Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as proteins in different functional states. Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity problem in single-particle cryo-EM. The majority of heterogeneous projection image classification methods are developed using supervised learning technology or require a large amount of a priori knowledge, such as the orientations or common lines of the projection images, which leads to certain limitations in their practical applications. In this paper, an unsupervised heterogeneous cryo-EM projection image classification algorithm based on autoencoders is proposed, which only needs to know the number of heterogeneous 3D structures in the dataset and does not require any labeling information of the projection images or other a priori knowledge. A simple autoencoder with multi-layer perceptrons trained in iterative mode and a complex autoencoder with residual networks trained in one-pass learning mode are implemented to convert heterogeneous projection images into latent variables. The extracted high-dimensional features are reduced to two dimensions using the uniform manifold approximation and projection dimensionality reduction algorithm, and then clustered using the spectral clustering algorithm. The proposed algorithm is applied to two heterogeneous cryo-EM datasets for heterogeneous 3D reconstruction. Experimental results show that the proposed algorithm can effectively extract category features of heterogeneous projection images and achieve high classification and reconstruction accuracy, indicating that the proposed algorithm is effective for heterogeneous 3D reconstruction in single-particle cryo-EM. Structural biology is an interdisciplinary field that focuses on the three-dimensional 3D) spatial structure, dynamic processes, and biological functions of biological macromolecules . Its maiD spatialThe structural heterogeneity of flexible biological macromolecules refers to the fact that the two-dimensional (2D) cryo-EM projection images involved in the 3D reconstruction are projected from different 3D structures, which is a prominent research topic in the field of cryo-EM 3D reconstruction and has received widespread attention from researchers . BiologiAlthough single-particle 3D reconstruction methods have been able to resolve the atomic-resolution 3D structures of biological macromolecules, there are still many difficulties in directly reconstructing multiple different 3D structures of the same biological macromolecule in different functional states using this technique. This is because an important prerequisite for single-particle 3D reconstruction techniques is that all 2D projection images involved in the reconstruction are projected from the same 3D structure, that is, all cryo-EM projection images are homogeneous ,22. DireThe heterogeneous 3D structures of flexible biological macromolecules are not directly observed, but reconstructed from the corresponding 2D projection images. In order to reconstruct the high-resolution heterogeneous 3D structure of biological macromolecules, all 2D heterogeneous projection images involved in the 3D reconstruction should be divided into several subsets of homogeneous projection images representing 3D structures of different biological macromolecules or different conformations of the same flexible biological macromolecule in different functional states, and then the traditional single-particle 3D reconstruction methods, such as RELION  and cryoIn recent years, researchers have proposed a large number of heterogeneous projection image classification algorithms using different methods to solve the structural heterogeneity problem of biological macromolecules in single-particle cryo-EM ,28,29. FIn the supervised classification algorithms, heterogeneous projection images are classified into different subsets of homogeneous projection images using a priori knowledge of heterogeneous 3D structures and conformational states of flexible biological macromolecules, where the critical process is projection matching. In other words, this kind of methods use known 3D structures as template structures to achieve heterogeneous projection image classification according to the similarity between cryo-EM heterogeneous projection images and synthetic projection images projected from the 3D template structures ,31. It ci and j, their polar Fourier transforms are usually discretized into 360 radial lines, and then the similarity between each pair of radial lines is computed to find the most similar pair of radial lines, which is the common line between the projection images i and j.In the unsupervised classification algorithms, heterogeneous projection images are classified into different subsets of homogeneous projection images based on traditional clustering algorithms according to the clustering characteristics of heterogeneous projection images. This kind of method calculates the distance metric between heterogeneous projection images based on the critical features of projection images such as the common lines between projection images and the class averages corresponding to the original projection images, and uses clustering algorithms combined with principal component analysis and maximum likelihood expectation maximization to achieve heterogeneous projection image classification. We proposed a weighted voting algorithm  that canWith the rapid development of machine learning methods, deep learning has become a breakthrough technique in the field of advanced machine learning, making great breakthroughs in many areas of pattern recognition. Deep learning models have strong data representation and feature learning capabilities, and have unique advantages in studying heterogeneous 3D reconstruction problems in single-particle cryo-EM ,42,43,44Autoencoder is one of the most commonly used generative neural network models that has been extensively applied to many unsupervised learning scenarios. Autoencoder models achieve feature representation learning of the data through consistency between the input of the encoder network and the output of the decoder network. The core task of heterogeneous projection image classification is to compute the category features that can be used to distinguish which 3D structure the heterogeneous projection images belong to. In this paper, an unsupervised heterogeneous projection image classification algorithm based on autoencoders is proposed, which can effectively classify heterogeneous projection images into homogeneous subsets without any a priori knowledge other than the number of heterogeneous 3D structures. Specifically, (1) a simple autoencoder model with multi-layer perceptrons and a complex autoencoder model with residual networks are implemented to extract the category features of heterogeneous projection images. For the convenience of description, the autoencoder model implemented by multi-layer perceptrons will be referred to as AE-MLP and the autoencoder model implemented by residual networks will be referred to as AE-RES in the remainder of this paper. The AE-MLP model needs to be trained in the iterative mode, while the AE-RES model can be trained in the one-pass learning mode ,53. (2) Compared to supervised heterogeneous projection image classification algorithms ,31, the Compared to traditional unsupervised heterogeneous projection image classification algorithms based on common lines ,33, the Compared to the heterogeneous projection image classification algorithms based on deep learning ,46,47, tCompared to other heterogeneous projection image classification algorithms ,33,45, tWe propose an unsupervised classification algorithm for heterogeneous projection images based on autoencoders, which relies only on the information of the heterogeneous projection images themselves and does not require any other prior knowledge such as the projection orientations.We implement a multi-layer perceptron-based autoencoder and a residual network-based autoencoder to transform heterogeneous projection images into latent variables, which can automatically extract the category features of heterogeneous projection images.Compared to other heterogeneous projection image classification algorithms, the proposed algorithm has a broader application scope, faster computation speed, and stronger robustness.The main contributions of this paper can be summarized as follows:The remainder of this paper is organized as follows. In In the experiments, the performance of the proposed unsupervised heterogeneous projection image classification algorithm based on autoencoders is demonstrated on synthetic heterogeneous cryo-EM projection image datasets. The source of structural heterogeneity of biological macromolecules in cryo-EM images is well known and broadly attributed due to compositional during sample grid preparation and conformational heterogeneity corresponding to different states of the biological macromolecules. Therefore, we have prepared two datasets, one is the discrete compositional heterogeneous conformational dataset composed of different biological macromolecules, and the other one is the conformational heterogeneous dataset composed of different conformations of the same flexible biological macromolecule in different functional states. The heterogeneous projection images are projected from different published 3D density maps and their orientations are uniformly distributed over the rotation group SO(3). The real cryo-EM projection images contain a high level of background noise with a signal-to-noise ratio of about 0.1. In order to make the synthetic noise projection images close to the real cryo-EM projection image, as in many studies that generate synthetic noisy projection images ,22, the The proposed AE-MLP and AE-RES models are implemented in the PyTorch software and are trained using the stochastic gradient descent (SGD) algorithm in a minibatch manner. All synthetic heterogeneous cryo-EM projection images are downsampled to images with a size of After dividing the heterogeneous cryo-EM projection images into homogeneous groups, the projection images in each group are used separately to reconstruct a preliminary 3D density map. The heterogeneous projection images are downsampled for classification, while the original, undownsampled projection images are used for 3D reconstruction. The Fourier-based 3D reconstruction package FIRM  embeddedIn this experiment, the classification performance of the AE-MLP and AE-RES models is validated on a relatively simple discrete compositional conformation cryo-EM dataset. The dataset contains synthetic projection images projected from three different ribosomes, namely the Trypanosoma brucei mitochondrial ribosome small subunit in complex with mt-IF3 (EMD-0230), the Trypanosoma brucei mitochondrial ribosome large subunit (EMD-0231), and the Trypanosoma brucei mitochondrial ribosome small subunit body in complex with mt-IF3 (EMD-0232) . The repAfter preprocessing these heterogeneous projection images, they are input into the AE-MLP and AE-RES models respectively to extract their category features. The heterogeneous projection images are encoded as 8D and 4096D latent variables respectively after training the AE-MLP model and the AE-RES model. The latent variables and the original heterogeneous projection images are reduced to 2D by using the UMAP dimensionality reduction algorithm. In this experiment, the classification performance of the AE-MLP and AE-RES models is validated on a highly heterogeneous cryo-EM dataset containing synthetic projection images projected from four conformations of the bL17-depleted large ribosomal subunit assembly intermediate  . The repThe category features of heterogeneous projection images belonging to the four conformations of the bL17-depleted large ribosomal subunit assembly intermediate are extracted by the AE-MLP model and the AE-RES model. The high-dimensional latent variables and the original heterogeneous projection images are reduced to 2D after dimensionality reduction using the UMAP algorithm. The visualization results of these 2D features are illustrated in The spectral clustering accuracy of the 2D features extracted by different models are listed in In general, both the AE-MLP model and the AE-RES model can effectively extract the category features of heterogeneous projection images. The first dataset is a relatively simple dataset of projection images belonging to discrete compositional conformations, and both the AE-MLP model and the AE-RES model can be applied to accurately classify heterogeneous projection images. The second dataset contains projection images belonging to four conformations of the bL17-depleted large ribosomal subunit assembly intermediate, which is very difficult to classify. The classification accuracy of the AE-MLP model and the AE-RES model on the second dataset is much higher than that of directly using the original projection images. The AE-MLP model requires iterations, but runs very fast due to its simple structure. The AE-RES model has a complex structure, but its learning and representation capabilities are so strong that the desired results can be obtained even in the one-pass learning mode.In this experiment, we reconstruct the preliminary 3D density maps of corresponding biological macromolecules based on the classification results of heterogeneous projection images to achieve heterogeneous cryo-EM 3D reconstruction. The results of heterogeneous cryo-EM 3D reconstruction are mainly affected by the accuracy of heterogeneous projection image classification, the number of projection images involved in the 3D reconstruction, and the projection orientations of the projection images. The main purpose of this paper is to propose an efficient heterogeneous projection image classification algorithm, so we use the ground-truth projection orientations of the projection images to reconstruct the preliminary 3D density maps. In this way, the reconstruction accuracy is only related to the classification accuracy of the projection images and the number of projection images involved in the reconstruction. The higher the classification accuracy of the projection images and the more projection images involved in the reconstruction, the higher the reconstruction accuracy. The heterogeneous projection images on the discrete compositional conformation dataset in In this section, the general framework of heterogeneous 3D reconstruction in single-particle cryo-EM is first presented, where the autoencoder model is used to automatically learn the category features of heterogeneous cryo-EM projection images. Furthermore, the implementation details of two specific autoencoder models are presented, including the AE-MLP model, a simple autoencoder model implemented by multi-layer perceptrons, and the AE-RES model, a complex autoencoder model implemented by deep residual networks.Autoencoder is one of the most commonly used deep generative network models for feature extraction, which achieves the latent variable representation of data by the consistency between the input of the encoder network and the output of the decoder network in a self-supervised manner. The input data are encoded as latent variables in a latent space after training the autoencoder model, which represent the learned features of the input data. Here, we present a new heterogeneous 3D reconstruction algorithm in single-particle cryo-EM, where an autoencoder model is first used to automatically learn the category features of heterogeneous projection images, and then the extracted high-dimensional features are reduced to 2D features using the UMAP algorithm, and finally these 2D features are used to classify heterogeneous projection images using the spectral clustering algorithm. After classifying the heterogeneous projection images into homogeneous subsets, the corresponding preliminary cryo-EM 3D volume in each homogeneous subset can be reconstructed by using the traditional single-particle 3D reconstruction methods, thus realizing the heterogeneous 3D reconstruction in single-particle cryo-EM.Step 1: Projection Image Preprocessing. To take advantage of the powerful image feature learning capability of the autoencoder model, the input projection image needs to undergo some preprocessing operations, including image downsampling and increasing the number of channels of the image. Image downsampling, that is, reducing the size of an image, is one of the basic operations in image processing, which can not only reduce the computational load of subsequent image processing algorithms, but also achieve preliminary denoising of images. In the proposed algorithm, the original heterogeneous projection images with a size of Step 2: Feature Extraction. The autoencoder model consists of an encoder network and a decoder network. The encoder network transforms the pre-processed projection images into latent variables in a latent space, while the decoder network converts these latent variables to generate images of the same size as the input images. A loss function is then computed based on the input and output images of the autoencoder model, and the error backpropagation algorithm is applied to train the autoencoder model to make the input and output of the autoencoder model as similar as possible by adjusting the network weights. After training the autoencoder model, the representation learning of heterogeneous projection images is achieved, and the category features of heterogeneous projection images are embedded into these latent variables of the autoencoder model.Step 3: Unsupervised Classification. This is a crucial step to achieve unsupervised classification of heterogeneous projection images, which consists of the following three key steps.−Step 3.1: Feature Dimensionality Reduction. In order to accommodate the performance of the autoencoder model, the dimensionality of the latent variables is usually greater than 2. Therefore, we use the UMAP dimensionality reduction algorithm  to reduc−D, which is further converted into an adjacency matrix i and j, is calculated as follows:k nearest neighbors of the 2D features of the projection images i and j, respectively, which can be found by the distance matrix D. The adjacency matrix i and j. The empirical value of the parameter k in the KNN algorithm can be calculated adaptively according to the total number of projection images N:Step 3.2: Spectral Clustering. The spectral clustering algorithm ,58 is onlgorithm  and a shlgorithm . Specifi−Step 3.3: Unsupervised Classification of Heterogeneous Projection Images. The adjacency matrix lgorithm  to perfoStep 4: Heterogeneous 3D Reconstruction in Single-Particle Cryo-EM. After classifying the heterogeneous projection images into homogeneous projection image subsets, traditional single-particle 3D reconstruction methods such as RELION  and cryoThe multi-layer perceptron is a classical multi-layer feed-forward fully connected neural network model with a simple network architecture and very powerful feature learning and representation capabilities. The multi-layer perceptron model is used to implement a relatively simple autoencoder model, AE-MLP. w and b denote the weights and bias values of the network, respectively.In the encoding process, the single-channel heterogeneous projection images with a size of In the decoding process, the 8D latent variables are first calculated by the ReLU activation function. Then, the calculation results are input to a hidden layer containing 512 neurons. Finally, the outputs of this hidden layer are decoded as output images with a size of L as small as possible.During the training process of the AE-MLP model, the network weights are updated to make the inputs and outputs of the AE-MLP model as similar as possible. Specifically, a loss function is first calculated based on the inputs and outputs of the AE-MLP model. The loss function used in the AE-MLP model in this paper is the Mean Squared Error (MSE) function, which is expressed as follows: The structure of the AE-MLP model is very simple, and the time required for one iteration during the training process is very short. However, in order to extract effective category features from the input heterogeneous projection images, the training of the AE-MLP model requires a significant amount of iterative computation. Therefore, we adopt an iterative training mode to train the AE-MLP model.Deep residual networks are very important deep learning models that use residual blocks with skip connections to construct the model architecture. They can effectively alleviate some problems in traditional deep learning models, such as gradient disappearance, gradient explosion, and model degradation . MeanwhiThe architecture of the encoder and decoder networks in the AE-RES model is asymmetric. The encoder network consists of an input layer, five residual downsampling modules ResDown, and a convolution layer, while the decoder network consists of six residual upsampling modules ResUp and a convolution layer. The ResDown module is mainly used to transform the input high-dimensional heterogeneous projection images into low-dimensional latent variables in the latent space, while the ResUp module is mainly used to generate the output projection images that consistent with the input projection images from these low-dimensional latent variables.In the AE-RES model, the heterogeneous projection images undergo a series of complex network computations to extract their category features. The input single-channel projection images are first downsampled to images with a size of Equation .U. The calculation results of the RReLU activation function are further processed sequentially through the 2D average pooling operation The ResDown and ResUp modules have the same architecture and calculation process. The main difference between them is that the ResDown module uses the 2D average pooling operation d “+” in b and theCompared to the AE-MLP model, the AE-RES model has a more complex architecture. However, the AE-RES model can effectively extract the category features of heterogeneous projection images even after one iteration. Therefore, we adopt the one-pass learning mode ,53 to trHeterogeneous projection image classification is an effective way for solving the structural heterogeneity problem of flexible biological macromolecules in single-particle cryo-EM. In this paper, we propose an unsupervised classification algorithm for heterogeneous cryo-EM projection images based on deep autoencoder models, which does not require any a priori knowledge such as the orientations or common lines of the heterogeneous projection images. We use multi-layer perceptrons and residual networks to implement the autoencoder model for extracting the category features of heterogeneous projection images. The multi-layer perceptron-based autoencoder is very fast to train, while the residual network-based autoencoder can extract the desired features in the one-pass learning mode. Image downsampling can reduce computational load and noise, while increasing the number of channels of images can enhance their semantic information. Applying the UMAP algorithm to reduce the high-dimensional latent variables to 2D can further aggregate the most important features, and applying the spectral clustering algorithm to cluster the 2D features can achieve higher classification accuracy. The classification performance of the proposed algorithm is demonstrated on a discrete compositional heterogeneous conformation dataset and a conformational heterogeneous dataset. Experimental results show that the proposed algorithm can accurately classify the projection images of discrete compositional conformations and also has advantages on the dataset of projection images of different conformations of a protein. In future work, we will further explore deep generative network models with more powerful learning and representation capabilities, and apply them to heterogeneous 3D reconstructions on real cryo-EM projection image datasets.


Following the acute phase of the COVID-19 pandemic, record numbers of people became economically inactive , or non-employed . A possible explanation is people leaving the workforce after contracting COVID-19. We investigated whether testing positive for SARS-CoV-2 is related to subsequent economic inactivity and non-employment, among people employed pre-pandemic.The data came from five UK longitudinal population studies held by both the UK Longitudinal Linkage Collaboration  and the UK Data Service . We pooled data from five long established studies . The study population were aged 25–65 years between March 2020 to March 2021 and employed pre-pandemic. Outcomes were economic inactivity and non-employment measured at the time of the last follow-up survey . For the UK LLC sample , COVID-19 infection was indicated by a positive SARS-CoV-2 test in NHS England records. For the UKDS sample we used self-reported measures of COVID-19 infection . Logistic regression models estimated odds ratios (ORs) with 95% confidence intervals (95%CIs) adjusting for potential confounders including sociodemographic variables, pre-pandemic health and occupational class.Testing positive for SARS-CoV-2 was very weakly associated with economic inactivity (OR 1.08 95%CI 0.68–1.73) and non-employment status (OR 1.09. 95%CI 0.77–1.55) in the primary analyses. In secondary analyses, self-reported test-confirmed COVID-19 was not associated with either economic inactivity (OR 1.01 95%CI 0.70–1.44) or non-employment status (OR 1.03 95%CI 0.79–1.35).Among people employed pre-pandemic, testing positive for SARS-CoV-2 was either weakly or not associated with increased economic inactivity or non-employment. Research on the recent increases in economic inactivity should focus on other potential causes. Considerable economic disruption has occurred internationally since the start of the pandemic , with maThe health consequences of experiencing COVID-19 are possible drivers of increased economic inactivity. Post-COVID-19 condition  has been linked to reductions in working capacity , substanOne approach is to use administrative data. Administrative data that combines healthcare data with data on labour market and other domains required to adjust for potentially sociodemographic confounding variables is not readily available . LongituUsing data available in the UK LLC, we aim to understand the relationship that COVID-19 status as indicated by a positive test for SARS-CoV-2 has with economic inactivity and employment status, among respondents who reported that they were employed or self-employed just before the pandemic. The use of linked UK LLC data is not without restrictions that might bias the sample; data was limited for some LPSs due to lack of consent for linkage. To evaluate the possible consequences of potential consent bias, we carried out analyses using unlinked data from UK Data Service (UKDS) for the same LPS and used self-reported measures of COVID-19 instead of linked health records.We use self-reported sociodemographic, economic and COVID-19 data on individuals from five UK population-based longitudinal studies where the self-reported data are both held in the UK LLC  and the UKDS . These studies include three cohort studies (age-homogenous within-study): the 1970 British Cohort Study (BCS70) , the 195Each longitudinal study sent surveys to their participants during the COVID-19 pandemic. These surveys contained demographic, health and COVID-19 related questions, and gathered information on employment status just before the pandemic, and employment status and economic activity during the pandemic. We derived two analytic samples using data pooled from the five longitudinal studies. Within the UK LLC TRE we selected people in England with permission to link to NHS data, aged 25 years and over in the first COVID-19 survey they participated in, and aged 65 or under at the time of assessing their economic activity and employment status at end of follow up (“UKLLC sample”). The younger age limit was selected to exclude those of younger ages when education would be a common alternative to employment. The older limit reflects statutory retirement age. In addition, we repeat analyses using a sample based on data available from the UKDS, from which the UKLLC sample was derived, without the restrictions of being resident in England and consenting to linkage to English NHS data (“UKDS sample”)The outcomes were economic inactivity  and employment status (in paid employment -reference category - versus not in employment). The distinction between the two measures is that being economically active is defined to include not only those who are in paid employment but also those who are unemployed and actively seeking work. In contrast for the employment status measure, the non-employed category includes both unemployed people who are looking for work and economically inactive people e.g. retired or long-term sick. To be as consistent as possible, we sought to use data from approximately the same period of each longitudinal cohort survey, which ranged from November/December 2020 to March 2021 , sex , household composition at start of pandemic ), self-reported key worker status during the pandemic (no/yes) and ethnicity . NHS records, if available, were used if ethnicity data was missing (5.4% of the sample) for the UKLLC sample. For the UKDS analyses ethnicity was not used. BCS70 ethnicity data was restricted to participants of a sweep that occurred in the year 2000, and only a simple non-white binary measure was available for ELSA. Adjusting for ethnicity would have led to a smaller sample size for the addition of a variable of limited utility. The pre-pandemic socioeconomic confounders were the National Statistics Socio-Economic Classification  and the highest level of education  or academic equivalents see Schneider 2011  into NVQAnalyses consisted of separate logistic regression models for the association between COVID-19 status and economic inactivity and employment status. Unadjusted and confounder adjusted analyses were conducted using the differing COVID-19 measures for the UKLLC and UKDS samples. Given that we pooled data from multiple studies with different study designs and did not have information on those who had not consented to linkage, we were not able to provide weighted estimations.To investigate the comparability of the self-reported and NHS testing data, we conducted two sets of additional sensitivity analyses. First, using the UKLLC sample we repeated the analyses using self-reported COVID-19 status as the exposure. Second, we ran the analyses using only people resident in England in the UKDS sample. In addition, we carried out analyses stratifying the UKDS sample by the following characteristics: age (under 50 years versus 50 and over), sex and NS-SEC , and self-rated health . We formally tested for effect modification by these characteristics using likelihood ratio tests.https://github.com/UKLLC/llc_0010). The analyses and coding of the survey data were carried out using Stata 17.0, while the graphs and coding of NHS data were carried out using R 4.3.1The code to derive all the variables and carry out the analyses is available from the UK LLC Github repository  0.82, 95% confidence interval (CI) 0.52 to 1.28) and there was a weak non-significant increased risk in adjusted analyses   that became null on adjustment  analyses. Likewise, there was little evidence that suspected COVID-19 was associated with economic inactivity in unadjusted (OR 0.87 95% CI 0.69 to 1.10) or adjusted analyses (OR 1.02 95% 0.80 to 1.29)  and only a very week non-significant increased risk in adjusted analyses   but there was little evidence of an association in adjusted analyses . There was little evidence that suspected COVID-19 was associated with employment status in unadjusted  and a week non-significant association in adjusted estimates  (see blue squares in The adjusted associations for both economic inactivity and employment status for different samples and different COVID-19 status measure combinations are also shown in The relationship between COVID-19 status and the economic inactivity and employment status measures stratified by age, sex, NS-SEC and self-reported health are shown in Our results come from five UK longitudinal population studies covering people in work at the start of the pandemic and adjusting for a wide range of potential confounders. They suggest that having SARs-CoV-2 confirmed by a test is either not associated or only has a small increased risk of being economically inactive and not being in work during the pandemic. There was also little evidence to suggest that this varied by important characteristics such as age, sex, and gender. Despite the reasonably large sample sizes for the samples, approximately 8,000 for the UKLLC sample and 14,000 for the UKDS sample, the width of confidence intervals does not rule out stronger associations entirely.The finding, for people employed prior to the pandemic, that COVID-19 has a small or null association with economic inactivity and non-employment is not entirely inconsistent with the current literature, which is in its early stages of development, and currently focused on post-COVID condition. A non-peer reviewed initial rapid scoping review suggests that the socioeconomic consequences of post COVID-19 are considerable , howeverth pandemic survey in March 2021. It is possible that differences between those who consented to linkage and those who did not may have introduced a small bias, which would be consistent with other studies (For some of the studies included in the UK LLC, linkage to NHS records was based on consent. For most studies, including BCS70, NCDS, ELSA, and NS, consent for record linkage was collected prior to the COVID-19 pandemic. USoc local governance requirements demanded specific consent for inclusion in UK LLC, and this was acquired during the 8 studies . Given tThe use of a directly observed positive test for SARS-CoV-2 and a self-reported measure of test-confirmed COVID-19 each come with advantages and disadvantages. Conceptually, test-confirmed COVID-19 is much more common than other potential measures related to COVID-19 such as post COVID-19 condition, which is experienced by only small proportion of those who are infected . In addiSurveys are good at capturing snapshots around the time the survey measure is recorded. A strength of our study is that the use of longitudinal survey data meant we could control for important pre-pandemic potential confounding factors such as education and occupational class. However, it is possible that we could not fully adjust for factors that might lead to behavioural changes reducing the risk of exposure to the SARs-CoV-2 virus and labour market engagement. To mitigate against this, we did adjust for shielding, keyworker status, age and self-rated health. The UK LLC is planning to add linked administrative employment data which may extend the scope and period of employment outcomes that can be investigated. However, use of administrative data is not without other sources of bias .For our chosen LPS, the UKLLC sample only included people who had consented to having their survey data linked to NHS records. In addition, despite being embedded within long standing cohorts, survey responses during the pandemic were lower than typically achieved. Thus, there may be some selection biases restricting generalisability of results and we did find slightly higher point estimates for the associations between suspected COVID-19 and the outcomes in the UKLLC sample, than the more general population sample. Given that we only have data within the UK LLC on people who had consented to linkage, options were limited. Regression analyses tend to be robust to missing data and more appropriate than other alternatives such as weighting and multiple imputation which could amplify biases when the data is missing not at random , 31. TheThe study also has many strengths. We have focused on people aged over 25 and in employment just prior to the pandemic. Thus, we are looking at the consequences of testing positive for COVID-19 in the group whose subsequent employment status and inactivity are mostly like to be due to the consequences of the pandemic. Data include pre-pandemic measures of NS-SEC and self-rated health. While the pre-pandemic measures are assessed in the first COVID-19 wave for NCDS, BCS70 and NS, this is still prior to the assessment of the employment outcome and thus longitudinal.Finally, pooling data across studies has enabled a much larger sample size than any of the individual studies alone would have allowed. Using the UK LLC, pooling data has enabled the use of more detailed ethnicity information than is commonly available for BCS70, ELSA and NCDS, and a more detailed level of education and social class than employed in other approaches such as pooled meta-analysis, which have been used to combine longitudinal population studies in other papers .Our results suggest either a small or no effect of COVID-19 among people who were employed during the pandemic. The direct consequences of SARS-CoV-2 infection are unlikely to be a major contributor to the large increases in economic inactivity seen following the pandemic, and therefore other explanations need to be investigated. Given the potentially small impact of COVID-19 among the working population, definitive answers are likely to require very large studies. This may require investments in studies to evaluate population health on a scale that was inconceivable prior to the pandemic. Alternatively, research should specifically target more vulnerable groups such as older people, or those already in poor health.Supplement 1


The clinical aspects of SARS-CoV-2 infection, as well as the COVID-19 vaccines’ safety, efficacy and effectiveness in pediatric patients with asthma, are crucial to adapting clinical management in this fragile population and for prevention strategies. The aim of this narrative review was to evaluate the impact of SARS-CoV-2 infection in children with asthma and the impact of COVID-19 vaccination. Systematic research using the principal medical databases was conducted using specific search query strings from the early spreading of COVID-19 globally until March 2023; further relevant data were drawn from the main national and supranational institutions. No significant differences in SARS-CoV-2 incidence and morbidity were found in asthmatic pediatric patients compared to non-asthmatic ones; however, subjects with uncontrolled asthma were found to be at increased risk of developing a serious disease during SARS-CoV-2 infection. Regarding COVID-19 vaccines, accumulating data support their safety, efficacy and effectiveness on asthmatic children regardless of asthma severity. Further cohort-based studies are needed as the evidence of new epidemic waves caused by new viral variants makes the current knowledge outdated. Since its initial spreading from the first months of 2020 to the present day, more than 740 million cases of COVID-19 have been recorded all over the world, with over 7.5 million deaths among the general population, with nearly half of these latter in Europe and North America . SeveralThe rapid development of COVID-19 vaccines made by the extraordinary efforts of the pharmaceutical industry and national and supranational regulators agencies and their subsequent extension to the pediatric population was a crucial element against the pandemic, especially for the category of frail subjects. Among these, it was initially debated whether the asthmatic pediatric population could be considered or not as a fragile category, because only a few studies have reported asthma as a risk factor for severe forms of COVID-19 ,7,8,9,10Systematic research on the principal databases  was conducted using specific search query strings see the , with a Comorbidities are known to be a risk factor for severe COVID-19. In a retrospective study including 177 children, 63% of those hospitalized with COVID-19 had comorbidity conditions compared to 32% of non-hospitalized ones with COVID-19, and 78% of critically ill children with COVID-19 had underlying conditions compared to 57% of hospitalized, non–critically ill patients with COVID-19 .Asthma is the most common chronic disease of childhood in the world, and asthma exacerbation is frequently caused by viral infections ,12. InitFurther studies have reported that young patients with well-controlled asthma did not develop severe COVID-19, probably due to low angiotensin-converting enzyme 2 (ACE2) expression in bronchial epithelial cells ,18. ACE2Although, in adults, the current evidence shows that asthma is associated with a higher risk of COVID-19-related hospitalization and mechanical ventilation , few stup = 0.012), but no significant difference was found concerning the other symptoms. However, there was no severe asthma case in this study group, and the authors concluded that asthmatic children with mild to moderate forms experienced COVID-19 with no great differences compared to their peers [Regarding clinical presentation, no significant differences were found between asthmatic and non-asthmatic children with COVID-19. The Global Asthma Network (GAN) conducted a global survey at 14 GAN centers from 10 different countries, collecting data between November 2020 and April 2021 . Overallir peers .Asthma control plays a central role in this category of children. A Scottish cohort study was done on all children from 5 to 17 years of age who were included in a specific dataset  . The aimThe hyperinflammation status is strictly linked with the severity and complications of SARS-CoV-2 infection . In asthSumming up, asthma represents a frequent comorbidity in hospitalized children with COVID-19, but the infection has a similar clinical course in patients with mild and moderate asthma compared to non-asthmatic ones. On the other hand, patients with severe and uncontrolled asthma are at a higher risk of developing a serious disease. Maintaining good asthma control is fundamental in the prevention of this risk. Further investigations are required to establish the real impact of asthma on the morbidity and mortality of COVID-19 in the pediatric population.Multiple guidelines on allergy care during the COVID-19 outbreak support that asthmatic children should continue with their current asthma medications while infected with COVID-19  if a good control for symptomatology has been reached . ReducinRegarding asthma exacerbation, prompt treatment with oral corticosteroids in moderate-to-severe asthma exacerbation is recommended, especially if patients are poorly responsive to bronchodilators, as indicated by the current guidelines ,21. In tIf a child is using a nebulized asthma relief medication, this should be switched to a metered dose inhaler (MDI) or dry powder inhaler under most circumstances . NebulizIn January 2020, the first genetic sequence of the SARS-CoV-2 virus was published . Since tCOVID-19 vaccines are safe, as seen in the registration trials and as also witnessed by the supranational pharmacovigilance systems with their regular reports . The twoThe registration trials for pediatric populations have shown excellent safety, immunogenicity and efficacy profiles ,47,48,49Available data regarding COVID-19 vaccines in asthmatic pediatric patients are few. Recommendations on times and doses of administration do not vary compared to other subjects of the same age; in addition, there is no evidence to support the hypothesis that patients with asthma, regardless of their age, are at increased risk of exacerbation when receiving the COVID-19 vaccination . In addiGreater attention must be paid to subjects with uncontrolled asthma, since these patients are considered at moderate risk of adverse reactions . In accoOne further aspect on which it is important to focus attention toward is vaccine hesitancy, since many parents have been hesitant to vaccinate their children with asthma . StudiesBiological drugs can block specific immune pathways that limit the cascade determining inflammation, and for this reason, they are used in the treatment of severe forms of asthma . Many moIn conclusion, in severe asthmatic pediatric patients, COVID-19 vaccines are strongly recommended alongside basic hygienic measures and the use of personal protective equipment .SARS-CoV-2 infection has a similar clinical course in patients with mild and moderate asthma compared to non-asthmatic ones. However, patients with severe and uncontrolled asthma are at a higher risk of developing a serious disease, so maintaining good asthma control is fundamental in the prevention of this possibility. These strategies could be implemented with close cooperation between pediatricians, patients and their families, explaining the importance of accurate treatment compliance, the necessity of periodic medical evaluations and providing them a written action plan with instructions on how to manage the reappearance or worsening of symptoms. COVID-19 vaccines are recommended in pediatric patients with asthma, who should continue to receive their prescribed asthma medications on the day of their COVID-19 vaccination or on the surrounding days. For subjects with severe persistent bronchial asthma, an observation of about 60 min after administration of the COVID-19 vaccine is required, whereas an interval of 2–7 days between the administration of the COVID-19 vaccine and biological therapy is necessary. However, further specific population studies that take into account the epidemiological peculiarities of this category of patients are needed to better understand the underlying causes of differences with other fragile population segments and to better act against the pandemic. In addition, further cohort-based studies are needed, as evidence of new epidemic waves caused by new viral variants makes the current knowledge outdated.


Iron oxide-copper-gold (IOCG) deposits are a globally important source of copper, gold and critical commodities. Despite their relevance, IOCG deposits remain an ill-defined clan, with a range of characteristics that has complicated development of the general genetic model. Here we focus on the Candelaria IOCG deposit in Chile and reveal that by using micro-textural and compositional variations in actinolite, a common alteration mineral found in many IOCG deposits, we can constrain the evolution of these systems. We demonstrate that Candelaria formed by the superposition of at least two pulses of mineralization with a late Cu-rich event overprinting and superimposed over an early, and probably higher temperature, iron oxide-apatite (IOA) mineralization event. These distinct pulses were likely caused by episodic injections of magmatic-hydrothermal fluids from crystallizing magmas at depth. Our data provide empirical evidence of grain-to-deposit scale compositional and potentially temperature changes in an IOCG system. The results support the use of actinolite chemistry as a novel approach to understand the formation of IOCG deposits and a potential tool for vectoring in exploration. By-product strategic elements including Co, U and Rare Earth Elements (REE) add to their attractiveness. Previous studies on important IOCG deposits such as Olympic Dam , Candelaria (Chile), and Salobo and Sossego (Brazil) have shown that they are closely related to basement penetrating fault systems2, and have undergone an early magnetite-actinolite event followed by main-stage mineralization of copper and other elements6. Even though there is abundant evidence for magmatic signatures in IOCG deposits—Geochemical and isotopic7—Most of these deposits show no apparent genetic relationship with exposed coeval igneous intrusions7. Intrusions are inferred at depth but the magmatic events leading to mineralization are not well defined. IOCG deposits also lack substantial quartz veining which has hindered the use of fluid inclusion and related studies to determine the temperature and composition of the mineralizing fluid(s)12. Geochemical proxies, particularly the chemical composition and Fe-O-H-S isotopic signatures of selected silicates, magnetite and pyrite, have provided some insights into the nature of the hydrothermal fluids22, but the thermal evolution of IOCG systems in space and time remains poorly constrained. In particular, the thermal and spatial evolution constraints of early Fe-rich and the main Cu(-Au) mineralization stage have not been addressed. Understanding this relationship is critical to development of a universally applicable genetic model.Iron oxide-copper-gold (IOCG) systems are among the world’s richest mineral deposits, making for highly profitable mining operations2)(Mg4.5–2.2Fe0.5–2.5)(Si8O22)(OH)2. Actinolite has been well reported in the mineral paragenesis of Andean IOCG deposits, including Candelaria, Mina Justa and El Espino25. Therefore, the chemical composition of actinolite can potentially be distinct between early and later mineralization stages in IOCG deposits, and consequently be used as a proxy for characterizing their temporal evolution. The chemical composition of actinolite has been previously used for determining temperature conditions in iron oxide-apatite (IOA) deposits, where experimental data demonstrate that the thermal stability of amphibole depends on its Fe# , which changes over a wide P-T range27.One of the most common minerals found in both (early) Fe-rich and (later) Cu-rich mineralization stages in IOCG deposits is actinolite,  volcanic-sedimentary Punta del Cobre Formation that is overlain by sedimentary marine sequences from the Lower Cretaceous (132–130 Ma) Chañarcillo Group28. District-wide, early calcic-sodic alteration is observed towards the northwestern side of the district followed by widespread magnetite-actinolite alteration that formed between ~120–116 Ma6. This early magnetite-rich alteration extends beyond all deposits in the district  and is observed as disseminated and pervasive magnetite-actinolite that can completely replace the volcanic host rocks, this style of alteration is common in IOA deposits .The Candelaria-Punta del Cobre district is located south of the city of Copiapó in northern Chile and comprises more than nine active IOCG mines, all interpreted to be part of the same hydrothermal system8. The main ore body in the Candelaria deposit is up to 400m thick in the central part of the deposit and thins towards the margins8. Actinolite occurs in veins together with massive granular aggregates or disseminations of magnetite or sulfides in the volcanic host rocks. In all its forms, actinolite is a common alteration mineral in the Candelaria deposit6 that is typically associated or with Cu mineralization  software package and output to Surfer® (v. 8).High-resolution quantitative X-ray wavelength dispersive spectrometry (WDS) maps of representative grains from both mineralization events were acquired on a JEOL 8530F field-emission electron probe microanalyzer equipped with five wavelength-dispersive spectrometers at the Center for Microscopy, Characterization and Analysis (CMCA), the University of Western Australia, Perth, WA. The elements Si, Ti, Al, Mn, Fe, Mg, Ca, Na and Cl were measured. Detection limit maps were acquired for these elements and applied as the minimum cut-off values. Map acquisition utilized a 15 keV accelerating voltage, 100 nA beam current and a fully focused beam. Pixel dimensions were chosen between as 0.5, 1 or 2 µm27. Each EPMA analysis was linked to a corresponding actinolite texture at the micro-scale,  and exporting this as a .txt map. The .txt maps were then processed in Python by using the Matplotlib packageCalculated actinolite Fe# range from ~0.19 to ~1 Figs.  and 3. TThe maps of Fe# in actinolite Fig.  reveal aThe intra- and inter-crystalline grain-scale Fe# variations are represented in histograms constructed from each pixel in the Fe# maps Fig. F. Two di27 suggesting that cluster-1 formed at a higher temperature than cluster-2. The composition of actinolite, can be sensitive to prograde and retrograde metamorphic reactions33, the amphibole morphology34, large changes in pressure conditions35, and the presence of coexisting minerals that can incorporate or buffer Fe and Mg33. In the Candelaria deposit, the composition of the host rocks is relatively uniform6, actinolite composition is independent of grain morphology or texture  increases with temperature in igneous and metamorphic environments39. The higher Ti contents of the deep and early actinolite grains suggest a higher temperature formation compared with the later, main stage actinolite grains associated with Cu mineralization. Titanium concentration in actinolite can also be affected by the coeval crystallization of magnetite40, which may also contribute to the observed trend between early and main-stage actinolite 20 associated with actinolite  in samples from the same drill hole21 as used in this study. These, and results from other workers42 suggest that Cu-Fe mineralisation and associated actinolite-rich alteration formed over a range of ~350-650 °C  in samples from the same drill hole21 are similar to the chemistry and isotopic signatures of actinolite from Andean IOA deposits 48. The early, higher-temperature event in the Candelaria-Punta del Cobre district has a similar mineralization/alteration paragenesis to Andean IOA deposits 51. Therefore, the early mineralization event in Candelaria is analogous to high-temperature Fe-rich mineralization in IOA deposits.The two clusters of actinolite compositions are interpreted as distinct hydrothermal episodes and are consistent with data from other IOCG and IOA deposits. The chemical composition of actinolite from cluster-1 Fig. F togethe54. The proposed model connects the results presented here with recent models of Fe transport for IOA deposits, which are the Cu-deficient representatives of the IOCG clan59. These two styles of mineralization (IOCG and IOA) commonly overlap in time and space, and several studies have proposed a genetic link between them, with Fe-dominated IOA systems corresponding to the deeper hotter roots of Cu-rich (IOCG) systems66, although in the present model we propose that the deposits can be superimposed.The observations and modeling reported here support an early, high-temperature hydrothermal pulse followed by a lower-temperature one, with magnetite and actinolite ubiquitous to both, but chalcopyrite only occurring in the latter. Figure 37Cl data from the Candelaria system indicate that hydrothermal fluids in the district were sourced from intermediate to mafic silicate magmas68, gabbro to diorite in composition, formed in the Upper Jurassic to Lower Cretaceous magmatic arc of northern Chile70. Dioritic magmas formed under these conditions would be enriched in volatiles together with Fe, Cu, S and Cl72. Exsolution of a magmatic-hydrothermal volatile phase from the silicate melt would start during cooling of the parental magma. Volatile exsolution may be triggered by prior magnetite crystallization, as demonstrated experimentally74, where exsolving magmatic-hydrothermal fluid bubbles nucleate on magnetite microlites to form a magnetite-fluid suspension that rises through the intrusive body74 scavenging metals, and forming an accumulation of a metal-rich fluid in the upper part of the magma body56  and Olympic Dam85 . Repeated, temporally distinct, fault reactivation may result in different episodes of hydrothermal activity as evidenced by actinolite micro-textures  fluids from the overlying basin87, which may have contributed additional sulfur aiding sulfide precipitation21  cooled to temperatures below ~550 °C precipitating chalcopyrite, due to the sharp drop in chalcopyrite solubility45 Fig. D. Furthet Fig. SM. As cool78. Such buoyancy-driven outgassing of magmatic-hydrothermal fluid from magma can efficiently transfer fluid-soluble elements such as those found in IOCG deposits77. At Candelaria, the data indicate that the first fluid pulse resulted in mineralization dominated by magnetite with actinolite and minor sulfide, whereas the second fluid pulse resulted in mineralization dominated by magnetite, actinolite and Cu-Fe sulfides, where the latter make the deposit economic. These observations are consistent with the first fluid transporting Fe, Ca, Mg and Si, and the second fluid transporting the former elements and, critically, also Cu and S. The chemistry of magmatic-hydrothermal fluid exsolved from silicate melt is controlled by the pressure, temperature, oxidation state and composition of the melt from which the fluid exsolves89. The low concentration of Cu and S in the first fluid pulse, evidenced by the modally minor amount of sulfide coeval with early magnetite and actinolite, is consistent with the evolution of a magmatic-hydrothermal fluid from a magma that had lost Cu and S to a sulfide crystal/liquid which was not subsequently resorbed prior to degassing and was not resorbed during the degassing event90. The fluid responsible for the subsequent Cu-sulfide rich pulse could have outgassed from the same evolving magma body and resulted in resorption of earlier formed Cu sulfide crystal/liquid, or could have evolved from a newly emplaced magma where the released volatiles overcame capillary resistance and ascended along permeable channels developed in overlying, older crystal mush78. The efficient ascent of a magmatic-hydrothermal fluid in such evolving magmatic systems is transient and dependent entirely on the rate of supply of ascending magmatic-hydrothermal fluid79. Such intermittent degassing events plausibly explain the superimposed magnetite-actinolite and younger magnetite-actinolite-Cu-sulfide mineralization events at Candelaria. The constraint on the timing of the evolving hydrothermal system is yet to be properly evaluated, although previous research in other IOCG/IOA districts such as the Marcona-Mina Justa have estimated that millions of years could separate de Fe-rich from the Cu-rich stage30. Understanding the time difference between these two stages in the Candelaria-Punta del Cobre district will be essential for better characterizing the magmatic evolution of the hydrothermal fluid source responsible for mineralization.The observation of two temporally distinct hydrothermal pulses is consistent with episodic replenishment of an evolving crustal magmatic system where the magmatic-hydrothermal fluid evolves from a newly emplaced magma that either underplates previously emplaced magma or forcibly intrudes and mixes with previously emplaced magma90. Further, the presence of minor sulfides in IOA deposits 51 and early, pre-Cu mineralization, magnetite-actinolite in IOCG deposits 91 point to a transition between IOA and shallow IOCG systems. However, those authors assumed that both IOA and IOCG mineralization were the result of a single cooling fluid. Although this may be the case for IOA deposits and small, vein-type IOCG systems64, our data point to at least two distinct pulses of hydrothermal activity involved in the formation the Candelaria IOCG system, which are superimposed. These pulses most likely correspond to hydrothermal fluids of contrasting temperatures, and possibly with distinct metal budgets derived from an evolving magmatic source(s).Our genetic model involving at least two distinct phases of hydrothermal activity introduces an important addition to how we understand the evolution and connection between IOA and IOCG mineralization styles. As noted, previous studies have suggested a spatial and temporal relationship between IOA and IOCG mineralization, where the IOA could represent the roots of an IOCG systemThe deposit-scale actinolite data presented here are used as a proxy to determine mineralization temperatures and provide a new tool to track the evolution of IOCG mineral systems. Furthermore, our results offer an explanation for how the Cu-deficient IOA-type mineralization relates spatially to Cu-rich IOCG deposits. We propose that both mineral systems can represent temporally distinct nonequivalent pulses albeit part of the same metallogenic system. We argue that IOA- and IOCG-type systems should not necessarily be viewed as related to formation depth, but rather be the result of temperature gradients and an evolving magmatic source affecting hydrothermal fluid composition and circulation. Most importantly, we argue that world-class IOCG systems are formed by distinct, episodic pulses of hydrothermal activity. The detailed understanding of the characteristics and key controls of the magmatic-hydrothermal evolution responsible for the formation of IOCG deposits, and therefore the factors that control metal transfer, is a critical step in the development of new strategies for successful exploration. Therefore, the results presented here open new opportunities for Cu exploration in districts that are historically rich in Fe.Supplementary Information 1.Supplementary Information 2.


GraphCPLMQA also outperforms state-of-the-art methods on 19, 035 models in CASP13 and CASP14 monomer test set.Model quality evaluation is a crucial part of protein structural biology. How to distinguish high-quality models from low-quality models, and to assess which high-quality models have relatively incorrect regions for improvement, are remain a challenge. More importantly, the quality assessment of multimer models is a hot topic for structure prediction. In this study, we propose GraphCPLMQA, a novel approach for evaluating residue-level model quality that combines graph coupled networks and embeddings from protein language models. The GraphCPLMQA consists of a graph encoding module and a transform-based convolutional decoding module. In encoding module, the underlying relational representations of sequence and high-dimensional geometry structure are extracted by protein language models with Evolutionary Scale Modeling. In decoding module, the mapping connection between structure and quality is inferred by the representations and low-dimensional features. Specifically, the triangular location and residue level contact order features are designed to enhance the association between the local structure and the overall topology. Experimental results demonstrate that GraphCPLMQA using single-sequence embedding achieves the best performance compared with the CASP15 residue-level interface evaluation methods among 9108 models in the local residue interface test set of CASP15 multimers. In CAMEO blind test (20 May 2022 to 13 August 2022), GraphCPLMQA ranked first compared with other servers ( Protein structure prediction plays an important role in biological research. In recent years, the development of deep learning has greatly advanced the transformation and progress of protein structure prediction. Many high-accuracy deep learning structure prediction methods have been developed, such as AlphaFold2 , RoseTTASince CASP7, many methods for assessing the quality of protein models have been developed . In partDeep learning networks can capture potential connections within proteins. Various neural networks that contain convolution, LSTM and graph networks, are used in model quality assessment methods, as ProQ3D , AngularProtein language models are widely used in protein modeling and design tasks, which are trained unsupervised on protein databases to obtain embedding representations. In protein modeling tasks, sequence embeddings from protein language models are used to infer structural information, such as IgFold , ESMFoldIn this work, we propose GraphCPLMQA based on a deep graph coupled neural network framework using protein language models. Embeddings representations are generated by the protein language model ESM, which reflect sequence and structural properties. The embeddings that supplemented by structural features are input into a deep graph coupled network. The network consists of two parts: (i) the graph encoding network learns the latent connection between sequence and structure. (ii) the transform-based convolutional decoding network obtains the mapping relationship between structure and quality to evaluate protein models. The results show that representations from language models and graph-coupled neural networks can learn the implicit relationship among the sequence, structure and quality, which further improve the accuracy of model quality.In this section, we described the GraphCPLMQA method in three parts, including training datasets, input features for proteins and network architecture. In addition, we provide two versions according to different sequence embedding types, namely the full version of GraphCPLMQA (GraphCPLMQA-MSA) and the single sequence version of GraphCPLMQA (GraphCPLMQA-single). The pipeline is shown in The training dataset of GraphCPLMQA was constructed from the Protein Data Bank (PDB). A total of 15, 054 proteins were selected from the PDB (19 November 2021) based on the following criteria: (i) minimum resolution <=2.5 Å, (ii) protein length within 50 ~ 400 residues, (iii) sequence similarity to any protein in the dataset <35%.For each protein of the training set, three different approaches were used to generate decoys (structure models): structural dihedral adjustment, template modeling and deep learning-guided conformational changes . For strMost current model quality assessment methods use one-hot encoding for sequence embedding. However, there is sometimes a problem with only using one-hot encoding. One-hot encoding cannot effectively represent the similarity or difference between amino acids, as it may fail to capture the underlying relationships in protein sequences. In addition, mapping sequences to structures using one-hot encoding may burden model quality assessment networks focused on structure learning. To characterize the structural information of protein models, some methods describe the relative positions of residues in different coordinate systems, such as 3DCNN, Ornate, DeepAccNet and DeepUMQA. However, these methods do not consider the implicit connections of residues in higher-dimensional spaces.In this work, embeddings of protein sequences and geometric structures from ESM were employed to capture sequence–structure relationships. We have devised two distinct versions of the methods to assess protein model quality, namely the GraphCPLMQA method, which utilizes the MSA language model, and the GraphCPLMQA-Single, which employs a single sequence language model. For GraphCPLMQA-Single, the residue-level sequence embedding (1280-dim) of the query sequence is generated by ESM2  at the lα coordinate of residue N (number of residues) small triangles outlining the fundamental shape of the protein structure. In each triangle To describe the protein structure, the triangular location feature was designed, which is inspired by the residue-level USR from DeepUMQA . The feawhere Contact order  is used where i finds the closest K residues in the space and records their relative indexes, which are converted into node features through the position encoding formula. See The positional order and properties of amino acids are critical to protein structure. The relative position encoding method in Transformer  was emplip atoms , which cIn the encoding module, a protein graph is typically represented as In the triangle graph transformer , the res follows :Where  follows :Where In invariant point attention , outputsTo further fine-tune the node information, an Equivalent Graph Neural Network (EGNN)  is utiliWhere In the decoding embedding module, we extract the node representation where In the structure-quality decoding module, a residual network based on a transform strategy is employed, which consists of main residual blocks and branch residual blocks (Error-Block and Cutoff-Block). Each residual block comprises three 2-dimensional convolutional layers with different expansion rate coefficients and a normalization operation. We take the GELU  activatiwhere The distance-error rom lDDT . FinallyWhere The graph-coupled network model is trained using a combination of model quality and geometric constraint loss terms. To improve the efficacy of the sequence-structure encoding module, we use actual geometric structure information  to constrain the encoding output (predicted residue coordinates and distances) by the mean square error between atomic coordinates and the L1 loss function. This approach helps in decoding the underlying information of structure and quality. In the decoding module, we compute the cross-entropy loss for the distance error and threshold, where the loss term for the threshold is the binary entropy. Finally, the loss function of the model quality is the mean squared error (MSE). The total loss is the sum of the losses of the geometric constraint and quality assessment as follows:where he AdamW  optimizeWe use the constructed structure dataset to train the graph coupled network, which is used to test the non-redundant CASP proteins. Moreover, we participated in the blind test of CAMEO and analyzed the quality assessment data. During the test, the global quality assessment  and the accuracy of the local structure quality  were used. Local QA describes the quality of each residue, where lDDT is used to evaluate the residue quality. Global QA describes the overall quality of the protein model structure by calculating the mean value of the Local QA. Pearson, Kendall , AUC 4747, Mean The performance of GraphCPLMQA is thoroughly tested on the model quality assessment datasets of CASP13, CASP14 and CASP15. GraphCPLMQA also participated in the continuous blind test of quality evaluation in CAMEO. All the test sets are constructed from the data provided by CASP and CAMEO official websites. The CASP13 and CASP14 test sets (CASP monomer test sets) were constructed by collecting models that were evaluated by all comparison methods and had a sequence similarity of <35% to the proteins in the training set. The CASP13 dataset collects 9390 structural models of 70 individual targets, and the CASP14 dataset collects 9645 models of 69 individual targets. The CASP15 test set (CASP multimer test set) was constructed by collecting structural models whose experimental structures had been released and length did not exceed 3000 residues and collected 9108 models of 34 multimer targets. Our training data (19 November 2021) was constructed before the CASP15, and the impact on the data is completely isolated in time. The CAMEO blind test set consists of 6 months CAMEO data (20 May 2022 to 13 August 2022), which contains 1590 models of 189 proteins. Details of data can be found in With the precision breakthrough of Alphafold2 in monomers, research into multimers has become a top priority. Similarly, assessing the quality of multimer interfaces is a future frontier and presents a challenge. The lack of effective MSA information greatly increases the difficulty of predicting and evaluating multimers. However, GraphCPLMQA-Single employs a single-sequence embedding to assess interface quality, as shown in GraphCPLMQA-Single is compared with nine methods in CASP15 for predicting multimer interfaces. On Pearson metrics, GraphCPLMQA-Single has improved by 23.6% compared with ModFOLDdockR , 51 266. ModFOLDWe compare with ModFOLDdockR (266) on each target. The results show that prediction accuracy of GraphCPLMQA-Single outperforms ModFOLDdockR . EvaluatThe above results show that the performance of GraphCPLMQA-Single surpasses other CASP15 methods. Although GraphCPLMQA-Single is trained on monomer data, it performs well on multimer interface evaluation. GraphCPLMQA-Single shows potential for extension to evaluate multimer interfaces, which may be attributed to the following reasons. First, the network has learned the evaluation mode of the local structural quality on proteins; second, the network takes the input protein structure as a whole, regardless of whether it is a multimer or a monomer; finally, the features of the network can describe structural and sequence information of the multimer. However, our method still has deficiencies in the interface evaluation of the CASP15 multimer test set. It can be seen from We developed the server ZJUT-GraphCPLMQA (server 46) based on the GraphCPLMQA method to participate in CAMEO-QE. In addition, more than 3134 protein models have been evaluated. In the competition, other participating servers included DeepUMQA2 , DeepUMQFor 9390 protein models of CASP13 and 9645 protein models of CASP14, GraphCPLMQA and GraphCPLMQA-Single compare performance with state-of-the-art methods on the CASP monomer dataset . In the The impact of the features and network architecture for GraphCPLMQA and GraphCPLMQA-Single on the non-redundant CASP monomer test datasets were analyzed –S13. At 1 was created by replacing the transformer strategy inverted bottleneck of GraphCPLMQA with residual block in the decoding module, and GELU [1. Regarding local metrics, there was a varying degree of decline in the prediction accuracy, while there was no significant change observed in global metrics. This indicated that the transformer strategy could further capture the local structural information. Second, the structural embeddings were removed from the language model in GraphCPLMQA1, leading to a decline in performance for GraphCPLMQA2. The high-dimensional structure features may imply some properties of protein structure that contribute to better learning of the network. Then, we changed the output mode of the encoding module (GraphCPLMQA3) and the connection architecture between modules (GraphCPLMQA4) on the GraphCPLMQA2 model. These operations may relatively weaken the sequence–structure relationship in the encoding module so that the encoding result has an impact on the decoding structure–quality relationship. Finally, based on GraphCPLMQA2 model, the triangle position and residual-level contact order features were removed (GraphCPLMQA5). The reduction in accuracy on the local metrics implies that these characteristics can supplement the portrayal of the local structure.To investigate the impact of the components, we modified the full version of GraphCPLMQA by removing some features and changing the network architecture. Different network models were retrained to test the results and analyze the effect of these modifications. First, GraphCPLMQAand GELU  was repland GELU  to creat1 and GraphCPLMQA-Single2 were retrained with the high-dimensional sequence embedding of the ESM-1b [3 network model. GraphCPLMQA-Single3 used the sequence embeddings of all layers of the ESM-1v language model by taking the mean. Although GraphCPLMQA-Single3 is based on GraphCPLMQA-Single2 using ESM-1v embedding, the results still show that this approach introduces significant noise that may affect the accuracy of the predictions. For GraphCPLMA-Single4, we removed the loss of geometric constraints and retrained the network model. The performance of the method decreased on all metrics, indicating that geometric constraints may potentially guide local accuracy evaluation.On the GraphCPLMQA-Single model, we analyze the effect of different single sequence language models on the performance of the method. Specifically, the network models GraphCPLMQA-Singlee ESM-1b  and ESM-e ESM-1b  languagehttps://github.com/deepmind/alphafold) to predict 69 sequences of CASP14. AlphaFold2 produced five output models for each sequence, resulting in a total of 345 models with pLDDT. GraphCPLMQA and GraphCPLMQA-Single assessed each model quality of AlphaFold2, respectively. The quality of GraphCPLMQA assessment exceeds the self-assessment of AlphaFold2 on MAE  model quality that combines graph coupled networks and embeddings from protein language models.We design a graph-coupled network based on an encoder-decoder module to establish a potential mapping relationship between sequence, structure and quality, which takes full advantage of the high-dimensional embedding of protein language models.To describe the protein structure and its complexity, we designed the triangle location feature and residue-level contact order.https://www.cameo3d.org/quality-estimation). GraphCPLMQA also outperforms state-of-the-art methods on 19 035 models in CASP13 and CASP14 monomer test set.Compared with CASP15 local/per-residue interface evaluation methods, GraphCPLMQA using single-sequence embeddings achieved the best performance among 9108 models in the local residue interface test set of CASP15 multimers. In CAMEO blind test (20 May 2022 to 13 August 2022), GraphCPLMQA ranked first compared with other servers (Supplementary_bbad420Click here for additional data file.All_Data_bbad420Click here for additional data file.


Our results demonstrate that GDP, Government intervention, testing and vaccination exert positive impacts on COVID-19 cases. We incorporate factors like weather to know how temperature impacts COVID-19 Cases. Our results demonstrate that magnitude of COVID-19 cases goes on upward fashion in winter days more. With reference to co-morbid conditions like diabetes, we notice that people with diabetes are more vulnerable to the infections, however due to the greater behavioral response, we obtain a negative association between co-morbid conditions and new COVID-19 cases. However, the intensity of COVID-19 cases is decimated with the improvement in health facilities and behavioral changes. Besides basic regression estimates, our instrumental variable estimates hold true in the line of regression results while underlying the relation with the COVID-19 cases. Interestingly, our results from alternate specification ensures that high human development with greater openness has resulted in more COVID-19 cases. Overall, our study belies the fact that vaccination and higher govt intervention can prevent COVID-19. Rather, a comprehensive policy is recommended on cross-country basis to overcome such challenge.  Specifications table*Method detailsThe COVID-19 outbreak has exerted devastating impact upon the economies globally. This study attempts to examine the economic impacts of COVID-19 for few Asian economies in this direction considering the cases of health infrastructures, co-morbid conditions, Government intervention and weather-related activities into the account. Economies especially in the South, South-Eastern, Eastern and neighboring parts have encountered economic severity and lockdowns with the challenges of mounting pressure on the health sectors post 2019. This study aims to flag some concerns post 2020 comprehensively in South and South-East Asia, considering the lethality and transmissibility of COVID-19 variants. Earlier literature in this direction fails to attain the comprehensive effects of COVID-19 post 2020 from the perspective of growth and health sector development COVID-19 has resulted in economic slowdowns, which in turn has led to firms’ shutdowns and employee lay-offs resulted in demand-supply mismatch across Asia ,16,17. EPast empirical work has significantly stressed upon the evidences of COVID-19 pandemic with various macroeconomic, financial and sectoral development for pre 2021 w.r.t scenarios. Most of the literature fail to address the economic and health sector scenarios aftermath 1st wave of COVID-19 prevalence especially in the Asian context. Our contribution in this direction is to show the evidence of linking COVID-19 infection with the human development, economic growth, govt initiatives and comorbid conditions of the people especially in the 2nd waves of infection prevalence in the developing blocs. We argue that economies especially in mid-2021 onwards have been highly infected with the virus’ infection upscale, which could be further underlined as the source of decimating economic growth, crumbling health infrastructure and high mortality in the region.As of date literature has started focusing exclusively upon studying the impacts of COVID from the different sectoral perspectives. COVID has impacted economic and financial systems globally across economies to a various degree Building on the epidemiological SIR Model, we propose an economic model considering the role of human development, GDP and health infrastructures for the 29 Highly COVID-19 Ravaged Asian Economies post 2020 from June 1, 2021 to June 1, 2022.In this section, we capture a brief analysis of previous literature pertaining to the economic effects of COVID-19 pandemics across globe and its repercussions. Considering several macroeconomic literatures on this COVID-19 infection transmission, it is well noticed that COVID led uncertainty has impacted global economy in varied manners. Step-1: We run a simple linear regression model considering both time and country effects followed by panel generalized least square models and PCSE model.Step-2: Next, we incorporate the instrumental variable 2 SLS model to overcome the endogeneity arising out of regression analyses. This study proposes 4 different instruments to see, how GDP and various COVID-19 induced factors vary with new COVID-19 cases.We utilize the Our World in Data database in terms of retrieving daily data for 29 economies especially from South, South-East, East and Central Asian regions. This database mostly captures the information on a daily basis pertaining to some of the pressing issues globally. Data period considered in this manuscript are from 1st June 2021 to 21st June 2022 for all the 29 Asian Economies on a daily basis.With reference to the empirical evaluation, we propose the following model with COVID-19 cases being the outcome variable, GDP being the major explanatory variable and weather, vaccination, stringency, health facilities as the control variables. The model is as follows.i and year t. Where In above At the outset, in our empirical strategy, we report the descriptive statistics for each of the variable used in our model. The descriptive statistics are reported in The We now provide a comparison of our region-specific results in order to draw more robust analysis by setting up the basic linear regression model. Based on the economic diversity, connectedness, growth and openness, the region has been divided into 3 different blocs- South Asia, South-East Asia and East-Central Asia See . The empAs seen from Here in This study investigated the nexus among COVID-19 cases, GDP, HDI, weather and various COVID-19 induced factors for 29 Asian economies post 2020. The results from full sample show that higher GDP and longer winter days accelerate the COVID-19 infection rate across the region. Full vaccination in the region as a whole exerts mild impact in controlling COVID-19. It is confirmed that triple strategies like high Government intervention, higher vaccination rate and improved testing intensity help decimate COVID-19 wave to a greater extent. Moreover, findings from our study confirm that in the region, joint COVID-19 strategy is still lacking, as evident from vaccination scale. Interestingly, we notice that hospital bed facilities overall in the region have been increased significantly, which itself indicates that COVID-19 has pushed the economies in the region to invest heavily in healthcare sectors. We also find that changing behavioral perspectives in terms of at least hand washing frequently has led to the controlling of COVID-19 significantly. In light of these findings, this study recommends that joint strategies in controlling COVID-19, equal vaccination rate, private participation in healthcare sector, higher testing rate and stimulating more public awareness ensure the smooth functioning of economies in long run. The study further suggests that improving GDP and opening up of economies should not be compromised at the cost of adopting a lackadaisical approach towards COVID-19 handling. Apart from the length of lockdown, several critical factors like health infrastructure, co-morbid conditions and macroeconomic characteristics have been identified in terms of analyzing the post 2020 Covid-19 scenario in these Asian economies. Considering such a wide diversity across economies, the study feels that a pan Asian health organization needs to be established to address the health inequality and backwardness in these economies. Second, health and economic opportunities for the socially disadvantaged, vulnerable groups along with the special focus on children and women need to be enhanced in these economies with a higher stake of private sector participation. Third, initiatives like social safety schemes, low cost mobile money transfer and innovative delivery approaches of medicine and health care facilities need to be deployed at the massive scale especially in the rural, semi-urban areas of the region.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The study included patients suffering from neurological irAEs at least grade ≥2 derived from two clinical networks  during a period of 5 years,8 Neurological irAEs affected the PNS in 59.2% and the CNS in 34.7%, respectively.8 The authors found that the rate of changeover from severe to minor disability was higher in patients suffering from melanoma compared with lung cancer.8 Similarly, the disease manifestations myositis and neuromuscular junction disorders were connected to a higher rate of changeover from severe to minor disability.8 Conversely, neurological improvement was lower when patients were older or had paraneoplastic-like syndromes. The findings are clinically important as they indicate that neurological recovery from ICI-induced PNS, and CNS damage is connected to baseline characteristics of the patients’ malignancy and age, which may help to guide treatment decision in future clinical studies, e.g. testing the hypothesis that immunosuppressive treatment needs to be different depending on these baseline characteristics. While the study can help to identify risk factors for non-response, the underlying mechanisms of neuroinflammation are not yet clarified. Different pathomechanistic steps have been previously reported for neuroinflammation including immune-mediated effects on astrocytes, microglia, oligodendrocytes and neurons e.g. in multiple sclerosis models or graft-versus-host disease affecting the CNS.9 The evolution of novel technological advances including genetically modified mouse models in combination with single-cell RNA-sequencing, and high resolution flow-cytometry based analysis of human CNS or PNS tissues, allows an increased depth of immune profiling of neuroinflammation,10 which could help to characterize and understand the pathomechanism behind irAEs affecting the PNS and CNS. Immune checkpoint inhibitors used in the clinic are diverse, targeting mainly PD-1, PD-L1, CATLA-4, TIM-3 and LAG-3. The authors found that the type of ICI used was not connected to later responsiveness of the neuroinflammation to immunosuppression.8 Conversely, the clinical presentation as myositis and neuromuscular junction disorders were predictive for a better response, suggesting that the biology and pathomechanism may be different in these manifestation compared with other clinical pictures such as limbic encephalitis and sensory neuronopathy, which were connected to low response rates. Currently, the pathomechanism of irAEs affecting the PNS and CNS are not well understood, and future research should analyse in controlled systems how ICI causes inflammation in the PNS and CNS including local immune cell types that may be involved.Immune-related adverse events (irAE) that occur after immune checkpoint inhibitor therapy (ICI) can affect various organs including the peripheral (PNS) and central nervous systems (CNS). Treatment of irAEs includes interruption of ICI-therapy and administration of glucocorticosteroids as well as multiple experimental approaches.


Spontaneous cerebrospinal fluid (CSF) rhinorrhea develops in patients without any history of trauma. Multiple factors have been theoretically debated. Also, localizing the defect may result in a challenge for the rhinologist. The common locations are the cribriform plate and the lateral recess of the sphenoid. Clival CSF rhinorrhea is rare, and only few cases have been reported so far. A 52-year-old female presented to the otolaryngology clinic with 7 years of history of left-side clear fluid rhinorrhea as a drop, which progressed to be runnier after she had pneumonia with severe cough secondary to COVID-19 infection. CSF was confirmed by a beta-2-transferrin test. During the perioperative evaluation, she developed meningitis which was treated with IV ceftriaxone and IV vancomycin antibiotics. The magnetic resonance imaging (MRI) and computerized tomography (CT) scan showed clival defect with pseudomeningocele which was initially not easy to see on CT. The patient underwent an endoscopic approach to the skull base to repair the defect with a pedicled septal flap. Also, a lumbar drain with intrathecal fluorescein administration was utilized. The postoperative course was uneventful without any complications. There was no evidence of recurrence with a 9-month follow-up postoperatively. Etiologies for CSF rhinorrhea are divided into traumatic and nontraumatic causes; nontraumatic causes are then subdivided based on the intracranial pressure (ICP), either normal or raised . SpontanA 52-year-old female, known to have hypertension and obesity with BMI 33, presented to the otolaryngology clinic complaining of 7-year history of left-sided clear rhinorrhea started as a drop with on and off headaches. Then, 3 months before presentation, she had developed pneumonia secondary to COVID-19, which resulted in severe cough, and at that time, she noticed her rhinorrhea became worse, which aggravated with bending forward. Her clear fluid was tested by beta-2-transferrin which confirmed CSF. Her CT skull base without contrast Figures  showed cThere are multiple factors that could possibly lead to spontaneous CSF leak, anatomical and physiological . AnatomiGiven its rare location, it is considered a challenge in terms of diagnosis and possibly surgical management. There were cases reported as being missed, leading the patient to present with recurrence because they were operated on without knowing the exact location of the defect and assuming the possibility of it being in the common areas . The sta