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Algorithmic bias in COVID-19 detection systems poses aserious threat to equitable pandemic response, asdemographicdisparities in model performance risk worsening healthoutcomes across vulnerable populations. We present anadoptedCausal Concept Bottleneck Model (C2BM) framework thatsystematically addresses fairness in multimodal COVID-19detection by learning interpretable concepts from chest CTscans and patient metadata. Our approach targets theCountry → Institution → COVID causal pathway throughprincipledinterventions, achieving substantial bias reduction: age andgender demographic parity differences decrease from 51.15%to 18.50% (64% reduction), gender disparate impact improvesfrom 0.6475 to 0.9812 (51% improvement), whilepreserving 98.45% diagnostic F1-score. Throughcomprehensive evaluation across four model variants, wedemonstrate that causal interventions enable stable andreproduciblefairness improvements without compromising clinicalutility. Our work establishes that principled causalreasoning canachieve practical fairness-accuracy trade-offs in COVID-19detection systems, providing actionable guidance forequitable healthcare AI deployment. 

Why parasites occur in certain hosts in certain locations has been a long-standing question among ecological and evolutionary parasitologists. Encounter and compatibility filters summarize the likelihood that a host and parasite will physically interact and establish an infection upon contact. Encounter and compatibility filters are not fixed and, among multiple locations, the abiotic environmental characteristics and biotic community composition that contribute to the filters often vary spatially and temporally. Abiotic variation may directly affect hosts or parasites —particularly parasites with one or more free-living stages— whereas the local biotic community may dilute or amplify parasite transmission. Unlike directly transmitted parasites, complex-life cycle parasites use multiple hosts, thus having life cycles that, we hypothesize, are highly susceptible to the effects of spatiotemporal environmental variation. We modeled infection probability relationships of endohelminths from post-metamorphic wood frogs (Rana [Lithobates] sylvatica) and northern leopard frogs (Rana pipiens) with wetland characteristics, landscape composition, and the anuran species within the local community. Parasites included complex-life cycle trematodes that use amphibians as definitive hosts (Haematoloechus spp., Glypthelmins quieta) or as intermediate hosts (Alaria sp., Neodiplostomum sp., echinostomatids, Lechriorchis) and nematodes with direct or indirect life cycles (Cosmocercoides, Oswaldocruzia). Although our results demonstrate that distributions of parasites with complex and direct life cycles are correlated with some abiotic and biotic characteristics of the environment, there were few general trends. Each parasite's distribution had its own unique relationship with wetland, landscape, and amphibian-community variables and there was overall low predictability for most species. One landscape feature — the number of wetlands within the vicinity of the site of amphibian capture — was commonly included in top models for leopard frogs and could be associated with how definitive hosts (e.g., amphibians, mammals, birds) and intermediate hosts (e.g., snails, odonates) use the landscape. The amphibian community at any given site also commonly affected infection probabilities, such that the local presence of other species tended to reduce infection probabilities in sampled frogs, lending support to the dilution effect at the landscape level. Our research highlights the need to consider spatiotemporal sampling, environmental variation, and host-community variation when studying parasite prevalence in any given component community. 

Abstract Protein succinylation is an important post-translational modification (PTM) responsible for many vital metabolic activities in cells, including cellular respiration, regulation, and repair. Here, we present a novel approach that combines features from supervised word embedding with embedding from a protein language model called ProtT5-XL-UniRef50 (hereafter termed, ProtT5) in a deep learning framework to predict protein succinylation sites. To our knowledge, this is one of the first attempts to employ embedding from a pre-trained protein language model to predict protein succinylation sites. The proposed model, dubbed LMSuccSite, achieves state-of-the-art results compared to existing methods, with performance scores of 0.36, 0.79, 0.79 for MCC, sensitivity, and specificity, respectively. LMSuccSite is likely to serve as a valuable resource for exploration of succinylation and its role in cellular physiology and disease. 

Abstract Protein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii , a model algal phototroph. An ensemble model combining convolutional neural networks and long short-term memory (LSTM) achieves the best performance in predicting phosphorylation sites in C. reinhardtii. Deemed Chlamy-EnPhosSite, the measured best AUC and MCC are 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing higher than those measures for other predictors. When applied to the entire C. reinhardtii proteome (totaling 1,809,304 S and T sites), Chlamy-EnPhosSite yielded 499,411 phosphorylated sites with a cut-off value of 0.5 and 237,949 phosphorylated sites with a cut-off value of 0.7. These predictions were compared to an experimental dataset of phosphosites identified by liquid chromatography-tandem mass spectrometry (LC–MS/MS) in a blinded study and approximately 89.69% of 2,663 C. reinhardtii S and T phosphorylation sites were successfully predicted by Chlamy-EnPhosSite at a probability cut-off of 0.5 and 76.83% of sites were successfully identified at a more stringent 0.7 cut-off. Interestingly, Chlamy-EnPhosSite also successfully predicted experimentally confirmed phosphorylation sites in a protein sequence (e.g., RPS6 S245) which did not appear in the training dataset, highlighting prediction accuracy and the power of leveraging predictions to identify biologically relevant PTM sites. These results demonstrate that our method represents a robust and complementary technique for high-throughput phosphorylation site prediction in C. reinhardtii. It has potential to serve as a useful tool to the community. Chlamy-EnPhosSite will contribute to the understanding of how protein phosphorylation influences various biological processes in this important model microalga. 

Phosphorylation, which is mediated by protein kinases and opposed by protein phosphatases, is an important post-translational modification that regulates many cellular processes, including cellular metabolism, cell migration, and cell division. Due to its essential role in cellular physiology, a great deal of attention has been devoted to identifying sites of phosphorylation on cellular proteins and understanding how modification of these sites affects their cellular functions. This has led to the development of several computational methods designed to predict sites of phosphorylation based on a protein’s primary amino acid sequence. In contrast, much less attention has been paid to dephosphorylation and its role in regulating the phosphorylation status of proteins inside cells. Indeed, to date, dephosphorylation site prediction tools have been restricted to a few tyrosine phosphatases. To fill this knowledge gap, we have employed a transfer learning strategy to develop a deep learning-based model to predict sites that are likely to be dephosphorylated. Based on independent test results, our model, which we termed DTL-DephosSite, achieved efficiency scores for phosphoserine/phosphothreonine residues of 84%, 84% and 0.68 with respect to sensitivity (SN), specificity (SP) and Matthew’s correlation coefficient (MCC). Similarly, DTL-DephosSite exhibited efficiency scores of 75%, 88% and 0.64 for phosphotyrosine residues with respect to SN, SP, and MCC.