To address this vulnerability, it becomes necessary to retrain the affected model against adversarial inputs as part of the software testing process. In order to make this process energy efficient, data scientists need support on which are the best guidance metrics for reducing the adversarial inputs to create and use during testing, as well as optimal dataset configurations.When using deep learning models, one of the most critical vulnerabilities is their exposure to adversarial inputs, which can cause wrong decisions  the accuracy, resource utilization, and execution time of retraining convolutional neural networks with the guidance of six different guidance metrics , (b) the accuracy and resource utilization of retraining convolutional neural networks with three different configurations .We reveal that adversarial retraining from original model weights, and by ordering with uncertainty metrics, gives the best model w.r.t. accuracy, resource utilization, and execution time.Although more studies are necessary, we recommend data scientists use the above configuration and metrics to deal with the vulnerability to adversarial inputs of deep learning models, as they can improve their models against adversarial inputs without using many inputs and without creating numerous adversarial inputs. We also show that dataset size has an important impact on the results. The use of DL systems ranges from applications in autonomous driving systems to applications in medical treatments, among others . Howeveri.e., not embedding any DL model, we may find a vast amount of testing approaches to ensure a certain level of robustness  using the information provided by a number of guidance metrics, which try to identify the most useful inputs according to the criteria of each metric, and (ii) adding adversarial inputs to augment the dataset employing different training inputs from the original training set. Still, questions arise: What is the accuracy, resource utilization, and execution time of using such metrics? What is the accuracy and resource utilization of using a certain retraining configuration? (the execution time of retraining configuration is not included in this question as the number of used inputs determines the computation expense of the retraining). Thus, it is needed to determine specific scenarios in which a particular guidance metric should be employed, as well as optimal retraining configurations. In this context, this work aims at comparing both guidance metrics and configurations for retraining DL models with adversarial inputs based on their accuracy, to improve the DL models’ performance , resource utilization and execution time of the metrics.We conduct a study based on five datasets, in which we evaluate and compare various retraining runs guided by six guidance metrics. We show that using uncertainty metrics for guiding the retraining of CNN models outperforms neuron coverage, surprise-based and random metrics w.r.t. accuracy  and resource utilization, of three configurations of retraining CNN models. In particular, when doing adversarial retraining from original model weights can achieve results that are aligned with green AI goals.In the same study, we conduct a comparison, in terms of accuracy .We provide a replication package, available online  the field of study of our work, namely deep learning (DL) system testing; (ii) the type of DL model we used, namely convolutional neural networks (CNN); (iii) the guidance metrics used in our study, namely: neuron coverage (NC), two surprise adequacy (SA) metrics: likelihood-based surprise adequacy (LSA) and distance-based surprise adequacy (DSA) and two uncertainty metrics: DeepGini (DG) and softmax entropy (SE); (iv) the adversarial attacks used to create adversarial inputs, namely Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM) and Carlini-Wagner (CW).In this section, we introduce Deep learning (DL) system testing has become a relatively new broad research field, in which every year the number of publications related to the subject increases . Aiming Convolutional neural networks (CNNs) are one of the most used DL architectures in image classification . A CNN cCurrent approaches to DL systems testing evaluate them according to a number of properties, either functional (such as accuracy or precision) or non-functional  . In ordeD be a trained DL model, composed of a set N of neurons. The neuron coverage NC of an input D is given byD.Likelihood-based surprise adequacy (LSA). Let D be a DL model trained on a set T of inputs. The likelihood-based surprise adequacy LSA of the input D is given by:L layer of D when T composed of all the inputs of the same class as H (matrix H .Distance-based surprise adequacy (DSA). Based on the distance between vectors representing the neuronal Activation Traces of the given input and the training data (using Euclidean distance). Let D be a DL model trained on a set T of inputs. The distance-based surprise adequacy DSA of the input D is given by:D and D when confronted with cument}x .The hypothesis of using uncertainty-based metrics as a guidance for retraining is that inputs with higher uncertainty are more likely to be misclassified, they are the most challenging inputs due to their low confidence. Some authors have studied this type of metric and proved that inputs with the highest uncertainty are the most informative for the DL model in a retraining process . In thisDeepGini (DG).DG value of input x with respect to D is given by:Softmax entropy (SE). This metric is very similar to DG. However, SE is based on the perspective of information theory  attack was proposed by Z and the hyperparameter From the reviewed literature, there are many studies focusing on the creation of adversarial inputs in order to uncover debilities and vulnerabilities of DL systems or models . Goodfelvia Intelligent Mutation Analysis), on this type of input.Other studies centered on being able to detect such adversarial inputs for different purposes. Although there are several techniques to face the problem of adversarial examples in ML literature , there iMost recently, As the use of adversarial inputs has been proved to provide many improvements, we identified a research gap there to take advantage of them during a retraining process. Moreover, we want to understand how to order an augmented dataset with adversarial inputs guided by metrics so that the retraining is efficient (with highest accuracy and using fewest inputs) for data scientists. To the best of our knowledge, our study is the first one that applies metrics to guide a retraining using an augmented dataset with adversarial inputs in order to improve the model accuracy against adversarial inputs and keeping the trade-off with resource utilization, in order to obtain computational benefits, which is key as the retraining phase is time consuming and the creation of adversarial images has a high computational cost .A comparison between state-of-the-art guidance metrics for a guided retraining.A comparison of three different configurations for doing a retraining against adversarial inputs.Guidelines to help data scientists to improve their CNN models against adversarial attacks from the perspective of green AI.In our study, we consider an augmented dataset with adversarial inputs obtained from the original training set using the FGSM, BIM and CW attacks. Therefore our study provides the following novel contributions:The goal  of this RQ1 - Does the use of guidance metrics impact the accuracy, the resource utilization, and the execution time required for the retraining of a CNN model?RQ2 - Does the configuration of the retraining of a CNN model impact the accuracy and the resource utilization required for retraining this model?Thus, we aim at answering the following research questions (RQ):Accuracy, measures the capability of the retraining phase of providing higher accuracy against adversarial inputs. We plan to measure the accuracy of the CNN models against an augmented test set, composed of the original test set and an adversarial test set. The latter is obtained from the original test set, applying the chosen adversarial attacks to each input.Resource utilization, which measures the input size needed to obtain the highest accuracy during the retraining phase.Execution time, which quantifies the time to compute each of the considered metrics for the corresponding dataset.Another variable that influences our conclusions is the dataset. We experiment with five different datasets of different size and from different domains. Hence, we define the dataset as a nominal variable indicating the used dataset for training and retraining the models.data collection and preprocessing which consists of the collection and preprocessing of raw data. Second, the model training which consists of the traditional training and proposed retraining phase. Finally, we provide answers to the RQs by analyzing our results w.r.t. DL testing properties.We evaluate the guidance metrics on CNNs using five multi-class, single-image classification datasets.e.g., weather conditions). While humans can easily classify them, for DL systems  still is a challenge.First, the German Traffic Sign Recognition Benchmark (GTSRB) dataset, with 43 classes containing 39,208 unique images of real traffic signs in Germany. It has been widely used in DL research . This tySecond, the Intel Image Classification (Intel) dataset with six classes containing 17,034 labeled images of natural scenes around the world, used in many studies as well . It was Third, the Canadian Institute for Advanced Research (CIFAR-10) dataset, with 10 classes containing 60,000 labeled images of animals and transports, such as dogs, cats, airplanes and ships . This daFourth, the Modified National Institute of Standards and Technology (MNIST) dataset, with 10 classes containing 70,000 labeled images of handwritten digits from 0 to 9 which are size-normalized and centered . This daFifth, the Fashion-MNIST dataset (Fashion), which considers 10 classes and also contains 70,000 labeled images of clothing, such as T-shirts, pullovers and sneakers . This daThe adversarial inputs are obtained using the FGSM, BIM and CW attacks from the foolbox library . Table 3“Train” in “Test” in M, against adversarial inputs. After obtaining a retrained model, Weights from scratch: Random initialization of the model’s weights.M):Original model weights . These weights are from the 20 data points by selecting the model that achieves the highest accuracy.The traditional training of a model consists of using an available original training set identified as Step 1. Create adversarial inputs for training and testing: First, we obtain the adversarial training set, “Adv. Train”, by applying the adversarial attacks to a subset of the original training set, “Train”. The number of the adversarial inputs for this case is a small proportion of the entire “Train” set, thus we do not increase the artificial inputs used in retraining and the original input distribution is not diminished either : Starting from scratch using the new adversarial inputs and original training set. The model “Train*” set ordered by highest score of LSA, DSA, DG, SE, NC and Random, respectively, starting from scratch.(a) Adversarial retraining (AR): Starting from the original model M using the new adversarial inputs and original training set. The model “Train*” set as in OAR with the difference that the model M weights.(b) Adversarial fine-tuning (AF): Starting from the original model M using only the new adversarial inputs. The model “Adv. Train” set, also ordered by highest score of LSA, DSA, DG, SE, NC and Random, respectively, starting from the original model M weights.(c) The retraining process is shown more extensively in For each configuration, we execute a retraining of the models guided by the six metrics and for each metric we obtain 20 data points as shown in “Test*”. “Test*” set and resource utilization of the experiments by dataset, configuration and metric used in each case. The “Original accuracy” column shows the accuracy of the original model M against the “Test*” set, which is low due to the adversarial inputs, the “Accuracy w.r.t. augmented test set” column shows the highest accuracy during its respective retraining and the “Resource utilization” column shows the input size to obtain that highest accuracy. In order to compare the impact that guidance metrics and retraining configurations have on the accuracy and resource utilization of the retrained models, we select the best model, according to the accuracy, during the retraining of the 20 data points for each combination of configuration and guidance metric  using less than a third of the available dataset, as shown in Overall, uncertainty metrics may be the best due to the fact that they identify the inputs that have a low confidence on their classification. Regarding SA metrics, the model has improved since they first identify the inputs that are most different from the previous training inputs. In that way, the model learns features that it missed during the first training, increasing the correct classifications of adversarial inputs. On the other hand, for NC metric it may be more difficult to identify good inputs for retraining because this metric identifies inputs that cover more neurons according to certain thresholds, which in retraining may not be significant.Guidance metrics and execution time. We obtained the time to calculate the metrics as stated in Uncertainty metrics have the best selection of inputs for the retraining as stated by the observed accuracy.Uncertainty metrics can reach a better model with fewer inputs. DSA also has an acceptable resource utilization.Uncertainty metrics execution time is insignificant compared to SA and NC metrics. The computation of NC values takes more than 3× in terms of execution time w.r.t. the time needed to obtain SA metrics.Key takeaways for RQ1:Configuration of the retraining and accuracy. According to the total number of used inputs, as expected, OAR and AR reached higher values of accuracy than AF. Nevertheless, between these two configurations, the benefits of using AR are greater because with fewer inputs  in which the retraining is done.Our experimental results focused on the retraining of models against adversarial inputs, taking into consideration the most used and practical state-of-the-art guidance metrics: LSA, DSA, DG, SE, NC, and Random. On top of that, we considered the manner , we have found that the size of the dataset greatly affects the results, because when using small datasets it is not so fast. Additionally, an important finding is a variable that we also considered: the configuration of retraining. All the related works have only experimented with their own way to execute the retraining and even sometimes they are not explicit with how they did the retraining process. As we observe in the results, the configuration can change the performance of the model considerably. These results confirm the importance on finding efficient configurations to retrain deep learning models. Considering the studied configurations, using the best configuration studied in this work (AR) can give data scientists a fast and efficient method of retraining.Regarding the computational complexity of the retraining process, we acknowledge that it increases the complexity of the entire training process. Indeed, a defense strategy against adversarial attacks is needed and a retraining process is one of the most effective strategies . In thatWhen using the combination of uncertainty metrics, especially SE metric, and AR retraining configuration, we observe that it can lead to results that are aligned with green AI research, which refers to research that takes into account computational costs to reduce the computational resources spent , as feweetc. In this way, we offer guidelines for data scientists in the context of testing with adversarial inputs during the retraining of CNNs for image classification:Guidance metrics and accuracy. Retraining with the guidance of uncertainty metrics can achieve CNN models with higher accuracy compared with the rest of the studied metrics.Guidance metrics and resource utilization. When prioritizing the number of inputs used for retraining, uncertainty metrics need fewer inputs to reach higher accuracy.Guidance metrics and execution time. When considering the execution time to obtain the metrics, uncertainty metrics are by far the best choice w.r.t. to other metrics, with the exception of random.Retraining configuration and accuracy. When prioritizing accuracy, retraining with original inputs and adversarial inputs from scratch (OAR) is the best option. However, retraining with original inputs and adversarial inputs from original model weights (AR) can reach similar accuracies without considerable differences and this configuration uses fewer inputs.Retraining configuration and resource utilization. Retraining with original inputs and adversarial inputs from original model weights (AR) has a good resource utilization, in addition to high accuracies w.r.t. the other configurations. Nevertheless, retraining with only adversarial inputs (AF) is the best option when prioritizing only resource utilization.Green AI. When considering efficiency without diminishing the accuracy of the model, retraining with original inputs and adversarial inputs from original model weights (AR), and the guidance of uncertainty metrics, especially SE, is the best way to retrain a CNN model in the mentioned context.Previous studies, our results, and new implementations of these metrics  aim to bIn this section, we report the limitations of our empirical study and some mitigation actions taken to reduce them as much as possible.Regarding construct validity, we include five original models using CNN architecture trained in five different datasets, respectively, in order to mitigate mono-operation bias. Derived from the metrics selected, we use LSA, DSA, DG, SE, NC, and Random metrics to guide the retraining runs, which may have potential threats. However, these metrics have been used in previous work as shown in the Related Works section, lowering this threat. And according to threats related to the configurations, we have reviewed the relevant literature and searched for retraining configurations using adversarial inputs.https://drivendata.github.io/cookiecutter-data-science), making them as simple as possible. To increase reliability in our study, we detail the procedure to reproduce our work: the process is shown in the Emperical study section, datasets and replication package are available online. Also, we address the randomness of our results by starting the retraining runs for each data point from their respective initial weights .Concerning conclusion validity, the quality of the DL models and implementations depends on the experience of the developers. To mitigate this, we provide the implementations organized and following the “Cookiecutter Data Science” project structure  the models are increasing their accuracy against the augmented test set with adversarial inputs as it was sought in the objectives of this work, and (ii) there are computational benefits of using certain metrics and configurations.In this work, we have studied DL testing metrics for guiding the retraining of models, and we adapted the models’ weights to be aware of adversarial inputs, as the retraining process involves adjusting the weights to better handle them. We performed an empirical study with the metrics and also considered three different configurations of retraining against adversarial inputs and did a comparison of the metrics and the configurations. In summary, we observe that The empirical study showed that the uncertainty metrics (such as DG and SE) as guidance for a retraining phase are useful for data scientists when using the following configuration: adversarial retraining from original model weights.With the previous configuration and metrics, we can improve the accuracy of the models against adversarial inputs by up to 13.3% on the GTSRB dataset, 7.7% on the Intel dataset, 14.4% on the CIFAR-10 dataset, 1.5% on the MNIST dataset and 9.0% on the Fashion-MNIST dataset without the need of using many inputs. Therefore, this can be done by using 34.7%, 73.8%, 30%, 25% and 75% (similar accuracy gain achieved since using 30% unlike Intel dataset) of the available inputs, on the GTSRB, Intel, CIFAR-10, MNIST and Fashion-MNIST datasets, respectively.Concerning SA metrics, DSA presents competitive values for dependent variables except for the execution time variable. Using random can guide to similar levels of accuracy but when using the recommended configuration, the computational benefits of using uncertainty metrics are that fewer inputs are required as discussed above in percentage, which in turn, translates into fewer adversarial inputs created. Regarding the NC metric, we do not recommend the use of it as it presents the greatest disadvantages on the studied variables. In cases where one-step adversarial retraining from scratch is considered, it can be a time-consuming process as it requires using almost 100% of the inputs to achieve similar accuracy to the recommended configuration.Additionally, we revealed that the size of the dataset is important when implementing the recommended metrics and configuration. Taking this into account, we need to assess whether it would be worth calculating the metrics when using only small datasets.In the next step, the reproduction of our experiments on different datasets of varied sizes, unbalanced datasets, and also non-images datasets  are requWhile the results of our study provide meaningful outcomes, there are several dimensions to empirically explore in the future. Thus, it would be relevant to evaluate this type of retraining process against adversarial inputs in the context of continual learning (CL), as there is scarce research  in addre


The aim of the study is to assess the effect of maternal prolonged oxygen exposure during labor on fetal acid–base status, fetal heart rate tracings, and umbilical cord arterial metabolites.The study was conducted as a secondary analysis.The study was set in three tertiary teaching hospitals in Beijing, China.Approximately 140 women in the latent phase of labor with no complications participated in the study.Participants were randomly allocated in a 1:1 ratio to receive either 10 L of oxygen per minute in a tight-fitting simple facemask until delivery or room air only.The primary outcome was the umbilical cord arterial lactate.Baseline demographics and labor outcomes were similar between the oxygen and room air groups; the time from randomization to delivery was 322 ± 147 min. There were no differences between the two groups in the umbilical cord arterial lactate , the number of participants with high-risk category II fetal heart rate tracings , or the duration of those high-risk tracings . Prolonged oxygen exposure significantly altered 91 umbilical cord arterial metabolites, and these alterations did not appear to be related to oxidative stress.Maternal prolonged oxygen exposure during labor did not affect either the umbilical cord arterial lactate or high-risk category II fetal heart rate tracings but might result in alterations to the umbilical cord arterial metabolic profile.www.clinicaltrials.gov, identifier NCT03764696. The facemask was checked by research nurses to ensure that it covered the nose and mouth during labor. The two interventions (O2 vs room air) were continued until delivery.At the point in the latent phase of labor , participants were assigned 1:1 to receive either OAll women received standard intrapartum care and began pushing down immediately at the onset of the second stage in a supine position. The electronic fetal monitoring was reviewed every 15 to 30 min in the first stage and continuously in the second stage of labor (2) was >5.25 mmHg, and the Uv PCO2 was >21.75 mmHg .The secondary outcome was the high-risk category II FHR tracings, including the number of women who developed high-risk category II FHR tracings during labor and the duration of those tracings. The high-risk category II FHR tracings were defined as any of the following features: baseline bradycardia, minimal or absent variability, recurrent variable decelerations, and recurrent late decelerations, as these features might suggest an increased risk for fetal hypoxia or acidemia . Two trat test or Mann–Whitney U test for continuous variables and the Chi-square test or Fisher exact test for categorical variables. The plasma UHPLC-MS/MS analyses included metabolite annotation, principal components analysis (PCA), partial least squares discriminant analysis (PLS-DA), differential metabolite identification, metabolic pathway enrichment, etc. The method of UHPLC-MS/MS analysis is available in We used a fixed sample size from the primary trial, and all randomized participants were included in this analysis. Data analysis was performed using the modified intention-to-treat principle. All randomized women were included in the high-risk category II FHR tracing analysis; 96.4% (135/140) participants with validated paired Uv and Ua gases were included in the Ua lactate analysis; and 85% (119/140) participants were included in the Ua metabolites analysis. The Kolmogorov–Smirnov test was used to analyze the distribution of continuous variables. Baseline characteristics and outcomes were compared between the two groups using the Student’s 2 and 70 received room air without interruption or crossover; 135 women were included in the Uv/Ua lactate analysis; all 140 women were included in the high-risk category II FHR tracing analysis; and 119 women were included in the Ua metabolite analysis. Baseline demographics and labor outcomes were similar between the O2 and room air groups. Most labor outcomes except Ua pH were similar between the two groups; the cesarean delivery rate was less than 3% in the trial; and the median duration of the first and second stages of labor was about 8 h , the Uv lactate ; in the Ua glucose , the Uv glucose ; or in some electrolytes, including Uv/Ua K+, Na+, Cl—, or Ca2+ , and there were no differences between the two groups for the individual components of the composite, including baseline bradycardia, minimal or absent variability, recurrent variable decelerations, or recurrent late decelerations   in the Ua plasma   or relieve high-risk category II FHR tracings in normal women during labor. Prolonged O2 exposure significantly altered some Ua metabolites; however, these alterations were not associated with a unique metabolic or signaling pathway and did not appear to be related to oxidative stress.The present trial was a negative study; it showed that prolonged O2 exposure (mean 322 min), in contrast to previous studies including Thorp et al. , which was the most studied marker in trials. It is thought that hyperoxia can cause maternal oxidative stress, which is controversial in the fetus. The recent systematic review showed O2 was associated with an increase in maternal MDA level but no significant difference in the Ua MDA level  by simple diffusion, and fetal Uv PO2 is about 28 mmHg  increased fetal O2 saturation substantially, as determined by fetal pulse oximetry. A Cochrane Review reported a very low quality of evidence that women receiving supplementary O2 had a higher mean UvPO2 than participants who received room air for cesarean section during regional anesthesia   , 25. In e stress , 27. In 2 could directly affect these metabolites through a literature search. The KEGG enrichment analysis revealed some potential metabolic or signaling pathways, but these pathways were not unique or unrelated to each other. The results might suggest that maternal hyperoxia had little effect on fetal metabolic status or that this analysis of metabonomics was spurious due to the heterogeneity of the small sample size. Fourth, the applicability of our study was low; the trial focused on changes in normoxia with further O2 administration but did not consider the effects of administering hyperoxia under hypoxic conditions. Maternal O2 during labor was usually administered to the fetus under hypoxic conditions; however, this trial did not include any high-risk fetuses with intrauterine asphyxia, abnormal FHR tracings, fetal growth restriction, etc.This RCT has several important limitations. First, the sample size of the trial was small; the study included 140 participants and found no differences between the two groups in the Ua lactate . To detect the 0.3 mmol/L difference in Ua lactate with 80% power and a 2-sided test of 0.05, we estimated that 450 women were needed in each group. Second, the main outcomes were not patient-relevant end points . The Ua 2 exposure in normal labor does not affect either the Ua lactate or high-risk category II FHR tracings. This way of administering O2 might result in alterations to the Ua metabolic profile, and these alterations did not appear to be related to oxidative stress.We conclude that prolonged intrapartum Ohttps://ngdc.cncb.ac.cn/omix/release/OMIX004347).The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: OMIX004347  and Seventh Medical Center of the Chinese PLA General Hospital (20/1/19) and the PLA Strategic Support Force Characteristic Medical Center (18/2/20). The patients/participants provided their written informed consent to participate in this study.FC: conceptualization, methodology, investigation, writing-original draft, writing-review and editing, and visualization. TD and YL: data analysis, writing-original draft, and writing-review and editing. LZ: writing-original draft review and editing. LC and WJ: conceptualization, methodology, and writing-original draft. YZ and YC: conceptualization, methodology, investigation, writing-original draft, writing-review and editing, visualization, and funding acquisition. All authors contributed to the article and approved the submitted version.


Saccharomyces cerevisiae Rdh54 is a known target of the Mec1/Rad53 signaling axis, which globally protects genome integrity during DNA metabolism. While phosphorylation of DNA repair proteins by Mec1/Rad53 is critical for HR progression little is known about how specific post translational modifications alter HR reactions. Phosphorylation of Rdh54 is linked to protection of genomic integrity but the consequences of modification remain poorly understood. Here, we demonstrate that phosphorylation of the Rdh54 C-terminus by the effector kinase Rad53 regulates Rdh54 clustering activity as revealed by single molecule imaging. This stems from phosphorylation dependent and independent interactions between Rdh54 and Rad53. Genetic assays reveal that loss of phosphorylation leads to phenotypic changes resulting in loss-of-heterozygosity (LOH) outcomes. Our data highlight Rad53 as a key regulator of HR intermediates through activation and attenuation of Rdh54 motor function.Rdh54 is a conserved DNA translocase that participates in homologous recombination (HR), DNA checkpoint adaptation, and chromosome segregation.   DNA double strand breaks (DSBs) are a dangerous type of DNA lesion that if unrepaired result in cell death or loss of genomic integrity . DSBs reSaccharomyces cerevisiae, Rad51 is aided in alignment of recipient and donor DNA by several accessory factors including Rad52, Rad54, Rdh54, and Rad55/57  , the cla kinases , with a  kinases . Rad53 p kinases ,51, at t kinases , and at  kinases . These kRdh54 is a target of the Mec1/Rad53 signaling axis , but it Here, we have identified and characterized Rdh54 mutants that lead to changes in Rdh54 clustering behavior on dsDNA, translocation activity on dsDNA, and interactions with the effector kinase Rad53. We also find this region to be a target of Rad53 whose kinase activity appears to down regulate the translocation activity of Rdh54 on dsDNA. Importantly, mutations in this region have phenotypic consequences on the distribution of LOH outcomes associated with HR. From this data we have classified Rdh54 mutations as translocation fast and slow versions of the enzyme linking these classes of mutants to different phenotypic outcomes. Finally, we have developed a model where Rdh54 activity at DNA repair sites is controlled by a dynamic equilibrium that balances recruitment of Rdh54 by Rad51 and dissociation of Rdh54 through translocation along dsDNA. This dynamic equilibrium helps regulate HR outcomes by a kinetic mechanism which is coordinated by Rad53 activity.S. cerevisiae strains used in this study were W303. All recombination outcome experiments were performed in the W303 background. Strains for spot assay experiments were BY4741 and were generated by transformation of rdh54Δ strains with pRS415 plasmids. The genotypes for all strains used in this study can be found in Most of the 600 of 0.2 and then allowed to grow to an OD600 of 1.0. Cells were then serially diluted and manually spotted on Synthetic Complete (SC) -Leu plates containing either no drug, 0.01% MMS or 25 μM CPT. Plates were incubated at 30°C for 2–3 days and imaged at 24, 48 and 72 h. For colony growth, overnight cultures were diluted 100-fold and then allowed to grow 2 h. Then 0.01% MMS or an equal volume of sterilized H2O was added to the cultures and allowed to grow 4 h. Colony forming units (CFU) were determined by plating 1000-fold diluted cultures on YPD + adenine sulfate plates. The plates were scored for colony growth and survival percentage was calculated by dividing the colonies from MMS-treated culture by the untreated culture.For complementation spot assays, overnight cultures were diluted back to an ODYeast strains of JBC206 and JBC207 and derivatives were crossed , and the600 of 0.2 and allowed to reach an OD600 of 0.4–0.5 followed by the expression of I-Sce1 through the addition of 2% galactose. Cells were allowed to grow for an additional 1.5 h, after which they were plated on YPD and allowed to grow for 48 h. After 48 h they were placed at 4°C overnight to allow further development of red color. The number of white, red, and sectored colonies was then counted followed by replica plating onto YPD + hygromycin B (200 μg/ml) and YPD + nourseothricin  for analysis of recombination outcomes. Strains were also replica plated on YNB (-Ura/-Met) + 2% dextrose to insure proper chromosome segregation  + 2% dextrose. CFU were determined by plating a dilution of the culture on non-selective media. After 3 days, the plates were scored for colony growth. The Ura + frequency was determined by dividing the adjusted CFU from the selective plate by the number of colonies on the non-selective plate. The mean and standard deviation were calculated for three independent experiments.The WT strains used for this study were described in . The gen600 was ∼1.0. Then 2% galactose  was added to the culture to induce DSBs. In an uninduced control group, 2% dextrose was added. The cells were allowed to grow for 2 h and harvested by centrifugation. Cell pellets were washed twice with 1× PBS  + 0.2 mM PMSF and frozen at –80°C for at least 30 min. Cells were then thawed and resuspended in lysis buffer . Glass disruptor beads were added, and the cells were bead beat with 1 min on and 1 min off for a total of 8 cycles. The cellular debris was then spun down at ∼1100 × g for 10 min at 4°C. The supernatant was then centrifuged at 26 712 × g for 10 min at 4°C. The supernatant was collected as whole cell extract and incubated with anti-RAD53  pre-coated Dynabeads Protein G  overnight at 4°C. To prepare the coated beads, they were first washed with PBST (PBS + 0.1% Tween-20) 3 times, then resuspended with PBST and antibody and incubated at 4 °C for 2 h. After incubation, the antibody was removed, and the beads were washed with PBST 3 times. Then the beads were ready to be used. For each 1 ml of cell lysate, 1 μl of anti-RAD53 antibody and 15 μl of Dynabeads were used. The immunocomplexes were washed with wash buffer  3 times and transferred to a new tube. The whole cell extract and immunocomplex were dissolved in SDS loading buffer. The samples were loaded on an SDS-PAGE, and the proteins were transferred onto a nitrocellulose membrane. The membrane was stained with Ponceau S staining solution  and blocked with 5% non-fat dry milk in TBST  for 1 h. The membrane was then incubated with the primary antibody  in TBST + 5% non-fat dry milk overnight at 4°C followed by incubation with secondary antibodies  at room temperature for 1 h. The immunoblots were visualized with enhanced chemiluminescence.A starter culture was grown in 5 ml YPD for 10 h. The culture was then inoculated into 100 ml of YP + 3% glycerol + 2% sodium lactate and allowed to grow overnight until the OD600 between 0.6 and 0.8 at 37°C. The temperature was lowered to 16°C and cells were induced with 0.5 mM IPTG overnight. Cells were harvested by centrifugation and stored at –80 °C. Cells were resuspended in buffer A  and lysed by freeze thaw. Cell lysate was sonicated on ice for 10 cycles with 65% duty cycle and 15 s on and 45 s off. Lysate was centrifuged at 10 000 × g for 45 min at 4°C. Clarified extract was then mixed with pre-equilibrated His-Pur Nickel-NTA resin and incubated in batch for 1 h at 4 °C. After 1 h the resin was centrifuged and washed 2× with buffer A, followed by 2× washes with buffer B . The resin was then added to a disposable column, and the protein was eluted in buffer C . Fractions were analyzed by SDS-PAGE and the peak fractions were pooled. The protein was further purified using a strong cation exchange column (HiScreen SP FF). The column was resolved using a 20–100% gradient of buffer D  and buffer E . Fractions were analyzed by SDS-PAGE. Peak fractions were pooled and concentrated by centrifugation at ∼1100 × g at 4°C using a 10 kDa MWCO Vivaspin 6 column. Protein was finally purified on a 120 ml S300 column (HiPrep Sephacryl S-300 HR) in SEC buffer . Peak fractions were pooled, and the protein was concentrated as described above. Purified protein was stored at –80°C. 6xHis-Rad53, 6xHis-GFP-Rad53, 6xHis-Rad53D339A and 6xHis-GFP-Rad53D339A were purified as previously described  cells bearing pET15-6xHis-Rdh54 expression plasmids were grown to an ODescribed . PlasmidIn vitro kinase assays were performed in Rad53 kinase buffer . 1 μM Rdh54 was incubated with 0.1 μM Rad53 for the defined periods of time. Reactions were quenched with SDS-PAGE loading buffer and resolved on a 10% SDS-PAGE impregnated with phostag reagent and 10 mM MnCl2. The gel was stained with Coomassie Brilliant blue and the shift in Rdh54 was directly observed. Mass spectrometry analysis was performed on Rdh54 samples phosphorylated in vitro that were excised from a Coomassie-stained SDS-PAGE.2, 200 ng/μl BSA, 1 mM DTT and 10% Glycerol) and contained 1 mg/ml sheared salmon sperm DNA, and 100 nM Rdh54.To measure ATP hydrolysis rates, a commercially available ADP-GLO kit was used. ATP hydrolysis reaction were performed in HR buffer .All single molecule experiments were conducted on a custom-built prism-based total internal reflection microscope (Nikon) equipped with a 488-nm laser , a 561-nm laser , and two Andor iXon EMCCD cameras. DNA substrates for DNA curtains experiments were made by attaching a biotinylated oligo to one end of the 50 kb Lambda phage genome, and an oligo with a digoxygenin moiety on the other. This allowed double tethering of the DNA between chrome barriers and chrome pedestals, as previously described ,73. FlowYf – Yi|) × 1000 bp/[|Xf – Xi|]) × frame rate], where Yi and Yf correspond to the initial and final pixel position and Xi and Xf correspond to the start and stop time (in seconds). Graphs of individual velocity and distances travelled were plotted in GraphPad Prism 9. The mean was determined from these graphs and significant differences between mutants was determined by performing a student's t-test of this data.The velocity and track length for Rdh54-mCherry molecules were measured by importing raw TIFF images as image stacks into ImageJ. Kymographs were generated by defining a 1-pixel wide region of interest (ROI) along the long axis of individual dsDNA molecules. Data analysis was performed from the kymographs. The start of translocation was defined when the Rdh54-mCherry molecule moved >2 pixels. Pauses were defined as momentary stalls in translocation that lasted 2–4 frames. Termination was defined by molecules that did not move for >10 frames. Velocities were calculated using the following formula [(|Photobleaching measurements were made by binding Rdh54-mCherry to the dsDNA and then observing the reaction without shuttering of the 561 nM laser at 25% power output (25 mW). Images were collected with 200 msec integration time. Kymographs were generated from individual DNA molecules and were visually inspected for irreversible changes in signal intensity. This was quantified by measuring the signal intensity over time and identifying defined drops. These drops in intensity were used to create a distribution of photobleaching steps. The intensity of individual drops was used to create a distribution to evaluate the mean intensity drop which correlates with the intensity for an individual fluorophore. Cluster size was then determined by subtracting the global background from the flow cell from each cluster, and then dividing by the mean intensity drop for an individual fluorophore. This created an estimated number of molecules per foci. Populations of bound Rdh54 under different conditions were statistically compared for significance using a non-parametric Mann–Whitney test in Graph Pad Prism 9.For Rdh54 binding experiments, 1 nM of 6xHis-Rdh54-mCherry was mixed with 6xHis-GFP-Rad53 and injected onto pre-formed DNA curtains. Initial binding was monitored with a frame rate of 1 frame/3 sec. Data were then processed as described above. The number of Rdh54 molecule bound by GFP-Rad53 were then scored for binding. For functional assays Rdh54-mCherry, Rdh54T851A-mCherry, or Rdh54S852A-mCherry were treated with catalytic amounts of Rad53 for 60 min in HR buffer. Translocation was then monitored by single molecule imaging and analyzed as described above.rdh54Δ haploid yeast strains. In addition to the serine/threonine to alanine mutations, we also tested the rdh54K318R allele which harbors a mutation in the Walker A box of Rdh54 preventing hydrolysis of ATP. We used this mutant because it has more severe phenotypes than the rdh54Δ strain . In contrast, the rdh54S852A and rdh54T851A,S852A alleles were not significantly different from the WT with 40 ± 4 and 48 ± 8 percent survival respectively  gene conversion, and LOH outcomes  located on homologous chromosomes into an active LEU2 gene . The two phosphomimic versions of Rdh54, rdh54T851D and rdh54S852D, were comparable to WT in conversion frequencies   Figure . SurprisT Figure . This surdh54 backgrounds. When using the homologous chromosome as a template for repair, LOH events can occur by both reciprocal (CO) and non-reciprocal (BIR) exchange of DNA sequences between chromosomes. RDH54 reduces BIR outcomes and reduces CO frequency by a mutation to the Walker A box, rdh54K318R , rdh54T851D , rdh54S852D  and rdh54S852A  as compared to WT (53 ± 10%)  the magnitude of this change was proportional to the loss of CO outcomes , rdh54S852A  and rdh54S852D  these changes were also accompanied by an increase in BIR outcomes , with smaller changes in rdh54T851D  and rdh54S852D . The change in the rdh54S852A mutant was comparable to the rdh54Δ strains  Figure . Surpriss Figure . The mags Figure . The rdhRDH54 has been implicated in template switching , a 2.5-fold reduction from WT. Surprisingly, there was no significant reduction in template switching observed for rdh54T851D, rdh54S852A, rdh54S852D or rdh54T851A,S852A strains   . Because domains  using th domains . The clo domains . We wereactivity . ThroughN = 27) group contained four Rdh54 molecules. Groups of one and three units were also observed but occurred less frequently than the other groups . We also measured cluster sizes for each of the Rdh54 mutants. We found there was no significant difference in cluster sizes between WT, and Rdh54T851A (P = 0.14), Rdh54T851D (P = 0.28), Rdh54S852D (P = 0.08) and Rdh54K318R (P = 0.38)  and 5.1 (P < 0.0001), respectively  Figure . In conty Figure . From thn = 132/142) indicating an interaction between the two populations of molecules. The mixed molecules co-translocated with a mean velocity of 68.5 ± 40.4 bp/s, a mean distance of 14.3 ± 8.8 kbp . We next performed classic translocase poisoning experiments where we mixed an equal amount of WT protein with either Rdh54K318R or Rdh54T851A. The hypothesis was that if proteins within a cluster were functionally dependent on each other, then incorporating mutant versions of the protein would poison the WT protein and reduce the velocity of the cluster. When we mixed an equal molar amount of Rdh54-mCherry with unlabeled Rdh54K318R, the mean velocity of the total population was reduced to 30.7 ± 20 bp/s , representing a significant change from WT    when unlrdh54K318R and rdh54T851A alleles would have altered CO/NCO ratios. We chose to test CO/NCO ratios because this was the most significant difference we observed in our initial experiments and would likely be sensitive enough to detect even a partial change in outcomes. We performed red/white recombination experiments with RDH54:rdh54K318R and RDH54:rdh54T851A strains. We found that the RDH54:rdh54K318R had a reduction in CO  in favor of NCO  outcomes. Likewise, there was a reduction in CO  and an increase in NCO outcomes  in the RDH54:rdh54T851A strain , and a track length of 12.1 ± 7.1 kb (N = 110)  and mean track length . In contrast, the Rdh54T851D mutant was not different from WT in either mean velocity  or mean track length , consistent with the results from the ATP hydrolysis experiments. The reduction in velocity of the Rdh54T851A mutant and the increase in ATP hydrolysis is consistent with a less efficient coupling of ATP hydrolysis to translocation. The Rdh54S852A mutant translocated with a mean velocity of 77 ± 54 bp/s (N = 41) and a mean track length of 9.3 ± 5.6 kb (N = 41). These values were not significantly different from the WT and suggest a mutant which is more efficient at coupling ATP hydrolysis to translocation. In contrast, Rdh54S852D had a significant decrease in velocity  and a small decrease in track length  relative to WT . We saw an insignificant difference in track length   Figure , E. Both) Figure ,61. For T Figure , E. When) Figure , E compaE. coli (E. coli. The GFP-Rad53 retains auto kinase function and can be purified in a phosphorylated form (P = 0.003), and twice as large at 10 nM Rad53 , suggesting that Rad53 may affect Rdh54 cluster size  , but only a weak correlation with the GFP-Rad53D339A mutant  Figure . This al) Figure  and a re) Figure . Finallyin situ interaction between Rad53 and Rdh54 using a co-immunoprecipitation experiment. We used a strain of yeast with an HO inducible break that lacked a donor DNA sequence. We used this strain because it had previously been used to demonstrate a genetic interaction between Rad53 and Rdh54 . In contrast, the Rdh54S852A mutant had reduced cluster size   Figure . This wa) Figure . This wa) Figure . From thin vitro kinase assay. We found that Rad53 was able to directly phosphorylate Rdh54 in vitro . This was significantly (P < 0.0001) different from the translocation velocity of 81.7 ± 51 bp/s observed for untreated Rdh54-mCherry, and comparable to the translocation velocity observed for Rdh54S852D (48 ± 33.7 bp/s). The translocation length for Rad53-treated Rdh54-mCherry (9.5 ± 4.5 kb)  was only slightly lower than that observed for WT and was the same as Rdh54S852D. When we measured the velocities of Rdh54T851A-mCherry we found a reduction in velocity to 20 ± 17 bp/s as compared to Rdh54T851A-mCherry untreated    Figure , N = 140) Figure . There w) Figure . However) Figure , or trac) Figure . TogetheIn this study, we investigated the regulation of the Rdh54 motor protein during homologous recombination. We identified a previously uncharacterized region of Rdh54 that is critical for function during HR. Mutations to this region caused changes in Rdh54 cluster formation on dsDNA, translocation along dsDNA, and interactions with the effector kinase Rad53. This region is a target of Rad53 whose phosphorylation of Rdh54 results in a reduction in translocation velocity on dsDNA. Importantly, mutants in this region have phenotypic consequences that result in changes in the distribution of LOH outcomes in yeast reporter strains. Based on these findings we present a model where the association and dissociation of Rdh54 clusters from HR repair foci are important for directing HR outcomes.It has been known for several years that Rdh54 is recruited to HR foci by Rad51 through an interaction with the disordered N-terminal region of Rdh54 ,30,81. Tin vivo and in vitro caused by significant de-coupling of ATP hydrolysis from translocation on dsDNA. There was no difference in Rdh54 cluster size in this mutant. In contrast, the phosphomimic version of this residue, T851D, had no discernable biochemical defect. Without a high-resolution structure it is difficult to determine how these mutations regulate cluster size and ATP hydrolysis rates. However, an attractive model is that Rdh54 may behave like other RecA proteins in forming a short filament along dsDNA. In this case adjacent protomers may form a shared active site like that observed in Rad51. There is evidence that Rad51 can be phosphorylated at the protomer-protomer interface which prevents binding to dsDNA , which is a combination of recruitment of Rdh54 by Rad51and the self-association of Rdh54 molecules, and the off-rate (koff), which is determined by how quickly Rdh54 translocates away from HR repair intermediates on dsDNA (Figure rdh54Δ where no clusters form (Figure Based on these data we propose a model in which the kinetic lifetime of Rdh54 at DNA repair foci helps determine recombination outcomes. The lifetime is controlled by the on-rate (kA Figure . The offA Figure . Likewism Figure .rdh54T851A,S852A allele restored WT function in our genetic assays. Biochemically, this mutant exhibited reduced cluster formation, and a reduction in translocation velocity along DNA. However, this mutant was slightly better at cluster formation than the S852A mutant, and it was better at coupling hydrolysis to translocation than the T851A mutant. Finally, it cannot be phosphorylated due to the mutation at the S852 site. This leads to a combination of variables that likely average out to yield the equivalent of a WT protein in vivo. Put another way, the mutant protein likely has the same lifetime as the WT protein at HR repair foci (Figure Importantly, we found that the i Figure .Rdh54 is a target of the Mec1/Rad53 signaling axis , and phyrad9Δ strains exhibit a reduction in BIR (rdh54K318R leads to an accumulation of hyperphosphorylated Rad53 in yeast strains that lack a homologous donor sequence (It has previously been shown that Rad53 has the opposite effect on the size of Rad9 clusters in which phosphorylation of the Rad9-BRCT domain leads to reduction in Rad9 cluster size . It has n in BIR . The oppsequence , suggestsequence . Ultimatin vivo imaging of Rdh54 clusters in the presence or absence of Rad53, or more refined in vitro phosphorylation experiments. Ultimately, these mutants provide an interesting new class of Rdh54 mutants that provide new insight into its function during HR.A caveat of our study is that we cannot exclusively determine if the phenotypes observed in T851A and S852A mutants are due to defects in phosphorylation or whether they are simply defective enzymes. These two outcomes are not mutually exclusive, but future work will be needed to determine more conclusively which of these models is correct. Experiments to differentiate these models may include S. cerevisiae model cluster formation is regulated by specific protein-protein interactions within Rdh54 and through an interaction with Rad53. However, this specific interaction may not be conserved in higher eukaryotes. The homolog of Rdh54 in higher eukaryotes is RAD54B. This homologous motor protein may regulate recombination outcomes through a similar clustering type mechanism that controls the on and off rate of this motor protein at HR repair centers. Interestingly, it has recently been shown that during meiosis in C. elegans RAD54-B is active when CHK2, a Rad53 homolog, is also active (Here we present a model where the lifetime of Rdh54 at HR foci helps regulate genetic outcomes. In the o active . Thus, egkad848_Supplemental_FileClick here for additional data file.


Reproductive health is important, but often neglected in cancer survivorship care. This study explored contraceptive use and factors associated with fertility testing among young adult survivors of childhood cancer in Germany.Young adult survivors of childhood cancer were identified through the German Childhood Cancer Registry and completed a mailed survey. Survivors were queried regarding contraceptive use, reproductive goals, uncertainty about fertility, and completion or interest in fertility testing. Multivariable stepwise logistic regression models were used to calculate Odds Ratios (OR) and 95% confidence intervals (CI) as a means of identifying factors associated with completion of and interest in fertility testing.N = 472; 57.8% female; aged 23.3 ± 1.5 years, and 14.9 ± 5.0 years from diagnosis), reported high rates of contraceptive use, including 61.2% using a single method, 30.6% dual methods, and 8.1% no/less effective methods. Few survivors had completed fertility testing (13.0%), although 58.8% were interested. Having been diagnosed during adolescence , greater uncertainty about fertility , and use of dual contraceptive methods  were associated with having completed fertility testing. Factors associated with interest in fertility testing included goals of wanting to have children  and greater uncertainty about fertility (OR = 1.19 95%CI: 1.06–1.33).Survivors (In this sample of young adults who survived childhood cancer, most reported contraceptive use. Few survivors had completed fertility testing, although more than half were interested. Interventions are needed to address potential barriers to fertility testing and help survivors manage fertility-related uncertainty. Improved cancer treatments have led to increased survival rates for childhood cancer, which most commonly include leukemia, lymphoma, or central nervous system tumors; with current survival rates exceeding 80% in Germany , across unplanned pregnancies and sexually transmitted infections (STIs) due to risky sexual behavior and lack of effective contraceptive use [believed they were infertile were four times less likely to use contraception than survivors who believed they were fertile. This is concerning as survivors can have inaccurate perceptions of their true infertility risk [While survivors of childhood cancer may experience challenges with fertility and family building, adolescence and young adulthood is also generally a time when people are at risk for tive use . Preventtive use , 24, buttive use . At the tive use –28. Conttive use . Specifiity risk , 18, 30 VIVE  that considered medical late effects of childhood cancer treatment and InRel (PI: Lehmann) that focused on intimate relationships [This study is part of the larger E-Surv collaboration which included two surveys: ionships .N = 2000 survivors of childhood cancer (see eligibility below). Survivors were invited to participate in both studies in a counterbalanced manner . Information packets were mailed to survivors, and in case of non-response, a reminder was sent after six weeks. After three months, survivors were invited to participate in the second survey of this project  irrespective of their previous (non-) response unless they explicitly opted out.The German Childhood Cancer Registry (GCCR) randomly selected Participants provided informed consent prior to completing the survey, and all procedures were described previously . This stEligible survivors were diagnosed with any type of cancer before age 18, were long-term survivors (≥ 5 years post-diagnosis), were emerging/young adults , were registered at the GCCR, and living in Germany at the time of data collection (2018–2019). Due to logistical delays after eligible survivors had been identified, participants were aged 21–26 years at study participation.p < 0.001) and more often female  than non-completers, but they did not differ by type of or age at diagnosis. Throughout the survey, participants were able to skip questions. For the current analyses, we excluded survivors who had children already (n = 29), who had not been pregnant/sired a pregnancy previously (n = 7), or who unsuccessfully tried to become pregnant for more than one year (n = 6). Another n = 12 survivors missed data for these items, resulting in a final sample of N = 472.Of the 2000 identified survivors, 526 completed the InRel survey. These completers were somewhat younger , and completed level of school education  were self-reported by survivors. Age and clinical data  were supplied by the GCCR  they used to prevent pregnancy at last sexual intercourse, with responses being categorized as hormonal methods , barrier method (condom), or no/less effective methods . One participant indicated use of a copper IUD and was included in the hormonal contraception group, due to its effectiveness in preventing pregnancies. These contraceptive methods were further categorized as single contraception  or dual contraception methods  to distinguish in their ability in preventing both pregnancies and STIs. Participants were also able to select that they were not using contraception because they were currently attempting a pregnancy.Reproductive goals were assessed by asking participants if it is a goal in their life to have biological children. Responses were dichotomized as no versus yes .Uncertainty of fertility status was measured with three face-valid items, including whether survivors tend to wonder about, are unsure, and believe that they might be infertile. Answers to these 3 items were measured on 4 or 5-point scales  and summed to an uncertainty score , with higher scores indicating greater uncertainty. Cronbach’s alpha was 0.75 in this sample.fertility testing and thus, if they knew their fertility status (“Have you have been tested to find out what your fertility status is?”). Response options included: (a) never having been tested or having been tested with results indicating (b) infertility, (c) impaired/sub-fertility, (d) fertility, or (e) unclear results. For analyses, participants were categorized as having completed fertility testing versus not.Participants were asked if they had ever undergone without prior fertility testing were queried regarding their interest in fertility testing. This question also explained that fertility testing would include producing a semen sample for males or blood work and ultrasounds for females. Responses were categorized as interested  or not interested (uncertain or would not consider if offered). Participants were also asked about reason(s) if they were not interested in completing fertility testing.Participants χ2, t-tests or ANOVAs, depending on the variable type and whenever subgroups were sufficiently large.Descriptive statistics of all measures of interest are presented. Differences in contraceptive use by sociodemographic  and medical factors  were tested using backward selection to explore which factors play a significant role, followed by entering reproductive goals and uncertainty in block 2, and finally contraceptive use in block 3. The same analyses were conducted for interest in fertility testing . Post-hoc power analyses indicated ample power (> 0.9) for the above analyses to detect even small effects (r = 0.2) given a sample of n = 472 survivors.Multivariable stepwise logistic regression models were used to calculate Odds Ratios (OR) of whether survivors had completed fertility testing. All usable basic sociodemographic and medical factors  and type of diagnosis (CNS vs. others) were entered in block 1 using N = 472) were on average 23.3 years old  and 14.9 years from cancer diagnosis . Most participants were female (57.8%), single (52.1%), and had completed a university entrance qualification , and most often diagnosed with leukemia (37.9%). Most participants had not experienced a relapse/second malignancy , n = 3 were currently trying to get pregnant, and n = 8 were missing. Thus, of all survivors (n = 369) with data on contraceptive use at last intercourse, n = 113 (30.6%) reported using dual methods  to prevent pregnancy, n = 226 (61.2%) used a single method , and n = 30 (8.1%) used no/less effective methods . These 30 survivors who used no/less effective methods were not included in further analyses due to small subgroup size. These survivors did not appear different from all other survivors, although relatively more had only completed a basic school education  nor by sex or type of diagnosis . Yet, single survivors were more likely to use dual contraceptive methods  than partnered survivors . In other words, most survivors who used single contraceptive methods were partnered  reported that having a biological child is a goal in their life. Uncertainty regarding fertility status varied , with higher scores indicating greater uncertainty, wonder, or worry about fertility status.Most participants  had never been tested and did not know their fertility status. The most common reasons for not having completed fertility testing were that participants did not think of it , believed they were not at risk for infertility , planned to be tested when they were older  or in a relationship , and/or lacked knowledge about where/how to access testing . Of the 61 participants who received fertility testing, 52.4% (n = 32) were told that they were fertile, 31.1% (n = 19) had impaired fertility, 13.1% (n = 8) reported being infertile, and 3.3% (n = 2) reported unclear test results.Most participants . Moreover, those with higher uncertainty about their fertility status , and those using dual  contraceptive methods relative to using either/single methods  participants responded yes, while 41.2% (n = 168/408) would decline testing . Factors associated with interest in obtaining fertility testing included wanting to have children in the future  and increased uncertainty about fertility  also provided reasons, including that they did not think they were at risk , did not want to have kids , or would be uncomfortable with test procedures . A smaller portion of participants also reported concerns regarding cost of procedures, feeling embarrassed, or fearing the results of fertility testing.Of those 8.8% n = 40/408 paIn this study of young adult survivors of childhood cancer, rates of contraceptive use were high, with almost one-third of survivors using both hormonal and barrier contraception. A small proportion of survivors had completed fertility testing, although over half of never-tested survivors reported interest in fertility testing. Uncertainty about fertility was identified as relevant for both having been tested, as well as being interested in testing. A cancer diagnosis during adolescence and using dual contraceptive methods were also associated with having completed testing, while goals of having a child in the future were strongly associated with interest in fertility testing.no contraception at last intercourse, and almost one-third report using both hormonal and barrier contraception methods [Most (> 90%) participants reported using one or more forms of contraception during their last intercourse, which is similar to reports of German national studies where < 10% of healthy young adults report using  methods , 33. Of  methods  and surv methods . In a sa methods . We also methods .Similar to previous studies of young adult cancer survivors, our findings demonstrate that far more survivors are interested in testing, than those who actually completed fertility testing , 21. TheDual (relative to single) contraceptive use was associated with a greater likelihood of having completed fertility testing. Among those who completed fertility testing, half were told they were fertile, likely encouraging them to prevent unintended pregnancies and resulting in using more than one contraceptive method. It may also be that survivors who had undergone fertility testing are more engaged in reproductive healthcare, having received additional counseling regarding pregnancy versus STI prevention, leading to dual methods of contraception.Greater uncertainty regarding fertility was associated with interest in fertility testing, which aligns with prior research , 37. HowMost participants (85.5%) reported a desire for having biological children, an important developmental milestone for many young adults and most childhood cancer survivors , 39. In Survivors diagnosed during adolescence were more likely to have completed fertility testing, suggesting that providers may be more likely to initiate fertility-related conversations with adolescent patients due to greater availability of cryopreservation options for older patients. Further, and unlike child-aged patients, adolescents are more able to engage in discussions surrounding fertility at the time of their cancer diagnosis, making them more attuned to their risks for infertility and subsequent efforts to test their fertility following treatment. It is also possible that survivors diagnosed during adolescence require more frequent oncology clinical visits during young adulthood to monitor for relapse, creating increased opportunities to initiate fertility-related discussions. Psychoeducation is warranted for all (long-term) survivors, regardless of age at diagnosis, to inform them of their infertility risk, fertility testing options, family building options, and to discuss alternatives to biological parenthood or a life without children. These conversations should be ongoing and tailored to survivors’ goals over time.Findings from this study should be considered in the context of its limitations. Data were collected through self-report, and outcomes of fertility testing and risk for infertility may be inaccurate. The sample may not be generalizable to young adult survivors of childhood cancer outside of Germany, as systemic differences in healthcare delivery, access to contraception, and costs vary across countries. Additionally, there may be a selection bias, such that survivors with higher education completed surveys more frequently. No validated measure exists to measure uncertainty about fertility in cancer survivors, but the self-developed 3-item measure included in this study showed good internal consistency. Other single-items included in the survey were deemed to have sufficient face validity by experts in the field and were comparable to questions used to assess contraceptive behaviors in national studies. As is the case in all cross-sectional studies, the temporal relationship between some variables could not be tested. Finally, additional psychological variables  were not assessed, and warrant further investigation to determine more factors related to the completion or interest in fertility testing.Young adult survivors of childhood cancer can have unique reproductive healthcare needs, including contraception to prevent unintended pregnancies and STIs, while also considering fertility testing to assess options for future biological parenthood. In our sample, the vast majority of survivors reported use of either single or dual methods of contraception. However, there is a large gap in care regarding fertility testing, with over half of the sample reporting interest, but < 15% having completed testing. As many participants were unaware of fertility testing options or wanted to wait until they were older, healthcare providers may need to discuss fertility testing multiple times after treatment. Further research in this area is needed to identify and address barriers to fertility testing among interested cancer survivors. Additionally, uncertainty regarding fertility was associated with both completion of, and interest in, fertility testing, suggesting a need for psychological support before and after fertility testing.


The relatively high fertility in the Nordic countries has attracted considerable academic and political interest. Still, the causal relationship between economic circumstances and fertility in the Nordic context is poorly understood. This paper estimates the effect of tax breaks and universal transfers on fertility in the Nordic context. We analyze the fertility effects of a regional child benefit and tax reform implemented in the northern municipalities of the Norwegian county Troms, using the southern municipalities of the same county as a plausible and empirically similar control group. We use a difference-in-difference/event study design, and estimate multivariate models on individual-level data from administrative registers for the full population. The reform increased fertility among women in their early 20 s. The effects are concentrated among unmarried women, who received the largest subsidies. Our findings suggest that favorable economic conditions have contributed to the relatively high fertility in the Nordic countries.The online version contains supplementary material available at 10.1007/s11113-023-09793-z. About a third of the world’s children, and nearly 90 percent of children in Europe and Central Asia, receive some sort of unconditional cash transfer , with no change in the comparable and bordering region Southern Troms (control region). We compare the fertility development for women in the two regions using difference-in-difference/event study designs. Our analysis is based on detailed, high quality data on fertility, education, income and marital status for the full female population in this region, drawn from administrative registers. To avoid bias from selective migration, place of residence and all potentially endogenous covariates are measured prior to the reform.When fully implemented, the reform increased the universal child allowance for children aged 0–18 years by about 3600 NOK yearly, about 575 1990-USD.Results show that the reform increased fertility among women in their early 20 s. The effect is most marked for first births, but there are also tendencies of increases in second and third birth. This finding is consistent across specifications and robustness tests. We find the strongest response in the age group where earned income is lowest, and the relative economic improvement due to the reform largest. There is also an increase in third births among women aged 35–39 at implementation, suggesting a lasting effect on fertility.Our sources of exogenous variation are two regional reforms, implemented in 1989–1990, that substantially improved the economic conditions of families and individuals in Northern Troms and Finnmark. The reforms were targeted at recruiting and retaining high skilled labor to the region and to improving the labor market for low skilled workers as part of a long tradition—with considerable political and public opinion support—of “district policies” aimed at maintaining population levels in remote parts of the country. Comparable reforms in other countries were implemented with the explicit aim of increasing fertility , from 2.5 children per woman in 1970 to 1.72 children per woman in 1980. This co-occurred with an increase in female labor force participation and educational attainment, spurring interest in the potential of institutions to ease the combination of paid work and child rearing. At implementation, important components of what was later to be termed the “Nordic model” of family support was not yet in place in Norway. 18 weeks of parental leave was introduced in 1977. From 1987 to 1993, the length of parental leave was gradually expanded to 42 weeks. In 1993, 4 weeks of paternity quota was introduced , nor the additional benefits introduced for Finnmark and North Troms are means-tested.Anticipatory effects are very unlikely. In August 1988, Prime Minister Gro Harlem Brundtland announced the intention to increase the cash allowance in Finnmark and North Troms, and to generally strengthen the “district policies” targeted at Northern Norway. The final decision was made by the Norwegian Parliament 20th December 1988. The child benefits were paid from January 1st the next year.Table For women in their 20 s and 30 s, the additional cash allowance amounted to about 3–5% of median yearly earnings, with the highest relative value found among women in their early 20 s. For teenagers, who rarely have employment as their main activity, the increase constitutes more than a third of the median yearly income .From 1 January 1990, substantial regional income tax deductions for all taxpayers were implemented in the reform region, increasing net wages in Nord-Troms and Finnmark.relative increase in economic resources was by far highest for individuals with lower earnings, the absolute increase in disposable income increased with (taxable) earnings.The income tax breaks consisted of two parts, an increase in the general (lump-sum) deduction available for all taxpayers and lower  tax rates for incomes at higher levels. The “base tax”, applying to taxable income at all levels, was set 3.5 percentage points below the rate for the rest of Norway. Additionally, the increase in tax for higher tax brackets, starting at 340 700 NOK, was reduced with 4 percentage points in the reform region, compared to the rest of Norway.The general deduction was increased by 10 000 NOK per individual, and twice this amount for single parents  student loans reduced by 10% (up to a limit of NOK 15.000) for each year residing and working in Northern Troms and Finnmark, aiming to retain and recruit highly educated individuals. Strictly speaking, the reduced student loan repayments increase disposable income both in the short and long run. While these changes are small and non-transparent, we cannot rule out a priori that they increased educational enrollment and thereby led to fertility postponement  welfare benefits, largely from the US. This includes studies of tax breaks for parents with low income, such as the Earned Income Tax Credit (EITC). Overall, studies with sound causal identification show either no effects, or very small negative effects, on fertility  parents through two channels: increased income  and reduced cost of a marginal child (through increased transfers). These changes can influence both fertility and labor supply, and responses to each outcome influence the other. In this section, we outline theoretical perspectives on effects of cash transfers and tax breaks on fertility.Two fundamental concepts from the microeconomic theory of fertility are crucial to understand the potential effect of the cash transfer increase on fertility: the direct cost of children and the income effect.In its simplest form, the microeconomic theory of fertility predicts that the demand for children will increase if the cost of raising a child falls Becker, . The dirincome effect on fertility, i.e. that family size increases in household income, all else equal . On the other hand, the higher wages allow for working fewer hours for the same household income (income effect on labor supply (ibid.)). Those who are already employed can respond most quickly to tax changes, so we expect larger effects on overall earnings than on the probability of being employed.However, the all else equal assumption may not hold, as the basic labour/leisure-model suggests that a change in wages is expected to also affect labor supply (hours worked)   on fertility from the cash transfer increase. We note that the increased income from cash transfers could lead that hours in paid work and thus gross labor market earnings to fall.Hours in paid work are unmoved Hours in paid work increase If we observe a fertility fall, we expect this to be due to the tax break. An increase in fertility, on the other hand, could theoretically be driven by both cash transfers and tax breaks. The empirical literature, however, points to cash transfers as the more likely driver of a fertility increase.As for tax breaks, pertaining to a wage increase, theory predicts that effects on fertility can be positive or negative. Empirical studies have suggested mainly no or negative effects. The fertility response to the tax break will depend on the labor market response, which cannot be inferred a priori, as it depends on the preference for income versus leisure. Thus, we formulate two contrasting expectations for the effect of tax breaks on labor supply and fertility: It should be noted that if the reform increased childbearing, it will also be expected to reduce female labor supply (at the ages where fertility is increased) , due to the larger tax breaks. This leads to the naive expectation that effects will be stronger for nonmarital fertility. On the other hand, as living with a partner before having a child has a practical advantage and is to some extent normatively expected  to the change in the fertility in the comparison region (Southern Troms). If trends in the two regions are parallel prior to the reform, but differ from the onset of the reform, this indicates a reform effect on fertility.An alternative way to describe the relationship between economic resources and fertility would be to correlate e.g. earnings, measured at the individual level, with fertility (see e.g. Hart ). A chalWe estimate difference-in-difference (DD) and event study (ES) models, which pertain to an ordinary least squares (OLS) or linear probability models (LPM) including fixed effects (i.e. a set of dummy variables) for region and year. The fixed effects net out change over time that is shared across regions, and any region specific factors that affects fertility but are constant over time. The main DD-specification takes the following form:If the trends in fertility across the region are similar (absent the reform), Eq. The dummy variables for negative values of duration, i.e. the pre-trend, provide a formal, indirect test of the assumption of parallel trends. We perform an F-test of the joint significance of the three estimated dummy variables for the pre-period .Standard errors are robust with clustering on municipality . Population registers include information on date of birth (for both mothers and children) and municipality of residence as of January 1st each year. Information on educational enrollment and completion was obtained from the National Educational Database (NUDB).Due to a tax reform implemented in 1983 that affected the treatment and reform region differently, our observation window starts in 1984. Our observation window is limited upwards by the introduction of the cash-for-care reform in 1998, which could change the economic incentives of childbearing and confound the effects of our reform  estimates. The seven reform municipalities in Northern Troms see Fig. , we exclOur main dependent variable is based on birth dates for all children ever born. For each year, we include children already born, as well as conceptions (imputed as birth date minus average length of pregnancy) leading to live births. From this variable we construct dummy variables for mothers’ parity, i.e. for having at least at least one, two, and three children in the given year.Our data distinguish between married women and unmarried  women. We construct a dummy variable for being registered as married in the current year. Data from the Medical Birth Registry shows that 80% of the unmarried women were cohabiting with the child’s father at the time of the birth . However, the share of children who lives with both parents falls with age, and co-residential unions are consistently less stable than marriages  used to adjust pensions and transfers,The natural logartim of earnings. Ajusted to 1984 value using the “base amount”.Our data includes gross earned income as reported to the tax authorities. The variable includes both labor market earnings and other pensionable transfers.We construct a set of dummies for educational attainment, distinguishing between mandatory (primary and lower secondary), high school and higher education. Missing education is included as a separate category. We also construct a dummy variable for being enrolled in education, taking one if the individual is registered as enrolled in education in the current year.As a first step towards establishing whether the reform and comparison region are similar, Table trends for the relevant outcomes are parallel. Thus, we proceed to test, first by visual inspection , there is a tendency of increase in number of children . With one exception, effects for all other outcomes and samples are statistically insignificant.12In the shorter panel, point estimates suggest a 5 percentage point increase in number of children in the youngest cohort (p<0.05). Point estimates for the share with one and two children are also positive, albeit not statistically significant, in this cohort. There is also, in line with the event study plots, a 1.4 percentage points increase in the share with three children in the oldest cohort  wages due to the tax break did not induce women to work longer hours. One explanation for this could be that women in Troms, when facing a wage increase, preferred to work the same number of hours for slightly higher pay, cf. the labor/leisure-model Borjas, . It is aa priori plausible control group, and for extensive (indirect) testing of the identifying assumption. The plausibility of our identifying assumption is also strengthened by the estimates being relatively unchanged by inverse probability weighing. The subsample estimations, crucial for the interpretation of our results, are obtained without endogenous conditioning . The inn et al. ) may verSome concerns with our analysis should be mentioned. First, our results hinge on identifying assumptions, and have a causal interpretation as long as these assumptions hold. Our indirect tests of these assumptions suggest that they are more plausible in some groups than others. In particular, in the youngest age groups, the estimated pre-trends are statistically similar, corroborating the validity of our results. Second, while theoretical expectations of how tax benefits and cash allowances interact can be outlined, studying each of these in isolation would make for a theoretically cleaner interpretation of the results. As it is, we are able to test that the reform did not affect women’s labor supply through other channels than increasing fertility . However, due to the combination of effects, we abstain from calculating price- and income elasticities, which again complicates precise comparison of reform effects across contexts.As our main effects are identified among unmarried women, it would be of both theoretical interest and policy relevance to investigate whether the effects are concentrated among non-union births, or births to cohabiting women. Unfortunately, limitations of register data on cohabitation in the sampling period do not permit such investigations. To the extent that single women are affected more strongly than cohabitors, this means that our effects will be diluted and thus biased downwards. We are not aware of survey data set on births to cohabitors that would allow us to both zoom in sufficiently to get a plausible control group, and to retain a sample size sufficient to identify effects of a meaningful size.As for all quasi-experimental studies, the external validity of estimates remains a concern. With publicly covered high-quality schooling (through university) and nearly free public high-quality health care, the direct cost of a child in Troms in around 1990 was relatively low compared to most other regions in today’s Western world. On the other hand, interest rates were revolving around 10 at the time of the reform,Our results illustrate how changes in the cost of a child can not only influence fertility, but also change the order of life courses, shifting some births from higher ages and formalized unions to lower ages, where unions are less likely to be formalized, or even formed. At the societal level lower mean age at birth can in and of itself have a lasting impact on population structure as generational lengths shorten Below is the link to the electronic supplementary material.


Due to the undoubtedly rather big success of artificial reproductive techniques attention in clinical reproductive medicine is mainly laid on progress in this field in order to achieve even better success rates. With regard to male infertility basic research has yielded also remarkable new results in terms of better understanding causes of male fertility disturbances where the majority of research efforts comes from genetic studies. Many new genes being involved in spermatogenesis and their potential relation to specific spermatogenic disturbances, in particular the meiotic division process, have been identified. This is, of course, an important development, which does, on the other hand, not really help the affected couple in their aim to become finally parents, because they only are informed why it does not work. In addition, there seems to be a gap between basic research in Andrology and its clinical application. What one has to keep in mind is the more or less unique structural principle of the human seminiferous tubules. Whereas in the majority of mammalians major areas of the testis are covered by the association of identical cells in a so-called stage with only minor differences of differentiation and hormonal regulation by testosterone and FSH, in the human these areas confine to a much smaller space. Therefore, areas that require a maximum of testosterone and a minimum of FSH are located directly next to those needing just the contrary relation of hormones. This leads inevitably to irritations of the development of germinal cells by minimal damaging factors. This is one of the reasons why we observe a highly varied picture of sperm morphology with only a very low percentage of normally shaped spermatozoa. Obviously, this still is enough to sustain normal reproductive capacity. Knowledge of these vulnerable basic mechanisms and regulatory factors including testicular anatomy, physiology, endocrinology and genetics ranging to potential environmental influences to this highly complicated process of spermatogenesis is prerequisite in order to understand male fertility and its disturbances and to look for reasonable solutions to overcome the problem.Approaches to management of disturbed male fertility comprise avoidance of potential harmful external influences, medical treatment or, if such measures are not applicable, cryopreservation of spermatozoa or testicular tissue before fertility damaging interventions. Most challenging tasks are new techniques such as stem cell culturing and germ cell transplantation.All these aspects are addressed in this Research Topic.Horvath-Pereira et al. report on the current state and future perspectives in using biomaterials for testicular bioengineering. The most promising approaches to preserve male fertility comprises testicular cryopreservation, and, in cases where this is not applicable germ cell transplantation and testicular grafts. In this highly forward-looking contribution the application of bioengineering with various types of biomaterials including extracellular matrix is highlighted, which sounds very promising, not least as these techniques can replace the requirement for experimental animals, a big progress.Dumont et al. have demonstrated that testicular tissue freezing has only minor impact on gene expression. In addition, they pointed out that a disorganized steroidogenic pathway and probably inflammation could explain differences between in vivo and physiological controls. Moreover, ways to further optimization of organotypic culture procedures are illustrated.As pointed out earlier the first steps of spermatogenesis are the most crucial ones for further development of the germ cells. One of the most challenging issues in the future represents suitable cryopreservation techniques of prepubertal testicular tissue for boys who have to undergo fertility damaging treatments, mostly because of cancer. In their elegant animal study, Bhattacharya et al. emphasize the necessity to generate translational progress in terms of clinical success in order to improve male infertility and to develop reversal male contraception methods. For this purpose identification of further FSH- and LH(T) responsive genes is required.Although gonadotrophins have been used since decades for treatment of male hypogonadism and infertility there are still some critical questions to be answered in order to optimize the management of male reproductive health. Lai et al. highlights the harmful influence of thoracic X-ray radiation on spermatogenesis in mice with all the consequences of increased pro-inflammatory cytokines. A problem that could be minimized if one were aware of it.The highly relevant contribution by So, this plethora of information providing new and promising perspectives for male fertility problems should be attractive for all being active in reproductive medicine.The author confirms being the sole contributor of this work and has approved it for publication.