DCE-MRI was performed for each patient before and after treatment (two cycles of NACT) to generate baseline and early follow-up images, respectively. Feature-level changes (delta) of the entire tumor were evaluated by calculating the relative net feature change (deltaRAD) between baseline and follow-up images. The voxel-level change inside the tumor was evaluated, which yielded a Jacobian map by registering the follow-up image to the baseline image. Clinical information and the radiomic features were fused to enhance the predictive performance. The area under the curve (AUC) values were assessed to evaluate the prediction performance. Predictive models using radiomics based on pre- and post-treatment images, Jacobian maps and deltaRAD showed AUC values of 0.568, 0.767, 0.630 and 0.726, respectively. When features from these images were fused, the predictive model generated an AUC value of 0.771. After adding the molecular subtype information in the fused model, the performance was increased to an AUC of 0.809 (sensitivity of 0.826 and specificity of 0.800), which is significantly higher than that of the baseline imaging- and Jacobian map-based predictive models . The level of tumor heterogeneity reduction  is higher in the NACT responders than in the nonresponders. The results suggested that changes in DCE-MRI features that reflect a reduction in tumor heterogeneity following NACT could provide early prediction of breast tumor response. The prediction was improved when the molecular subtype information was combined into the model.Breast tumor morphological and vascular characteristics can be changed during neoadjuvant chemotherapy (NACT). The early changes in tumor heterogeneity can be quantitatively modeled by longitudinal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is useful in predicting responses to NACT in breast cancer. In this retrospective analysis, 114 female patients with unilateral unifocal primary breast cancer who received NACT were included in a development ( Neoadjuvant chemotherapy (NACT) is commonly used in treatment of locally advanced or large operable breast cancers with the aim of downstaging before surgery . The achDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is routinely used in clinical practice, provides morphological tumor characteristics and functional information about the blood flow, vascular status and angiogenesis . A systeThe NACT regimen usually takes six to eight cycles to finish the whole treatment procedure. Longitudinal imaging is usually performed during the procedure to monitor and evaluate treatment response. The changes of tumor heterogeneity in DCE-MRI between the preoperative and early NACT  may provide information for early prediction of the eventual treatment outcome. Previous studies have demonstrated evidence of longitudinal changes in pharmacokinetic parameters , tumor sTo this end, attempts have been conducted by aligning intermediate MRI to baseline images to evaluate changes in tumor heterogeneity in a voxel-by-voxel manner. A previous study implemented an accurate image registration technique using a parametric response map (PRM), which can provide quantitative voxel-based information regarding heterogeneous changes within the tumor during treatment . The nonTo predict NACT responses in breast cancer, changes in tumor heterogeneity were evaluated both in voxel-by-voxel and feature-level manners using longitudinal DCE-MR images. Radiomic features were extracted at baseline and post-treatment images and the voxel-level map of volumetric change before and after early NACT. Additionally, feature-level changes in tumor heterogeneity were evaluated by calculating the relative net radiomic feature change between baseline and follow-up images (deltaRAD). Predictive models were then established using the radiomic features derived from these images. Our comprehensive analyses demonstrated how the heterogeneity changes in DCE-MRI before and after early NACT could affect the accuracy of prediction of the response to NACT.The framework of this study is illustrated in This study was approved by the Institutional Review Board of Fudan University Shanghai Cancer Center. Due to the retrospective nature of this study, use of a consent form was waived. The data collection and selection procedure in these two cohorts are illustrated in in situ hybridization (FISH)  . Tumor sn = 61), the images were acquired using a 3.0-T scanner . DCE-MRI was acquired with a fat-suppressed T1-weighted imaging sequence, which generated one precontrast (S0) followed by five or eight sequential postcontrast image series after injection of a gadobutrol-based contrast agent. The time interval between the first postcontrast image and S0 was 90 s, while the time intervals between the subsequent image series were 43 or 44 s.Imaging was performed following the specific requirements of the hospital. For the development dataset (n = 53), DCE-MRI was acquired using a dedicated 1.5-T breast magnetic resonance imaging system . The imaging system generated one precontrast image and three postcontrast images at 120, 245, and 371 s after beginning the intravenous administration of gadobutrol injection. The detailed imaging parameters for these two datasets are shown as in For the testing dataset  bias correction was implemented to reduce the effect of MR imaging artifacts. Images from the patients were resampled to the same spatial resolution for feature extraction. The tumor region-of-interest (ROI) was identified by using a spatial Fuzzy C-means method on the third postcontrast image series where the highest enhancement valuate were usually achieved .Voxelwise volumetric changes were evaluated by aligning the follow-up images to preoperative scans by finding an optimal and deformable transformation for image registration . Based on = 14), first-order statistics (n = 18), texture features using gray level cooccurrence matrix (GLCM) (n = 24), gray-level run-length matrix (GLRLM) (n = 16), gray-level size-zone matrix (GLSZM) (n = 16) and gray-level dependence matrix (GLDM) (n = 14). The imaging heterogeneity of the entire tumor was evaluated based on the subtraction images between the intermediate image series that unusually exhibited the maximum enhancement signal and the precontrast image. Radiomics features were calculated on the pre- and post-treatment images and the Jacobian map.Features were extracted from the tumor ROI using a publicly available radiomics analysis software, Pyradiomics . For eacith radiomics feature  was performed to compare continuous variables between the development and the testing groups. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the performance of the predictive model. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. The sensitivity and specificity were determined at the operation point at ROC curve by using the Youden index by the maximum sum of the specificity and the sensitivity. Statistical tests with p values less than 0.05 were considered significant.The distributions of the histopathological information of the molecular subtypes, menopausal status, family history between the development and testing groups were compared by using the α and γ were tuned using a grid search method in each iteration with a 10-fold cross-validation framework. An optimized model with the selected feature subset and the tuned model parameters was established using all the samples in the development set and was applied to the testing set to evaluate the model performance. Statistical analysis and machine learning methods were performed using R (version 4.0) and Matlab .A support vector machine (SVM) with a Gaussian kernel was used as a base classifier for prediction. Predictive model establishment and model tuning were performed on the development set and were tested on the testing set. SVM-recursive feature elimination (RFE) was used to rank the features that were most relevant to the target, and these were then sequentially added into the predictive model. The feature sets were fed into the predictive model, in which the SVM parameters n = 70, 61.4%) who had an MP grade of no more than three  were defined as the nonresponders. No significant differences in histological information were observed between the development and testing datasets  calculated on the Jacobian map of tumors is illustrated in f 4.9e−5 . This rep = 2.45e−4). This result suggested that a higher level of this texture feature, which reflects a higher voxelwise spatial rearrangement heterogeneity of the shrinkage/expansion pattern inside a tumor during NACT, is more likely to be associated with a worse response to NACT.In addition to statistical features, examples of texture features derived from tumor Jacobian maps are illustrated in p = 0.09) differences between the responders and nonresponders  decrease in a statistical feature (energy) after early NACT was observed in the responders than in the nonresponders (A more significant (sponders . This fep = 0.006), while the difference was not significant in the nonresponders (p = 0.241). This feature measures the level of the fineness and coarseness of the texture of an object, in which a high value is correlated with high gray-level heterogeneity within the tumor. The results suggest that the level of tumor heterogeneity reduction is higher in the NACT responders than in the nonresponders.An example of a texture feature  is also illustrated in To evaluate the collective effect of longitudinal radiomics, the features from different images were combined and evaluated. The individual features from the images at baseline, post-treatment, Jacobian map and deltaRAD features were evaluated, and the results showed that features from the follow-up image have the highest performance , while the deltaRAD features and Jacobian map-based features showed intermediate performance .p = 0.028) and the Jacobian map (p = 0.019) based predictive model.Radiomic features from these images were used separately to establish predictive models in the development set and was tested on the testing set . Among tDuring NACT, the breast tumor size and morphological and functional changes are associated with the eventual treatment outcomes. In this study, the pattern of the changes in tumor heterogeneity during NACT was evaluated using baseline and post-treatment images to predict responses to NACT in breast cancer. The voxelwise shrinkage/expansion inside the tumor and the feature-level changes of the entire tumor were both obtained. Radiomics features from longitudinal images and the changes in tumor heterogeneity were fused for the prediction. The molecular subtype information was combined with radiomics features, which generated an increased prediction performance.2-weighted MR images was independently associated with pCR in non-triple-negative breast cancer  and texture features  decreased after early NACT, while the level of feature reduction was lower in the responders than in the nonresponders. On the other hand, the voxelwise volumetric reduction inside tumors was significantly associated with the responders. Additionally, texture features  were reduced after early NACT, and the level of the reduction was higher in patients who responded to NACT than in those who did not. In our results, tumor heterogeneity was decreased after early NACT, and more importantly, the high level of reduction in heterogeneity was associated with good response to NACT. This indicated that decreased heterogeneity within a tumor may likely be exhibit in the patients who benefitted from the NACT.In addition to the evaluation of voxel-vize volumetric changes by image registration, longitudinal feature-level changes between the baseline and follow-up images were also evaluated for their associations with tumor response to NACT. In our study, the performance of the model based on vascular characteristics measured by DCE-MRI was higher than that of the model based on morphologic features, which is partly consistent with the findings of a previous study that dynamic features have better accuracy in response prediction than tumor size . Our resWe observed a relatively lower performance in terms of AUC for features from the baseline images. A related study identified significant change in the tumor maximum diameter between the responders and nonresponders . In our Despite the potential significance of tumor radiomics using longitudinal images in this study, several limitations should also be addressed. First, only the tumor region was analyzed for image feature extraction. It would also be interesting to analyze the peritumoral tissues that surrounds the tumor  to conduIn conclusion, longitudinal changes in tumor heterogeneity at the voxel and feature levels were examined to determine their contribution to the prediction of tumor response. It was found that molecular subtypes add more predictive power in assessing the response to NACT.


SARS-CoV-2 is one of three recognized coronaviruses (CoVs) that have caused epidemics or pandemics in the 21st century and that likely emerged from animal reservoirs. Differences in nucleotide and protein sequence composition within related β-coronaviruses are often used to better understand CoV evolution, host adaptation, and their emergence as human pathogens. Here we report the comprehensive analysis of amino acid residue changes that have occurred in lineage B β-coronaviruses that show covariance with each other. This analysis revealed patterns of covariance within conserved viral proteins that potentially define conserved interactions within and between core proteins encoded by SARS-CoV-2 related β-coranaviruses. We identified not only individual pairs but also networks of amino acid residues that exhibited statistically high frequencies of covariance with each other using an independent pair model followed by a tandem model approach. Using 149 different CoV genomes that vary in their relatedness, we identified networks of unique combinations of alleles that can be incrementally traced genome by genome within different phylogenic lineages. Remarkably, covariant residues and their respective regions most abundantly represented are implicated in the emergence of SARS-CoV-2 are also enriched in dominant SARS-CoV-2 variants. The ~30kb genome size of all SARS-related CoVs renders sequence alignment and pairwise distance methods effective for phylogenic studies and determining genetic events that correlate with their adaption to the human host. While nucleic acid sequence-based phylogenies are informative, they clearly have limitations as not all single nucleotide polymorphisms are equal. For example, single-strand RNA viruses possess significant base-pairing in regions of their genomes that can result in different fitness costs even for synonymous mutations because higher-ordered RNA structures and non-coding regions can impact replication, transcription, and recognition by the host immune system8. Nucleotide polymorphisms also distinctly influence encoded amino acid (AA) residues depending on their position in a codon and thus further protein expression is often correlated with codon frequency or cognate tRNA abundance9. Similarly, codon usage has been studied in the context of mutational pressure and natural selection13. For example, the presence of repeating rare codons in the SARS-CoV-2 Spike protein corresponding to the furin cleavage site (FCS) has been explored as circumstantial evidence for genetic manipulation14. Mutational pressure rather than translational selection is however reported in other work to be one dominant factor in the observed codon usage in other human RNA viruses16. In RNA viruses, the host immune system can impose additional selective pressure on genomic nucleotide content; thus the high frequency of A- and U-ending codons and underrepresentation of CpG dinucleotides in RNA viruses including CoVs is attributed primarily to cytosine deamination and the pressure exerted by innate immune mechanisms19. Comparative analysis of SARS-CoV-2 to closely related CoVs suggests that C-to-U conversion played a significant role in the evolution of the SARS-CoV-220. In sum, codon usage, nucleotide sequences, and the AA content may affect virus adaptation with the latter being most relevant to protein folding, stability, function, and adaptive immune recognition in the host.The prior emergence of SARS-CoV and MERS-CoV as human pathogens is attributed to zoonotic viruses that transferred from bats to civets and camels, respectively, while SARS-CoV-2 is most similar to viruses isolated from both bats and pangolins3. An ancestor to SARS-CoV-2 is yet to be established with certainty, but both residues within the Spike RBD that interact with the host receptor angiotensin-converting enzyme 2 (ACE-2), and a unique furin cleavage site are believed to have contributed to its adaption to the human host and its enhanced transmission25. RATG13, one of the closest bat CoV relatives to SARS-CoV-2 that shares ~96% nucleotide identity26, is measured to have a reduced affinity for human ACE-2 when both are compared and this is in part conferred by residues in Spike27. However, molecular evidence for ACE-2 affinity being the primary determinant in host specificity for CoVs is also confounding. SARS-CoV and SARS-CoV-2 viruses that infect human cell lines via the ACE-2 receptor are found to vary in their ability to infect bat cell lines suggesting that the host range of β-coronaviruses is not only specified by Spike RBD-ACE-2 interactions28.Many previous and ongoing efforts to study human CoV virus-host interactions are focused on AA residues and domains within the quaternary and tertiary structure of the Spike trimer protein. For SARS-CoV, the stepwise adaption from the ancestral bat CoV to variants that infect civet, human, and even laboratory mice is well-understood and can be traced to the domains in the Spike protein that confer specificity to the host, especially in the context of residues within the receptor-binding domain (RBD)30. Subsequent dominant emerging variants within this clade notably possess additional mutations in Spike and other genes. Moreover, a broad and diverse collection of variant mutations are represented independently in other distinct lineages32. In Spike, some of these mutations are attributed to immunity evasion, host-receptor interactions, and Spike structure and conformational dynamics33. Distinct attributes or roles of each of these mutations are yet to be entirely elucidated and the actual extent of the contribution of any allele may be intricate. Importantly, it is not yet known what is the contribution of each of these single mutations in the context of other mutations and in general, the effects of mutations in many proteins other than Spike have been less studied or are completely unknown.Evidence for the selective adaptability and the plasticity of Spike protein domains has been documented by the existence of single and multiple mutations that have been temporally enriched in newly dominant variant lineages during the ongoing pandemic. For example, the Spike D164G allele, a stand-alone defining mutation of the dominant SARS-CoV-2 A2a clade that emerged early in the pandemic, has been demonstrated to increase viral fitness and infectivity, possibly by influencing proteolytic processing, incorporation of Spike protein in the virion, and conformational states of the Spike proteinHere we report the results of an investigation that sought to use the evolutionary history of sarbecoviruses to identify the most-conserved interactions between AA residues in key proteins encoded by CoV viruses most related to SARS-CoV and SARS–CoV-2. Specifically, we identified all covariant AA pairs and also larger correlated tandem model-based networks (clusters) of AA residues that exhibited statistically high frequencies of covariance with each other. We examined conserved covariance between protein sequences to uncover new insights into CoV evolution through the identification of apparent inter and intra-protein interactions. We propose that these covariant interactions of residues are important for virus evolution and may drive adaption to other hosts and influence transmission and pathogenicity by emergent variant viruses.38. By generating a phylogenic tree to assist in identifying such phylogenic effects, AA variability at covariant residues can also be traced using tree topology and even branch length and therefore analyzed in the context of evolution39.We first estimated the extent of the evolutionary relatedness of 169 β-coronaviruses using whole-genome nucleotide sequences . We postNsp1 of the 16 polyprotein-encoding gene Orf1ab through to the termination codon of the N gene that encodes the nucleocapsid protein , Civet (10), Pangolin (6), and human (6). SARS-CoV is notably represented as both civet and human isolates and SARS-CoV-2 is represented by an initial reference sequence isolated in Wuhan during December 2019.We aligned the deposited nucleotide (nt) sequences of 169 unique lineage β-coronaviruses between the initiation codon of 45. Though certain genomic regions are predicted to be prone to recombination events that can lead to mosaicism during the evolution in CoVs49, we did not alter our phylogenic analysis based on the exclusion or inclusion of any single core gene or gene region, apart from hypothetical gene ORF10. In an unrooted tree, distinct lineages are apparent; several CoVs are most related to SARS-CoV, a set of sublineages of CoVs more closely related to SARS-CoV-2, and a third distinct group of β-coronaviruses more closely related to HKU-3. Only a small subset of viruses in this analysis are predicted to be most closely related to SARS-CoV and SARS-CoV-2 relative to all other CoVs represented in this phylogeny. How these viruses differ from those more distantly related in the context of amino acid residue covariance was one aim of the comparative analysis presented here.The topology and structure of the maximum likelihood (ML) tree  generateThe relatedness of SARS-CoV-2 to certain bat and pangolin CoVs supports the emergence of this virus from a zoonotic reservoir. Using our nucleotide alignment-based tree, we identify RATG13 and other more recently identified bat CoVs from Laos to be most closely related to SARS-CoV-2, designated as Group 1. Clusters of other pangolin and bat CoVs, some nearly clonal, comprise the next tier of related assemblages designated as Group 2 and 3, respectively. A small number of other CoVs, designed Groups 4 and 5, are less closely related to those in Groups 1–3 CoVs but also distinct from other SARS-CoV and HKU-3-related viruses in our generated phylogeny. All CoVs in groups 1–5 are more likely to share a common ancestor with SARS-CoV-2 and we propose mutational and/or recombination events and also selective processes have generated the observed diversity within this subclade  and 2B.ORF1a and ORF1b which together encodes at least 16 smaller non-structural proteins (nsps) when processed by a viral protease. Others include genes for S (Spike), the two viroporins ORF3a and E (envelope), M (membrane), and N (nucleocapsid). Our goal was to align the AA sequences of these proteins to identity covariant pairs and residues within and between core non-structural and structural viral proteins. The alignment resulted in a 9458 AA consensus sequence with only 1.5% of sites being gaps with low coverage and 2% with residue conservation of less than 80%.We selected 149 CoVs represented in our phylogenic reconstruction based on the availability of annotated proteins and aligned the amino acids of core proteins using identified open reading framed (ORFs) common to all genomes. This includes the conserved CoV polyprotein genes called When the AA sequences for each protein are aligned and compared, the degree of conservation varies between each protein and also within individual and discrete protein domains. Genes that encode nonstructural proteins (nsp)s with roles in RNA metabolism and genome replication  are among the most highly conserved in AA identity. Others that encode nsp2, nsp3, and nsp4 are highly variable. The NTD and RBD of Spike show the most significant variability in both residue identity and length. In contrast, the sequence of much of the CTD of the S1 and the entire S2 subunit of Spike is highly conserved. The channel-forming E (envelope) viroporin protein is one of the most highly conserved proteins in contrast to the viroporin ORF3a that exhibits high variability in its NTD and other CTD subdomains. We propose that this is evidence of evolutionary pressure on residues in CoV proteins and that certain protein subdomains may be under higher selective pressure than others.50. After binding to the ACE-2 receptor, cleavage of S by furin and or other proteases is critical to conformational changes that allow viral envelope-host membrane fusion and subsequent viral RNA entry into the host cytosol25. Other changes in S are less understood. For example, certain residues in the NTD of SARS-CoV-2 Spike are present in other unrelated CoVs but are notably absent in the corresponding regions of SARS-CoV51. Conversely, there are residues in many CoVs with no positional equivalent in SARS-CoV-2. To address gaps in the alignment, we have indicated such residues with a “Z” designation to accommodate possible covariance between residues and such deletion occurrences.In addition to greater AA sequence variability in some genes, individual residues and continuous sections of AA are either uniquely present or absent in a portion of CoVs. One key aim of our study was to identify and evaluate the covariance of all residues in our selected proteins among these 149 CoVs without bias. We predicted that his analysis might reveal the existence of critical amino acid residue conservation or changes that would possibly correlate with changes in the virus-host range or biological properties. In this regard, SARS-CoV-2 is recognized to possess unique sequences not present in other closely related CoVs including those that define and enhance a furin cleavage site (FCS) of the Spike protein54. Because covariant AA residues change in concert with each other, they can define critical AA-AA residue interactions within a protein or in homologous or heterologous protein-protein pairs or instead indicate phylogenetic relatedness based on their co-existence38. These putative residue interactions may provide new insights into the evolution and relatedness of the CoV family of viruses55. To survey the frequency of covariance among a reference collection CoVs, we identified correlative pairs and also assembled groups of three or more (here designated as ‘clusters’) of covarying amino acid residues using a correlating tandem model56. These clusters are not typically generated using other typical pairwise algorithms tailored for determining protein structure or docking interfaces. We chose the FastCoV approach for its distinct quality in identifying larger networks of putative compensatory mutations generated by selection and adaptation which seems well-suited for studying the emergence of viruses similar to SARS-CoV and SAR-CoV-256. This differs from DCA-based and other covariance approaches used to predict co-evolving residue interactions that may use corrected and weighted correlative data that can also be coupled with other various predictive secondary structure motifs to assist in protein structure and interaction predictions57. Our approach simply provides a raw covariance purity and percentage score for pairs and larger networks of covariant residues with no goal for structure-based predictions. We selected a purity threshold (0.7) based on our small sampling size and extracted 973,649 unique pairs and 741 clusters. In this preliminary analysis, we identified a collection of gaps and also unique sequences selectively conserved in some CoVs and we concluded that such deletions or insertions, like residues, may also covary with AA sequences in proteins. Deletions were temporarily substituted as rare alternate amino acids in the alignment and covariance was analyzed to reveal putative covariance between all residues and also deletions. This expanded the total number of unique correlating residue pairs  and clusters (769) (Covariance is a quantitative measurement of how often the identity of one AA is correlated to the identity of another AA or AAs in either the same protein or in a completely different proteinrs (769) . We idenAll CoVs genomes, clusters, and residues were graphed using a force mapping algorithm . This inTo provide information about the phylogenic distribution of any cluster that may be due to ancestry, an average taxonomic distribution score (ATDS) was calculated for each cluster based on the number of CoVs present in a given cluster and their average distribution based on branch lengths estimated in the ML tree . Though Of the 1,089,836 unique pairs with varying degrees of residue identity at each two positions, we identified 522,336 correlative AA residue pair positions and also calculated the number of unique amino acid identities that can exist for each position in the pair , nearly half (154) of these are located in the Spike protein. Various structures of Spike trimer are elucidated and well-studied due to the roles in receptor recognition, cell entry, and interactions with monoclonal antibodiesposition . ResidueWe examined the identity and distribution of covariant residues within the lineages of CoVs most closely related to SARS-CoV-2 identified in this work designated as Groups 1–5 . We reasGroup 5 exhibits the most numerous and distributed covariant residues identified in distinct clusters . This is62. We vetted residues in Groups 1–3 because we hypothesized these covariant alleles might be important for selection, adaptation, and viral fitness for the most closely SARS-CoV-2-related viruses in human, pangolin, and bat hosts. Furthermore, we identified covariant residues co-present in two or in all three of these groups. By definition, the AA identity of individual covariant residues is not highly conserved in CoVs, but instead, their conserved identity varies with other residues. Thus any covariant pair or cluster of residues may indicate a direct or indirect conserved interaction between AAs important during the adaption of a CoV. We find a common subset of conserved covariant residues between both bat and pangolin CoVs with those closely related to SARS-CoV-2 in Group 1. These may indicate specific interactions between residues and residue identities especially relevant to the biology of SARS-CoV-2 and related CoVs.Because CoVs in Groups 1–3 are most closely related to SARS-CoV-2 based on their nucleotide identity, we focused on these and examined covariant residues in Spike –C. ResidA majority of the Spike-specific covariant residues common to clusters in Groups 1–3 are located within discrete domains primarily in the S1 domain  and 3C. The resolution and extent of our pan-CoV covariance analysis are in part defined by the number of distinct genomes and also their relatedness. Roles for all identified covariant residues in all proteins cannot be readily ascertained, but this generated data may be further validated as more SARS-CoV-2 protein structures become available. Because of the vast scope and magnitude of SARS-CoV-2 infections during the ongoing pandemic and availability of whole genomes sequenced, we supposed covariant residues may also be apparent in the millions of sampled clinical strains over 18 months. Genes enriched residues with covariant relationships that appear to be co-present in both the pan and clinical covariant analysis are more likely to be of special interest to SARS-CoV-2 biology. We extracted and stringently selected whole-genome sequences that were nearly or entirely complete to avoid artifacts that may bias our analysis and then compared the positions of covariant residues of 252,102 randomly selected sequences deposited between December 2019 and August 2021 .Due to the near clonality of SARS-CoV-2 sequences, we were unsurprised to find only 1.2% of the total covariant residues when compared to those identified in pan-CoV analysis. 13,041 uniquely represented pairs of AA residues can be reduced to 6,137 correlative pairs for all proteins. As observed in the pan-analysis, the distribution is exceedingly skewed toward several genes encoding proteins including Spike. When the distribution of every single residue identified in both the pan and clinical analysis is compared gene-by-gene, regardless of the positions of correlative partner, we discovered the contributed representation for each encoded protein follows a similar trend  and 4B. We accounted for the distribution of intra- and inter-protein residue pairs identified in both analyses . We reasThe enrichment of residue pairs in the subdomains of Spike and ORF3a were of special interest . First, We find evidence that subsets of these 31 residue pairs likely interact directly or are positioned proximal to one other within particular regions of the Spike protein –E. In a 31. When both residue pairs are present in a dominant variant (49), we find 76% of these are remarkably present together in the same variant lineage  of 394 total represented residues are found in 15 dominant variants including the two current Omicron sublineages lineage . This ob63. We chose to limit our pan-analysis to only lineage B β-coronaviruses for our work based on this very principle.For both nucleotide and amino acid identity-based approaches, the sequence conservation in either complete or partial regions of CoV genomes continues to be applied to understand the relatedness between β-coronaviruses and SARS-CoV-2. This extends to the emergence of SARS-CoV-2 as a human pathogen responsible for a global pandemic and its continued adaptation. In this work, we examined the conservation of correlative covariant pairs and even clusters of amino acids that appear to change in concert with one another across the entire genome. We acknowledge apparent covariant mutations can also be a simple consequence of spontaneously emerged mutations and other common concurrent mutations at less conserved sites. Conversely, these could be a result of spontaneous mutations that imposes a selection for one or even more compensatory mutations at other sites to maintain or even increase viral fitness. Both instances are certain to be present in this dataset. An applied hypergeometric probability distribution predicts the overlap of 538 covariant pairs between the pan and clinical datasets is exceedingly significant . We acknWe find evidence of true covariance in this work when we compare the total number of independent changes present in each pair with a known structure. In Spike, the probability of either a direct or possible indirect interaction by one flanking residue increased from 5% to 24% when the minimum number of unique AA changes in any given residue pair of two identities were compared to those with five. Furthermore, for covariant residues with increased independent residue representation in the pan covariance analysis, these were also more likely to be identified in the clinical covariance analysis. Notably, when residues identified in both analyses are compared, 90% are found within the same gene and enriched in certain genes with important virus-host interactions such as Spike. This observation is consistent with an expected model where intraprotein covariance is predicted to be more abundant than that for interprotein, including proteins that form homotrimers such as Spike.68, but not yet investigated comprehensively in the context of pan-covariance to our knowledge. Notably, many NTD covariant hotspots are also deletions identified in SARS-CoV Spike protein when aligned to SARS-CoV-251. Though clinical SARS-CoV-2 covariance data should by definition reveal co-present residues common to variants, the independence occurrence of these in the Pan-CoV covariance is intriguing. We note these clinical sequences were collected through August 2021, over three months prior to the first emergence of Omicron, and yet we identify some Omicron-specific residues and pairs in this work. We propose all covariant residues identified in Spike and other conserved CoV proteins in this work serve as one reference for possible future single and multiple mutations that might arise in dominant variants. These could inform about epitopes and antigenic regions that are possibly vulnerable to enriched mutations also in part due to covariance such as specific regions in the Spike NTD. Furthermore, these may reveal key residues that contribute to yet undiscovered interactions between viral proteins of SARS-CoV-2 including Spike and ORF3a as discussed in an earlier description of our initial results55.A benefit of comparing residues from both covariant analyses is demonstrated by their co-presence and enrichment in dominant circulating variants. Mutations and deletion-enriched hotspots in the Spike NTD described in this work have been recently identified and studied as antigenic regions responsible for antibody escape70. All genomes and accession numbers are provided in Orf1a gene through the stop codon of the N gene71. Protein sequences for NSP1 through NSP16, Spike, ORF3a, E, M, and N of 149 CoVs were concatenated and then aligned using the MAFFT iterative consistency-based setting (G-INS-i)71. For clinical samples, we initially selected a total of 882,364 sequences isolated, sequenced, and deposited in GISAID between December 2019 and August 21, 2021 based on their near completeness (>95%) of sequence coverage for all proteins used in pan-CoV amino acid covariance. This facilitated the inclusion and identification of sequences with small deletions and rare insertions. Due to computational limits, two independent sets of 126,051 randomly selected sequences of the 882,364 were aligned using MAFFT using respective references of each alignment to maintain identical length  with 1000 replicates using bootstrap resampling analysis72. This tree file is available in We inferred phylogeny by reconstructing a maximum likelihood (ML) tree with IQTree after first testing and comparing 286 DNA models by creating initial parsimony trees scored according to Bayesian information criterion (BIC) using IQTree ModelFinderFor all genomes that belong to each cluster, the sum of branch lengths between every possible pair was extracted from the tree file and averaged to calculate the Average Taxonomic Distribution Score (ATDS). This relative score is provided as additional metadata in the Gephi Force mapping file.56. Alignment files for both pan and clinical CoVs were substituted to provide a “W’ in place of absent/deleted residues. Using the known position of true “W”, residues, all “W” deletions were replaced as “Z” to indicate absence following analysis. We set a purity score (0.7) for stringency cutoffs in both the pan-CoV and clinical-CoV sequence alignment. A raw table of predicted covariant pairs is provided as 73. All clusters and residues and their respective occurrence in CoVs for both analyses are tabulated in 73. This data is provided in Pairwise and multiple residue covariance and scores were calculated using FastCov74. To accommodate minor sequence and structure variability between Spike proteins in the pan-CoV analysis, the position of any two residues identified to interact in SARS-CoV-2 was extended by one flanking position both amino and carboxyl to each residue when calculating possible interactions for all 149 CoVs. Residues in Spike were mapped onto the PDB structure for Spike (7JJI.pdb) using PyMol (v.2.3.4)76.The Arpeggio program was used to calculate inter and intramolecular interactions between residues in the 7JJI.PDB file 59,74. ToCoVariants.org and enabled by GISAID77. The WHO label for each variant is used for reference.Mutations identified in previous and dominant circulating variants of clinical interest were extracted from data compiled by Hypergeometric probability was applied in R using the abundance and distribution of single residues in each analyzed gene in the pan and clinical covariance datasets. Residue identity by position was approximated for the pan covariance and then numbered by position in SARS-CoV-2. For comparative analyses of covariant pairs identified in both analyses across all genes, residue identity by position was approximated for the pan covariance and then numbered by position in SARS-CoV-2. The total number of unique residues identified as covariant in each independent analyses and the total pairs co-present (overlap) was examined as above by applying a hypergeometric probability formula.78.Circular graphing of key collections of residues was graphically plotted using CircosSupplement 1Supplement 2Supplement 3Supplement 4Supplement 5Supplement 6Supplement 7Supplement 8Supplement 9


Strong recent clinical evidence links the presence of prominent oscillations of ventricular repolarization in the low-frequency range (0.04–0.15 Hz) to the incidence of ventricular arrhythmia and sudden death in post-MI patients and patients with ischaemic and non-ischaemic cardiomyopathy. It has been proposed that these oscillations reflect oscillations of ventricular action potential duration at the sympathetic nerve frequency. Here we review emerging evidence to support that contention and provide insight into possible underlying mechanisms for this association. Oscillations of ventricular repolarization measured from the electrocardiogram (ECG) T-wave vector have recently been shown to be one of the strongest predictors of arrhythmia and sudden death in cardiac patients in a large prospective multicentre study. The results provide clear evidence that a fluctuating pattern of ventricular repolarization at a frequency <0.1 Hz, when enhanced, is highly predictive of ventricular arrhythmia and sudden cardiac death in cardiac patients.Despite the obvious importance of these findings, the link between oscillation of the ECG T-wave vector and ventricular arrhythmia is at present unclear. Understanding the electrophysiological basis for this association is important in order to refine the PRD and facilitate its use as a potential clinical tool for risk stratification. Furthermore, the link between oscillatory behaviour of repolarization and ventricular arrhythmia may provide valuable insight into arrhythmia mechanisms. In this regard, several key questions arise. What does the ECG T-wave vector angle represent? It has been proposed that these oscillations of T-wave dynamics represent the effect of phasic changes in sympathetic activation on ventricular repolarization possibly associated with changes in action potential duration (APD) related to different layers of the myocardium. But does APD exhibit oscillations? If so what drives the oscillatory behaviour? What are the electrophysiological mechanisms linking APD oscillation and ventricular arrhythmia? Do LF oscillations of ventricular repolarization interact with proarrhythmic mechanisms such as beat-to-beat variability of repolarization and T-wave alternans? Here we review emerging evidence for a mechanistic basis to help answer these questions.Figure 1). In Figure 2, PRD recordings are shown from a post-MI patient who survived a 5-year period (left) and a patient who died 8 months after an MI (right). Typical PRD oscillations are seen which are of much greater amplitude in the patient who died compared to the survivor. It was proposed that these T-wave dynamics represent oscillations of repolarization which in turn reflect oscillations of ventricular APD.,The ECG T-wave vector reflects the spatiotemporal orientation of the repolarization wavefront with respect to the body surface. Oscillations of the T-wave vector referred to as PRD occur in the LF range . Oscillations typically occurred over a fairly narrow range within the LF spectrum . Low-frequency oscillations were subsequently observed in a number of other studies including recordings from the left and right ventricular endocardium,,,Oscillatory behaviour is a ubiquitous property throughout many biological systems. However, it is only relatively recently that ventricular APD has been shown to exhibit oscillatory behaviour.et al. sympathetic provocation was induced by the Valsalva manoeuvre during steady-state pacing in patients with an ICD. Recordings of left ventricular epicardial APD (ARI) showed an increase in LF power.The question arises as to what drives these rhythmic fluctuations of APD. It was suggested that LF oscillations of the ECG T-wave vector could be related to the characteristic oscillations of sympathetic nerve activity at this frequency.CaL) and the slowly activating delayed rectifier current (IKs).in silico modelling have recently demonstrated a biphasic response of ventricular APD in the immediate few beats following abrupt beta-adrenergic stimulation.,CaL) and the slower time constants of slow component of the delayed rectifier current (IKs). The fast time constant of inward ICaL current results in initial APD lengthening. After a few beats, outward IKs catches up, counterbalances ICaL, and induces APD shortening,Figure 5). Pueyo et al.CaL/IKs mismatch following beta-adrenergic stimulation as described above could play a major role. For their computations, they simulated an oscillatory pattern of beta-adrenergic stimulation by consecutive 10 s on/off sequences of isoprenaline. A biphasic APD response was observed for each isoprenaline application, with initial transient APD prolongation accompanied by dominant ICaL followed by APD shortening accompanied by dominant IKs.Figure ).What are the cellular mechanisms whereby phasic sympathetic activation generates an LF oscillatory pattern of APD? Beta-adrenergic stimulation initiates a signalling cascade in cardiac myocytes through G protein activation of adenyl cyclase which enhances cyclic AMP production and activation of protein kinase A. Protein kinase A phosphorylates multiple targets including regulating the L-type calcium current .Beta-adrenergic stimulation increases cardiac contractile function by excitation contraction couplingCaL) and the slow component of the delayed rectifier potassium current (IKs) together with MEF.Thus the foregoing provides a possible framework whereby phasic LF sympathetic stimulation may induce an oscillatory pattern of ventricular APD at the same frequency through cellular mechanisms which include a mismatch between the time constants of the L-type calcium current  and reduced slow component of the delayed rectifier potassium current (IKs). They found that the LF power of APD oscillations was substantially increased and early afterdepolarizations and runs of triggered activity were observedFigure 7). In an established A-V block dog model, ventricular APD was measured using monophasic action potentials.Kr blocker) showed that LF power of APD was larger in inducible chronic A-V block dogs.The autonomic nervous system, particularly enhanced sympathetic activity, has long been known to play an important role in arrhythmogenesis.,Kr, ICaL, and the inward rectifier potassium current (IK1).Beat-to-beat variability of ventricular repolarization (BVR) has been shown to be proarrhythmic in a wide range of experimental models and humans particularly when enhanced in response to beta-adrenergic stimulation.,In the foregoing, we have reviewed evidence supporting the contention that LF oscillations of ventricular repolarization are related to the effect of rhythmic sympathetic nerve activity on APD. However, the PRD is an electrocardiographic phenomenon and could also be influenced by structural changes and functional properties of the myocardium. For example, it has been suggested that the different intrinsic properties of myocardial cells across the ventricular wall observed in single cells may play a role.,,Low-frequency oscillatory behaviour of ventricular repolarization may provide a novel approach to both risk stratification and mechanisms of arrhythmogenesis. There is urgent need to improve risk stratification for the use of ICD devices for the prevention of ventricular arrhythmia and sudden cardiac death. Current guidelines from the American Heart Association, American College of Cardiology, and the European Cardiac Society recommend prophyllactic ICD implantation in patients with ischaemic and non-ischaemic cardiomyopathy with ejection fraction below 35%.Ks in response to beta-adrenergic stimulation together with MEF; the generation of afterdepolarizations and triggered activity in the presences of disease conditions and interaction between LF oscillations of ventricular APD with other proarrhythmic mechanisms.The powerful role of LF oscillatory dynamics of ventricular repolarization in the prediction of ventricular arrhythmia and sudden cardiac death is now firmly established.E.P. and D.A.S.-P. were supported by the Eur Research Council under grant agreement ERC-StG 638284 by Ministerio de Ciencia e Innovacion (Spain) through project PID 2019-105674RB-100 and by European Social Fund (EU) and Aragon government through BSICoS group (T39/2OR) and project LMP 124–18. Computations were performed by the ICTS NANBIOSIS (HPC) Unit at University of Zaragoza.Conflict of interest: none declared.


Coronary artery disease is one of the leading causes of death worldwide, and ST-elevation myocardial infarction (STEMI) is one of its most serious manifestations. While STEMI itself is an ominous sign, there are other sinister electrocardiogram (EKG) patterns that are associated with increased morbidity and mortality, one of which is STEMI with right bundle branch block (RBBB). Blood supply to the right bundle comes from the left coronary circulation. Intuitively, RBBB in the setting of anterior wall myocardial infarction would indicate more extensive myocardial involvement and thus portend a worse prognosis. This case presents the significance of the association of new RBBB with critical lesions of the left anterior descending artery (LAD), therefore a low threshold for emergent coronary angiography and percutaneous coronary intervention (PCI).A 63-year-old man with a known history of non-insulin-dependent diabetes mellitus (NIDDM), hypertension, and hypertriglyceridemia non-compliant with medications presented to the emergency department (ED) after a visit with his primary care physician, with a chief complaint of exertional substernal chest pain for a one-week duration. His EKG on arrival showed significant ST-segment elevation with an atypical EKG pattern showing RBBB in V1-V2 with ST depression in reciprocal leads. Cardiac biomarkers showed an initial troponin I value of 0.441 ng/mL. Due to his persistent, worsening chest pain and associated nausea with episodes of vomiting, he was taken for an emergent cardiac catheterization that revealed a 100% lesion in his proximal LAD. The procedure was complicated by the development of cardiogenic shock requiring intra-aortic balloon pumps and vasopressors. A successful primary PCI was performed with drug-eluting stent (DES) to the 100% lesion in the proximal LAD and DES to the 80% lesions in the mid LAD, with 0% residual stenosis after the intervention. There was thrombolysis in myocardial infarction (TIMI) 0 flow pre-procedure and TIMI 3 flow post-intervention. Left ventriculography revealed anterolateral akinesis, apical akinesis, and diaphragmatic hypokinesis with an estimated ejection fraction (EF) of 20%. Transthoracic echocardiogram was repeated prior to discharge. Left ventricular (LV) systolic function was normal by visual assessment, and EF was noted to be ~55%. The patient continued on dual antiplatelet therapy and the rest of goal-directed medical therapy for coronary artery disease post-procedure.New-onset RBBB in the patient with typical STEMI in the context of ischemic symptoms should raise suspicion of critical proximal LAD coronary occlusion. It is increasingly being recognized as one of the significant EKG patterns for occlusive myocardial infarction associated with the worst outcome and mortality, highlighting the need to pay critical attention to these patients. Given the poor prognosis of these patients in the setting of acute myocardial infarction (AMI), it is essential to minimize the delay in initiating reperfusion therapy as they can potentially benefit from emergent intervention. Coronary artery disease is associated with increased morbidity and is one of the leading causes of death worldwide . One of A 63-year-old man with a known history of non-insulin-dependent diabetes mellitus (NIDDM), hypertension, and hypertriglyceridemia who was non-compliant with medications presented to our institution after visiting his primary care physician with a chief complaint of exertional substernal chest pain for a one-week duration. On the day of the presentation, he was given aspirin in the ambulatory clinic due to worsening symptoms, which did not alleviate his pain. He had no known cardiac history. His EKG on arrival revealed a new RBBB with significant ST-segment elevation in leads V1-V2  and vasopressor support. A successful primary PCI was performed with a drug-eluting stent (DES) of the 100% lesion in the proximal LAD and 80% lesions in the mid LAD with a 0% residual stenosis. There was thrombolysis in myocardial infarction (TIMI) 0 flow pre-procedure and TIMI 3 flow post-intervention  of 20%. Transthoracic echocardiogram was repeated prior to discharge. LV systolic function was normal by visual assessment with an EF of 55%. The patient continued with dual antiplatelet therapy and guideline-directed medical therapy for acute myocardial infarction (AMI) disease post-procedure. The IABP was removed on hospital day 2.Myocardial infarction remains a leading cause of mortality worldwide despite all of the recent advancements in medical management and device therapy. The most common cause is acute vessel closure secondary to plaque rupture causing prolonged ischemia, which results in myocardial cell death evidenced by an increase in cardiac enzymes, specifically troponin. Arriving at a diagnosis of MI requires integration of clinical findings, patterns on the ECG, laboratory data, observations from imaging procedures, and on occasion pathological findings.Although STEMI accounts for 38% of all myocardial infarction in the United States, the STEMI mortality rates supersede the non-STEMI (NSTEMI) rates .The case presented here highlights the association between a new RBBB in STEMI and AMI. A meta-analysis by Hazem et al. to investigate the relationship between AMI and new or old RBBB found a greater all-cause 30-day mortality compared to those without a bundle branch block . SimilarWang et al. also evaluated the prognostic value of a new RBBB with proximal LAD occlusion and found that a new RBBB is likely to have a higher incidence of cardiogenic shock and increased long-term mortality . In thesNew-onset RBBB in a patient with typical STEMI in the context of ischemic symptoms should raise suspicion of critical proximal LAD coronary occlusion. It is increasingly being recognized as one of the significant EKG patterns for occlusive myocardial infarction associated with the worst outcome and mortality, highlighting the need to pay critical attention to these patients. Given the poor prognosis of these patients in the setting of an AMI, it is essential to minimize the delay in initiating reperfusion therapy as they can potentially benefit from the emergent intervention.


Callitris columellaris varied with inter-annual variation in the amount, intensity and frequency of rainfall events over the last century and between semi-arid  and tropical  biomes in Australia. We used linear and polynomial regression models to investigate the strength and shape of the relationships between growth (ring width) and rainfall. At semi-arid sites, growth was strongly and linearly related to rainfall amount, regardless of differences in the seasonality and intensity of rainfall. The linear shape of the relationship indicates that predicted future declines in mean rainfall will have proportional negative impacts on long-term tree growth in semi-arid biomes. In contrast, growth in the tropics showed a weak and asymmetrical (‘concave-down’) response to rainfall amount, where growth was less responsive to changes in rainfall amount at the higher end of the rainfall range  than at the lower end . The asymmetric relationship indicates that long-term growth rates of Callitris in the tropics are more sensitive to increased inter-annual variability of rainfall than to changes in the mean amount of rainfall. Our findings are consistent with observations that the responses of vegetation to changes in the mean or variability of rainfall differ between mesic and semi-arid biomes. These results highlight how contrasting growth responses of a widespread species across a hydroclimatic gradient can inform understanding of potential sensitivity of different biomes to climatic variability and change.Globally, many biomes are being impacted by significant shifts in total annual rainfall as well as increasing variability of rainfall within and among years. Such changes can have potentially large impacts on plant productivity and growth, but remain largely unknown, particularly for much of the Southern Hemisphere. We investigate how growth of the widespread conifer,  Spatial patterns of average primary productivity and carbon fluxes among biomes can be strongly predicted by spatial variation in mean annual rainfall –4. HowevThe sensitivity of plant productivity to changes in the mean or variability of rainfall depends on the slope and shape (linear or non-linear) of the relationship between them , 13, 14.The shape of the relationship between rainfall and productivity tends to differ between water-limited and mesic biomes . InMuch of what is known about the temporal relationship between rainfall and productivity is based on studies that have monitored responses of above-ground primary productivity to water availability under either natural or manipulated rainfall regimes, but these are generally over relatively short time frames  andHere, we use tree-ring records and high-quality instrumental rainfall data that span more than 110 years and include both extreme dry and extreme wet conditions to investigate the shape and strength of temporal relationships between rainfall and tree growth in Australia. We use a spatial network of five tree-ring chronologies encompassing a broad climatic and latitudinal (12–33°S) range across northern and western Australia to also investigate how temporal relationships between growth and rainfall vary between mesic  and water-limited (semi-arid) biomes. While these are relatively few sites compared to other continents, the western half of Australia is a remarkable area in the world to develop a tree-ring network across a large latitudinal range as this region is without significant changes in topography and shows strong spatial coherence in rainfall patterns , 23. ConCallitris columellaris F.Muell., one of the few Australian tree species that produces clear growth rings. C. columellaris exhibits hydraulic traits  that arustralia , 26. ConOur first aim was to quantify the slope and shape of the temporal relationship between annual tree growth and rainfall amount and determine if these relationships differ between semi-arid and mesic biomes. Based on previous studies of plant productivity responses to inter-annual variation in rainfall, we expect that the relationship between tree growth and rainfall in semi-arid biomes will be strong and of either a linear or ‘concave-up’ form, whereas in the mesic  biomes, we expect the relationship to be weaker and of a ‘concave down’ form. The relationship between rainfall amount and tree growth may also differ widely among biomes with similar mean annual rainfall depending on the seasonal distribution of rainfall. In particular the seasonal timing, duration, frequency, and intensity of rain events may be as important for driving growth as the amount of rainfall that is delivered , 27, 28.Callitris columellaris across its range  and semi-arid interior  as well as higher rainfall areas, including the tropical north of the continent where mean annual rainfall can exceed 1,200 mm [Plant productivity and growth is primarily limited by water availability across the vast majority of the Australian continent , 30. Acce e.g., –34). C. . C. Call1,200 mm –37.C. columellaris, which includes C. intratropica R.T.Baker & H.G.Sm. and C. glaucophylla Joy Thomps. & L.A.S.Johnson as taxonomic synonyms of C. columellaris following the convention of Farjon [https://florabase.dpaw.wa.gov.au/browse/profile/8466). Samples were collected with permission from the Western Australian Department of Biodiversity, Conservation and Attractions under Flora Collection License SW019556 issued to A. O’Donnell.All of the trees used in this study are the single species f Farjon  and the C. columellaris trees from five sites spanning much of the latitudinal gradient of mainland Australia and encompassing three distinct climate zones  and Carlton Hill (CHR) are located in the monsoonal tropical zone of northern Australia . The norC. columellaris forms an open woodland restricted to a fire protected, south-facing, shallow gully on skeletal and rocky soils.The Juna Downs (CJD) site is located in the sub-tropical semi-arid Pilbara region of northwest Australia  and Lake Deborah , are both located in the Mediterranean semi-arid climate zone of southwest Australia . The cliWhile rainfall seasonality varies among the northern and southern sites, temperature seasonality does not. For all sites, the coldest months are in the Austral winter (Jun-Aug) and the warmest are in the Austral summer (Dec-Feb). However, the temperature range varies greatly among sites . While mWe used five chronologies of ring-width indices: three published  and twohttp://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software). Further details on the sample size, detrending methods and statistical quality can be found in O’Donnell et al. [To account for potential age-related decline in ring width, we statistically detrended the raw ring-width measurements to remove age-related (non-climatic) trends and converted them to ring-width indices as residuals from the detrending curve . The LTYl et al.  for the l et al.  for the l et al. . Howeverhttps://www.longpaddock.qld.gov.au/silo/). The estimated interpolation error of the SILO data has been thoroughly examined by Jeffrey et al. [http://www.bom.gov.au/climate/data/). We calculated the mean of daily rainfall data from the stations meeting these criteria for each site. The spatial coverage and number of available stations  varied by site–we found only three stations that met our criteria for the KOR site while the other sites had 6–8 records available , but nevertheless, slightly weaker than the best annual period  to test if a polynomial model fit the data significantly better than a simple linear model. We then compared the goodness of fit among the different models, selecting the ‘best’ model(s) as the one(s) with the highest R2. If other models with the same number of parameters had an R2 within 0.02 of the best model (Δ R2 < 0.02), we considered these to be equally supported as the ‘best’ models. For these data, this Δ R2 threshold is more conservative than the generally accepted criterion of Δ AIC > 2.We calculated total rainfall for annual and growing season periods for each site to use as variables to predict tree growth (as RWI) in regression models. For each of these rainfall amount variables, we fit both a simple linear model and a quadratic polynomial model with RWI as the response variable and the rainfall variable as the predictor, using the age of R . We usedIn order to determine whether other attributes of the rainfall distribution influence tree growth, we also calculated a range of annual rainfall variables from daily rainfall data that represent the intensity, frequency and intermittency of rainfall from all rain days . We usedx for both annual and growing season rainfall; 2; At all sites, tree growth (as RWI) was significantly and positively related to rainfall amount  but the In contrast, the relationship between growth and rainfall amount in the tropics was better described by a concave-down polynomial model than a simple linear model  was positively related to rain intensity across all biomes, with the exception of the wet-tropical KOR site, where rain intensity was the highest of the five sites . The numOur study used tree-ring records to provide a century-long perspective on how temporal variation in the amount, intensity and timing of rainfall impacts the growth response of trees and how these relationships differ among biomes. We found that the sensitivity of growth to changes in rainfall amount differs substantially between semi-arid and tropical biomes of Australia and is the highest (greatest slope) in semi-arid biomes. This finding is consistent with observations that the sensitivity of primary production to year-to-year changes in rainfall decreases along continental-scale gradients of increasing mean annual rainfall e.g., , 21, 49), 4921, 4C. columellaris in semi-arid Australia is strongly and symmetrically (linearly) related to rainfall amount; i.e., growth responses to both wet and dry years are of a proportional magnitude. This linear relationship holds across the 10-fold range of rainfall observed in the last century  growth responses to cool-season rainfall amount in trees in the semi-arid west of the United States [Growth of d States . Howeverd States , 51, whed States , 52. Whid States –52 indicThe steep and symmetric (linear) relationship between tree growth and rainfall amount in semi-arid Australia suggests that changes in the mean amount of rainfall are likely to have significant effects on mean growth rates; a decline in mean rainfall would result in a proportional decline in mean growth rates and vice versa –7. For sIn contrast to semi-arid biomes, tree growth in the Australian tropics showed a negative asymmetrical (concave down or ‘saturating’) response to rainfall amount , which i2 and slope); however, the variance explained (R2) is within the range that has been found for trees growing in mesic biomes elsewhere , which potentially doesn’t fully capture the rainfall period that is most important to growth in the wet tropics; that is, rainfall in the transitional months both before (Sep-Nov) and after (Mar-Jun) the main wet season  may also partially explain the relatively weak relationship between growth and rainfall amount.The sensitivity of growth to inter-annual variation in rainfall amount is weakest in the most mesic biome we examined . Thet season . In addiWhile there is large uncertainty around potential future changes in mean rainfall for tropical northern Australia , the relCallitris columellaris) growing across a latitudinal gradient that also encapsulates a gradient in hydroclimatic stress to reveal potential growth responses to changing rainfall patterns across mesic and water-limited biomes of Australia. We expect our findings are more broadly applicable to other shallow-rooted woody species; however, additional research efforts are needed both in Australia and elsewhere to better understand how the long-term sensitivity of tree growth to rainfall variability and amount varies among species and how, for example, this relates to observations of asymmetry made at much larger spatial scales.In this study, we have used an “indicator” species  total rainfall and (f-j) monthly mean minimum and maximum temperatures at the five Callitris columellaris sites. Boxplots show the median (50th percentile) as the centre horizontal line, the interquartile range (25th to 75th percentile) as the bottom and top horizontal line, the range (0.3–99.7th percentile) as vertical lines, and extreme values (<0.7 or >99.3 percentile) of mean monthly temperatures as dots. Numbers inside or next to boxes in f-o are the Pearson correlation values for the respective season or annual period. LDE showed no significant correlation between RWI and minimum temperature for any period > 3 months. Rainfall data are from Australian Bureau of Meteorology stations see . Tempera(TIFF)Click here for additional data file.S2 Figa) Total annual rainfall and b) annual raw ring width (sample mean) of the five Callitris columellaris sites. Boxplots show the median (50th percentile) as the centre horizontal line, the interquartile range (25th to 75th percentile) as the bottom and top horizontal line, the range (0.3–99.7th percentile) as vertical lines, extreme values (<0.7 or >99.3 percentile) and the mean (diamonds) of annual total rainfall and ring width. Note: Raw ring widths are the sample mean ring width measurement (in mm) for each year. Raw ring widths have not been detrended to remove age-related (non-climatic) growth trends and are shown here only to provide an indication of differences in actual ring widths (growth rates) among sites and rainfall zones .(TIFF)Click here for additional data file.S3 FigMeasures of signal strength, the expressed population signal (EPS) and the RBAR, and the sample depth (n) of each of the a) LTY, b) LDE and c) CHR chronologies for the period > 1900 CE. RBAR provides an indication of chronology signal strength (common variance) and is independent of sample size . The EPS(TIFF)Click here for additional data file.S4 FigEach point represents data for one year. Lines represent fitted quadratic polynomial models.(TIFF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


The substrates of ubiquitylation have been recognized as protein ever since the ubiquitin modifier was discovered decades ago. However, this conventional perception is fundamentally changed by a recent finding that lipopolysaccharide (LPS) molecules are ubiquitylated by host cells to trigger cell-autonomous immunity against bacterial invasion.Ubiquitylation (or ubiquitination) has been documented to be of vital importance in virtually all cellular and physiological pathways. The ubiquitylation process is accomplished through a stepwise enzymatic cascade typically catalyzed by three different enzymes: E1 (ubiquitin-activating enzyme), E2 (ubiquitin-conjugating enzyme), and E3 (ubiquitin ligase). The sequential action of the E1–E2–E3 cascade results in covalent attachment of ubiquitin to the selected substrates. Until very recently, ubiquitylation has been recognized as an exclusive post-translational protein modification. However, a recent landmark study by Otten and colleagues revealed that host cells combat invading bacteria in the cytosol by tagging the lipid A moiety of bacterial lipopolysaccharide (LPS) with ubiquitin, which leads to subsequent autophagic degradation of the pathogens .Salmonella enter host cells by receptor-mediated endocytosis followed by forming a membrane-bound vacuolar compartment termed bacteria-containing vacuole (BCV), which further develops into a replicative niche. To propagate and disseminate, bacteria inside the BCV need access the host cytosol and finish a cytoplasmic lifestyle through vacuolar rupture  in human cells (S. Typhimurium that escaped from the invasion vacuoles at 4 h after infection (HAI). The authors then prepared wild type and O-antigen polymerase defective mutant of S. Typhimurium from infected cells at 4 HAI for analysis of the ubiquitin coat formation, which led to the discovery that the ubiquitylated substrate were LPS molecules instead of proteins. The finding was further supported by the observation that the low molecular weight ubiquitin bands in the O-antigen polymerase defective mutant of S. Typhimurium and the majority of ubiquitin smear in wild type bacteria are heat resistant .Otten and colleagues strived to fill the knowledge gap by decoding the ubiquitin coat of an cells . They fiSalmonella mutants defective in the biosynthesis of the core polysaccharides and O-antigen indicated that changes in the ubiquitylation patterns match the alterations in LPS length and molecular weight, which further confirmed LPS is the target of ubiquitylation on cytosolic S. Typhimurium.LPS is a group of structurally related glycolipids constituting the outer leaflet of the outer membrane of most Gram-negative bacteria. LPS molecules show a typically tripartite structure: a hydrophobic domain known as lipid A, a short “core” oligosaccharide, and a long-chain, repeat-unit polysaccharide (O-antigen repeats)  Figure . Many keS. Typhimurium isolated from the infected host cells under alkaline conditions and found the treatment resulted in hydrolysis of the ubiquitin chains attached to LPS. Thus, ubiquitin molecules attach to LPS through ester-linkage.Ubiquitin molecules are known to append to the protein targets through isopeptide bonds with amino groups (amide-linkage) or occasionally through ester bonds with hydroxy groups (ester-linkage). The authors tested whether amino groups that can be introduced onto LPS by numerous transferases are essential for LPS ubiquitylation by deleting genes that encode the transferases. They found loss of function in either one or all the genes exerted no impairments to LPS ubiquitylation, which excluded the known amino groups in LPS as sites of ubiquitylation. To examine the possibility of ester-linked ubiquitin, the authors treated O-antigen polymerase defective mutant of RNF213 by siRNA and CRISPR technology, respectively, revealed that lack of RNF213 abolished LPS ubiquitylation on cytosolic S. Typhimurium in both human and mouse cells. In vitro ubiquitylation reaction found that purified RNF213 ubiquitylated LPS in a classical manner where ATP and E1 and E2 enzymes are required. When lipid A alone and various truncated versions of LPS purified from Salmonella enterica ser. Minnesota strains were used as substrate of the in vitro reaction, lipid A was found to be the minimal form of LPS being ubiquitylated by RNF213.To pinpoint the E3 enzyme responsible for the ubiquitylation of LPS, Otten and colleagues executed a procedure of extensive fractionation and purification of the HeLa cell lysates and tested each fraction for the E3 activity that is responsible for LPS ubiquitylation, leading to the identification of RNF213 , an E3 tRNF213 showed the dynein-like ATPase core was indispensable for LPS ubiquitylation. Surprisingly, the RING domain was not required for the ubiquitylation of LPS by RNF213. Neither did the mutations in RNF213 that are associated with the moyamoya disease patients cause defect in LPS ubiquitylation. Instead, a C-terminal RNF213 region that is adjacent to the RING domain and embedded with a conserved 27-amino-acid stretch consisting of four cysteine (C) and two histidine (H) amino acid residues (CHC3H) named by the authors as RZ finger (RNF213–ZNFX1 finger)   is essenS. Typhimurium and the dynein-like AAA + module and E3 ligase activity were indispensable for generating a stable RNF213 coat. The authors observed that RNF213-deficient mouse embryonic fibroblast (MEF) cells couldn’t produce a ubiquitin coat surrounding the S. Typhimurium cells. The RNF213-deficient MEF cells also failed to recruit the ubiquitin interacting autophagy cargo receptors thus caused an impairment in antibacterial autophagy and presented an increased proliferation of the intracellular S. Typhimurium. The authors thus concluded that RNF213 plays a positive role in host immune response to bacterial infection in a ubiquitin-dependent manner.The ubiquitin coat associated with cytosolic bacteria serves as a signal for the xenophagy pathway, which plays a key role in cell-autonomous immunity. Microscopy studies showed that RNF213 colocalized with the ubiquitin coat by forming a layer of RNF213 surrounding the cytosol-invading S. Typhimurium, indicating that RNF213-mediated LPS ubiquitylation functions to initiate LUBAC-mediated immune signaling. To date, evidence for the involvement of LUBAC in host immunity against intracellular Gram-positive bacteria is very limited. A recent work revealed that RNF213 also facilitates restricting the growth of Listeria monocytogenes in an E3 ligase activity-dependent but ubiquitylation-independent manner  that catalyzes formation of Met1-linked linear ubiquitin chains was previously shown to contribute to cell-autonomous immunity and requires pre-existing ubiquitin for its own recruitment  Noad et. Otten at manner , which pin vitro, raising the possibility that S. Typhimurium may utilize DUBs derived from the host or synthesized and secreted by the pathogen to remove ubiquitin from LPS for promoting infections. OTULIN, a well-known Met1-specific host DUB was recently shown to function at the host–Salmonella interface and down-regulates NF-κB signaling by restricting Met1-ubiquitin chains formation  stability through their deubiquitylating activity  USP2 ormation . In addiactivity . Howeverbona fide E2 is recruited as RNF213 partner in the cell remain to be elucidated. Answers to these questions may facilitate the decoding of the complex ubiquitin biology and offer insights into ubiquitylation-dependent cell-autonomous defense against cytosolic bacterial invaders.The discovery of bacterial LPS as the target of host ubiquitin E3 RNF213 defies the conventional perception that targets of ubiquitylation are proteinaceous and raises the question whether other non-proteinaceous biomolecules, such as host cell lipids also serve as targets of the ubiquitylation machinery. The finding that RNF213-mediated LPS ubiquitylation acts as an initiator of LUBAC-dependent antibacterial autophagy sheds light on the molecular mechanism underlying ubiquitylation-dependent clearance of cytosol-invading bacteria. In addition to LUBAC, LRSAM1, Parkin, and SMURF1 have also been identified as E3 ligases that contribute to antibacterial, autophagy-associated ubiquitylation . It woul


The chest X-ray is considered a significant clinical utility for basic examination and diagnosis. The human lung area can be affected by various infections, such as bacteria and viruses, leading to pneumonia. Efficient and reliable classification method facilities the diagnosis of such infections. Deep transfer learning has been introduced for pneumonia detection from chest X-rays in different models. However, there is still a need for further improvements in the feature extraction and advanced classification stages. This paper proposes a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search Optimization Algorithm (ASSOA). The first stage is the feature learning and extraction processes based on a Convolutional Neural Network (CNN) model named ResNet-50 with image augmentation and dropout processes. The ASSOA algorithm is then applied to the extracted features for the feature selection process. Finally, the Multi-layer Perceptron (MLP) Neural Network’s connection weights are optimized by the proposed ASSOA algorithm (using the selected features) to classify input cases. A Kaggle chest X-ray images (Pneumonia) dataset consists of 5,863 X-rays is employed in the experiments. The proposed ASSOA algorithm is compared with the basic Squirrel Search (SS) optimization algorithm, Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) for feature selection to validate its efficiency. The proposed (ASSOA + MLP) is also compared with other classifiers, based on (SS + MLP), (GWO + MLP), and (GA + MLP), in performance metrics. The proposed (ASSOA + MLP) algorithm achieved a classification mean accuracy of (99.26%). The ASSOA + MLP algorithm also achieved a classification mean accuracy of (99.7%) for a chest X-ray COVID-19 dataset tested from GitHub. The results and statistical tests demonstrate the high effectiveness of the proposed method in determining the infected cases. I.Medical images are teeming with many features that can be considered for inspection. Generally, many processes in Computer-Aided System (CAD), such as pre-processing, isolating Regions of Interest (ROIs), and feature extracting process, can help to get the accurate classification of the diseases More training phases with large datasets are mainly required to apply the pre-trained networks for medical image classification tasks. Hence, in some cases, adopting these types of networks as classifiers are often not the preferred method to apply the CNN to the diagnosis tasks of CAD For a specific problem, optimization can be the most reliable solution between all available solutions, especially towards multi-dimensional space This paper proposes a classification method to classify infected cases from the chest X-ray images. The method can decrease detection costs significantly. First, a feature learning stage is developed using the CNN model named ResNet-50 with image augmentation as a pre-processing and dropout as post-processing. Second, the features are extracted to start the feature selection process. A proposed Advanced Squirrel Search Optimization Algorithm (ASSOA) is developed for feature selection. The advanced classification stage starts to classify the infected cases using the optimized Multilayer Perceptron Neural Network (MLP) by the proposed ASSOA algorithm. ASSOA’s basic rule in the classification stage is to optimize the connection weights of MLP to improve accuracy. A dataset from Kaggle, chest X-ray images (Pneumonia) dataset •An Advanced Squirrel Search Optimization Algorithm (ASSOA) is developed for feature extraction and classification.•The proposed ASSOA algorithm adds horizontal, vertical, diagonal, and exponential movements to the basic moves in the search process of the basic SS algorithm.•A new agents’ relocation equation is modeled in the proposed ASSOA algorithm, affecting local and global optima under specific conditions.•A classification method for chest X-ray images is proposed based on the ASSOA algorithm.•The classification method is tested using a dataset from Kaggle with 5,863 chest X-ray images.•The classification method is also tested for a chest X-ray COVID-19 dataset from GitHub.•Wilcoxon rank-sum and ANOVA statistical tests are performed to ensure the proposed ASSOA algorithm quality.This paper’s main contributions are as follows:The next sections of this paper are as follows: II.Large datasets availability and the recent advances in deep learning models have led to the possession of power-assisted algorithms, which beats the medical professionals in various clinical image resolution. These images are such as cancer classification et al. achieved an accuracy of (90.1%) in et al. in Recent research has implemented several automatic pneumonia detection systems based on chest X-rays et al. in An AI approach to diagnosing COVID-19 and other types of pneumonia is already developed in et al.et al. in Nour Therefore, building a classification method for various infections is one of the most critical issues prohibitively expensive for mass adoption. Deep transfer learning has been introduced for pneumonia detection from chest X-rays in different literature models. However, there is still a need for more improvements in the feature extraction and classification stages.III.This section introduces the chest X-ray datasets used in this paper and will also discuss the essential CNN deep transfer learning, multilayer perceptron neural network, and the original Squirrel Search (SS) optimization algorithm.A.Chest (Pneumonia) X-ray images from Kaggle dataset B.In traditional learning, the model is isolated and based mainly on specific tasks and particular datasets CNN’s Residual Network (ResNet) is known as an efficient CNN model C.Feed-forward neural networks are considered supervised machine learning methods consisting of neurons distributed over fully connected layers. The first (input) layer maps the network input variables, and the last layer is the output one. Layers between the first and last layers are called hidden layers Based on the value of D.The Squirrel Search (SS) basic optimization algorithm simulates the search process of flying squirrels The flying agents’ locations is in matrix form as follows: Case 1:Location of Case 2:Location of Case 3:Location of New location generation mathematically for each flying squirrel is considered as one of the three following cases:Algorithm 11:Initialize SS population 2:Initialize SS parameters 3:Calculate fitness function 4:Sort flying squirrels locations in ascending order5:Find the first best individual 6:Find the next three best individuals 7:Find the normal individuals 8:whiledo9:for .A.The ResNet-50 model is applied in this stage as a part of the proposed method for features extraction from the chest X-ray images in the fully connected layer by altering the nodes and doing a fine-tuning based on the input dataset. Each input image is resized to After the resizing and normalization, the output image B.The proposed ASSOA algorithm adds horizontal, vertical, diagonal, and exponential movements to the basic moves in the search process of flying squirrels, as shown in Mathematically, the ASSOA algorithm assumes the squirrels are flying in directions shown in Case 1:Location of Case 2:Location of Case 3:Location of For the random value Case 4:Location of Case 5:Location of Case 6:Location of In the ASSOA algorithm, the new location generation for each flying squirrel is updated as in the following cases. For a random value The seasonal constant , validation images of (20%), testing images of (20%). The data in the training process is used to train the CNN model. In contrast, the validation process data is applied for verification purposes, and the testing data evaluated the efficiency of the proposed method for the unknown chest X-ray cases.A.The classification accuracy of the four CNN models namely AlexNet B.In this scenario, the efficiency of feature selection by the proposed ASSOA algorithm is investigated. ASSOA algorithm performance is compared with the basic Squirrel Search (SS) optimization algorithm The p-values of the ASSOA algorithm are tested compared to SS, GWO, and GA algorithms by Wilcoxon’s rank-sum test. The employed test can get if there is a significant difference between the ASSOA algorithm and other algorithms. If the p-value < 0.05, this indicates that the ASSOA algorithm results are significantly different from other algorithms. If p-value >0.05, this indicates that the algorithm results have no significant difference. The p-value results in this scenario are shown in C.The last scenario checks the classification accuracy of the ASSOA algorithm based on MLP (ASSOA + MLP) in comparison with other algorithms of SS + MLP, GWO + MLP, and GA + MLP. The classification performance is tested for chest X-ray cases and other cases based on chest X-ray COVID-19. The configuration of the proposed ASSOA algorithm and the compared algorithms are shown in 1)The results of the ASSOA + MLP algorithm and other algorithms regarding accuracy are shown in Wilcoxon’s rank-sum and ANOVA tests are performed in this scenario to get the ASSOA + MLP algorithm’s p-values compared to SS + MLP, GWO + MLP, GA + MLP classification algorithms. These tests can indicate the significant difference between the ASSOA + MLP algorithm results and compared algorithms. The output p-values are shown in The possible problems can be observed from the residual values, and residual plots rather than the original dataset plot since some datasets are not good candidates for classification. The ideal case is achieved if the residual values are equally and randomly spaced around the horizontal axis. The residual value can be calculated as  with the mean and sum of the residuals are equal to zero. A residual plot is used to present the vertical axis’s residual values and the independent variable on the horizontal axis. The homogeneity of variance (heteroscedasticity) provides a visual examination between the prediction errors and the predicted dependent variable scores. The heteroscedasticity plot, shown in Figure 12, can quickly determine any violation and easily improve the research findings’ accuracy. Homoscedasticity describes a situation in which the error term  is the same across the independent variables’ values.The quantile-quantile (QQ) plot is also shown in Figure 12. It is known as a probability plot. It is mainly used by plotting their quantiles against each other to compare two probability distributions. It is noted from the figure that the points distributions in the QQ plot are approximately fit on the line. Thus, the actual and the predicted residuals are linearly related, confirming the proposed ASSOA + MLP classifier’s performance to identify the chest X-ray images.2)A chest X-ray COVID-19 dataset ANOVA test is also performed for this experiment to test the ASSOA + MLP algorithm’s p-values compared to SS + MLP, GWO + MLP, GA + MLP classification algorithms. The output p-values are shown in VI.The experiments are divided into three different scenarios to assess the proposed method performance to classify chest X-ray images. According to the promising performance, the first scenario shows that the features can be extracted from the earlier layers of the ResNet-50 model. The extracted features are fed to the next scenario for feature selection. The second scenario shows the robustness and reliability of the ASSOA algorithm in finding the optimal subset of features in a reasonable amount of time. In this scenario, Wilcoxon’s rank-sum test emphasizes the superiority of the proposed ASSOA algorithm and shows that the algorithm is statistically significant. In the third scenario, the experiments show that the proposed algorithm (ASSOA + MLP) can achieve a mean accuracy of (99.26%) and an AUC value equal to (0.9875) within (135) seconds to classify a new input X-ray chest image which outperforms other algorithms. The ASSOA + MLP algorithm also achieved a classification mean accuracy of (99.7%) for a chest X-ray COVID-19 dataset. Wilcoxon’s rank-sum and ANOVA tests confirm the proposed algorithm’s superiority and that the algorithm is statistically significant. The results and statistical tests demonstrate the high effectiveness of the proposed method in determining the infected cases.VII.Developing a classification model for diagnosing infected cases is considered one of the most critical problems, which is still much too pricey for the mass selection. This paper proposes a classification model to detect the afflicted instances from the chest X-ray images, which may dramatically minimize the diagnosis prices, particularly in cultivating nations. The training and feature extraction processes are based on a convolutional neural network (CNN) based model (ResNet50) with fine-tuning and image augmentation. The X-ray images’ classification to viral, normal, and bacterial, and popular scenarios are based upon an MLP neural network along with the proposed ASSOA algorithm. In this work, the chest X-ray images (Pneumonia) dataset composed of 5,863 X-ray images are utilized in the experiments. In the proposed model, a transfer learning technique is applied during the training stage and feature extraction. Experimental results show the proposed classification model’s efficiency in classifying the afflicted situations and a mean accuracy of (99.26%), which surpasses the cutting-edge strategies discovered in the literature. The proposed (ASSOA + MLP) algorithm also achieved a classification mean accuracy of (99.7%) for another chest X-ray COVID-19 dataset.