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Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction.

AirLift is the first read remapping tool that enables users to quickly and comprehensively map a read set, that had been previously mapped to one reference genome, to another similar reference. Users can then quickly run a downstream analysis of read sets for each latest reference release. Compared to the state-of-the-art method for remapping reads (i.e., full mapping), AirLift reduces the overall execution time to remap read sets between two reference genome versions by up to 27.4×. We validate our remapping results with GATK and find that AirLift provides high accuracy in identifying ground truth SNP/INDEL variants.

Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we introduce a novel deep learning framework, AutoSamp, based on variational information maximization that enables joint optimization of sampling pattern and reconstruction of MRI scans. We represent the encoder as a non-uniform Fast Fourier Transform that allows continuous optimization of k-space sample locations on a non-Cartesian plane, and the decoder as a deep reconstruction network. Experiments on public 3D acquired MRI datasets show improved reconstruction quality of the proposed AutoSamp method over the prevailing variable density and variable density Poisson disc sampling for both compressed sensing and deep learning reconstructions. We demonstrate that our data-driven sampling optimization method achieves 4.4dB, 2.0dB, 0.75dB, 0.7dB PSNR improvements over reconstruction with Poisson Disc masks for acceleration factors of R = 5, 10, 15, 25, respectively. Prospectively accelerated acquisitions with 3D FSE sequences using our optimized sampling patterns exhibit improved image quality and sharpness. Furthermore, we analyze the characteristics of the learned sampling patterns with respect to changes in acceleration factor, measurement noise, underlying anatomy, and coil sensitivities. We show that all these factors contribute to the optimization result by affecting the sampling density, k-space coverage and point spread functions of the learned sampling patterns.

Rift Valley fever (RVF) is a mosquito-borne zoonotic viral disease endemic to Africa and the Middle East. Live-attenuated RVF vaccines have been studied for both veterinary and human use due to their strong immunogenicity and cost-effective manufacturing. The live-attenuated MP-12 vaccine has been conditionally approved for veterinary use in the U.S.A., and next-generation live-attenuated RVF vaccine candidates are being actively researched. Assessing the virulence phenotype of vaccine seeds or lots is crucial for managing vaccine safety. Previously, preweaning 19-day-old outbred CD1 mice have been used to evaluate the MP-12 strain. This study aimed to characterize the relative virulence of three live-attenuated RVF vaccine strains in 19-day-old inbred C57BL/6 mice: the recombinant MP-12 (rMP-12), the RVax-1, and the ∆NSs-∆NSm-rZH501 strains. Although this mouse model did not show dose-dependent pathogenesis, mice that succumbed to the infection exhibited distinct brain pathology. Mice infected with ∆NSs-∆NSm-rZH501 showed an infiltration of inflammatory cells associated with infected neurons, and focal lesions formed around virus-infected cells. In contrast, mice infected with rMP-12 or RVax-1 showed a minimal association of inflammatory cells in the brain, yet the virus spread diffusely. The preweaning model is likely useful for evaluating host responses to attenuated RVFV strains, although further refinement may be necessary to quantitate the virulence among different RVFV strains or vaccine lots.

Antagonistic relationships such as host-virus interactions potentially lead to rapid evolution and specificity in interactions. The Orsay virus is so far the only horizontal virus naturally infecting the nematode C. elegans. In contrast, several related RNA viruses infect its congener C. briggsae, including Santeuil (SANTV) and Le Blanc (LEBV) viruses. Here we focus on the host's intraspecific variation in sensitivity to these two intestinal viruses. Many temperate-origin C. briggsae strains, including JU1264 and JU1498, are sensitive to both, while many tropical strains, such as AF16, are resistant to both. Interestingly, some C. briggsae strains exhibit a specific resistance, such as the HK104 strain, specifically resistant to LEBV. The viral sensitivity pattern matches the strains' geographic and genomic relationships. The heavily infected strains mount a seemingly normal small RNA response that is insufficient to suppress viral infection, while the resistant strains show no small RNA response, suggesting an early block in viral entry or replication. We use a genetic approach from the host side to map genomic regions participating in viral resistance polymorphisms. Using Advanced Intercrossed Recombinant Inbred Lines (RILs) between virus-resistant AF16 and SANTV-sensitive HK104, we detect Quantitative Trait Loci (QTLs) on chromosomes IV and III. Building RILs between virus-sensitive JU1498 and LEBV-resistant HK104 followed by bulk segregant analysis, we identify a chromosome II QTL. In both cases, further introgressions of the regions confirmed the QTLs. This diversity provides an avenue for studying virus entry, replication, and exit mechanisms, as well as host-virus specificity and the host response to a specific virus infection., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Alkan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Heartland virus (HRTV) is an emerging tick-borne bandavirus that causes a febrile illness of varying severity in humans, with cases reported in eastern and midwestern regions of the United States. No vaccines or approved therapies are available to prevent or treat HRTV disease. Here, we describe the genetic changes, natural history of disease, and pathogenesis of a mouse-adapted HRTV (MA-HRTV) that is uniformly lethal in 7- to 8-week-old AG129 mice at low challenge doses. We used this model to assess the efficacy of the ribonucleoside analog, 4'-fluorouridine (EIDD-2749), and showed that once-daily oral treatment with 3 mg/kg of drug, initiated after the onset of disease, protects mice against lethal MA-HRTV challenge and reduces viral loads in blood and tissues. Our findings provide insights into HRTV virulence and pathogenesis and support further development of EIDD-2749 as a therapeutic intervention for HRTV disease., Importance: More than 60 cases of HRTV disease spanning 14 states have been reported to the United States Centers for Disease Control and Prevention. The expanding range of the Lone Star tick that transmits HRTV, the growing population of at-risk persons living in geographic areas where the tick is abundant, and the lack of antiviral treatments or vaccines raise significant public health concerns. Here, we report the development of a new small-animal model of lethal HRTV disease to gain insight into HRTV pathogenesis and the application of this model for the preclinical development of a promising new antiviral drug candidate, EIDD-2749. Our findings shed light on how the virus causes disease and support the continued development of EIDD-2749 as a therapeutic for severe cases of HRTV infection., Competing Interests: G.R.B. and G.R.P. are coinventors on patent WO 2019/1736002 covering the composition of matter and use of EIDD-2749 and its analogs as an antiviral treatment. All other authors declare that they have no competing interests.

Non-silent single nucleotide genetic variants, like nonsense changes and insertion-deletion variants, that affect protein function and length substantially are prevalent and are frequently misclassified. The low sensitivity and specificity of existing variant effect predictors for nonsense and indel variations restrict their use in clinical applications. We propose the Pathogenic Mutation Prediction (PMPred) method to predict the pathogenicity of single nucleotide variations, which impair protein function by prematurely terminating a protein's elongation during its synthesis. The prediction starts by monitoring functional effects (Gene Ontology annotation changes) of the change in sequence, using an existing ensemble machine learning model (UniGOPred). This, in turn, reveals the mutations that significantly deviate functionally from the wild-type sequence. We have identified novel harmful mutations in patient data and present them as motivating case studies. We also show that our method has increased sensitivity and specificity compared to state-of-the-art, especially in single nucleotide variations that produce large functional changes in the final protein. As further validation, we have done a comparative docking study on such a variation that is misclassified by existing methods and, using the altered binding affinities, show how PMPred can correctly predict the pathogenicity when other tools miss it. PMPred is freely accessible as a web service at https://pmpred.kansil.org/, and the related code is available at https://github.com/kansil/PMPred., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Rift Valley fever is a zoonotic viral disease transmitted by mosquitoes, impacting both humans and livestock. Currently, there are no approved vaccines or antiviral treatments for humans. This study aimed to evaluate the in vitro efficacy of chemical compounds targeting the Gc fusion mechanism. These compounds were identified through virtual screening of millions of commercially available small molecules using a structure-based artificial intelligence bioactivity predictor. In our experiments, a pretreatment with small molecule compounds revealed that 3 out of 94 selected compounds effectively inhibited the replication of the Rift Valley fever virus MP-12 strain in Vero cells. As anticipated, these compounds did not impede viral RNA replication when administered three hours after infection. However, significant inhibition of viral RNA replication occurred upon viral entry when cells were pretreated with these small molecules. Furthermore, these compounds exhibited significant inhibition against Arumowot virus, another phlebovirus, while showing no antiviral effects on tick-borne bandaviruses. Our study validates AI-based virtual high throughput screening as a rational approach for identifying effective antiviral candidates for Rift Valley fever virus and other bunyaviruses.

Copy number variants (CNV) are shown to contribute to the etiology of several genetic disorders. Accurate detection of CNVs on whole exome sequencing (WES) data has been a long sought-after goal for use in clinics. This was not possible despite recent improvements in performance because algorithms mostly suffer from low precision and even lower recall on expert-curated gold standard call sets. Here, we present a deep learning-based somatic and germline CNV caller for WES data, named ECOLE. Based on a variant of the transformer architecture, the model learns to call CNVs per exon, using high-confidence calls made on matched WGS samples. We further train and fine-tune the model with a small set of expert calls via transfer learning. We show that ECOLE achieves high performance on human expert labelled data for the first time with 68.7% precision and 49.6% recall. This corresponds to precision and recall improvements of 18.7% and 30.8% over the next best-performing methods, respectively. We also show that the same fine-tuning strategy using tumor samples enables ECOLE to detect RT-qPCR-validated variations in bladder cancer samples without the need for a control sample. ECOLE is available at https://github.com/ciceklab/ECOLE ., (© 2024. The Author(s).)

The NSs protein is a major virulence factor in bunyaviruses, crucial for viral pathogenesis. However, assessing NSs protein function can be challenging due to its inhibition of cellular RNA polymerase II, impacting NSs protein expression from plasmid DNA. The recombinant Rift Valley fever virus (RVFV) MP-12 strain (rMP-12), a highly attenuated vaccine strain, can be safely manipulated under biosafety level 2 conditions. Leveraging a reverse genetics system, we can engineer rMP-12 variants expressing heterologous NSs genes, enabling functional testing in cultured cells. Human macrophages hold a central role in viral pathogenesis, making them an ideal model for assessing NSs protein functions. Consequently, we can comprehensively compare and analyze the functional significance of various NSs proteins in human macrophages using rMP-12 NSs variants. In this chapter, we provide a detailed overview of the preparation process for rMP-12 NSs variants and introduce two distinct human macrophage models: THP-1 cells and primary macrophages. This research framework promises valuable insights into the virulence mechanisms of RVFV and other bunyaviruses and the potential for vaccine development., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)