Advances in microscopy have increased output data volumes, and powerful image analysis methods are required to match. In particular, finding and characterizing nuclei from microscopy images, a core cytometry task, remains difficult to automate. While deep learning models have given encouraging results on this problem, the most powerful approaches have not yet been tested for attacking it. Here, we review and evaluate state-of-the-art very deep convolutional neural network architectures and training strategies for segmenting nuclei from brightfield cell images. We tested U-Net as a baseline model; considered U-Net++, Tiramisu, and DeepLabv3+ as latest instances of advanced families of segmentation models; and propose PPU-Net, a novel light-weight alternative. The deeper architectures outperformed standard U-Net and results from previous studies on the challenging brightfield images, with balanced pixel-wise accuracies of up to 86%. PPU-Net achieved this performance with 20-fold fewer parameters than the comparably accurate methods. All models perform better on larger nuclei and in sparser images. We further confirmed that in the absence of plentiful training data, augmentation and pretraining on other data improve performance. In particular, using only 16 images with data augmentation is enough to achieve a pixel-wise F1 score that is within 5% of the one achieved with a full data set for all models. The remaining segmentation errors are mainly due to missed nuclei in dense regions, overlapping cells, and imaging artifacts, indicating the major outstanding challenges. As generating brightfield images is relatively quick and noninvasive, it would benefit many protocols to be able to segment nuclei directly from them. However, this rewarding task remains challenging5 and is therefore the main focus of this work.Microscopes collect images from scales of atoms and molecules to cells and tissues. While visual inspection can guide intuition, automated image processing is central for distilling understanding from the gathered data. Computational analysis approaches have evolved together with the instrumentation, with a plethora of methods developed to date.6 object detection,7 and segmentation.8 The cell microscopy analysis community and the larger cytometry field in general have taken note and exapted the useful ideas.911 One of the earliest popular approaches that utilized convolutional neural networks for nuclear segmentation from fluorescence images was DeepCell.12 Many more have been proposed since.1320 U-Net,21 later superseded by U-Net++,22 has also been introduced as a plugin for ImageJ23 to make the models accessible for biological image analysis. Overall, classical methods have been outperformed by deep learning techniques for nuclear segmentation, justifying the substantial interest in them.4 However, while segmenting the more challenging brightfield cell images has also been attempted,24 there remains a performance gap compared with fluorescence segmentation.Over the last decade, deep learning-based approaches have advanced image classification,2527 The key advances in image segmentation have come from properly accounting for context.28 The early approach was to use the features extracted in earlier layers as inputs into deeper ones,29 followed by considering a broader context for the segmented object.31 Training was further improved by data augmentation,33 and objects of different scales better handled with scale-robust architectures.31 These recent ideas and advanced networks could improve nuclear segmentation performance as well, but have not yet been utilized for this purpose.The rapid development of deep learning has continuously provided new insights that could also impact practical solutions.22 Deeplabv3+,30 Tiramisu,25 and a modified version of U-Net,5 as well as a novel streamlined PPU-Net architecture, for identifying nuclei. To gain a deeper understanding, we investigate the causes of variable performance across cell lines and images, the sources of error, and the data requirements for training successful segmentation models.Here, we tackle the nucleus segmentation problem in brightfield and fluorescence images with current state-of-the-art deep learning approaches. We evaluate the architectures of U-Net++,To evaluate modern deep learning methods for the nuclear segmentation task, we use two data sets containing a total of eight different cell lines, five alternative neural network architectures, and a unified training process that includes models with different amounts of training data, transfer learning, label smoothing, data augmentation, and multiple evaluation metrics .We evaluated four convolutional neural networks that cover the successful architectural features for image segmentation: skip connections, atrous convolution, pyramid pooling, and dense blocks. All models are end-to-end trained encoder–decoder networks with a downsampling contraction path, an upsampling expansion path, and a bottleneck to connect them. Inspired by the surveyed literature, we also propose a new architecture (PPU-Net) to strike a balance between model size and performance. We describe each of the models in detail below.21 has already been adapted for brightfield nuclei segmentation.5 The architecture has four main components: a contraction path, an expansion path, a bottleneck to connect them, and skip connections to enhance localization by transferring high-resolution features from the contraction to the expansion path . The contraction path in our implementation contains 15 convolution layers that use 3 × 3 convolution filters followed by rectified linear unit (ReLU)34 activation layers, with a 2 × 2 max pooling layer and a skip connection to the upsampling path every third convolution. The expansion path consists of 15 corresponding convolution layers, followed by ReLU activation layers, with an upsampling layer every third convolution. There is a bottleneck block between the encoder and the decoder with three convolution layers. Each convolution layer in the contraction path, expansion path, and bottleneck has 64 filters. In total, the architecture has 1.3 million trainable parameters .As a baseline, the successful U-Net22 enriches the skip links between the contraction and expansion paths. To achieve this, a series of dense convolutions are added to the encoder feature maps, and their output concatenated to the decoder counterparts . Both contraction and expansion paths consist of four blocks. Each block consists of two convolution layers, followed by batch normalization, followed by 2 × 2 max pooling in the downsampling path, and transposed convolution for the upsampling path. The bottleneck consists of a similar block, but without max pooling. The skip connection pathways from the first, second, and third contraction path blocks to the corresponding expansion path blocks consist of three, two, and one block, respectively. Each convolution layer in the first block of the contraction path, the corresponding block in the expansion path, and the skip connection pathway between them has 32 filters of size 3 × 3. The second, third, and fourth blocks have 64, 128, and 256 filters, respectively, and the convolution layers in the bottleneck have 512 filters each. In total, U-Net++ has ~9 million trainable parameters .U-Net++30). Like the other considered models, its most recent member, Deeplabv3+, has the form of an encoder–decoder network, with the Xception backbone35 for sharper boundaries of segmented objects. The published encoder module consists of four parts: entry flow, middle flow, exit flow, and atrous spatial pyramid pooling . The entry, middle, and exit flows have 3, 16, and 2 blocks, respectively, each consisting of two 3 × 3 separable convolutions,36 and another one with a stride of 2 for downsampling. The input to each block is concatenated to its output. The decoder branch combines the output of the encoder with feature maps of low-level features. Atrous spatial pyramid pooling uses filters of various sizes for the skip connections. Here, we use the published Deeplabv3+ with the Xception backbone, and the output stride parameter set to 16 to balance performance, accuracy, and speed.30 In total, Deeplabv3+ has ~41 million trainable parameters .The Deeplab family of models has evolved to be some of the most sophisticated in the field25 uses dense blocks37 for segmentation . In a dense block, feature map inputs to each convolutional layer are concatenated to the outputs of all further convolutional layers in that block, introducing deep supervision and feature reuse. The Tiramisu has a contraction path of five dense blocks, each followed by a transition-down block of batch normalization, ReLu, and 2 × 2 max pooling, and an expansion path of five dense blocks, each followed by a transition-up block (3 × 3 transposed convolution with a stride of 2). The dense blocks in the contraction and expansion paths use  and  layers, respectively. The bottleneck dense block between the two paths contains 15 layers. Finally, a single convolution layer is added to the beginning of the contraction path. Each convolution layer in the dense block has sixteen 3 × 3 filters, while the first convolution layer has forty-eight 3 × 3 filters. In total, this network has ~9.4 million parameters .The Tiramisu architectureFig. 1E). Its relevant features attempt to crystalize different aspects of progress in the segmentation models above. First, PPU-Net exploits both short and long skip connections. This was motivated by a demonstration that both types help to achieve better performance and faster convergence; the short skip connections also stabilize parameter updates and help in solving a vanishing gradient problem that prevents parameters from being effectively updated.32 Second, to cover the necessary global and subregional context without losing spatial relations, PPU-Net employs the hierarchical pooling module (pyramid pooling31) in long skip connections.Inspired by the diversity of successful ideas in the field, we designed our own pyramid pooling U-Net architecture (PPU-Net) . This architecture is the second smallest after standard U-Net with ~2.1 million trainable parameters, which is 5% of Deeplabv3+, the largest tested architecture. It is also the fastest at segmenting an image, taking 0.23 s, on average .The PPU-Net consists of a contraction path, an expansion path, a bottleneck between them, and a skip pathway connection that link the features in the contraction path to the ones in the expansion path. There are 10 blocks in the contraction path, 10 blocks in the expansion path, and 2 blocks in the bottleneck between the paths. A block comprises two 3 × 3 convolutions, each of which is followed by batch normalization and ReLU activation layers. Each convolution layer in such a block has 64 filters. The contraction path block output is processed by the pyramid pooling module in the skip pathway, and concatenated to the input of the corresponding block in the expansion path. This module integrates information from five different scale levels by average pooling the feature maps with pool sizes of 16 × 16, 8 × 8, 4 × 4, 2 × 2, and 1 × 1 and strides that equal the pool size. The output of each pooling level is processed by sixteen 1 × 1 convolutions, followed by batch normalization and ReLU activation layers. The output is rescaled bilinearly to match the input dimensions, and concatenated with the input again , epithelial cells from kidney tissue of adult dogs (MDCK), human hepatocellular carcinoma cells (HepG2), human breast cancer cells (MCF7), mouse embryonic fibroblast cells (NIH3T3), human alveolar basal epithelial cells (A549), and human fibrosarcoma (HT1080) were seeded into collagen type 1-coated CellCarrier-384 Ultra Microplates  using 48 wells for each. The cells were fixed in formaldehyde  and stained with 10 µg/mL Hoechst 33342 . Images were acquired using a 20× water immersion objective on an Opera Phenix high-content screening system (PerkinElmer) in confocal mode for both brightfield and fluorescence modalities. A total of 3024 images of size 1080 × 1080 pixels (1 pixel = 0.59 µm) were acquired for each modality, with nine fields of view from each well (432 combined) for each of the seven cell lines, and 353 cells in each field of view, on average . This is referred to as “seven cell line” data.Two data sets were used in this study with a total of eight different cell lines. Their provenance has been described previously,Suppl. Table S1). This is referred to as the “LNCaP” data. For both seven cell line and LNCaP data, one modality was acquired first on all wells and fields of view, and the second one in a subsequent round.Second, human prostate adenocarcinoma  cells were seeded into the 384 wells of a CellCarrier Ultra (PerkinElmer) microplate, fixed in formaldehyde, and stained using DRAQ5 fluor  to label nuclear DNA. A total of 784 images of 2556 × 2156 pixels (1 pixel = 0.325 µm) were acquired using a 20× objective in both confocal mode to capture fluorescence images and brightfield mode on a CellVoyager 7000  instrument, giving an average of 681 cells per image  with expert quality control to optimize parameters was used to generate ground truth masks from fluorescence images of nuclear stains for the seven cell lines, as described in Fishman et al.38All the experiments were conducted using a Tesla V100-PCIE-16GB Graphics Processing Unit, and the architectures were built using the Keras framework with TensorFlow backend v1.14.0.39 was used to optimize binary cross-entropy loss. Each architecture was trained for up to 500 epochs with  iterations. The learning rate was selected as described below, and reduced by a factor of 10 once the validation loss was not improving for 10 consecutive epochs. Training was terminated completely if validation loss was not improving for 20 consecutive epochs. Batches of size 16, 8, 8, 4, and 8 images were used for U-Net, U-Net++, Deeplabv3+, Tiramisu, and PPU-Net networks, respectively, chosen based on the available processing budget.40 All networks have an input size of 288 × 288 pixels.The five models  were trained on the seven cell line data set (separately on fluorescence and brightfield images), using 2016 images for training, 504 images for validation, and 504 for testing; and on LNCaP, using 628 images for training, 78 for validation, and 504 for testing. The Adam optimizer41 We used the strategy introduced in Smith40 to select it separately for each model. We monitored loss during training each model for a few epochs, while gradually increasing the learning rate from a very small value (1e-10) to a very large one (10). The candidate optimal learning rate was manually identified as the value that gives the largest change in loss . We then performed full training for the models using different learning rates around the candidate and selected the best. Selected rates for the brightfield and fluorescence data sets are 1e-5, 1e-3, 5e-4, 5e-4, and 5e-4; and 3e-5, 3e-4, 5e-4, 5e-5, and 3e-4 for U-Net, U-Net++, Deeplabv3+, Tiramisu, and PPU-Net, respectively.Learning rate is among the most critical hyperparameters for training neural networks.To simulate situations in which a data set of a few images is available, different models using an increasingly different number of images were trained. We randomly extracted 1, 2, 4, 8, 16, 32, 64, and 286 images of the A549 cells from the seven cell line data. Those data sets are used to train seven models from each network architecture. The same models are also trained two more times, once with label smoothing and another time with data augmented using five basic data augmentation techniques . The models were evaluated using the A549 cell line held-out data.42 The targets were softened such that the positive label 1 is reduced by a smoothing factor and the negative label 0 is increased by the same factor. We selected the best smoothing factor by conducting a grid search on models trained using factors of 0.05, 0.10, 0.15, and 0.20; selected the best performing of each architecture; and evaluated them on held-out data (Suppl. Table S2). The selected factors are 0.05, 0.15, 0.20, 0.20, and 0.15 for U-Net, U-Net++, Deeplabv3+, Tiramisu, and PPU-Net, respectively.In label smoothing, the cross-entropy loss is optimized against soft targets.33 to the vanilla U-Net. Third, we increased the number of connections as well as introduced batch normalization, and further modified the number of filters in the convolution layers. We gradually increased the number of filters in the contraction path to be 64, 128, 256, and 512 filters while decreasing them symmetrically in the expansion path. Finally, we excluded half of the U-Net++ convolutions in the skip connection layers.To test the effect of making different selections for network architecture, we evaluated three aspects that distinguish U-Net from U-Net++. First, we doubled the number of U-Net pathway connections. Second, we added batch normalizationTo evaluate the models’ performance across domains, we fine-tuned a model trained on a source domain (one data set) using images from the target domain (another data set), as well as using images from both source and target domains. In both cases, we used an increasing number of images  to fine-tune the model. When fine-tuning on images from multiple domains, the same number was picked from both domains. We examined two sets of source and target domains. First, we used six cell lines from the seven cell line data set as the source domain and the remaining cell line as the target domain, and repeated for each cell line, introducing a small domain shift of the different line, but still considering images from the same acquisition experiment. Second, we used the LNCaP data set as a source domain and the seven cell line data collected on another instrument as a target domain, introducing a large domain shift of the imaging instrumentation and laboratory undertaking the work. We conducted the experiments of this section on the lightest model (U-Net), as we expect domain shift, rather than model differences, to dominate quality.The LNCaP data set was used to learn about the impact on segmentation performance with different numbers of unique focal planes as network input. To keep the model’s number of parameters constant, the number of input channels was fixed to nine, and the number of input focal planes was varied from nine copies of a single plane to nine different planes. The order in which to add planes was experimentally determined. First, we trained a different model for each plane and picked the best input plane based on evaluation. Then, we repeated the experiment to pick the next best plane out of eight possible variants in addition to the previously picked one.43 We filtered out objects smaller than 25 pixels and filled out holes smaller than 25 pixels using remove_small_objects and remove_small_holes, respectively, from the same package.To evaluate models, we first postprocessed the image probability maps they produce. All results are based on pixel-level outputs binarized at a 0.5 cutoff unless detailed otherwise. Objects are detected by clustering the interconnected positive pixels into objects using measure.label from the skimage package.5 To quantify object-level accuracy, we measure the intersection over union (number of pixels in intersection of two objects divided by number of pixels in their union [IoU]) for pairs of segmented and ground truth objects, and consider a ground truth object detected at an IoU threshold if there is a segmented object with an IoU value to it above the threshold. We report the F1 score for object detection across IoU thresholds ranging from 0.5 to 0.95 with a step of 0.05, as well as averaged over these thresholds (object-wise F1 score), as established earlier.4 We also record the number of merges (more than one ground truth object overlaps a predicted one), splits (a single object in the ground truth overlaps multiple predicted objects), and missed objects (ground truth objects that are not detected). To detect splits and merges, which lead to a small object overlap by definition, we used an IoU threshold of 0.1. To detect missed objects, we used an IoU threshold of 0.6, which gives a good balance between strict overlap and not missing objects entirely.We used both pixel-wise and object-wise metrics  to quantFinally, we assigned a likely cause of error to mislabeled pixels. First, we assigned errors to noisy input data if at least four out of five models agree on the same prediction in a fluorescence image that is opposite to the ground truth label, and calculate the fraction of errors that these pixels account for. Second, to quantify the error contributed by imaging artifacts, we manually annotated artifacts in 20 images and recalculated performance outside those anomalous regions to derive a difference in error that can then be ascribed to the artifacts.Suppl. Tables S3 and S4). All models detected nuclei in the fluorescence images with high pixel-wise F1 scores  , as well as object-wise scores  (Suppl. Table S3). This confirms that the signal in the fluorescence channel is clear enough to be detected regardless of the model used , as has been observed before.5 We therefore consider fluorescence segmentation a solved problem to a practical limit, and the rest of the results focus on the more challenging brightfield modality.We assessed the performance of four state-of-the-art and one novel segmentation model on the seven cell line and LNCaP data sets (Suppl. Table S3) in the seven cell line data set; and 84.0%, 86.2%, 87.7%, 85.8%, and 87.4% in the LNCaP data set, demonstrating that the classical U-Net model is outperformed by newer architectures . This trend is consistent across individual images  as well as within specific cell lines, and therefore is likely due to real performance differences, rather than biases from individual images or cell lines.Unlike for fluorescence images, models’ performances on brightfield images have a wider range. The methods achieved pixel-wise F1 scores of 81.3%, 85.0%, 86.2%, 85.6%, and 86.5% for U-Net, U-Net++, Deeplabv3+, Tiramisu, and PPU-Net, respectively (4) to identify correctly segmented nuclei. In line with pixel-wise results, PPU-Net slightly outperformed other models in the seven cell lines and was on a par with Deeplabv3+ in the LNCaP data set. Classical U-Net was inferior  . Importantly for cytometry applications, we confirmed that the object detection quality is echoed in the ability to recapitulate object properties. Indeed, a higher object-wise F1 score also gives a better match to ground truth object solidity and size .Next, we assessed object segmentation performance, using the area intersection over union . This trend held across all data, as well as within images from individual cell lines , with the exception of the MDCK line, which has a narrow range of low cell densities. Regression line slopes per model ranged between −1.2 × 10–4 and −1.4 × 10–4, suggesting that the models did not differ substantially in the degradation of their performance as cell densities decreased. Overall, dense images are more challenging to segment into individual objects, as expected.It is important to understand when brightfield segmentation can be expected to be successful and what the quality determining factors are. We first tested whether cell density influences segmentation performance and found a negative correlation between the number of cells per image and the pixel-wise F1 score . The objects in the largest decile have at least a 9% greater median pixel-wise F1 score than the objects in the smallest decile for all models in both the seven cell line and LNCaP data sets . We also wondered whether models cope differently with images that have variability in object size, but saw no effect  .Next, we considered whether the size of nuclei impacts the accuracy of prediction. We observed that larger objects are segmented more accurately by all models in the complete data set . Second, we added batch normalization,33 which also improved the pixel-wise F1 score by 0.02. We then increased the number of connections as well as introduced batch normalization, and further modified the number of filters in the convolution layers, improving the score by 0.026 compared with baseline. Finally, we excluded half of the U-Net++ convolutions in the skip connection layers but did not observe substantial deterioration in performance (0.001 in pixel-wise F1 score) . These results suggest that multiple architecture choices are responsible for the largest observed model performance differences, and that changing the number of parameters alone is not sufficient to achieve large changes to performance.As we found models to have a range of performances, we attempted to understand which model features are responsible for the differences. We suspected that the lower performance of U-Net could be ascribed to the model representation capacity  and training approach. To test this, we evaluated three aspects that distinguish it from the otherwise similar U-Net++ architecture. First, we doubled the U-Net pathway connections, and this improved the pixel-wise F1 score by 0.006 . Based on the severity of the contamination, all models struggle to delineate a nucleus and can miss it entirely. We observed likely cases of biological contamination during cell culture  , as well as particle contamination during acquisition  . Second, predicted nucleus boundaries are shifted between brightfield and fluorescence modalities . We confirmed visually that the image registration was correct overall, and other cells in this field of view are concordant, and therefore hypothesize that the mismatch is due to a true physical shift between brightfield and fluorescence modalities, for example, if cells are moving when not properly adhered to the plate. Third, there can be low contrast between the cell nuclei and the background . This lack of signal can lead models to miss nuclei or to merge objects. Finally, out-of-focus cells can still be visible in fluorescence images, but are difficult to detect in brightfield, as their signal is distorted . These findings are consistent with error modes previously characterized in these data.5The results of the second-generation deep learning models for brightfield nuclei detection were better than earlier reports,Fig. 3E). Together, these results suggest that anomalies and noisy labels contribute about 15% of the errors.We next attempted to quantify the relative contribution of the various errors. To do so, we first picked 20 images with evidence of sample contamination and recalculated performance outside of manually annotated anomalous regions only. An average of 10.7%  of misclassified pixels were caused by those anomalies, and filtering out the anomalous regions improved the pixel-wise F1 score by 1.6%–1.9% per image. This suggests that artifacts are a substantial but not the major source of remaining pixel errors in segmentation. Second, we found that 53%–61% of errors are due to false-negative pixels, consistent with either underprediction at object boundaries or missing entire objects, contributing more errors compared with false-positive pixels. Finally, we estimate 3%–5% of misclassified pixels to be due to noise in the ground truth labels . However, it also had a large proportion of undetected nuclei, on average, across all the thresholds (35% in both data sets) . Taking into account all types of errors and requiring a stricter object overlap, PPU-Net had the best object-wise performance by a narrow margin over Tiramisu and Deeplabv3+ in the seven cell line data set, with F1 scores of 59.8%, 58.2%, and 58.1%, respectively, and U-Net++ and U-Net having lower scores of 55.4% and 48.8% . In the LNCaP data set, PPU-Net and Deeplabv3+ had on-par performance data with object-wise F1 scores of 54.6% and 54.9%, respectively.Next, we considered object-level errors of splits, merges, and missing nuclei at a range of pixel classification thresholds . Compared with other advanced models, Tiramisu had the smallest number of total merges  and splits , on average, considering 176,946 and 53,828 objects in the seven cell line and LNCaP data sets, respectively .The results so far were obtained on a large training data set that has thousands of annotated images. In practice, annotation is expensive, and a limited number of annotated images are available. Hence, an important practical question, especially for advanced architectures with millions of parameters, is how many training images are sufficient for optimal performance. Predictably, the model performance improves with more annotated images (42) and data augmentation improve performance under limited training data. We found that nearly all models performed better using each of those strategies compared with training models without any of them . Deeplabv3+ did not perform well given a small number of training images using standard or label smoothing strategies, which can be due to its large number of parameters , but other models improved performance by 0.5%–2%, on average, when label smoothing was used. Data augmentation improves the performance of all models more, with an average increase in pixel-wise F1 score from 9% to 11%. Moreover, using only 16 images with data augmentation is enough to achieve a pixel-wise F1 score that is within 5% of the one achieved with a full data set for all models. Improvements provided by both strategies diminish when the data set size increases.We tested whether label smoothing . Conversely, a model trained on a subset of six of the seven cell lines in one imaging experiment, and applied on the seventh, only performed 5% worse than the best one trained on that cell line .Next, we considered transfer learning to deal with limited training data. First, we used a model that was trained on one data set (source domain) and to segment images in another (target domain). Intuitively, the performance then indicates how distant the target and source domains are. None of the transferred models perform near the optimum in another domain, and the domain shift is most marked for cells from another imaging experiment on different instrumentation  (C). To avoid the loss of performance on the source domain, we then fine-tuned the model using data from both domains. This retained F1 scores within 2% of the optimum on the source while improving the score on the target domain as the number of fine-tuning images was increased (C). Therefore, if the goal is to use the model in different domains, training data should be maintained for all of them, and the fine-tuning data set should reflect this.Next, we used an increasing number of images from the target domain to fine-tune the model. Fine-tuning improves performance on the target domain but degrades performance on the source domain . Additional planes gave diminishing returns, with scores within 1.7% of the two-plane performance, on average.Finally, we asked whether increasing the number of focal planes that are used in training improves segmentation performance. We found that adding one additional plane increased the pixel-wise F1 score by 3.3% on average, ranging from 4% in U-Net to 2.8% in DeeplabV3+  to well-performing  ones, and proposed PPU-Net, a novel architecture for this task. PPU-Net segments nuclei as accurately as the comparable alternative models while featuring smaller size, shorter training time, and quicker prediction. We noticed that the performance of complex models like Deeplabv3+ degrades when the amount of training data is small. We identified the number of focal planes, cell density, and nuclear size (but not its variability across cells), to influence segmentation quality, and established that a small number of ground truth images combined with substantial augmentation is sufficient for training a well-performing model. To our knowledge, these are the first experiments to segment nuclei from brightfield cell microscopy images with very deep neural networks, novel insights into their performance, and the most accurate segmentations presented to date.5 but not yet as accurate as fluorescence-based segmentation approaches. Part of this improvement can be ascribed to advances in methodology, where the network size, qualitative features, and training approaches all had an effect on the outcome quality. In concordance with prior work, we observed modality-specific error sources, such as low contrast, likely physical shifts, and noisy ground truth labels. Some of these, such as physical shift, are systematic and unlikely to be improved by more complex models. Others, like out-of-focus cells, could be optimized by dedicated data acquisition and training. Upon inspecting the errors, we believe that there is room for further improvement to the current models, mainly by avoiding anomalous regions, having noisy labels in training data, and better segmentation of smaller objects.The second generation of deep learning models for brightfield nuclei detection were superior to the initial tests on the same data,4 and Fishman et al.5), providing more training data improves the ability to accurately identify nuclei. While data acquisition is not limiting, annotating ground truth in brightfield modality can be a substantial bottleneck, even when fluorescence-guided nuclear segmentation is available. Various data augmentation techniques, such as signal-preserving orthogonal rotations and reflections, as well as lossy general rotations and scalings, can all help bootstrap additional signal for the same data, and thereby improve training for models that do not take these invariants into account. Soft labeling, or intuitively allowing false-positive and false-negative rates in the ground truth, also improves outcomes. Therefore, when compute time and cost are not limiting, but data set sizes are, we recommend augmenting the training data.As expected, and observed before , additional signal (more photons inform of their location), or a simpler context . Conversely, the most difficult nuclei to segment were small and densely packed, in particular, for the HepG2 cell line. The dense packing problem is a general standing issue in instance segmentation. Dedicated object delineation models and bespoke data sets outside the scope of this work are needed to establish the best way of attacking this in cell microscopy.The quality of brightfield cell nucleus segmentations is such that they are useful in practice. A major future direction is to expand this approach to segment entire cells, which would aid cytometry applications, especially in cases of relatively dense cultures. Substantial additional training data, as well as innovation in handling dense and overlapping objects, are required to make progress in this direction.Click here for additional data file.Supplemental material, sj-pdf-1-jbx-10.1177_24725552211023214 for Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images by Mohammed A. S. Ali, Oleg Misko, Sten-Oliver Salumaa, Mikhail Papkov, Kaupo Palo, Dmytro Fishman and Leopold Parts in SLAS Discovery


The aim of the study was to assess the occurrence and nature of immune-related endocrine adverse events (irAEs) among patients with non-small-cell lung cancer (NSCLC) treated with nivolumab.Methods: The study group included 35 patients  with NSCLC in stage IIIB  and IV  who were treated with nivolumab.Results: Of the studied patients, 34.3% (n = 12) developed endocrine irAEs (irAE group): 22.9% (n = 8) hyperthyroidism and 8.6% (n = 3) hypothyroidism, and in one case, hypophysitis was observed. The median irAEs onset time was 2 months. In the group of patients with thyroid disorders, permanent hypothyroidism eventually developed in 58.3%. The severity of the analyzed irAEs ranged from mild to moderate (Grade 1–2); the case of hypophysitis was estimated as Grade 3. The comparison of progression-free survival time (PFS) between the two groups showed longer PFS in patients in the irAE group (p = 0.021). Patients with irAE were treated significantly longer with nivolumab and they received more doses of nivolumab, however in Cox analysis we did not find patients with irAE to experience progression later than patients without them.Conclusions: Nivolumab therapy is associated with an increased risk of endocrine adverse effects, particularly thyroid dysfunction. Endocrine adverse effects can be successfully treated pharmacologically and usually do not require discontinuation of immunotherapy. The relationship between a better cancer prognosis in patients who developed endocrine irAE has not been found. The immune checkpoint inhibitors (ICIs) are a new class of anti-cancer drugs that have already proved their efficacy, revolutionizing treatment in many types of cancer, e.g., melanoma, non-small-cell lung cancer (NSCLC), and renal cancer. Their mechanism of action is to restore the function of the immune system that was blocked by a cancer that affects T and B lymphocytes, natural killer (NK) cells, and macrophages .Focusing on NSCLC, in Poland, NSCLC patients currently have access to the following ICIs and are only reimbursed for this indication: nivolumab and pembrolizumab (programmed cell death protein (PD-1) inhibitors) and atezolizumab (programmed cell death ligand 1 (PD-L1) inhibitor). For first-line treatment, pembrolizumab can be used in NSCLC patients with high expression of PD-L1 on tumor cells (≥50%). No combination of immunotherapy with chemotherapy is available for Polish patients. For second-line treatment, regardless of PD-L1 expression, two drugs can be used, nivolumab and atezolizumab, both for non-small-cell lung cancer .Nivolumab, a monoclonal antibody that inhibits the PD-1 molecule, is primarily located on activated T lymphocytes as well as on NK cells, that is, on the immune system cells involved in the anti-cancer response , 4. The With the emergence of immunotherapy in oncological treatment, we started to observe adverse events different from those normally associated with classical chemotherapy. Those differences result from the effect of ICIs on the immune system and its stimulation, which leads to autoimmune reactions, especially in the endocrine system, gastrointestinal tract, skin, and respiratory system . ToleranAmong patients treated with nivolumab, common adverse events such as rash, musculoskeletal pain, fatigue, pruritus, diarrhea, and nausea, are observed in approximately 20% of patients . AdverseAmong the endocrinopathies, the most observed are thyroid dysfunctions: hyperthyroidism 0.8–15.3%) and hypothyroidism (2.6–10.1%) .3% and h–19. HypoA recent analysis of patients with NSCLC treated with anty-PD1 inhibitors found a positive association between thyroid dysfunction and a possible improvement in survival in patients who developed this complication –27. The The study group included 35 patients  with NSCLC in stage IIIB  and IV . Histopathology included squamous cell carcinoma , adenocarcinoma , and not-otherwise-specified (NOS) NSCLC  treated with nivolumab. In this article, we present the data of all patients who were enrolled to an early open-access program of nivolumab treatment in second and further lines of therapy before it was widely accessible and reimbursed in Poland.EGRF gene mutation, after failure of treatment with tyrosine-kinase inhibitors. In one of the enrolled patients, the ALK rearrangement in the absence of PD1 expression was identified during immunotherapy. Yet, nivolumab treatment was continued because of good therapeutic effect; in the long term, complete remission was achieved [Eligibility criteria included histologically or cytologically confirmed NSCLC in stage IIIB or IV  after faachieved . All patachieved .All patients started treatment with nivolumab from November 2016 to January 2017. Treatment was continued to disease progression, death, or unaccepted tolerance. Retrospectively, all data from the beginning of the treatment until January 2020 were collected, involving 36 months of follow-up. Nivolumab was administered intravenously every 2 weeks at a dose of 3 mg/kg, after modification of the characteristics of the medicinal product at a constant dose of 240 mg at two weeks intervals. The characteristics of the study group are shown in Thyroid function was assessed at baseline (before starting nivolumab) and every 4 weeks during treatment. We assessed the occurrence of adverse events affecting hormonal functions with a possible immunological mechanism (immune-related adverse events (irAEs)) related to nivolumab treatment. Adverse events were evaluated according to CTCAE v5.0 . SubclinThe time to occurrence of adverse events, recovery status, the use of systemic steroid therapy in the treatment of selected irAEs, and the impact of the presence of endocrine irAEs on the efficacy of lung cancer treatment were assessed. Patients were divided into two groups. The first comprised patients with endocrine-immunological adverse events (irAE group) and the second included patients without these effects (non-irAE group). In each group, we evaluated progression-free survival (PFS) and overall survival (OS) using the Kaplan–Meier method and log-rank test. Progression-free survival was defined as the time from start of treatment to documented disease progression or death. The irAE group included patients who had any hormonal disorders during immunotherapy (both de novo and change in severity or character of pre-existing endocrine disorders).In addition, to reduce immortal time bias, a Cox regression model with time-dependent variables was used to verify whether subjects developing subsequent endocrine complications experienced cancer progression later.In 48.5% of patients, we retrospectively determined PD-L1 expression in archived material using the immunohistochemical (IHC) method with the SP263 antibody clone in the BenchMark GX stain . We attempted to assess the association of endocrine irAEs occurrence with PD-L1 expression.2 test was conducted. If the expected frequency in cells was too small, the Fisher’s exact test (for 2 × 2 tables) or Monte Carlo simulation (for larger tables) was performed. A 5% inference error and the related significance level p < 0.05, indicating the existence of statistically significant differences or dependencies, were assumed. Statistical analysis was performed in SPSS software version 25 .The obtained results were subjected to statistical analysis. The values of the analyzed parameters measured on the nominal scale were characterized by the number and percentage, and on the quantitative scale by the mean, median, and standard deviation. To identify any differences a Mann–Whitney test or χPrior to initiating nivolumab treatment, all patients were euthyroid. Among the observed subjects, 14 patients had a prior history of endocrine diseases, including 5 patients diagnosed with nodular goiter. Four of them had no evidence of thyroid gland dysfunction during nivolumab therapy. However, in one of these patients, 2 months after treatment initiation, subclinical hyperthyroidism developed. The most likely mechanism of this abnormal finding was destructive thyroiditis (thyrotoxicosis).The characteristics of patients who developed endocrine disorders most probably in the immunological mechanism  are presented in In the examined group, hyperthyroidism was observed in eight patients (22.9%); in six of them, thyroid dysfunction was transient, most probably due to destructive thyroiditis. In three patients, hypothyroidism was observed manifesting either the aggravation of pre-existing Hashimoto’s disease or reflecting de novo development of autoimmune thyroiditis .In the group of patients with thyroid disorders, persistent hypothyroidism eventually developed in seven cases (58.3%) and normal thyroid function was restored in two cases (16.6%); in one case, subclinical hyperthyroidism remained. One patient died because of pulmonary adverse effects.In one patient , 11 months after nivolumab therapy initiation, patient reported difficulties in reading, weakness, worse well-being, and weight loss. In laboratory studies hyperkalaemia and hyponatraemia was reported. In the hormonal assessment, secondary hypothyroidism was detected and diagnostics of pituitary function were performed. Eventually, panhypopituitarism and hyperprolactinemia  and temporal visual field defects were diagnosed. The MRI revealed a 26 × 16 × 14 mm pathological mass and irregular contrast enhancement, an enlargement of sella turcica with suprasellar expansion and stalk thickening. The diagnosis of autoimmunological hypophysitis was established (Grade 3); in effect, immunotherapy was interrupted. Replacement therapy with hydrocortisone and levothyroxine was initiated. When the irAEs diminished (Grade 1), the immune therapy was resumed. The regression of the pituitary lesion regression was observed (16 × 10 × 15 mm) in the control MRI performed six months later . SecondaThe analyzed endocrine irAEs ranges in severity from mild to moderate (Grade 1–2). Only the case of hypophysitis was estimated as Grade 3. The information on the introduced treatment is presented in EGFR mutation, ALK rearrangement, or the reason for treatment discontinuation. A higher incidence of adenocarcinoma in the non-irAE group was found (p = 0.011). Previous radiotherapy  was more common (close to significant) in patients with irAEs (p = 0.07).When comparing the two groups of patients , no relaNo differences were identified in terms of PD-L1 expression between the groups, yet these data were available for less than half of the patients.To evaluate the potential influence of endocrine irAEs on patients’ prognosis, the two groups were compared in terms of progression free survival (PFS). Median progression free survival, according to Kaplan-Meier survival analysis, in the patients without endocrine adverse events was 2 months (95%CI: 1.50–2.50), while for patients with endocrine events was 9 months (95% CI: 3.50–26.0). The occurrence of endocrine complications statistically significantly reduced the risk of disease progression , patients with endocrine events had shown insignificantly longer OS median (54.5 months), when compared with those without endocrine adverse events (33.5 months). Moreover, the presence of endocrine events had insignificantly impact on reduced risk of survival  and were in good general condition. No patient died or discontinued nivolumab treatment because of hormonal disorders: in most of the cases, observation or symptomatic treatment was sufficient.To minimize the possibility of the influence of the time that has passed since the start of the study and the classification the subjects into the group experiencing complications  Cox analysis was performed. Affiliation to the group of people with irAE was the time-dependent covariance variable. The conducted analysis did not show that the proposed model explain the variability of the time to progression. It has not been shown that subjects with endocrine complications experienced progression later than subjects without them.Our study was a single-center, retrospective study on the incidence and nature of endocrine adverse effects during immunotherapy for advanced NSCLC. The incidence of endocrine irAEs was high 34.3%), with thyroid disorders being the most common. The prevalence of endocrine disorders was higher than in clinical trials (up to 14.2%) , 32–34 b.3%, withThe most common pathomechanism of thyroid function disorders during PD-1 inhibitors treatment is destructive thyroiditis with transient hyperthyroidism or thyrotoxicosis, followed by thyroid function normalization or hypothyroidism development , 43, 44.Several studies have proved that underlying thyroid dysfunction is a risk factor of endocrinological adverse events , 45–47. The basis of the mechanism in thyroid disorders is the increased cellular response according to the action of the PD-1 molecule on the activated immunocompetent cells. This is often accompanied by a humoral component, expressed by the presence of anti-thyroid antibodies. In the present study, antibodies were elevated in only part of the irAE group; in some of them, the assay was not available, which complicated inference. The presence of antibodies in literature ranges between 16% and 80% , 44, 48,An important aspect in the field of irAE is the stratification of patients in terms of the risk of their occurrence. At present, no clear recommendations regarding monitoring of the thyroid function are available, apart from the recommendation of periodic  control of thyroid hormones. Routine assessment of thyroid antibodies before starting immunotherapy could help identify patients at increased risk and would facilitate further monitoring .Hypophysitis is a rare complication in nivolumab monotherapy (up to 0.6%) , 20. In Similar to other studies , 61, 62,In our study, the median occurrence of endocrine disorders was 2 months, which agrees with the literature data , 40, 46.In the studied population, no relationship was found between the risk of irAEs and demographic factors, which corresponds to other types of studies , 12, 46,In the irAEs group, we observed a trend toward more frequent radiotherapy (RT) prior to nivolumab treatment. We found no correlation between radiotherapy and the occurrence or severity of irAE risk, however, reports state that irradiation can modulate the immune response, e.g., by increasing the expression of MHC I antigens and other adhesion molecules on tumor cells and thereby increase their immunogenicity –69. The Significantly higher PFS in the irAE group was found in the analyzed population. The literature also reports a positive effect on survival rates or response to cancer treatment in patients who experienced irAEs , 74–78. Higher PD-L1 expression in tumor tissue is associated with a better response to treatment , 80, 81,Nivolumab therapy is associated with an increased risk of endocrine adverse effects, particularly thyroid dysfunction, which implies the need for hormonal control during the therapeutic process. Endocrine adverse effects can be successfully treated pharmacologically and usually do not require discontinuation of immunotherapy. The relationship between a better cancer prognosis in patients who developed endocrine irAE has not been found.


KIF11, WAC, and CRBN. Then, we focused on common variants and identified six genes associated with ADHD : MANBA, UQCC2, HIVEP2, FOPX1, KANSL1, and AUH. Furthermore, HIVEP2, FOXP1, and KANSL1 were nominally associated with autism spectrum disorder (ASD) , as well as HIVEP2 with anxiety , and FOXP1 with aggression , which is in line with the symptomatology of the rare disorders they are responsible for. In conclusion, inspecting Mendelian disorders and the genes responsible for them constitutes a valuable approach for identifying new risk genes and the mechanisms of complex disorders.Attention-deficit hyperactivity disorder (ADHD) is a complex neurodevelopmental disorder characterized by hyperactivity, impulsivity, and/or inattention, which are symptoms also observed in many rare genetic disorders. We searched for genes involved in Mendelian disorders presenting with ADHD symptoms in the Online Mendelian Inheritance in Man (OMIM) database, to curate a list of new candidate risk genes for ADHD. We explored the enrichment of functions and pathways in this gene list, and tested whether rare or common variants in these genes are associated with ADHD or with its comorbidities. We identified 139 genes, causal for 137 rare disorders, mainly related to neurodevelopmental and brain function. Most of these Mendelian disorders also present with other psychiatric traits that are often comorbid with ADHD. Using whole exome sequencing (WES) data from 668 ADHD cases, we found rare variants associated with the dimension of the severity of inattention symptoms in three genes:  Attention-deficit hyperactivity disorder (ADHD) is characterized by symptoms of hyperactivity, increased impulsivity, and/or inattention, affecting approximately 5% of children and adolescents and 2.5% of adults worldwide . There iThe contribution of genetic factors to ADHD is estimated to be around 70–80% in children and adults . ADHD syhttps://www.omim.org/). Around 7000 rare diseases have been described, affecting 350 million people globally, and often including a wide range of symptoms. The vast majority are characterized by Mendelian inheritance, and most of them (80%) have a known genetic cause [Some known rare Mendelian diseases, such as Phenylketonurya, Adrenoleukodystrophy, Tuberous sclerosis, Fragile X, Muchopolysacaridosis type III, Hyperlysinemia, or Joubert syndrome, include symptoms of hyperactivity and/or inattention. These disorders also present with other psychiatric symptoms that are comorbid with ADHD, as described in the Online Mendelian Inheritance in Man (OMIM) database  with the search term “hyperactivity OR hyperactive OR attention OR inattention”. We did not include a search for the presence of impulsivity only, since this is frequently present in many disorders related to aggression and not hyperactivity. Therefore, we included impulsivity only when it is presented with hyperactivity, to avoid the inclusion of rare disorders with symptomatology not directly related to ADHD.We queried the OMIM database  [This search returned 182 OMIM entries that were manually curated to discard 18 entries not related to ADHD, as well as 27 entries without a known associated gene, ending up with 137 OMIM entries linked to 139 genes. From those entries, we retrieved information on related psychiatric and neurological disorders . From a http://www.impactadhdgenomics.com) and is described in more detail by Rovira et al. [To assess the contribution of rare variants in the OMIM gene list, we analyzed the presence of high-impact rare variants within those genes and their associations with ADHD symptom dimensions in a sample of adult patients with ADHD. The dataset comprised 668 German adults with persistent ADHD, diagnosed according to DSM-IV criteria; the mean age was 34 (ranging from 18–65), with 50.8% of the sample being males. This sample is part of the International Multicenter persistent ADHD Collaboration  or a 10% False Discovery Rate (FDR). As WES analyses were not performed for variants in the X chromosome, only genes located in the autosomes could be analyzed.Using a gene-based approach, we analyzed the cumulative effect of high-impact rare variants in the OMIM-derived genes with ADHD symptoms dimensions (inattention and hyperactivity/impulsivity symptoms). The statistical analyses were performed using the Multi-marker Analysis of GenoMic Annotation (MAGMA v1.07) software, with a SNP-wise mean model for the gene-based association analysis, adjusting for genomic principal components as covariates. We considered the genes to be associated if they reached the Bonferroni-corrected adjusted threshold for the number of genes tested  and 10% FDR.The contribution of common variants (MAF > 0.01) in the OMIM gene list to ADHD was assessed through a gene-based association analysis using MAGMA v1.06  and the https://www.med.unc.edu/pgc/download-results/, accessed on 10 April 2019), except for impulsive personality traits, where results from the gene-based association analyses were obtained directly from the published manuscript . As a result, we obtained a list of 139 genes responsible for 137 OMIM phenotypes containing at least one ADHD trait; there were 65 genes related to hyperactivity, 18 to inattention, and 56 to both symptoms . From thWe further annotated whether these 137 disorders also presented with other psychiatric or neurological comorbidities, frequently observed in ADHD patients, such as autism or aggressive behaviour. From the list of 139 genes known to cause these disorders, 57 genes were related to disorders that also present with symptoms of ASD, 49 with aggression, 15 with anxiety, and 14 with OCD . Other pWe tested the possible contribution of rare variants to hyperactivity/impulsivity symptom dimensions and to inattention symptoms in a clinical sample of 668 adults with persistent ADHD. From the original list of 139 OMIM genes, we were able to extract the set of rare variants from 111 autosomal genes, with 101 of them presenting at least one high-impact rare variant .KIF11, WAC, and CRBN), were significantly associated with inattention symptoms in this sample, with inattention being present in the Mendelian disorders they are responsible for  for the enrichment of rare variants in the permutation-based analysis.Subsequently, we used a permutation procedure to assess if the burden of high-impact variants identified in the OMIM gene list was higher than expected due to chance. We identified 667 high-impact variants in 103 OMIM-genes. We observed a trend close to statistical significance  and one with a short attention span (AUH) .We analyzed the contribution of common genetic variants in the OMIM gene list to ADHD, using a gene-based approach on the largest GWAS meta-analysis of the disorder, which included 19,099 cases and 34,194 controls [an (AUH) . HIVEP2 MANBA is differentially expressed in the cerebellum  and in the cerebellar hemisphere . It is important to note, however, that the information for most gene–tissue combinations was not available. We also inspected if common genetic risk variants in these six genes were associated with changes in subcortical brain volumes, using the summary statistics of a GWAS meta-analysis of seven MRI volumetric measures from the ENIGMA Consortium [HIVEP2 were associated with changes in the nucleus accumbens (p = 4.87 × 10−3), and AUH and MANBA in the pallidum . We also identified nominal associations for the MANBA gene with subcortical volumes of the hippocampus (p = 0.032) and the thalamus (p = 0.045).We explored whether the six genes associated with ADHD were predicted to be differentially expressed in cases versus controls in different brain areas, using MetaXcan . We obsensortium . We founFollowing the previously identified ADHD-associated genes, we observed that all the Mendelian disorders with hyperactivity symptoms also presented other psychiatric comorbid traits such as ASD, aggression, anxiety, impulsivity, or OCD . We inspHIVEP2, FOXP1, and KANSL1 using a GWAS of 18,382 cases and 27,969 controls . The gene UQCC2 did not show association with ASD.For ASD we found associations with the genes controls . AssociaHIVEP2 gene using a GWAS of 7016 cases and 14,745 controls  when combining ADHD with the comorbid trait in a gene-based meta-analysis . We did KIF11, that was significantly associated with ADHD in the analysis of rare variants, was also nominally associated with aggression, consistently with the symptomatology in this Mendelian disorder  and six carrying common genetic variants that are associated with ADHD . Furthermore, for HIVEP2, FOXP1, and KANSL1, which are causal genes for rare disorders presenting with psychiatric conditions that are frequently comorbid with ADHD, we also found associations with these comorbidities.In our study, we followed a new approach to identify novel risk genes for ADHD. We searched for Mendelian disorders with a known causal gene that present with hyperactivity and/or inattention among their symptomatology, and identified 137 OMIM phenotypes and 139 genes related to them. They are enriched in genes involved in neurological processes, brain function, and development. Nine of those genes were highlighted in our study, three of which showing association with rare variants related to the severity of ADHD symptoms (p = 1.85 × 10−13) [A total of 137 rare disorders with an ADHD-related phenotype were identified, representing 2.2% of the 6138 OMIM entries with a known molecular basis. A similar number (95 disorders) was found in a previous study investigating symptomatology featuring aggressive behavior in rare disorders . Among t× 10−13) . In rode× 10−13) . The fun× 10−13) .TBC1D2, was pointed to as a risk gene in a previous study in three families with multiple ADHD-affected members, with an apparent dominant inheritance pattern [Remarkably, one of the genes included in the OMIM gene list,  pattern . This geKIF11, WAC, and CRBN, which carry high-impact genetic variants associated with inattention severity. All disorders related to these genes include ADHD among their clinical manifestations: KIF11 is responsible for a disorder with microcephaly and lymphedema (OMIM #152950), WAC for Desanto-Shinawi syndrome (#616708), and CRBN is responsible for a recessive intellectual disability syndrome (#616708). The gene KIF11 encodes for a kinesin, CRBN, which is a protein involved in ubiquitination, and WAC, a molecular adaptor involved in ubiquitination and several biological processes such as the cell cycle and autophagy.The analysis of rare variants in the OMIM gene list pinpointed three genes, MANBA, UQCC2, HIVEP2, FOPX1, and KANSL1) were related to rare disorders presenting with hyperactivity, and one (AUH) with a short attention span. MANBA is responsible for β-mannosidosis (#248510), UQCC2 for Mitochondrial complex III deficiency (#615824), KANSL1 for Kooln-de Vries Syndrome (#610443), AUH for 3-methylglutaconic aciduria type I (#250950), and both HIVEP2 and FOXP1 are related to syndromes with intellectual disability .Analysis of common variants highlighted six genes associated with ADHD, five of which (MANBA, encoding for β-mannosidase, has been found to be associated with ADHD at a gene-wide significant level in our study. The encoded protein is a lysosomal enzyme that has been especially related to kidney disease but also to psychiatric disorders. Previous studies identified MANBA as a gene that is associated with schizophrenia and nicotine dependence [pendence , as wellpendence .HIVEP2, FOXP1, and KANSL1 have been found to be associated with ADHD and also were nominally associated with ASD in our study. HIVEP2 and FOXP1 code for transcription factors and both have previously been related to autism [HIVEP2 has also been linked to schizophrenia and substance use disorders [FOXP1 was also found to be associated with aggression in our study, but, to our knowledge, it has not been previously related to aggressive behavior. One of the loci associated with ADHD in the largest GWAS meta-analysis pointed to FOXP2, another member of the same family [FOXP2 forms homo- and/or heterodimers with FOXP1 and FOXP4 [o autism ,35,36. Hisorders ,38,39, aisorders ,41, whicnd FOXP4 ,43, and nd FOXP4 . Thus, tSeveral limitations of this study should be taken into account. On one hand, the sample size for the rare variant analysis is limited, and this could have prevented us from identifying associations of other genes with the dimension of the severity of ADHD symptoms. On the other hand, the statistical power of the GWAS meta-analysis, used for identifying common variants, is still limited, especially for OCD and impulsive personality traits, in which we could not identify any gene associated, potentially due to the limited sample size of these studies. Also, common variants could not be investigated separately in the different symptom domains of ADHD. Causal genes for Mendelian disorders presenting only with impulsivity, and not in combination with hyperactivity, were not investigated, which could have hindered our ability to identify additional relevant genes for ADHD. Finally, rare and common variants within genes located on non-autosomal chromosomes could not be assessed in this study. Therefore, further studies would be of help to confirm the role of these OMIM genes in ADHD.Overall, our results delineate an alternative way to identify candidate genes for complex disorders through exploring the shared phenotypic space with Mendelian diseases. We have seen that genes related to hyperactivity and/or inattention symptoms in Mendelian rare disorders could be relevant in the common and complex forms of ADHD, and have identified mechanisms that are potentially involved in neurodevelopment and brain function. Following this strategy, we have identified nine novel candidate genes for ADHD, three of them being of special interest for their potential pleiotropic role in several ADHD comorbidities.


Because weevils are the most damaging pests of sweetpotato, the development of cultivars resistant to weevil species is considered the most important aspect in sweetpotato breeding. However, the genes and the underlying molecular mechanisms related to weevil resistance are yet to be elucidated. In this study, we performed an RNA sequencing-based transcriptome analysis using the resistant Kyushu No. 166 (K166) and susceptible Tamayutaka cultivars. The weevil resistance test showed a significant difference between the two cultivars at 30 days after the inoculation, specifically in the weevil growth stage and the suppressed weevil pupation that was only observed in K166. Differential expression and gene ontology analyses revealed that the genes upregulated after inoculation in K166 were related to phosphorylation, metabolic, and cellular processes. Because the weevil resistance was considered to be related to the suppression of larval pupation, we investigated the juvenile hormone (JH)-related genes involved in the inhibition of insect metamorphosis. We found that the expression of some terpenoid-related genes, which are classified as plant-derived JHs, was significantly increased in K166. This is the first study involving a comprehensive gene expression analysis that provides new insights about the genes and mechanisms associated with weevil resistance in sweetpotato. Ipomoea batatas (L.) Lam.) is a member of the Convolvulaceae family that is widely cultivated in the tropical and temperate zones. As a valuable source of carbohydrates, vitamins, fiber, and minerals, sweetpotato is considered one of the most important crops in the world, with an annual production of over 100 million tons globally [Sweetpotato (Cylas formicarius (Fabricius) and West Indian sweetpotato weevil (Euscepes postfasciatus (Fairmaire)), are known as the most damaging pests of sweetpotato worldwide [Two weevil species, namely orldwide ,3, causiorldwide . In Japaorldwide . Particuorldwide . Ipomeamorldwide ,6.Cylas puncticollis Boheman) and discovered the presence of a functional RNAi pathway that may be used as a new strategy for controlling this pest [Ipomoea trifida have been previously released [I. trifida is considered a model sweetpotato species, its high-quality genome and gene sequences can be utilized as a reference for the transcriptome analysis of other sweetpotato varieties.To develop resistant varieties, the identification of resistance genes and the elucidation of the mechanisms underlying weevil resistance in sweetpotato are necessary. Previous studies have reported the differences in weevil susceptibility among sweetpotato varieties ,8,9. Yadhis pest . Using nhis pest . Howeverhis pest ,18,19,20released ,22,23. BIn this study, we performed an RNA sequencing (RNA-seq)-based transcriptome analysis using resistant and susceptible sweetpotato cultivars to comprehensively analyze the differentially expressed genes (DEGs) that respond to the feeding damage caused by weevils. Specifically, we aimed to identify the related genes and to elucidate the molecular mechanisms associated with weevil resistance in sweetpotato.Euscepes postfasciatus (Fairmaire))  were inoculated into the sweetpotato tubers of weevil-resistant Kyushu No. 166 (K166) and susceptible Tamayutaka cultivars. The degree of damage was investigated by counting the number of eggs on the surface of tuberous roots at 3 days after inoculation and the total number of insects at 15 and 30 days after inoculation. In addition, the growth stages of the insects were investigated at 30 days after inoculation. At 3 days after inoculation, the average number of eggs per tuberous root was 23.1 and 23.4 for K166 and Tamayutaka, respectively, and no significant difference was detected between the two (p < 0.05) in the total number of insects between the two cultivars  with two replicates. Sequencing with HiSeqX produced a total of 219,712,549 reads for all samples . After preprocessing, a total of 209,834,637 reads  were obtained . The ave trifida . Using e trifida , confirm trifida . From th trifida .In addition, we investigated the number of DEGs across four comparisons  and the overlaps between each set of DEGs. Venn diagram analysis indicated that a total of 68 (55 + 3 + 3 + 7) DEGs were differentially expressed in both T_ino vs. T_con and K_ino vs. K_con conditions , suggestGene ontology (GO) analysis was performed using OmicsBox for the functional annotation of the identified DEGs between K166 and Tamayutaka at 30 days after inoculation. In the biological process category, the top two enriched GO terms for the set of genes upregulated in K166 were “metabolic process” and “cellular process”, followed by terms related to phosphorylation such as “phosphate-containing compound metabolic process”, “phosphorylation”, and “protein phosphorylation” a. In terBombyx mori L.). JHs and JH analogues (JHAs or juvenoids) are known to prolong larval life by inhibiting the larval–pupal and pupal–adult transitions [itf09g05600.t1, itf12g13950.t1, itf09g05580.t1, itf13g04680.t1, and itf12g14020.t1) were highly expressed in K166  had significantly increased expression in K166 (itf09g05600.t1 and itf09g05580.t1) were found to be very closely located on chromosome 9, with a physical distance of approximately 2 kilobase (kb) from each other. The amino acid sequences of the two genes are also highly conserved  were detected between the two cultivars, reflecting the difference in their genetic backgrounds. Based on the pedigree information , the genPlants possess a complex defense system against diverse pests and pathogens and a response system composed of pathogen detection, signal transduction, and defense response . Plants In contrast, the expression of genes related to the oxidation-reduction process and oxidoreductase activity were upregulated in the susceptible Tamayutaka cultivar. Sweetpotato contains several phytoalexins, collectively known as furanoterpenoids, such as ipomeamarone and its precursor dehydroipomeamarone, ipomeanine, 1-ipomeanol, 4-ipomeanol, and 1,4-ipomeadiol ,29,30. IKr-h1), which is induced by the JH via a JH receptor, plays a key role in the repression of insect metamorphosis [BR-C) functions as a “pupal specifier” in the larval–pupal transition; JH-inducible Kr-h1 binds to the BR-C promoter region and represses its transcription, resulting in the inhibition of larval–pupal transition [In the weevil inoculation test, there was no significant difference in the number of eggs at 3 days after inoculation or the total number of insects at 15 days after inoculation between the resistant and susceptible cultivars. In contrast, there was a large difference in the total number of insects and the growth stage at 30 days after inoculation. These results suggest that weevils have no preferred spawning sites between resistant and susceptible cultivars. However, the weevil growth, particularly pupation, was suppressed in K166, indicating that one possible mechanism of weevil resistance is the inhibition of weevil development and reproduction. We tested this hypothesis by investigating the JHs involved in the suppression of insect metamorphosis. A Krüppel homolog 1 gene  mapping and GWAS using the F1 populations derived from K166 and Tamayutaka. By investigating the DEGs in the selected QTL region, we can identify the candidate genes controlling weevil resistance in K166.Euscepes postfasciatus (Fairmaire)) were used for the inoculation test, and each sweetpotato sample was placed in a plastic case. We prepared two experimental plots (control and inoculation) with three biological replicates. In the inoculation plot, one sweetpotato tuber root was inoculated with 10 adults . The degree of damage to the tuberous roots was investigated at 3, 15, and 30 days after inoculation. At 3 days after inoculation, the number of eggs on the surface of the tuberous root was counted. At 15 and 30 days after inoculation, the sweetpotato samples were dissected, and the degree of damage was investigated by counting the number of larvae, pupae, and adults in the tuberous roots.The weevil-resistant K166 and susceptible Tamayutaka cultivars were chosen for this study. K166 is the progeny of a cross between Kyukei98160-1 and Murasakimasari. Tamayutaka is derived from a cross between Kanto No. 33 and Kuroshirazu . The breThe tuberous roots from the control and inoculation groups with two replicates were collected. Total RNA was extracted using the phenol–chloroform method. Lithium chloride (LiCl) precipitation was performed to remove any impurities, and the extracted RNA was purified using RNeasy Plant Mini Kit . The RNA yield (ng/µL) was measured using Qubit fluorometer , whereas the RNA quality was confirmed by agarose gel electrophoresis. The RNA-seq library was prepared using the KAPA mRNA HyperPrep Kit . The library concentration of each sample was also measured using Qubit (Thermo Fisher Scientific). All samples were pooled in equal volumes to prepare the libraries for RNA-seq, and sequencing with HiSeqX  yielded 150-base pair (bp) paired-end reads.I. trifida [https://pachterlab.github.io/eXpress/index.html, accessed on 24 June 2019), and the DEGs were analyzed using edgeR [The obtained RNA-seq data were analyzed using the following procedures. First, the quality of the paired-end reads was determined using FastQC . Second, trifida  as the r trifida . Fourth,ng edgeR . Fifth, ng edgeR .−3). Subsequent GO mapping was performed using the Blast2GO mapping against the latest version of the GO database to obtain the functional labels [−6 and an annotation cutoff of 55. Bar plots of the enriched GO terms were created for three categories: biological process, cellular component, and molecular function. For the candidate genes involved in weevil resistance, the homology of their amino acid sequences was confirmed using BioEdit. In addition, the functional gene domains were searched against several protein databases, including ProDom  [https://pfam.xfam.org, accessed on 5 November 2019) [http://smart.embl-heidelberg.de, accessed on 5 November 2019) [http://pantherdb.org/, accessed on 5 November 2019) [For the GO analysis, the FASTA sequences of the DEGs were imported into OmicsBox version 1.2 (BioBam) and aligned to the NCBI Viridiplantae NR database using blastx search  , Pfam (her 2019) , SMART (er 2019) , and PANer 2019) , using Ier 2019) .


Background: Little is known about the effect of milk intake on all-cause mortality among Chinese adults. The present study aimed to explore the association between milk intake and all-cause mortality in the Chinese population. Methods: Data from 1997 to 2015 of the China Health and Nutrition Survey (CHNS) were used. A total of 14,738 participants enrolled in the study. Dietary data were obtained by three day 24-h dietary recall. All-cause mortality was assessed according to information reported. The association between milk intake and all-cause mortality were explored using Cox regression and further stratified with different levels of dietary diversity score (DDS) and energy intake. Results: 11,975 (81.25%) did not consume milk, 1341 (9.10%) and 1422 (9.65%) consumed 0.1–2 portions/week and >2 portions/week, respectively. Milk consumption of 0.1–2 portions/week was related to the decreased all-cause mortality . In stratified analysis, consuming 0.1–2 portions/week was associated with decreased all-cause mortality among people with high DDS and energy intake. Conclusions: Milk intake is low among Chinese adults. Consuming 0.1–2 portions of milk/week might be associated with the reduced risk of death among Chinese adults by advocating health education. Further research is required to investigate the relationships between specific dairy products and cause-specific mortality. Milk and dairy products play critical roles in providing multiple essential nutrients and energy for humans  and are Although full-fat or low-fat milk has been proved to prevent multiple chronic diseases such as fractures, diabetes, and cancer, together with mortality ,6,7,8, rP25, P75) milk and its product intake in China was 20.5  g/day [Although milk intake has been gradually increased in China, it is still at a low level because of perennial dietary habits . The ave0) g/day  during 10) g/day , and alss 20.5 5., 75.0 g/The health effects of milk, a major dietary source of calcium, among Chinese adults may be very different from those in developed countries. In addition, in the context of unbalanced status of economic development, culture, and lifestyle in China, nutrition status and quality among Chinese adults are especially different. Therefore, the role of milk plays might be quite different with different dietary diversity and energy intake. Nevertheless, little Chinese research has revealed the health effects of milk on certain diseases ,22,23, aTo address these gaps in the previous research literature, the present study aimed (1) to explore the association between milk intake and all-cause mortality, and (2) to further explore the association among people with varied dietary quality.Data used in this study were obtained from China Health and Nutrition Survey (CHNS), which is a large-scale, ongoing longitudinal and household-based survey initiated in 1989; nine additional waves were collected in 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, and 2015. The detailed study design can be found on the study website . BrieflyTo investigate association between milk intake and all-cause mortality, our sample included data in the seven waves collected from 1997 to 2015 (dietary data in 2015 were not available). Data in 1989 and 1993 was excluded because dietary data in the two years were not correctly coded and could not be identified. Participants were excluded with: (1) baseline age <18 years or missing; (2) missing dietary data; (3) extreme average energy intake with <520 kcal/day or >8000 kcal/day in all the waves of participation; or (4) participation time <4 years. A total of 14,738 participants were included in this study .Dietary data were collected by trained interviewers over three consecutive days within a week at individual and household levels. The three consecutive days were selected randomly from Monday to Sunday. For individual dietary intake, all the foods consumed  by participants over the previous 24 hours were reported. Types, quantities of all food consumed, and dining places were recorded by the interviewers with the help of food models and pictures. Household food and condiment consumption were calculated by recording the changes of inventory from the beginning to the end of a three-day survey, including all purchased, homemade, and processed food .Milk was identified as liquid milk, yogurt, and milk powder (100 g yogurt or 12.5 g milk powder were converted to 100 g liquid milk according to their protein ratios in fresh milk according to CDG–2016) . Other tDietary quality was evaluated in this study by dietary diversity score (DDS) and energy intake. DDS was developed according to CDG–2016 , which cAll-cause mortality was assessed according to information reported in each wave of the survey, with updating of live or deceased status in the household registration system. Dates of death were recorded if a decease occurred. Years of follow-up were calculated from the enrolment time (1997) to death or censoring.2) and divided into four levels according to the Working Group on Obesity in China: <18.5 kg/m2, 18.5–23.9 kg/m2, 24–27.9 kg/m2, and ≥28 kg/m2 [Participants were required to complete a questionnaire and a physical examination with the assistance of trained surveyors in each wave. The questionnaire contained: (1) sociodemographic information, including age (continuous variable), sex , education , place of residence , and individual income ; (2) information about personal lifestyle behaviors, including smoking status , and alcohol intake , physical activity (MET-hour/week); and (3) information about chronic disease history (no or yes), including hypertension, diabetes, fracture, and cancers. Height and weight were measured by surveyors with standard methods. Body mass index (BMI) was calculated by dividing weight (kg) by the square of height (mχ2) tests and one–way analyses of variance (ANOVAs) were used to compare basic categorical and continuous variables of the milk intake groups. The association between milk and all-cause mortality was explored using Cox proportion hazard regression and further stratified with different levels of DDS and energy intake. The Cox proportion hazard regression included two adjusted models: Model 1 adjusted for age, sex, education, place of residence, and individual annual income; Model 2, based on Model 1, further adjusted for smoking status, alcohol intake, physical activity, BMI, chronic disease history, vegetable intake, fruit intake, red meat intake, DDS, and energy intake. The same procedure mentioned above was conducted for stratified analyses of dietary quality (including DDS and energy intake). DDS and energy intake were not adjusted in their corresponding stratified analyses. Restricted cubic spline (RCS) Cox regression was additionally performed to explore the nonlinear dose–response relationship between milk intake and all-cause mortality, adjusting for the same confounders as Model 2 in Cox proportion hazard regression. The number of knots with max R2 and Dxy in the RCS Cox regression model was selected. In sensitivity analysis, we applied inverse probability of treatment weight (IPTW) Cox proportion hazard regression to balance off the confounding factors between milk intake and all-cause mortality. In short, as a type of propensity score method, IPTW balances the distributions of confounding factors among different milk intake groups in the analysis phase, and its strength is no loss of sample size compared to propensity score match [Chi-square (re match . Confounp < 0.05 (two-tailed). Statistical analyses were performed with R 3.6.2 .Statistical significance was determined as P25, P75) DDS and daily energy intake were 3.27  and 2046.15  kcal/day, respectively. The differences in age, education, place of residence, individual annual income, smoking status, alcohol intake, BMI, chronic disease history, physical activity were significant, and sex was not statistically different among them.Sociodemographic and dietary characteristics at baseline are presented in Dietary quality and intake of other foods are shown in P25, P75) follow-up of 10.31  years with 157,528.10 person-years, 642 deaths occurred. All-cause mortality was 4.30/1000 person-years for no milk consumption, 2.53/1000 person-years for 0.1–2 portions milk/week, and 3.35/1000 person-years in the group consuming >2 portions milk/week, respectively. In the unadjusted and Model 2 Cox proportion hazard regression models, consuming 0.1–2 portions/week was associated with lower all-cause mortality compared to the no milk consumption group, while no association was observed between consuming >2 portions/week and all-cause mortality.Association between milk intake and all-cause mortality are shown in The results of the IPTW Cox proportion hazard regression were similar to those of the aforesaid Cox regression: consuming 0.1–2 portions/week but not >2 portions/week was related to the lower risk of death in comparison to no milk consumption.In the low DDS group, the results of univariate, multivariate, and IPTW Cox proportion hazard regression were similar: consumption of 0.1–2 portions/week and >2 portions/week did not change all-cause mortality compared to no milk consumption. Among participants with high DDS, in adjusted models, consuming 0.1–2 portions/week was associated with the reduction of all-cause mortality, while the phenomenon was not observed in the unadjusted or IPTW models.In the low energy intake group, the results of all Cox proportion hazard regression showed no association of consuming milk at 0.1–2 portions/week and >2 portions/week with the risk of death compared to no milk consumption. For those with high energy intake, the unadjusted, adjusted, and IPTW Cox proportion hazard regression models showed significant associations between milk consumption of 0.1–2 portions/week and all-cause mortality.R2 and Dxy in the models. Among the overall population, milk intake of about 0.1–3 portions/week was associated with decreased all-cause mortality, whereas higher-level intake (>3 portions/week) was not. In the stratified analyses, the association in the high DDS group were similar to the overall population, and milk intake of 0.1–2 portions was related to the lower risk of death among participants with high energy intake; however, the associations in the low DDS and low energy intake groups were not observed.Apart from IPTW weighted Cox proportion hazard regression, other sensitivity analyses did not substantially modify our findings. The results of the association between milk intake at baseline and all-cause mortality remained stable .In this large, national, and prospective cohort study with a long follow-up time, we first explored the association between cumulative average intake and milk intake on all-cause mortality in Chinese adults. In contrast to most reports from European and North American countries, properly increased intake of milk  could be associated with reduction of all-cause mortality in China.As a type of nutrient-dense food, milk is rich in energy, protein, essential minerals, and vitamins, such as phosphorus, potassium, vitamins A, D, and B12, riboflavin, niacin, and especially calcium . Most diIn recent decades, the role of milk in health has been controversial. Ming Ding and colleagues drew the conclusion from three large cohort studies in the United States that high total dairy consumption was not associated with the risk of mortality . In addiSeveral recent meta-analyses have also concluded that drinking milk or dairy had no benefit to health, but even increased the risk of cancer and mortality ,42,43, wP80 was even 17.8 (SD: 5.9) servings/day, or about (4218.6 ± 1398.3) mL/day. In addition, in the RCS Cox regression, we found that the decreased risk of all-cause mortality was seen only in the milk intake group of 0.1–2 portions/week rather than the milk intake group of >2 portions/week. This phenomenon might result from the rough grouping of milk intake, which impeded the evaluation of the actual effect of milk intake. The health effects of high milk intake in the Chinese population still need to be evaluated, especially for vulnerable groups such as children and elderly people.In this study, we observed the association between milk intake of 0.1–2 portions/week and the decreased risk of death, and these results are supported by several previous studies conducted in China ,22,23 anAs demonstrated by previous literature, the health effect of dairy/milk depends on people’s proper dietary and nutritional status. Our results further examined the associations between milk intake and all-cause mortality stratified by dietary diversity and the results showed that drinking 0.1–2 portions/week was associated with the decreased all-cause mortality only among people with high DDS. The results suggest that milk intake of 0.1–2 portions/week might generate higher health benefits among the population with insufficient calcium intake. While no significant association was found among the population consuming >2 portions/week, which might be explained by our rough grouping of milk intake because of the limited number of populations who consumed milk and excessive milk intake might not be beneficial and even do harm to human health. Considering synthetically the results of our study and other studies involving Chinese adults ,21,49, mAs an important component of DDS and a sign of a healthy diet, greater milk consumption was associated with higher dietary diversity and a higher intake of other healthy foods . Recent When considering energy intake, milk consumption of 0.1–2 portions/week was related to the lower all-cause mortality in the high energy intake group. One cohort study showed that a moderate energy intake  decreased all-cause mortality by 26%, compared to a low energy intake  . In our Several limitations of this study should be mentioned. First, only liquid milk, yogurt, and milk powder were included in our study, and other types of dairy products were not taken into consideration in analyses because of their low consumption among the Chinese. The obvious difference in the range of dairy products consumed between the people in developed countries and the Chinese might be one of the reasons for different conclusions between our study and other studies in developed countries. Second, milk intake was roughly categorized into three groups because of the limited number of participants who consumed 0.1–1 portion/week and excessive milk, so the results for 0.1–2 portions/week and >2 portions/week might be misestimated. Third, data on nutrient supplement intake were not available in the CHNS, which might misestimate the association between milk intake and all-cause mortality. Fourth, since cause-specific mortality was not available in CHNS, the contribution of milk intake to cause-specific mortality could not be identified and needs further investigation. Finally, potential confounding bias cannot be measured and adjusted completely, and residual confounding cannot be entirely ruled out. However, we identified the confounding factors as far as possible and meanwhile avoid excessive adjustment, and the stable results of sensitivity analyses suggest that our results are credible.In conclusion, we observed milk intake in the current population to be far lower than the recommendation of CDG–2016. The study further revealed that the consumption of 0.1–2 portions/week of milk products might be associated with the reduced all-cause mortality, and the association remains when further stratified by the dietary quality and in sensitivity analyses, which demonstrate the reliability of our results. Therefore, the heath effect of a certain food may differ in varied populations. Combined with the fact that milk intake in the Chinese population was far lower than the recommendation of CDG–2016, health education, health promotion, and encouragement toward increased milk intake to 0.1–2 portions/week are urgently needed.Further research is required to investigate the association between specific dairy products and cause-specific mortality among Chinese, and further determine the specific requirements of each kind of dairy. More studies on certain populations, such as children and the elderly, should also be taken into consideration.