Cerenkov Luminescence Imaging (CLI) for Cancer Therapy Monitoring

In molecular imaging, positron emission tomography (PET) and optical imaging (OI) are two of the most important and thus most widely used modalities^1-3. PET is characterized by its excellent sensitivity and quantification ability while OI is notable for non-radiation, relative low cost, short scanning time, high throughput, and wide availability to basic researchers. However, both modalities have their shortcomings as well. PET suffers from poor spatial resolution and high cost, while OI is mostly limited to preclinical applications because of its limited tissue penetration along with prominent scattering optical signals through the thickness of living tissues.Recently a bridge between PET and OI has emerged with the discovery of Cerenkov Luminescence Imaging (CLI)^4-6. CLI is a new imaging modality that harnesses Cerenkov Radiation (CR) to image radionuclides with OI instruments. Russian Nobel laureate Alekseyevich Cerenkov and his colleagues originally discovered CR in 1934. It is a form of electromagnetic radiation emitted when a charged particle travels at a superluminal speed in a dielectric medium^7,8. The charged particle, whether positron or electron, perturbs the electromagnetic field of the medium by displacing the electrons in its atoms. After passing of the disruption photons are emitted as the displaced electrons return to the ground state. For instance, one ¹⁸F decay was estimated to produce an average of 3 photons in water⁵. Since its emergence, CLI has been investigated for its use in a variety of preclinical applications including in vivo tumor imaging, reporter gene imaging, radiotracer development, multimodality imaging, among others^4,5,9,10,11. The most important reason why CLI has enjoyed much success so far is that this new technology takes advantage of the low cost and wide availability of OI to image radionuclides, which used to be imaged only by more expensive and less available nuclear imaging modalities such as PET.Here, we present the method of using CLI to monitor cancer drug therapy. Our group has recently investigated this new application and validated its feasibility by a proof-of-concept study¹². We demonstrated that CLI and PET exhibited excellent correlations across different tumor xenografts and imaging probes. This is consistent with the overarching principle of CR that CLI essentially visualizes the same radionuclides as PET. We selected Bevacizumab (Avastin; Genentech/Roche) as our therapeutic agent because it is a well-known angiogenesis inhibitor^13,14. Maturation of this technology in the near future can be envisioned to have a significant impact on preclinical drug development, screening, as well as therapy monitoring of patients receiving treatments.     

Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons

System identification techniques—projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)—provide state-of-the-art performance in predicting visual cortical neurons’ responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron’s receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron’s receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons.     

Dietary Restriction at Old Age Lowers Mitochondrial Oxygen Radical Production and Leak at Complex I and Oxidative DNA Damage in Rat Brain

Previous studies in mammalian models indicate that the rate of mitochondrial reactive oxygen species ROS production and the ensuing modification of mitochondrial DNA (mtDNA) link oxidative stress to aging rate. However, there is scarce information concerning this in relation to caloric restriction (CR) in the brain, an organ of maximum relevance for ageing. Furthermore, it has never been studied if CR started late in life can improve those oxidative stress-related parameters. In this investigation, rats were subjected during 1 year to 40% CR starting at 24 months of age. This protocol of CR significantly decreased the rate of mitochondrial H₂O₂ production (by 24%) and oxidative damage to mtDNA (by 23%) in the brain below the level of both old and young ad libitum-fed animals. In agreement with the progressive character of aging, the rate of H₂O₂ production of brain mitochondria stayed constant with age. Oxidative damage to nuclear DNA increased with age and this increase was fully reversed by CR to the level of the young controls. The decrease in ROS production induced by CR was localized at Complex I and occurred without changes in oxygen consumption. Instead, the efficiency of brain mitochondria to avoid electron leak to oxygen at Complex I was increased by CR. The mechanism involved in that increase in efficiency was related to the degree of electronic reduction of the Complex I generator. The results agree with the idea that CR decreases aging rate in part by lowering the rate of free radical generation of mitochondria in the brain.     

Abundance of rare and elusive species: Empirical investigation of closed versus spatially explicit capture–recapture models with lynx as a case study

Effective conservation and management require reliable monitoring methods and estimates of abundance to prioritize human and financial investments. Camera trapping is a non-invasive sampling method allowing the use of capture–recapture (CR) models to estimate abundance while accounting for the difficulty of detecting individuals in the wild. We investigated the relative performance of standard closed CR models and spatially explicit CR models (SECR) that incorporate spatial information in the data. Using simulations, we considered 4 scenarios comparing low versus high detection probability and small versus large populations and contrasted abundance estimates obtained from both approaches. Standard CR and SECR models both provided minimally biased abundance estimates, but precision was improved when using SECR models. The associated confidence intervals also provided better coverage than their non-spatial counterpart. We concluded SECR models exhibit better statistical performance than standard closed CR models and allow for sound management strategies based on density maps of activity centers. To illustrate the comparison, we considered the Eurasian lynx (Lynx lynx) as a case study that provided the first abundance estimates of a local population in France. © 2012 The Wildlife Society.     

Differential gene expression detection using penalized linear regression models: the improved SAM statistics

SUMMARY: Differential gene expression detection using microarrays has received lots of research interests recently. Many methods have been proposed, including variants of F-statistics, non-parametric approaches and empirical Bayesian methods etc. The SAM statistics has been shown to have good performance in empirical studies. SAM is more like an ad hoc shrinkage method. The idea is that for small sample microarray data, it is often useful to pool information across genes to improve efficiency. Under Bayesian framework Smyth formally derived the test statistics with shrinkage using the hierarchical models. In this paper we cast differential gene expression detection in the familiar framework of linear regression model. Commonly used test statistics correspond to using least squares to estimate the regression parameters. Based on the vast literature of research on linear models, we can naturally consider other alternatives. Here we explore the penalized linear regression. We propose the penalized t-/F-statistics for two-class microarray data based on [Formula: see text] penalty. We will show that the penalized test statistics intuitively makes sense and through applications we illustrate its good performance. AVAILABILITY: Supplementary information including program codes, more detailed analysis results and R functions for the proposed methods can be found at http://www.biostat.umn.edu/~baolin/research CONTACT: baolin@biostat.umn.edu SUPPLEMENTARY INFORMATION: http://www.biostat.umn.edu/~baolin/research.     

Assessing the Impact of Case Sensitivity and Term Information Gain on Biomedical Concept Recognition

Concept recognition (CR) is a foundational task in the biomedical domain. It supports the important process of transforming unstructured resources into structured knowledge. To date, several CR approaches have been proposed, most of which focus on a particular set of biomedical ontologies. Their underlying mechanisms vary from shallow natural language processing and dictionary lookup to specialized machine learning modules. However, no prior approach considers the case sensitivity characteristics and the term distribution of the underlying ontology on the CR process. This article proposes a framework that models the CR process as an information retrieval task in which both case sensitivity and the information gain associated with tokens in lexical representations (e.g., term labels, synonyms) are central components of a strategy for generating term variants. The case sensitivity of a given ontology is assessed based on the distribution of so-called case sensitive tokens in its terms, while information gain is modelled using a combination of divergence from randomness and mutual information. An extensive evaluation has been carried out using the CRAFT corpus. Experimental results show that case sensitivity awareness leads to an increase of up to 0.07 F1 against a non-case sensitive baseline on the Protein Ontology and GO Cellular Component. Similarly, the use of information gain leads to an increase of up to 0.06 F1 against a standard baseline in the case of GO Biological Process and Molecular Function and GO Cellular Component. Overall, subject to the underlying token distribution, these methods lead to valid complementary strategies for augmenting term label sets to improve concept recognition.     

Apoptotic cell-mediated immunoregulation of dendritic cells does not require iC3b opsonization

A number of recent studies show that activation of CR3 on dendritic cells (DCs) suppresses TLR-induced TNF-alpha and IL-12 production and inhibits effective Ag presentation. Although the proposed physiologic role for these phenomena is immune suppression due to recognition of iC3b opsonized apoptotic cells by CR3, all of the aforementioned investigations used artificial means of activating CR3. We investigated whether iC3b opsonized apoptotic cells could induce the same changes reported with artificial ligands such as mAbs or iC3b-opsonized RBC. We explored the kinetics of iC3b opsonization in two models of murine cell apoptosis, gamma-irradiated thymocytes and cytokine deprivation of the IL-3 dependent cell line BaF3. Using a relatively homogenous population of early apoptotic cells (IL-3 deprived BaF3 cells), we show that iC3b opsonized apoptotic cells engage CR3, but this interaction is dispensable in mediating the anti-inflammatory effects of apoptotic cells. TLR-induced TNF-alpha and IL-12 production by bone marrow-derived DCs occurs heterogeneously, with apoptotic cells inhibiting only certain populations depending on the TLR agonist. In contrast, although apoptotic cells induced homogeneous IL-10 production by DCs, IL-10 was not necessary for the inhibition of TNF-alpha and IL-12. Furthermore, because the ability of iC3b opsonization to enhance phagocytosis of apoptotic cells has been controversial, we report that iC3b opsonization does not significantly affect apoptotic cell ingestion by DCs. We conclude that the apoptotic cell receptor system on DCs is sufficiently redundant such that the absence of CR3 engagement does not significantly affect the normal anti-inflammatory processing of apoptotic cells.     

Neural network‐derived Potts models for structure‐based protein design using backbone atomic coordinates and tertiary motifs

Designing novel proteins to perform desired functions, such as binding or catalysis, is a major goal in synthetic biology. A variety of computational approaches can aid in this task. An energy‐based framework rooted in the sequence‐structure statistics of tertiary motifs (TERMs) can be used for sequence design on predefined backbones. Neural network models that use backbone coordinate‐derived features provide another way to design new proteins. In this work, we combine the two methods to make neural structure‐based models more suitable for protein design. Specifically, we supplement backbone‐coordinate features with TERM‐derived data, as inputs, and we generate energy functions as outputs. We present two architectures that generate Potts models over the sequence space: TERMinator, which uses both TERM‐based and coordinate‐based information, and COORDinator, which uses only coordinate‐based information. Using these two models, we demonstrate that TERMs can be utilized to improve native sequence recovery performance of neural models. Furthermore, we demonstrate that sequences designed by TERMinator are predicted to fold to their target structures by AlphaFold. Finally, we show that both TERMinator and COORDinator learn notions of energetics, and these methods can be fine‐tuned on experimental data to improve predictions. Our results suggest that using TERM‐based and coordinate‐based features together may be beneficial for protein design and that structure‐based neural models that produce Potts energy tables have utility for flexible applications in protein science.     

Comparison of haplotype inference methods using genotypic data from unrelated individuals

OBJECTIVE: Haplotypes are gaining popularity in studies of human genetics because they contain more information than does a single gene locus. However, current high-throughput genotyping techniques cannot produce haplotype information. Several statistical methods have recently been proposed to infer haplotypes based on unphased genotypes at several loci. The accuracy, efficiency, and computational time of these methods have been under intense scrutiny. In this report, our aim was to evaluate haplotype inference methods for genotypic data from unrelated individuals. METHODS: We compared the performance of three haplotype inference methods that are currently in use--HAPLOTYPER, hap, and PHASE--by applying them to a large data set from unrelated individuals with known haplotypes. We also applied these methods to coalescent-based simulation studies using both constant size and exponential growth models. The performance of these methods, along with that of the expectation-maximization algorithm, was further compared in the context of an association study. RESULTS: While the algorithm implemented in the software PHASE was found to be the most accurate in both real and simulated data comparisons, all four methods produced good results in the association study.     

Topology models for 37 Saccharomyces cerevisiae membrane proteins based on C-terminal reporter fusions and predictions

We provide experimentally based topology models for 37 integral membrane proteins from Saccharomyces cerevisiae. A C-terminal fusion to a dual Suc2/His4C topology reporter has been used to determine the location of the C terminus of each protein relative to the endoplasmic reticulum membrane, and this information is used in conjunction with theoretical topology prediction methods to arrive at a final topology model. We propose that this approach may be used to produce reliable topology models on a proteome-wide scale.     

Uncertainty-aware and interpretable evaluation of Cas9–gRNA and Cas12a–gRNA specificity for fully matched and partially mismatched targets with Deep Kernel Learning

The choice of guide RNA (gRNA) for CRISPR-based gene targeting is an essential step in gene editing applications, but the prediction of gRNA specificity remains challenging. Lack of transparency and focus on point estimates of efficiency disregarding the information on possible error sources in the model limit the power of existing Deep Learning-based methods. To overcome these problems, we present a new approach, a hybrid of Capsule Networks and Gaussian Processes. Our method predicts the cleavage efficiency of a gRNA with a corresponding confidence interval, which allows the user to incorporate information regarding possible model errors into the experimental design. We provide the first utilization of uncertainty estimation in computational gRNA design, which is a critical step toward accurate decision-making for future CRISPR applications. The proposed solution demonstrates acceptable confidence intervals for most test sets and shows regression quality similar to existing models. We introduce a set of criteria for gRNA selection based on off-target cleavage efficiency and its variance and present a collection of pre-computed gRNAs for human chromosome 22. Using Neural Network Interpretation methods, we show that our model rediscovers an established biological factor underlying cleavage efficiency, the importance of the seed region in gRNA.     

Transfer of radionuclides to ants, mosses and lichens in semi-natural ecosystems

There is a scarcity of data on transfer of both natural and anthropogenic radionuclides to detritivorous invertebrates for use in the assessment of radiation exposure. Although mosses and lichens have been extensively used in biomonitoring programs, the data on transfer of radionuclides to these species are limited, particularly for natural radionuclides. To enhance the available data, activity concentrations of ¹³⁷Cs, ²²⁶Ra and ²²⁸Ra were measured in ants, mosses and lichens and corresponding undisturbed soil collected from semi-natural ecosystems in Serbia and Montenegro and biota/soil concentration ratios (CR) calculated. Since the majority of internal dose to biota is expected to come from ⁴⁰K, the activity concentrations of this radionuclide were also determined. The mean CR values for ¹³⁷Cs, ²²⁶Ra and ²²⁸Ra in ants analyzed in this study were found to be 0.02, 0.06 and 0.02, respectively. The mean CR values of radionuclides in mosses were found to be 2.84 for ¹³⁷Cs, 0.19 for ²²⁶Ra and 0.16 for ²²⁸Ra, while those in lichens were found to be 1.08 for ¹³⁷Cs, 0.15 for ²²⁶Ra and 0.13 for ²²⁸Ra. The CR values obtained in this study were compared with default CR values used in the ERICA Tool database and also with those reported in other studies.     

The necessity of connection structures in neural models of variable binding

In his review of neural binding problems, Feldman (Cogn Neurodyn 7:1–11, 2013) addressed two types of models as solutions of (novel) variable binding. The one type uses labels such as phase synchrony of activation. The other (‘connectivity based’) type uses dedicated connections structures to achieve novel variable binding. Feldman argued that label (synchrony) based models are the only possible candidates to handle novel variable binding, whereas connectivity based models lack the flexibility required for that. We argue and illustrate that Feldman’s analysis is incorrect. Contrary to his conclusion, connectivity based models are the only viable candidates for models of novel variable binding because they are the only type of models that can produce behavior. We will show that the label (synchrony) based models analyzed by Feldman are in fact examples of connectivity based models. Feldman’s analysis that novel variable binding can be achieved without existing connection structures seems to result from analyzing the binding problem in a wrong frame of reference, in particular in an outside instead of the required inside frame of reference. Connectivity based models can be models of novel variable binding when they possess a connection structure that resembles a small-world network, as found in the brain. We will illustrate binding with this type of model with episode binding and the binding of words, including novel words, in sentence structures.     

On the efficiency of score tests for homogeneity in two-component parametric models for discrete data

Summary In many applications of two-component mixture models for discrete data such as zero-inflated models, it is often of interest to conduct inferences for the mixing weights. Score tests derived from the marginal model that allows for negative mixing weights have been particularly useful for this purpose. But the existing testing procedures often rely on restrictive assumptions such as the constancy of the mixing weights and typically ignore the structural constraints of the marginal model. In this article, we develop a score test of homogeneity that overcomes the limitations of existing procedures. The technique is based on a decomposition of the mixing weights into terms that have an obvious statistical interpretation. We exploit this decomposition to lay the foundation of the test. Simulation results show that the proposed covariate-adjusted test statistic can greatly improve the efficiency over test statistics based on constant mixing weights. A real-life example in dental caries research is used to illustrate the methodology.     

Identification of large-scale genomic variation in cancer genomes using in silico reference models

Identifying large-scale structural variation in cancer genomes continues to be a challenge to researchers. Current methods rely on genome alignments based on a reference that can be a poor fit to highly variant and complex tumor genomes. To address this challenge we developed a method that uses available breakpoint information to generate models of structural variations. We use these models as references to align previously unmapped and discordant reads from a genome. By using these models to align unmapped reads, we show that our method can help to identify large-scale variations that have been previously missed.     

Microbial mats for multiple applications in aquaculture and bioremediation

Microbial mats occur in nature as stratified communities of cyanobacteria and bacteria, but they can be cultured on large-scale and manipulated for a variety of functions. They are complex systems, but require few external inputs. The functional uses of mats broadly cover the areas of aquaculture and bioremediation. Preliminary research also points to promising uses in agriculture and energy production. Regarding aquaculture, mats were shown to produce protein, via nitrogen fixation, and were capable of supplying nutrition to tilapia (Oreochromis niloticus). Current research is examining the role of mats in the nitrification of nutrient-enriched effluents from aquaculture. Most research has addressed bioremediation, within which two majors categories of contaminants were examined: metals and radionuclides, and organic contaminants. Mats sequester or precipitate metals/radionuclides by surface absorption or by conditioning the surrounding chemical environment, thus bioconcentrating the metal/radionuclide in a small volume. Organic contaminants are degraded and may be completely mineralized. For agriculture mats hold promise as a soil amendment and nitrogen fertilizer. The use of mats in biohydrogen production has been verified, but is in a preliminary phase of development. We propose a comprehensive closed system based on microbial mats for aquaculture and waste management.     

Identification of unstructured model for subtilin production through Bacillus subtilis using hybrid genetic algorithm

Subtilin production is favorable when Bacillus subtilis 168 is subjected to stress condition such as nutrient scarcity. A mathematical model underlying such growth process has immense applicability in determining the optimal operating conditions at industrial scale. We present this work with multiple objectives of a) selection of a substrate for creating the minimal nutrient media for B. subtilis thereby enhancing subtilin production, b) experimental study of the growth along with morphological characteristics of B. subtilis and product profile in nutrient scarcity condition and c) identification of an optimal unstructured model for subtilin production using a computational framework. First, we show that subtilin can be produced while B. subtilis is grown using galactose and B. subtilis undergoes morphological changes and takes filamentous shape. We then constructed a series of plausible models and used a hybrid method combining Genetic Algorithm and gradient based search methodologies, for model selection. The estimated kinetic parameters and the stoichiometric analysis indicate that the B. subtilis growth/death, product profile and respiratory mechanism undergo specific modifications in galactose as an adaptive response. Current study provides an inexpensive platform to produce subtilin and the predictive framework presented here has potential applications for large scale production of subtilin.     

Experimentally based topology models for E. coli inner membrane proteins

Membrane protein topology predictions can be markedly improved by the inclusion of even very limited experimental information. We have recently introduced an approach for the production of reliable topology models based on a combination of experimental determination of the location (cytoplasmic or periplasmic) of a protein's C terminus and topology prediction. Here, we show that determination of the location of a protein's C terminus, rather than some internal loop, is the best strategy for large-scale topology mapping studies. We further report experimentally based topology models for 31 Escherichia coli inner membrane proteins, using methodology suitable for genome-scale studies.