Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations.

Numerous experimental studies have indicated that alteration and dysregulation in mircroRNAs (miRNAs) are associated with serious diseases. Identifying disease-related miRNAs is therefore an essential and challenging task in bioinformatics research. Computational methods are an efficient and economical alternative to conventional biomedical studies and can reveal underlying miRNA-disease associations for subsequent experimental confirmation with reasonable confidence. Despite the success of existing computational approaches, most of them only rely on the known miRNA-disease associations to predict associations without adding other data to increase the prediction accuracy, and they are affected by issues of data sparsity. In this paper, we present MRRN, a model that combines matrix reconstruction with node reliability to predict probable miRNA-disease associations. In MRRN, the most reliable neighbors of miRNA and disease are used to update the original miRNA-disease association matrix, which significantly reduces data sparsity. Unknown miRNA-disease associations are reconstructed by aggregating the most reliable first-order neighbors to increase prediction accuracy by representing the local and global structure of the heterogeneous network. Five-fold cross-validation of MRRN produced an area under the curve (AUC) of 0.9355 and area under the precision-recall curve (AUPR) of 0.2646, values that were greater than those produced by comparable models. Two different types of case studies using three diseases were conducted to demonstrate the accuracy of MRRN, and all top 30 predicted miRNAs were verified.

The accurate prediction and characterization of cancerlectin by a combined machine learning and GO analysis.

Cancerlectins, lectins linked to tumor progression, have become the focus of cancer therapy research for their carbohydrate-binding specificity. However, the specific characterization for cancerlectins involved in tumor progression is still unclear. By taking advantage of the g-gap tripeptide and tetrapeptide composition feature descriptors, we increased the accuracy of the classification model of cancerlectin and lectin to 98.54% and 95.38%, respectively. About 36 cancerlectin and 135 lectin features were selected for functional characterization by P/N feature ranking method, which particularly selects the features in positive samples. The specific protein domains of cancerlectins are found to be p-GalNAc-T, crystal and annexin by comparing with lectins through the exclusion method. Moreover, the combined GO analysis showed that the conserved cation binding sites of cancerlectin specific domains are covered by selected feature peptides, suggesting that the capability of cation binding, critical for enzyme activity and stability, could be the key characteristic of cancerlectins in tumor progression. These results will help to identify potential cancerlectin and provide clues for mechanism study of cancerlectin in tumor progression.

Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics.

The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58% and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control.

Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products.

Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe ("myocommata") and red muscle ("myotome") pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.

Prediction of Potential miRNA-Disease Associations Based on a Masked Graph Autoencoder.

Biomedical evidence has demonstrated the relevance of microRNA (miRNA) dysregulation in complex human diseases, and determining the relationship between miRNAs and diseases can aid in the early detection and prevention of diseases. Traditional biological experimental methods have the disadvantages of high cost and low efficiency, which are well compensated by computational methods. However, many computational methods have the challenge of excessively focusing on the neighbor relationship, ignoring the structural information of the graph, and belittling the redundant information of the graph structure. This study proposed a computational model based on a graph-masking autoencoder named MGAEMDA. MGAEMDA is an asymmetric framework in which the encoder maps partially observed graphs into latent representations. The decoder reconstructs the masked structural information based on the edge and node levels and combines it with linear matrices to obtain the result. The empirical results on the two datasets reveal that the MGAEMDA model performs better than its counterparts. We also demonstrated the predictive performance of MGAEMDA using a case study of four diseases, and all the top 30 predicted miRNAs were validated in the database, providing further evidence of the excellent performance of the model.

A novel near infrared spectroscopy analytical strategy for soil nutrients detection based on the DBO-SVR method.

Soil is the basis of agricultural production and accessing accurate information on soil nutrients is essential. Traditional methods of soil composition detection, which are based on chemical analysis, are characterized by being costly and polluting. Spectroscopic analysis has proven to be a rapid, non-destructive and effective technique for predicting soil properties in general and potassium, phosphorus and organic matter in particular. However, previous research on soils has rarely combined optimization algorithms with machine learning techniques, which has led to suboptimal model accuracy and convergence speed. In this study, a total of 184 soil samples were collected from three cities of Linhai, Yueqing and Longyou County, Zhejiang Province, China. After measuring pH values, alkali-hydrolyzable nitrogen (SAN), available phosphorus (SAP), available potassium (SAK) and soil organic matter (SOM) contents, along with their corresponding spectroscopic measurements, nine pretreatment methods and their combinations are adopted. A novel assessment model, integrating support vector machine and dung beetle optimization algorithm (DBO-SVR), is proposed to predict pH values and SAN, SAP, SAK, SOM content. Meanwhile, the DBO algorithm is compared with three mainstream optimization algorithms (particle swarm optimization (PSO), whale optimization algorithm (WOA) and grey wolf optimizer (GWO)). Results showed that the DBO-SVR model was shown best performance with Rp, RMSEP and RPD of 0.9842, 0.1306, 5.6485 respectively for prediction of pH value, with Rp, RMSEP and RPD of 0.8802, 15.0574 mg/kg and 2.0508, respectively for assessment of SAN content, with Rp, RMSEP and RPD of 0.9790, 12.8298 mg/kg, and 4.5132, respectively for assessment of SAP content, with Rp, RMSEP and RPD of 0.8677, 22.5107 mg/kg, and 1.9546, respectively for assessment of SAK content, and with Rp, RMSEP and RPD of 0.9273, 2.6427g/kg , and 2.1821, respectively for assessment of SOM content. This study demonstrates that the combination of near-infrared (NIR) spectroscopy and the DBO-SVR algorithm is capable of predicting soil nutrient composition with greater accuracy and efficiency.

Effects of interleukin-18 on natural killer cells: costimulation of activation through Fc receptors for immunoglobulin.

The antitumor activity of monoclonal antibodies is mediated by effector cells, such as natural killer (NK) cells, that express Fc receptors for immunoglobulin. Efficacy of monoclonal antibodies, including the CD20 antibody rituximab, could be improved by agents that augment the function of NK cells. Interleukin (IL)-18 is an immunostimulatory cytokine that has antitumor activity in preclinical models. The effects of IL-18 on NK cell function mediated through Fcγ receptors were examined. Human NK cells stimulated with immobilized IgG in vitro secreted IFN-γ as expected; such IFN-γ production was partially inhibited by blocking CD16 with monoclonal antibodies. IL-18 augmented IFN-γ production by NK cells stimulated with immobilized IgG or CD16 antibodies. NK cell IFN-γ production in response to immobilized IgG and/or IL-18 was inhibited by chemical inhibitors of Syk and several other kinases involved in CD16 signaling pathways. IL-18 augmented antibody-dependent cellular cytotoxicity (ADCC) of human NK cells against rituximab-coated Raji cells in vitro. IL-18 and rituximab acted synergistically to promote regression of human lymphoma xenografts in SCID mice. Inasmuch as IL-18 costimulates IFN-γ production and ADCC of NK cells activated through Fc receptors in vitro and augments antitumor activity of rituximab in vivo, it is an attractive cytokine to combine with monoclonal antibodies for treatment of human cancer.

Prediction of CRISPR-Cas9 off-target activities with mismatches and indels based on hybrid neural network.

The CRISPR/Cas9 system has significantly advanced the field of gene editing, yet its clinical application is constrained by the considerable challenge of off-target effects. Although numerous deep learning models for off-target prediction have been proposed, most struggle to effectively extract the nuanced features of guide RNA (gRNA) and DNA sequence pairs and to mitigate information loss during data transmission within the model. To address these limitations, we introduce a novel Hybrid Neural Network (HNN) model that employs a parallelized network structure to fully extract pertinent features from different positions and types of bases in the sequence to minimize information loss. Notably, this study marks the first application of word embedding techniques to extract information from sequence pairs that contain insertions and deletions (Indels). Comprehensive evaluation across diverse datasets indicates that our proposed model outperforms existing state-of-the-art prediction methods in off-target prediction. The datasets and source codes supporting this study can be found at https://github.com/Yang-k955/CRISPR-HW.

DapBCH: a disease association prediction model Based on Cross-species and Heterogeneous graph embedding.

The study of comorbidity can provide new insights into the pathogenesis of the disease and has important economic significance in the clinical evaluation of treatment difficulty, medical expenses, length of stay, and prognosis of the disease. In this paper, we propose a disease association prediction model DapBCH, which constructs a cross-species biological network and applies heterogeneous graph embedding to predict disease association. First, we combine the human disease-gene network, mouse gene-phenotype network, human-mouse homologous gene network, and human protein-protein interaction network to reconstruct a heterogeneous biological network. Second, we apply heterogeneous graph embedding based on meta-path aggregation to generate the feature vector of disease nodes. Finally, we employ link prediction to obtain the similarity of disease pairs. The experimental results indicate that our model is highly competitive in predicting the disease association and is promising for finding potential disease associations.

Further Characterization of Multi-Organ DEARE and Protection by 16,16 Dimethyl Prostaglandin E2 in a Mouse Model of the Hematopoietic Acute Radiation Syndrome.

Survivors of acute radiation exposure suffer from the delayed effects of acute radiation exposure (DEARE), a chronic condition affecting multiple organs, including lung, kidney, heart, gastrointestinal tract, eyes, and brain, and often causing cancer. While effective medical countermeasures (MCM) for the hematopoietic-acute radiation syndrome (H-ARS) have been identified and approved by the FDA, development of MCM for DEARE has not yet been successful. We previously documented residual bone marrow damage (RBMD) and progressive renal and cardiovascular DEARE in murine survivors of H-ARS, and significant survival efficacy of 16,16-dimethyl prostaglandin E2 (dmPGE2) given as a radioprotectant or radiomitigator for H-ARS. We now describe additional DEARE (physiological and neural function, progressive fur graying, ocular inflammation, and malignancy) developing after sub-threshold doses in our H-ARS model, and detailed analysis of the effects of dmPGE2 administered before (PGE-pre) or after (PGE-post) lethal total-body irradiation (TBI) on these DEARE. Administration of PGE-pre normalized the twofold reduction of white blood cells (WBC) and lymphocytes seen in vehicle-treated survivors (Veh), and increased the number of bone marrow (BM) cells, splenocytes, thymocytes, and phenotypically defined hematopoietic progenitor cells (HPC) and hematopoietic stem cells (HSC) to levels equivalent to those in non-irradiated age-matched controls. PGE-pre significantly protected HPC colony formation ex vivo by >twofold, long term-HSC in vivo engraftment potential up to ninefold, and significantly blunted TBI-induced myeloid skewing. Secondary transplantation documented continued production of LT-HSC with normal lineage differentiation. PGE-pre reduced development of DEARE cardiovascular pathologies and renal damage; prevented coronary artery rarefication, blunted progressive loss of coronary artery endothelia, reduced inflammation and coronary early senescence, and blunted radiation-induced increase in blood urea nitrogen (BUN). Ocular monocytes were significantly lower in PGE-pre mice, as was TBI-induced fur graying. Increased body weight and decreased frailty in male mice, and reduced incidence of thymic lymphoma were documented in PGE-pre mice. In assays measuring behavioral and cognitive functions, PGE-pre reduced anxiety in females, significantly blunted shock flinch response, and increased exploratory behavior in males. No effect of TBI was observed on memory in any group. PGE-post, despite significantly increasing 30-day survival in H-ARS and WBC and hematopoietic recovery, was not effective in reducing TBI-induced RBMD or any other DEARE. In summary, dmPGE2 administered as an H-ARS MCM before lethal TBI significantly increased 30-day survival and ameliorated RBMD and multi-organ and cognitive/behavioral DEARE to at least 12 months after TBI, whereas given after TBI, dmPGE2 enhances survival from H-ARS but has little impact on RBMD or other DEARE.

A data-driven noise reduction method and its application for the enhancement of stress wave signals.

Ensemble empirical mode decomposition (EEMD) has been recently used to recover a signal from observed noisy data. Typically this is performed by partial reconstruction or thresholding operation. In this paper we describe an efficient noise reduction method. EEMD is used to decompose a signal into several intrinsic mode functions (IMFs). The time intervals between two adjacent zero-crossings within the IMF, called instantaneous half period (IHP), are used as a criterion to detect and classify the noise oscillations. The undesirable waveforms with a larger IHP are set to zero. Furthermore, the optimum threshold in this approach can be derived from the signal itself using the consecutive mean square error (CMSE). The method is fully data driven, and it requires no prior knowledge of the target signals. This method can be verified with the simulative program by using Matlab. The denoising results are proper. In comparison with other EEMD based methods, it is concluded that the means adopted in this paper is suitable to preprocess the stress wave signals in the wood nondestructive testing.

Dynamic and Static Representation Learning Network for Recommendation.

Existing review-based recommendation methods learn a latent representation of user and item from user-generated reviews by a static strategy, which are unable to capture the dynamic evolution of users' interests and the dynamic attraction of items. Here, we propose a dynamic and static representation learning network (DSRLN) to improve the rating prediction accuracy by exploring fine-grained representations of users and items. Specifically, we built DSRLN with a dynamic representation extractor to model the dynamic evolution of users' interests by exploring the inner relations of an interaction sequence, and with a static representation extractor to model the users' intrinsic preferences by learning the semantic coherence and feature strength information from reviews. To identify the different influences of dynamic and static features for different users, a personalized adaptive fusion module was designed using a weighted attention mechanism. Extensive experiments on five real-world datasets from Amazon demonstrated the superiority of the proposed model, and the additional ablation studies verified the effectiveness of the components designed in the DSRLN model.

Age and Sex Divergence in Hematopoietic Radiosensitivity in Aged Mouse Models of the Hematopoietic Acute Radiation Syndrome.

The hematopoietic system is highly sensitive to stress from both aging and radiation exposure, and the hematopoietic acute radiation syndrome (H-ARS) should be modeled in the geriatric context separately from young for development of age-appropriate medical countermeasures (MCMs). Here we developed aging murine H-ARS models, defining radiation dose response relationships (DRRs) in 12-month-old middle-aged and 24-month-old geriatric male and female C57BL/6J mice, and characterized diverse factors affecting geriatric MCM testing. Groups of approximately 20 mice were exposed to ∼10 different doses of radiation to establish radiation DRRs for estimation of the LD50/30. Radioresistance increased with age and diverged dramatically between sexes. The LD50/30 in young adult mice averaged 853 cGy and was similar between sexes, but increased in middle age to 1,005 cGy in males and 920 cGy in females, with further sex divergence in geriatric mice to 1,008 cGy in males but 842 cGy in females. Correspondingly, neutrophils, platelets, and functional hematopoietic progenitor cells were all increased with age and rebounded faster after irradiation. These effects were higher in aged males, and neutrophil dysfunction was observed in aged females. Upstream of blood production, hematopoietic stem cell (HSC) markers associated with age and myeloid bias (CD61 and CD150) were higher in geriatric males vs. females, and sex-divergent gene signatures were found in HSCs relating to cholesterol metabolism, interferon signaling, and GIMAP family members. Fluid intake per gram body weight decreased with age in males, and decreased after irradiation in all mice. Geriatric mice of substrain C57BL/6JN sourced from the National Institute on Aging were significantly more radiosensitive than C57BL/6J mice from Jackson Labs aged at our institution, indicating mouse source and substrain should be considered in geriatric radiation studies. This work highlights the importance of sex, vendor, and other considerations in studies relating to hematopoiesis and aging, identifies novel sex-specific functional and molecular changes in aging hematopoietic cells at steady state and after irradiation, and presents well-characterized aging mouse models poised for MCM efficacy testing for treatment of acute radiation effects in the elderly.

Stress wave signal denoising using ensemble empirical mode decomposition and an instantaneous half period model.

Stress-wave-based techniques have been proven to be an accurate nondestructive test means for determining the quality of wood based materials and they been widely used for this purpose. However, the results are usually inconsistent, partially due to the significant difficulties in processing the nonlinear, non-stationary stress wave signals which are often corrupted by noise. In this paper, an ensemble empirical mode decomposition (EEMD) based approach with the aim of signal denoising was proposed and applied to stress wave signals. The method defined the time interval between two adjacent zero-crossings within the intrinsic mode function (IMF) as the instantaneous half period (IHP) and used it as a criterion to detect and classify the noise oscillations. The waveform between the two adjacent zero-crossings was retained when the IHP was larger than the predefined threshold, whereas the waveforms with smaller IHP were set to zero. Finally the estimated signal was obtained by reconstructing the processed IMFs. The details of threshold choosing rules were also discussed in the paper. Additive Gaussian white noise was embedded into real stress wave signals to test the proposed method. Butterworth low pass filter, EEMD-based low pass filter and EEMD-based thresholding filter were used to compare filtering performance. Mean square error between clean and filtered stress waves was used as filtering performance indexes. The results demonstrated the excellent efficiency of the proposed method.

Multitask Non-Autoregressive Model for Human Motion Prediction.

Human motion prediction, which aims at predicting future human skeletons given the past ones, is a typical sequence-to-sequence problem. Therefore, extensive efforts have been devoted to exploring different RNN-based encoder-decoder architectures. However, by generating target poses conditioned on the previously generated ones, these models are prone to bringing issues such as error accumulation problem. In this paper, we argue that such issue is mainly caused by adopting autoregressive manner. Hence, a novel Non-AuToregressive model (NAT) is proposed with a complete non-autoregressive decoding scheme, as well as a context encoder and a positional encoding module. More specifically, the context encoder embeds the given poses from temporal and spatial perspectives. The frame decoder is responsible for predicting each future pose independently. The positional encoding module injects positional signal into the model to indicate the temporal order. Besides, a multitask training paradigm is presented for both low-level human skeleton prediction and high-level human action recognition, resulting in the considerable improvement for the prediction task. Our approach is evaluated on Human3.6M and CMU-Mocap benchmarks and outperforms state-of-the-art autoregressive methods.

Optimizing and Profiling Prostaglandin E2 as a Medical Countermeasure for the Hematopoietic Acute Radiation Syndrome.

Identification of medical countermeasures (MCM) to mitigate radiation damage and/or protect first responders is a compelling unmet medical need. The prostaglandin E2 (PGE2) analog, 16,16 dimethyl-PGE2 (dmPGE2), has shown efficacy as a radioprotectant and radiomitigator that can enhance hematopoiesis and ameliorate intestinal mucosal cell damage. In this study, we optimized the time of administration of dmPGE2 for protection and mitigation against mortality from the hematopoietic acute radiation syndrome (H-ARS) in young adult mice, evaluated its activity in pediatric and geriatric populations, and investigated potential mechanisms of action. Windows of 30-day survival efficacy for single administration of dmPGE2 were defined as within 3 h prior to and 6-30 h after total-body γ irradiation (TBI). Radioprotective and radio-mitigating efficacy was also observed in 2-year-old geriatric mice and 6-week-old pediatric mice. PGE2 receptor agonist studies suggest that signaling through EP4 is primarily responsible for the radioprotective effects. DmPGE2 administration prior to TBI attenuated the drop in red blood cells and platelets, accelerated recovery of all peripheral blood cell types, and resulted in higher hematopoietic and mesenchymal stem cells in survivor bone marrow. Multiplex analysis of bone marrow cytokines together with RNA sequencing of hematopoietic stem cells indicated a pro-hematopoiesis cytokine milieu induced by dmPGE2, with IL-6 and G-CSF strongly implicated in dmPGE2-mediated radioprotective activity. In summary, we have identified windows of administration for significant radio-mitigation and radioprotection by dmPGE2 in H-ARS, demonstrated survival efficacy in special populations, and gained insight into radioprotective mechanisms, information useful towards development of dmPGE2 as a MCM for first responders, military personnel, and civilians facing radiation threats.

Establishing Pediatric Mouse Models of the Hematopoietic Acute Radiation Syndrome and the Delayed Effects of Acute Radiation Exposure.

Medical countermeasures (MCMs) for hematopoietic acute radiation syndrome (H-ARS) should be evaluated in well-characterized animal models, with consideration of at-risk populations such as pediatrics. We have developed pediatric mouse models of H-ARS and delayed effects of acute radiation exposure (DEARE) for efficacy testing of MCMs against radiation. Male and female C57BL/6J mice aged 3, 4, 5, 6, 7 and 8 weeks old (±1 day) were characterized for baseline hematopoietic and gastrointestinal parameters, radiation response, efficacy of a known MCM, and DEARE at six and 12 months after total-body irradiation (TBI). Weanlings (age 3 weeks) were the most radiosensitive age group with an estimated LD50/30 of 712 cGy, while mice aged 4 to 8 weeks were more radioresistant with an estimated LD50/30 of 767-787 cGy. Female weanlings were more radiosensitive than males at 3 and 4 weeks old but became significantly more radioresistant after the pubertal age of 5 weeks. The most dramatic increase in body weight, RBC counts and intestinal circumference length occurred from 3 to 5 weeks of age. The established radiomitigator Neulasta® (pegfilgrastim) significantly increased 30-day survival in all age groups, validating these models for MCM efficacy testing. Analyses of DEARE among pediatric survivors revealed depressed weight gain in males six months post-TBI, and increased blood urea nitrogen at 12 months post-TBI which was more severe in females. Hematopoietic DEARE at six months post-TBI appeared to be less severe in survivors from the 3- and 4-week-old groups but was equally severe in all age groups by 12 months of age. Similar to our other acute radiation mouse models, there was no appreciable effect of Neulasta used as an H-ARS MCM on the severity of DEARE. In summary, these data characterize a pediatric mouse model useful for assessing the efficacy of MCMs against ARS and DEARE in children.

[Inversion of Water Quality Parameters Based on UAV Multispectral Images and the OPT-MPP Algorithm].

Unmanned aerial vehicle (UAV) multispectral remote sensing can be used to monitor multiple water quality parameters, such as suspended solids, turbidity, total phosphorus, and chlorophyll. Establishing a stable and accurate water quality parameter inversion model is a prerequisite for this work. The matching pixel-by-pixel (MPP) algorithm is an inversion algorithm for high resolution features of UAV images; however, it is associated with problems of excessive computation and over-fitting. To overcome these problems, the optimize-MPP (OPT-MPP) algorithm is proposed. In this study, Qingshan Lake in Hangzhou City, Zhejiang Province, was used as the research area. Forty-five samples were collected to construct the OPT-MPP algorithm inversion model for two water quality parameters:the suspended sediments concentration (SS) and turbidity (TU). The results showed that the optimal suspended sediment concentration inversion model had a determination coefficient (R2) of 0.7870 and a comprehensive error of 0.1308. The optimal turbidity inversion model had a R2 of 0.8043 and a comprehensive error of 0.1503. Hence, the inversion of the spatial distribution information for water quality parameters in each experimental area of QingShan Lake was realized by using the optimal models of the two established parameters.

Development of a Model of the Acute and Delayed Effects of High Dose Radiation Exposure in Jackson Diversity Outbred Mice; Comparison to Inbred C57BL/6 Mice.

Development of medical countermeasures against radiation relies on robust animal models for efficacy testing. Mouse models have advantages over larger species due to economics, ease of conducting aging studies, existence of historical databases, and research tools allowing for sophisticated mechanistic studies. However, the radiation dose-response relationship of inbred strains is inherently steep and sensitive to experimental variables, and inbred models have been criticized for lacking genetic diversity. Jackson Diversity Outbred (JDO) mice are the most genetically diverse strain available, developed by the Collaborative Cross Consortium using eight founder strains, and may represent a more accurate model of humans than inbred strains. Herein, models of the Hematopoietic-Acute Radiation Syndrome and the Delayed Effects of Acute Radiation Exposure were developed in JDO mice and compared to inbred C57BL/6. The dose response relationship curve in JDO mice mirrored the more shallow curves of primates and humans, characteristic of genetic diversity. JDO mice were more radioresistant than C57BL/6 and differed in sensitivity to antibiotic countermeasures. The model was validated with pegylated-G-CSF, which provided significantly enhanced 30-d survival and accelerated blood recovery. Long-term JDO survivors exhibited increased recovery of blood cells and functional bone marrow hematopoietic progenitors compared to C57BL/6. While JDO hematopoietic stem cells declined more in number, they maintained a greater degree of quiescence compared to C57BL/6, which is essential for maintaining function. These JDO radiation models offer many of the advantages of small animals with the genetic diversity of large animals, providing an attractive alternative to currently available radiation animal models.

Lifelong Residual bone Marrow Damage in Murine Survivors of the Hematopoietic Acute Radiation Syndrome (H-ARS): A Compilation of Studies Comprising the Indiana University Experience.

Accurate analyses of the delayed effects of acute radiation exposure in survivors of the hematopoietic acute radiation syndrome are hampered by low numbers of mice for examination due to high lethality from the acute syndrome, increased morbidity and mortality in survivors, high cost of husbandry for long-term studies, biological variability, and inconsistencies of models from different laboratories complicating meta-analyses. To address this, a compilation of 38 similar hematopoietic acute radiation syndrome studies conducted over a 7-y period in the authors' laboratory, comprising more than 1,500 irradiated young adult C57BL/6 mice and almost 600 day-30 survivors, was assessed for hematopoietic delayed effects of acute radiation exposure at various times up to 30 mo of age. Significant loss of long-term repopulating potential of phenotypically defined primitive hematopoietic stem cells was documented in hematopoietic acute radiation syndrome survivors, as well as significant decreases in all hematopoietic lineages in peripheral blood, prominent myeloid skew, significantly decreased bone marrow cellularity, and numbers of lineage-negative Sca-1+ cKit+ CD150+ cells (KSL CD150+; the phenotype known to be enriched for hematopoietic stem cells), and increased cycling of KSL CD150+ cells. Studies interrogating the phenotype of bone marrow cells capable of initiation of suspension cultures and engraftment in competitive transplantation assays documented the phenotype of hematopoietic stem cells in hematopoietic acute radiation syndrome survivors to be the same as that in nonirradiated age-matched controls. This compilation study adds rigor and validity to our initial findings of persistent hematopoietic dysfunction in hematopoietic acute radiation syndrome survivors that arises at the level of the hematopoietic stem cell and which affects all classes of hematopoietic cells for the life of the survivor.