juxta‐arterial or doubly committed and muscular based on their geographical location.By the end of fourth week of gestation, the bulboventricular cavity formed after the conus cordis and proximal part of the bulbus cordis have merged into a primitive ventricle.VSD, it seems that failure and maldevelopment of fibrous continuity between the aortic root and tricuspid valve is the main reason for the tunnel formation of VSD.The defining feature of the reported patient was the presence of a pathway within the cavity of the right ventricle that coursed blood flow from the left ventricle into the lateral side of the right ventricular cavity by a channel. According to the location of the Several previous studies have revealed that muscular and perimembranous VSDs could be closed spontaneously depending on the site of the defect. It has been hypothesized that an aneurysm of the membranous septum and leaflet of the tricuspid valve was the most responsible for the closure in perimembranous VSD.Although large‐sized VSDs are highly symptomatic at early childhood and should be treated promptly to prevent Eisenmenger, approximately 90% of isolated small VSDs close spontaneously at an early age or rarely remain asymptomatic until older age.Our case is noteworthy because the patient did not have any history of cardiac surgery and this type of congenital tunnel shaped VSD has not been reported in the literature.Mohammad Reza Eftekhari: Methodology; project administration; resources; writing – original draft; writing – review and editing. Roya Parkhideh: Writing – original draft; writing – review and editing. Meysam Khoshavi: Resources; writing – review and editing. Akram Nakhaee: Project administration; resources; writing – original draft; writing – review and editing.The authors have no relevant financial or non‐financial interests to disclose.The authors have no conflicts of interest to declare relevant to this article's content.Written informed consent was obtained from the patient to publish this report in accordance with the journal's patient consent policy.Video S1.Click here for additional data file.Video S2.Click here for additional data file.Video S3.Click here for additional data file.Video S4.Click here for additional data file.Video S5.Click here for additional data file.


Ne) has not been quantified directly in Pacific salmon Oncorhynchus spp., even though they can make up a large percentage of the total male spawners. We investigated the contribution of precocial males (“jacks”) to Ne in a naturally spawning population of Coho Salmon O. kisutch from the Auke Creek watershed in Juneau, Alaska. Mature adults that returned from 2009 to 2019  were genotyped at 259 single‐nucleotide polymorphism (SNP) loci for parentage analysis. We used demographic and genetic methods to estimate the effective number of breeders per year (Nb). Jack contribution to Nb was assessed by comparing values of Nb calculated with and without jacks and their offspring. Over a range of Nb values (108–406), the average jack contribution to Nb from 2009 to 2015 was 12.9% (SE = 3.8%). Jacks consistently made up over 20% of the total male spawners. The presence of jacks did not seem to influence Nb/N. The linkage disequilibrium Ne estimate was lower than the demographic estimate, possibly due to immigration effects on population genetic processes: based on external marks and parentage data, we estimated that immigrant spawners produced 4.5% of all returning offspring. Our results demonstrate that jacks can influence Nb and Ne and can make a substantial contribution to population dynamics and conservation of threatened stocks.Alternative life‐history tactics are predicted to affect within‐population genetic processes but have received little attention. For example, the impact of precocious males on effective population size ( In turn, Ne and Nb are affected by variation in survival and reproductive rates associated with alternative life histories  and several species of Pacific salmon (Oncorhynchus spp.) exhibit alternative life‐history tactics: some males reach sexual maturity at smaller size and younger age compared to typical full‐sized males that mature after gaining more mass after spending more time in the ocean  compare Nb and Ne estimates among methods; (iii) estimate age‐specific contributions to Nb and Ne using life tables, and (iv) quantify the reproductive success of immigrants into this population. We found that jack life history contributes substantially to Nb and Ne. Immigrants were among the adults that reproduced successfully and their contribution influenced Ne and likely the population genetic dynamics of the population. We discuss the implications of our findings for small and declining populations of Pacific salmon.We used a long‐term and detailed demographic and genetic dataset to determine the contribution of jacks to the effective number of breeders and effective population size over multiple generations in a naturally spawning population of Pacific salmon in Southeast Alaska. To achieve this, we quantified adult‐to‐adult reproductive success of Auke Creek coho salmon over 11 years. We determined the percentage of successful reproducers that had strayed into this population, based on comprehensive genetic sampling of returning adults and marking of out‐migrating smolts (juveniles switching to marine habitats). Our specific objectives were to (i) quantify the contribution of jacks to 22.1This study was conducted on the Coho Salmon population in the Auke Lake drainage in Juneau, Alaska, USA. Tissue samples and demographic information were collected at a permanent two‐way weir located between Auke Lake and Auke Bay that is operated by the National Oceanic and Atmospheric Administration (NOAA). Counts of out‐migrating smolts and returning adults have been recorded annually since 1980 and genetic samples have been collected since 2009. The weir catches all out‐migrating smolts and the smolts are released downstream after their adipose fin is clipped and they receive a coded‐wire tag. The weir also captures all returning adults, which can be identified as originating from Auke Creek by the absence of an adipose fin. As each adult is manually transported over the weir, an axillary process is removed and stored in 95% ethanol and each individual is assigned a sex/spawning type  based on morphological characteristics. Roughly one‐third of returning adults are sampled for length and age. Adults with intact adipose fins could either be adults that strayed into Auke Creek from other populations or were smolts that avoided capture and tagging at the weir; we used parentage (described below) to distinguish between these two possibilities. This study did not require ethics approval because we exclusively analyzed archived tissue samples collected by NOAA and not live fish. This study used data from adults returned between 2009 and 2019. Auke Creek Coho Salmon eggs are laid in the fall and after hatching in the spring, individuals spend either 1 or 2 years rearing in freshwater before smolting and migrating to sea. Jacks spend 6 months and full‐size females and males spend 1 full year at sea before returning to spawn /2 ; genotyping error rate was assumed to be 0.01 ; the allowed maximum number of mismatching alleles was five for dyads and seven for triads; and the minimum loci typed per individual was 150 (out of 251). Parentage assignments with posterior probability <0.9 were discarded. We expected assignment error to be small because we genotyped such a large proportion of the population and the panel had 122 loci with minor allele frequency >0.25. We determined whether fish captured in 2013–2015 with intact adipose fins were likely strays  or were unmarked locals . We then quantified the percent of returning adults from stray parents that spawned in 2013–2015. For additional details, see King et al.  in the population each year, and from this demographic information an inbreeding effective population size (NeD) was calculated for each generation , while Nb estimates are annual. First, we calculated NbD of each sex for each return year from 2013 to 2019 using population size (N), mean number of offspring (k¯), and variance in the number of offspring per individual (Vk) with the following equation . Individuals that were not successfully genotyped were included in the calculations of sex‐specific Nb and were assumed to have the same mean and variance in reproductive success as those successfully genotyped of the same sex. In 2010, of the 816 individuals that returned, 109 were not assigned a sex. In each of 400 iterations, we randomly assigned the unsexed fish a sex (given a 50:50 sex ratio) and then calculated NbDf, NbDm, and NbD. To estimate 2010 NbD, we used the mean NbD from all 400 iterations. Estimates of NbD for 2010 and Ne for generations including 2010 using a 50:50 sex ratio were almost identical to using the average sex ratio (58:42) from 2009, 2011, 2012, 2013, 2014, and 2015.Coho Salmon return to Auke Creek up to 4 years after being spawned, so for each generation we calculated four NbD and NeD relative to the population size, we calculated two ratios. In NbD/N, N represents the number of individuals returning to spawn in a single year, whereas N in NeD/N represents the total number of individuals returning over the entire generation (which includes multiple spawning/return years).To examine the sizes of NbD, we also calculated NbD using the same dataset but removing all jack parents and their offspring . We expect the bias introduced by calculating NbD this way to be small given the high rate of genotyping success  we achieved.We made three choices when excluding jacks from the dataset. First, we excluded the year 2010, because there was a high proportion of unidentified sex/type  samples that year and this analysis relied on comparing totals of full‐size males and jacks in the population. Second, we excluded all parents of unidentified sex/type across all years. Third, mean and variance in reproductive success were calculated with the number of retained individuals that were successfully genotyped. We assumed that the individuals not successfully genotyped had the same reproductive success as those that were successfully genotyped. In the calculation of 2.4Nb was estimated using the linkage disequilibrium (LD) method (NbLD) . For NbLD and NbS, we analyzed individual cohorts  for each year 2009 to 2015. Individuals were grouped into cohort years using parentage‐based aging. Bias in the estimates of NbLD was corrected using the haploid number of chromosomes  Hill,  as impleD) Hill,  and the D) Hill,  in the p2.5Life tables describe the abundance, survivorship rate, and offspring produced for each age group in a population or species. Life tables can be used to calculate the number of offspring produced from each age group, based on the proportions of individuals at each age and the mean and variance in number of offspring of individuals in each age group  and samples of all possible returning offspring from these individuals . To create each life table, we first determined the number of individuals in each age class. For males, we used the age classes 1.0, 1.1–2.0, and 2.1, where the number before the decimal denotes the inferred number of years an individual spent rearing in freshwater and the number after the decimal denotes the number of years an individual spent in the ocean before returning to spawn. The corresponding ages of those age classes are 2, 3, and 4 years old, respectively. Freshwater age was deduced by subtracting the saltwater age  from the total parentage‐based age of an individual. For females, we used the age classes 1.1 and 2.1  because all females spend a full year at sea.k = 2). With the age‐specific information, we calculated the total number of offspring produced, the net variance in reproductive success for the whole table, the effective number of breeders, and the generation length (g). Each sex‐specific g was calculated as the sum of each age class multiplied by their relative contribution of offspring. We then combined the male and female table information to determine overall NbLT, g, and NeLT (NeLT = g × NbLT).For each age class within the group, we determined the number of individuals, mean number of offspring produced, and variance in number of offspring produced. We scaled the mean and variance of all age classes to a stable population (33.1NbD was 12.9% (SE = 3.8%), and closer inspection of their contribution to NbD shows they increased NbD via male‐specific values, NbDm, with little impact on female‐specific values, NbDf (Table NbDm was 23.9% (SE = 6.3%), and jacks increased NbDm in every year except 2012. In contrast, the mean annual jack contribution to NbDf was 0.7% (SE = 2%). In two of 6 years, NbDf without jacks was greater than NbDf with jacks because excluding jacks decreased the female variance in reproductive success in those years which increased NbDf. In 2012, NbDf without jacks was larger than NbDf with jacks and NbDm without jacks did not change, so the overall NbD without jacks (174) was larger than NbD with jacks (170) and jack contribution was negative (−2.9%). NbD/N did not notably change when jacks were excluded because jacks affect both the numerator and denominator of this ratio.We created a multi‐year, multi‐generation pedigree from genotypes collected from Coho Salmon captured at the Auke Creek weir in 2009–2019. These data allowed us to assign offspring to parents, to determine the reproductive success of jacks and full‐sized adults, and to determine the proportion of offspring produced by immigrants into this population from elsewhere. The mean annual jack contribution to .9% SE = .8%, and NbDf each year ranged between 50 and 168, while NbDm ranged from 60 to 260. In all 6 years analyzed, NbDm exceeded NbDf. Combined, the total NbD ranged from 109 to 407  were likely strays (assigned with high confidence to no Auke Creek parents). Most strays were not successful at producing offspring, but strays produced 4.5% of all offspring from 2013 to 2015 that returned to Auke Creek as adults in subsequent years (through 2019).3.2NbLD (range: 98–271) and NbS (range: 74–180) followed the same overall pattern of NbD , and NeLT was 2099  than full‐size males , but similar mean‐adjusted variance. Age‐4 females had similar reproductive success (bx = 0.55) to age‐3 females (bx = 0.53), but their higher abundance resulted in a greater contribution of offspring than age‐3 females.The composite life tables for males and females created from our demographic and reproductive success data from 2013 to 2015 revealed that the overall generation length was 3.72 years , 4Ne. First, jacks made a substantial contribution to Ne, but their contribution varied widely across years. Jack Coho Salmon in Auke Creek made up a large proportion of the spawning males in some years and contributed a significant number of offspring. Excluding jacks from Ne calculations could bias these estimates, resulting in faulty inferences about the reproductive contributions of full‐size males, and cause incorrect conclusions about the status of populations being monitored using Ne. Second, different methods to estimate Nb produced similar results and reflected changes in adult abundance over time, despite variation in the Nb/N ratio. We also detected a small, but potentially important genetic contribution of immigrants into this population.Our study yielded several important insights into the influence of life‐history variation on 4.1Nb , but the magnitude of the contribution was highly variable from year to year. Removing jacks decreased the number of males and typically increased the variance in reproductive success which lowered Nbm. Reductions in NbD were primarily caused by the smaller numbers of individuals because removing jacks had little effect on Nb/N (the difference between the two estimates was consistently <0.05). Similarly, a study on Atlantic Salmon found that including mature male parr increased Ne without changing Ne/N  is comparable to the values reported for a wide range of species. Frankham  and LD estimate (198) were very similar  and LD Nb (53) in a population of Brown Trout Salmo trutta.All Ne from the demographic method (NeD = 613) was larger than the LD method (NeLD = 514). The genetic estimates may be lower than the demographic estimate because of immigration effects from fluctuations in population size across generations, or physical linkage of marker loci. The legacy effects of immigration, sweepstake events, and past small population sizes can carry over among years and affect genetic estimates of Ne  of 159 individuals in 2018, and it remains unclear whether the trend will continue. There was a slight increase in 2019 (457), 2020 (309), and 2021 (365), but these years were still low compared to the long‐term average (prior to 2014) return of 924 total adults and 675 full‐size adults. The escapement goal set for Auke Creek Coho Salmon (jacks excluded) is 200–500  at high (0.64) and low (0.21) jack frequencies. The highest Nb/N (0.3) occurred at a moderate jack frequency (0.46). The additional study of the relationship between jack frequency and the magnitude of influence of jacks on Nb/N would help to quantify the value of life‐history variants such as jacks as a buffer for Nb and Ne over a range of population sizes, including small populations of conservation concern.Although our results show jacks tend to increase Ne and immigration data suggest it is important to consider Auke Creek Coho in a regional context. The Ne values we observed of at least a few hundred per generation suggest that a loss of genetic variation is not a short‐term threat to Auke Creek Coho Salmon. We also identified that strays (immigrants) contributed 5% of returning adult offspring. Strays will add genetic variation that can be adaptive, maladaptive, or neutral, but we expect local genetic variation will be maintained at higher levels than predicted strictly from the local Ne estimates above to reflect the Ne of populations in the region linked by migrants (Palstra & Ruzzante, From a conservation perspective, the We declare no conflicts of interest.Figure S1:Click here for additional data file.


Uncertainties of regional precipitation projections are substantial, and users of such projections face the so‐called practitioners dilemma: a plethora of projections with different models from different ensembles of different types and generations are available. But the consistency of these projections has not been systematically assessed, such that no clear guidance about the use of these ensembles exists. Therefore, we systematically compare, separately for each season, projections of mean precipitation and extremes of daily precipitation over Europe across a wide range of climate model ensembles. We specifically consider the global climate model ensembles CMIP3, CMIP5, Coupled Model Intercomparison Project Phase 6 (CMIP6), and HighresMIP as well as the regional climate model ensembles ENSEMBLES and EURO‐CORDEX. All considered ensembles agree in their large‐scale patterns of changes for both mean and extreme daily precipitation, but at the regional scale, substantial discrepancies and inconsistencies are evident. Within and across ensemble spread is strongest in summer, in particular for the drying trend over the Mediterranean and Eastern Europe. CMIP5 and CMIP6 are broadly consistent. The regional climate model (RCM) ensembles modify the signals of the driving global climate models indicating potential added value. The high resolution of the RCM and HighresMIP ensembles seems to be key over the Alps for summer precipitation. Our study provides important information for users of climate projections as it helps to establish continuity across generations and types of climate models, and aids the design of new climate impact studies. Identification of robust and conflicting projections of mean and extreme precipitation across Europe in six climate model ensemblesFor mean precipitation changes, ensembles differ in the strength of Mediterranean and south‐eastern European summer dryingFor extreme precipitation changes, regional climate models often alter the changes projected by their driving global models Our focus is on climate model differences. Hence, we minimize the other sources of uncertainty by (a) defining changes between the end of the 21st century and 20th century for high emission scenarios to reduce the influence of internal variability; and (b) by expressing changes per Kelvin warming to approximately remove the influence of different scenarios. A comprehensive analysis of the causes of climate model differences is far beyond the scope of this study.22.1We analyze simulations from the following climate model ensembles: The GCM ensembles CMIP3 , CMIP5 (24 models), and CMIP6 (27 models) increasing from about 300 to 150 km to 100 km, respectively. In HighresMIP, models of different resolution have been compared. We restrict our analysis to the four available high‐resolution models with a horizontal resolution of approximately 50 km. From the ENSEMBLES project, 14 models with a 25 km resolution have been considered, and 39 models from EURO‐CORDEX with a resolution of 12 km.The GCM generations have been forced with different emission scenarios. To minimize the influence of internal variability, we have used the strongest scenario for most ensembles: the SRES A1B scenario for CMIP3 and ENSEMBLES, the RCP8.5 scenario for CMIP5 and EURO‐CORDEX, and the SSP5‐8.5 scenario for CMIP6. In the case of the SRES scenario, we did not use the strongest scenario A2 but instead A1B because of data availability and we aim to include as many models of each ensemble as possible. To enable a direct comparison of the different scenarios, we have expressed the simulated changes per degree Kelvin warming (see below).2.2Our study aims at comparing climate model uncertainties across different ensembles in precipitation projections over Europe. We considered seasonal means and extremes of daily precipitation for boreal winter (DJF), spring MAM), summer (JJA), and autumn (SON). Extreme precipitation is defined as 20‐year return values of each considered season separately  between the same reference periods to obtain the CCS in percent per degree warming:To increase the signal‐to‐noise ratio , we calculate change signals from two far apart baseline (CTRL) and future (SCEN) periods, different for each specific ensemble: 2081–2100 versus 1981–2000 for CMIP3; 2071–2100 versus 1971–2000 for CMIP5, CMIP6, ENSEMBLES and EURO‐CORDEX; and 2021–2050 versus 1950–1979 for HighresMIP. For CMIP3, only 20‐year time slices were available. Because of internal variability, this shorter period slightly deteriorates the signal‐to‐noise ratio compared to the other ensembles (see discussions below). To enable a direct comparison of the different scenarios used , we have expressed each signal as a change per Kelvin warming. This approach has been widely used in the most recent IPCC report consistencies between ensembles, we have chosen the hatching—different to the IPCC—to highlight robustness within ensembles.An initial analysis considers multi model mean changes for each of the different ensembles. Robustness and significance of changes within each ensemble is assessed similar to the method described in the recent IPCC assessment report  and g(x) is defined as ∫ min(f(x), g(x))dx. We estimate overlaps between different ensembles assuming that the CCS of the models within one ensemble are approximately normally distributed, Nμ,σ2, with expectation μ and standard deviation σ. This measure was compared with the Anderson‐Darling test, which provided similar results . The effect of the shorter time period for CMIP3 is hardly visible, resulting in slightly smaller and more patchy robust and significant areas compared to CMIP5 and CMIP6. An important question is whether RCMs modify the change signals of their driving GCMs. As Figure The main patterns of seasonal mean precipitation changes are well known  the strength of Mediterranean summer drying, where CMIP5 and EURO‐CORDEX project a weaker change than all other ensembles; and (b) the summer drying over Eastern Europe, where EURO‐CORDEX projects no change, but all other ensembles a drying. The RCMs tend to alter GCM changes toward wetter changes. Daily extreme precipitation is projected to increase over large parts of Europe (evidence for the Mediterranean is weak because of the low amount of non‐zero events), with varying magnitude across models and ensembles. Within‐ and across‐ensemble spread is higher than for mean precipitation, and especially high for summer. The strongest increases are found in the EURO‐CORDEX ensemble. CMIP5 and CMIP6 are broadly consistent, with notable differences to CMIP3. The ENSEMBLES and EURO‐CORDEX ensembles both modify the signals of the driving CMIP3 and CMIP5 models such that they are broadly consistent with each other. These modifications thus hint at potential added value of the RCMs at projecting regional changes in extreme precipitation. In particular over the Alps resolution may be important: all high resolution ensembles, ENSEMBLES, EURO‐CORDEX, and HighresMIP, project an increase in extreme summertime precipitation, whereas standard resolution GCMs project no changes. These findings are consistent with the results from Giorgi and Gutowski , who fouOur results provide important information for users of climate information. First, they help to establish continuity across generations and types of climate models, and thus also climate impact studies based on these models: where can impact studies relying on precipitation projections be considered “still valid” because the underlying climate model ensembles sample the known model uncertainty, and where are amendments required? Similarly, our results help to plan new impact studies: where does, for example, the EURO‐CORDEX ensemble comprehensively sample known climate model projections, and where do other ensembles have to be considered additionally?Our study explores differences and does not provide any explanations of inconsistencies between ensembles. It does, however, point to cases—regions, seasons and aspects of precipitation—where further process based analyses are required to understand and reconcile these inconsistencies: can they be attributed to the representation of specific physical mechanisms? And if so, can some of these representations be ruled out as implausible? Such fitness‐for‐purpose studies could ultimately help to exclude in‐appropriate models and to understand where standard GCM projections may suffice, where the added value of RCMs or high resolution GCMs is necessary to generate credible projections, or where RCMs may even distort GCM signals. But such a comprehensive process‐based analysis is far beyond the scope of this study. As such, our study provides an initial step to resolve the practitioners dilemma, and thus contributes to the distillation of regional climate information. But as a next step, region specific and process‐based studies are required to understand the mechanisms underlying regional climate model biases (e.g., Maraun et al., The authors declare no conflicts of interest relevant to this study.Supporting Information S1Click here for additional data file.


Abiotic factors are usually considered key drivers of species distribution at macro scales, while biotic interactions are mostly used at local scales. A few studies have explored the role of biotic interactions at macro scales, but all considered a limited number of species and obligate interactions. We examine the role of biotic interactions in large‐scale SDMs by testing two main hypotheses: (1) biotic factors in SDMs can have an important role at continental scale; (2) the inclusion of biotic factors in large‐scale SDMs is important also for generalist species. We used a maximum entropy algorithm to model the distribution of 177 bat species in Africa calibrating two SDMs for each species: one considering only abiotic variables (noBIO‐SDMs) and the other (BIO‐SDMs) including also biotic variables (trophic resource richness). We focused the interpretation of our results on variable importance and response curves. For each species, we also compared the potential distribution measuring the percentage of change between the two models in each pixel of the study area. All models gave AUC >0.7, with values on average higher in BIO‐SDMs compared to noBIO‐SDMs. Trophic resources showed an importance overall higher level than all abiotic predictors in most of the species (~68%), including generalist species. Response curves were highly interpretable in all models, confirming the ecological reliability of our models. Model comparison between the two models showed a change in potential distribution for more than 80% of the species, particularly in tropical forests and shrublands. Our results highlight the importance of considering biotic interactions in SDMs at macro scales. We demonstrated that a generic biotic proxy can be important for modeling species distribution when species‐specific data are not available, but we envision that a multi‐scale analysis combined with a better knowledge of the species might provide a better understanding of the role of biotic interactions. Traditionally abiotic factors are considered the main drivers of biodiversity at macro‐scales, whereas biotic factors are considered important almost only at local scales. Modeling the distribution of 177 African bat species, we demonstrated the importance of considering biotic interactions in SDMs at macro‐scales regardless of the species’ ecology. Traditionally, abiotic factors like climate are considered the main drivers of biodiversity at macro scales , whereas biotic factors are considered important almost only at local scales   by using climate variables only, climate variables plus the occurrence of four larval host plants as biotic variable, and biotic variables only. According to their findings, the inclusion of a biotic interaction can significantly alter species' distribution at macro scales for both the current time and under future climate change scenarios. These results were not unexpected, since the study is focused on one butterfly species highly dependent on the three host plant species during its larval stage.Wisz et al.  reviewedCampephilus magellanicus) and its preys in South America. They demonstrated that the inclusion of biotic interactions  helped in defining the niche and distribution of a specialist predator at the continental scale. All these analyses have been performed considering relatively simple systems, with a good level of knowledge, and focusing on specialist species, leaving an open discussion on the generalizability of their results.In the last few years, only a handful of studies have been added, focusing on trophic  and one SDM calibrated with both biotic and abiotic variables (hereafter BIO‐SDM). We compared variable importance in the two sets of SDMs to test two main hypotheses: (1) biotic factors in SDMs can have an important role at the continental scale and (2) the inclusion of biotic factors in large‐scale SDMs is important also for generalist species.Bats represent one of the most successful radiations among mammals, with more than 1400 species, a global distribution (except for the polar regions), and a huge variety of ecological niches Simmons, . Their t22.1We considered all 314 species of bats present in Africa .To limit autocorrelation in the data, for the remaining 177 species, we kept only one location per 1 kmhttp://batbase.org/) and Wilson and Mittermeier  and diet specialization (number of food items registered in the diet) from the Bat Eco‐Interactions database  and ArcGis Pro 2.8.3 (ESRI ©).We included in our analyses as many variables as possible among those potentially important in shaping species distribution in bats  as a proxy for roost availability, assuming that more complex topographies are associated with greater availability of rock crevices .Inland water availability is a critically important factor for many bat species, especially in hot and dry areas such as those present in the African continent  obtained from Chelsa V2.1  are important predictors of habitat suitability for many bat species both directly given their physiological constraints  which gives the number of people living in each 30 arc‐seconds resolution pixel. We considered this variable as a proxy for human disturbance represented by threats like habitat loss and roosts disturbance  as well as persecution and harvesting pressure for human–wildlife conflicts   as explanatory variables.No information is available on trophic resource availability for bats at the scale of the entire African continent. Therefore, we included in the analyses a model of trophic resource richness as a proxy for biotic factors. From Cosentino and Maiorano , we seleTo exclude collinearity between predictors, we performed a variance inflation factor (VIF) analysis with all predictors, and we retained only variables with a VIF < 3  considering only abiotic variables  and the other (BIO‐SDM) including all abiotic variables plus trophic resource availability. To calibrate the two SDMs with the same number of explanatory variables, we added to the noBIO‐SDM a dummy variable set to zero over entire Africa, therefore ensuring a full comparability between the two models  improvement of the predictive power for BIO‐SDMs compared to noBIO‐SDMs , being the most important variable for 35 species in BIO‐SDMs , a high number of items in the diet (p < .001), and a small colony size (p < .05), while the body mass gave no significant result . For almost 22% of the species, the changes in potential distribution covered more than 10% of the study area Appendix . SpeciesAppendix .Tropical forests and shrublands host the areas with the highest percentage of species showing a change in their potential distribution between BIO‐SDMs and noBIO‐SDMs Figure . More th4Traditionally, biogeographical analyses and species distribution models consider biotic factors at local scales only, while climatic variables dominate at regional to continental scales  based on high taxonomic level of food resources which is not necessarily representative of the trophic niche of a species. Valuable alternatives are represented by abundance or biomass of prey species, and species‐specific trophic links or food‐web data with approaches such as joint species distribution models , we found no sign of collinearity among our predictors. We performed a classical VIF analysis which, however, is able to detect only linear relationships Table . We alsoDespite the potential uncertainties of our study, we provide evidence of the importance of including biotic interactions in SDMs at macro scales. Trophic resource richness was particularly important for generalist species confirming also our second hypothesis. In this framework, proxies for trophic interactions like species richness have been proven to be useful in SDMs when species‐specific data are not available. Nevertheless, despite the improvements that have been made in the modeling frameworks to investigate this question, bridging the state of knowledge on species interactions remains a fundamental and urgent challenge, particularly in the regions with the highest level of biodiversity .Francesca Cosentino: Conceptualization (lead); data curation (lead); formal analysis (lead); methodology (lead); writing – original draft (lead); writing – review and editing . Ernest Charles James Seamark: Resources (supporting); supervision (supporting); writing – review and editing . Victor Van Cakenberghe: Resources (supporting); supervision (supporting); writing – review and editing . Luigi Maiorano: Conceptualization (lead); formal analysis (supporting); methodology (lead); supervision (lead); writing – review and editing .Data S1: Supporting InformationClick here for additional data file.


An efficient and cheap energization of microbial biocatalysts is essential in current biotechnological processes. A promising alternative to the use of common organic or inorganic electron donors is the semiconductor nanoparticles (SNs) that absorb light and transfer electrons (photoelectrons) behaving as artificial photosynthetic systems (biohybrid systems). Excited photoelectrons generated by illuminated SNs are highly reductive and readily accepted by membrane‐bound proteins and electron shuttles to drive specific cell reduction processes and energy generation in microbes. However, the operational mechanisms of these hybrid systems are still poorly understood, especially at the material–microbe interface, and therefore the design and production of efficient biohybrids are challenging. Some major limitations/challenges and future prospects of SNs as microbial energization systems are discussed. Hungarian physiologist Albert Szent‐Györgyi, who won the Nobel Prize in 1937 for the discovery of vitamin C, stated that “life is nothing but an electron looking for a place to rest.” This sentence summarizes very accurately that a flux of electrons is required for life maintenance. This flux initiates with the entrance of electrons in the organism from one electron donor. Microbes have evolved to accept electrons from a huge amount of donors. Organic compounds or inorganic materials, such as metals, metalloids, sulphur or hydrogen, transfer their electrons to carriers which initiate the electron flux that finally provides the energy needed by the cells. In current biotechnological processes, an efficient and cheap energization of the biocatalysts is a key issue for achieving competitive yields. In this sense, a promising alternative to the use of common organic or inorganic electron donors are the semiconductor nanoparticles (SNs) that absorb light and transfer electrons (photoelectrons) behaving as artificial photosynthetic systems that self‐repair and self‐generate , high surface area, ease of interaction at microbe–material interface because of their nanoscale dimensions 1–20 nm), and biocompatibility with living cells  that react with cadmium cations (Cd2+). Sulphide anions are present in the thiol groups of molecules rich in cysteine that, in addition to serving as sulphur source, stabilize the SNs  by desulfhydrase enzymes which break the carbon–sulphur bonds of cysteine , while they are self‐regenerating, with low waste generation, and operating at an efficiency (product yield based on solar energy input) higher than 80%  by incorporating CO2 under illumination with red wavelengths  and electron shuttles  to drive specific cell reduction processes and energy generation in microbes  production from glucose by Saccharomyces cerevisiae  of large organic pollutants into low‐molecular‐weight by‐products  synthesis from fructose by 3N4) and carbon dots (CDs), that exhibit low toxicity and cost, high water solubility, photostability and tunability (with an absorption wavelength around 600 nm), have been developed  production. Coupling g‐C3N4 with catalase, efficient for degradation of H2O2 generated during photocatalytic water splitting, improved further the production of PHB  to the electron transport molecules located in the cell (inner) membrane  are often added to bacteria–SNs systems to quench holes generated by the absorption of light  involved in the electron transfer from the photo‐excited CdS nanoparticles. It is thought that electrons taken up by these enzymes are further shuttled across the cell membrane by quinones  are piezoelectric and, hence, sound at determined frequency promotes electron transfer ; writing – original draft ; writing – review and editing . Manuel Carmona: Conceptualization ; writing – original draft ; writing – review and editing . Eduardo Diaz: Conceptualization ; funding acquisition (lead); writing – original draft ; writing – review and editing .The author declares no potential conflict of interest.The content and authorship of the submitted manuscript has been approved by all authors, and all prevailing local, national and international regulations and conventions, and normal scientific ethical practices, have been respected.