GetPrimers: A generalized PCR-based genetic targeting primer designer enabling easy and standardized targeted gene modification across multiple systems.

Genetic targeting (e.g., gene knockout and tagging) based on polymerase chain reaction (PCR) is a simple yet powerful approach for studying gene functions. Although originally developed in classic budding and fission yeast models, the same principle applies to other eukaryotic systems with efficient homologous recombination. One-step PCR-based genetic targeting is conventionally used but the sizes of the homologous arms that it generates for recombination-mediated genetic targeting are usually limited. Alternatively, gene targeting can also be performed via fusion PCR, which can create homologous arms that are orders of magnitude larger, therefore substantially increasing the efficiency of recombination-mediated genetic targeting. Here, we present GetPrimers (https://www.evomicslab.org/app/getprimers/), a generalized computational framework and web tool to assist automatic targeting and verification primer design for both one-step PCR-based and fusion PCR-based genetic targeting experiments. Moreover, GetPrimers by design runs for any given genetic background of any species with full genome scalability. Therefore, GetPrimers is capable of empowering high-throughput functional genomic assays at multipopulation and multispecies levels. Comprehensive experimental validations have been performed for targeting and verification primers designed by GetPrimers across multiple organism systems and experimental setups. We anticipate GetPrimers to become a highly useful and popular tool to facilitate easy and standardized gene modification across multiple systems.

Physics-driven learning of Wasserstein GAN for density reconstruction in dynamic tomography.

Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction and density reconstruction methods may not provide the high accuracy needed in many applications and can break down in the presence of unmodeled or anomalous scatter and other experimental artifacts. Incorporating machine-learning models could prove beneficial for accurate density reconstruction, particularly in dynamic imaging, where the time evolution of the density fields could be captured by partial differential equations or by learning from hydrodynamics simulations. In this work, we demonstrate the ability of learned deep neural networks to perform artifact removal in noisy density reconstructions, where the noise is imperfectly characterized. We use a Wasserstein generative adversarial network (WGAN), where the generator serves as a denoiser that removes artifacts in densities obtained from traditional reconstruction algorithms. We train the networks from large density time-series datasets, with noise simulated according to parametric random distributions that may mimic noise in experiments. The WGAN is trained with noisy density frames as generator inputs, to match the generator outputs to the distribution of clean densities (time series) from simulations. A supervised loss is also included in the training, which leads to an improved density restoration performance. In addition, we employ physics-based constraints such as mass conservation during the network training and application to further enable highly accurate density reconstructions. Our preliminary numerical results show that the models trained in our frameworks can remove significant portions of unknown noise in density time-series data.

Multi-layer clustering-based residual sparsifying transform for low-dose CT image reconstruction.

PURPOSE: The recently proposed sparsifying transform (ST) models incur low computational cost and have been applied to medical imaging. Meanwhile, deep models with nested network structure reveal great potential for learning features in different layers. In this study, we propose a network-structured ST learning approach for X-ray computed tomography (CT), which we refer to as multi-layer clustering-based residual sparsifying transform (MCST) learning. The proposed MCST scheme learns multiple different unitary transforms in each layer by dividing each layer's input into several classes. We apply the MCST model to low-dose CT (LDCT) reconstruction by deploying the learned MCST model into the regularizer in penalized weighted least squares (PWLS) reconstruction. METHODS: The proposed MCST model combines a multi-layer sparse representation structure with multiple clusters for the features in each layer that are modeled by a rich collection of transforms. We train the MCST model in an unsupervised manner via a block coordinate descent (BCD) algorithm. Since our method is patch-based, the training can be performed with a limited set of images. For CT image reconstruction, we devise a novel algorithm called PWLS-MCST by integrating the pre-learned MCST signal model with PWLS optimization. RESULTS: We conducted LDCT reconstruction experiments on XCAT phantom data, Numerical Mayo Clinical CT dataset and "LDCT image and projection dataset" (Clinical LDCT dataset). We trained the MCST model with two (or three) layers and with five clusters in each layer. The learned transforms in the same layer showed rich features while additional information is extracted from representation residuals. Our simulation results and clinical results demonstrate that PWLS-MCST achieves better image reconstruction quality than the conventional filtered back-projection (FBP) method and PWLS with edge-preserving (EP) regularizer. It also outperformed recent advanced methods like PWLS with a learned multi-layer residual sparsifying transform (MARS) prior and PWLS with a union of learned transforms (ULTRA), especially for displaying clear edges and preserving subtle details. CONCLUSIONS: In this work, a multi-layer sparse signal model with a nested network structure is proposed. We refer this novel model as the MCST model that exploits multi-layer residual maps to sparsify the underlying image and clusters the inputs in each layer for accurate sparsification. We presented a new PWLS framework with a learned MCST regularizer for LDCT reconstruction. Experimental results show that the proposed PWLS-MCST provides clearer reconstructions than several baseline methods. The code for PWLS-MCST is released at https://github.com/Xikai97/PWLS-MCST.

Predictability limit of partially observed systems.

Applications from finance to epidemiology and cyber-security require accurate forecasts of dynamic phenomena, which are often only partially observed. We demonstrate that a system's predictability degrades as a function of temporal sampling, regardless of the adopted forecasting model. We quantify the loss of predictability due to sampling, and show that it cannot be recovered by using external signals. We validate the generality of our theoretical findings in real-world partially observed systems representing infectious disease outbreaks, online discussions, and software development projects. On a variety of prediction tasks-forecasting new infections, the popularity of topics in online discussions, or interest in cryptocurrency projects-predictability irrecoverably decays as a function of sampling, unveiling predictability limits in partially observed systems.

Influence of varying degrees of malnutrition on IGF-I expression in the rat diaphragm.

This study evaluated the impact of varying degrees of prolonged malnutrition on the local insulin-like growth factor-I (IGF-I) system in the costal diaphragm muscle. Adult rats were provided with either 60 or 40% of usual food intake over 3 wk. Nutritionally deprived (ND) animals (i.e., ND60 and ND40) were compared with control (Ctl) rats fed ad libitum. Costal diaphragm fiber types and cross-sectional areas were determined histochemically. Costal diaphragm muscle IGF-I mRNA levels were determined by RT-PCR. Serum and muscle IGF-I peptide levels were determined by using a rat-specific radioimmunoassay. The body weights of Ctl rats increased by 5%, whereas those of ND60 and ND40 animals decreased by 16 and 26%, respectively. Diaphragm weights were reduced by 17 and 27% in ND60 and ND40 animals, respectively, compared with Ctl. Diaphragm fiber proportions were unaffected by either ND regimen. Significant atrophy of both type IIa and IIx fibers was noted in the ND60 group, whereas atrophy of all three fiber types was observed in the diaphragm of ND40 rats. Serum IGF-I levels were reduced by 62 and 79% in ND60 and ND40 rats, respectively, compared with Ctl. Diaphragm muscle IGF-I mRNA levels in both ND groups were similar to those noted in Ctl. In contrast, IGF-I concentrations were reduced by 36 and 42% in the diaphragm muscle of ND60 and ND40 groups, respectively, compared with Ctl. We conclude that the local (autocrine/paracrine) muscle IGF-I system is affected in our models of prolonged ND. We propose that this contributes to disordered muscle protein turnover and muscle cachexia with atrophy of muscle fibers. This is particularly so in view of recent data demonstrating the importance of the autocrine/paracrine system in muscle growth and maintenance of fiber size.

Insulin-like growth factor I prevents corticosteroid-induced diaphragm muscle atrophy in emphysematous hamsters.

The aim of this study was to evaluate whether recombinant human insulin-like growth factor I (rhIGF-I) could attenuate or prevent diaphragm (DIA) fiber atrophy with corticosteroid (CS) administration to emphysematous (EMP) hamsters. DIA muscle IGF-I responses to CS administration with and without exogenous rhIGF-I administration were evaluated. Three groups were studied: 1) EMP; 2) EMP + triamcinolone (T; 0.4 mg.kg-1.day-1 im); and 3) EMP + T + IGF-I (600 microg/day by constant infusion). After 4 wk, the DIA was analyzed histochemically and biochemically (IGF-I mRNA levels by RT-PCR and endogenous and exogenous IGF-I peptide levels immunochemically). Body weights of EMP-T progressively decreased, while those of EMP and EMP-T-IGF-I remained stable despite similarly reduced food intake in both T groups. DIA weight was reduced with T but preserved with rhIGF-I infusion. DIA fiber proportions were similar among the groups. The cross-sectional areas of types I, IIa, and IIx fibers were reduced (17 to 31%) with T administration but unchanged with rhIGF-I infusion. DIA IGF-I mRNA levels were similar across all groups. By contrast, the endogenous DIA IGF-I levels were reduced (41%) in the EMP-T-IGF-I animals. Total DIA IGF-I levels (endogenous + exogenous) were still significantly reduced. IGF-I immunoreactivity confirmed this reduction in all DIA fibers. We conclude that DIA fiber atrophy with T was completely prevented by exogenous rhIGF-I administration. This effect was likely mediated by the pharmacological influences of exogenously administered rhIGF-I. We speculate that this results from increased bioavailability of free IGF-I to react with muscle receptors. Reduced endogenous IGF-I levels in the DIA likely reflect a negative-feedback influence. These results may have important clinical implications for treatment options to offset the adverse effects of CS on the respiratory muscles in patients with chronic lung disorders.