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Wide field measurements of nitric oxide (NO) signaling could help understand and diagnose the many physiological processes in which NO plays a key role. Magnetic resonance imaging (MRI) can support particularly powerful approaches for this purpose if equipped with molecular probes sensitized to NO and NO-associated targets. In this review, we discuss the development of MRI-detectable probes that could enable studies of nitrergic signaling in animals and potentially human subjects. Major families of probes include contrast agents designed to capture and report integrated NO levels directly, as well as molecules that respond to or emulate the activity of nitric oxide synthase enzymes. For each group, we outline the relevant molecular mechanisms and discuss results that have been obtained in vitro and in animals. The most promising in vivo data described to date have been acquired using NO capture-based relaxation agents and using engineered nitric oxide synthases that provide hemodynamic readouts of NO signaling pathway activation. These advances establish a beachhead for ongoing efforts to improve the sensitivity, specificity, and clinical applicability of NO-related molecular MRI technology.

Summary Predictive learning from medical data incurs additional challenge due to concerns over privacy and security of personal data. Federated learning, intentionally structured to preserve high level of privacy, is emerging to be an attractive way to generate cross-silo predictions in medical scenarios. However, the impact of severe population-level heterogeneity on federated learners is not well explored. In this article, we propose a methodology to detect presence of population heterogeneity in federated settings and propose a solution to handle such heterogeneity by developing a federated version of Deep Regression Forests. Additionally, we demonstrate that the recently conceptualized REpresentation of Features as Images with NEighborhood Dependencies CNN framework can be combined with the proposed Federated Deep Regression Forests to provide improved performance as compared to existing approaches. Availability and implementation The Python source code for reproducing the main results are available on GitHub: https://github.com/DanielNolte/FederatedDeepRegressionForests. Contact ranadip.pal@ttu.edu Supplementary information Supplementary data are available at Bioinformatics Advances online.

PurposeBuilding information modeling (BIM) is a process of creating an intelligent virtual model integrating project data from design to construction and operation. BIM models enhance the process of communicating the progress of construction to stakeholders and facilitate integrated project delivery, coordination and clash detection. However, barriers within the construction industry in Ethiopia has led to slow BIM adoption in the country. The aim of this paper is to identify perceived BIM barriers, provide a platform to quantify their importance and develop a regression model to link individual's personal/professional attributes to their perception of BIM barrier.Design/methodology/approachTo address the objectives of this research, an online survey was developed to collect feedback from construction professionals in Ethiopia on 20 major adoption barriers extracted from a thorough review of literature. Relative importance index and strength of consensus metric were employed to identify the significance of barriers. This was then succeeded by performing exploratory factor analysis to determine the major constructs of BIM barriers which was then used to develop a multivariate regression model linking respondents' personal attributes to their perception of BIM barrier.FindingsResults revealed the importance of project complexity and BIM maturity level in prioritizing barriers that are more relevant under various contexts. More specifically, results indicated the following study highlights: Project complexity led to higher perceived weights for lack of appropriate physical/cloud infrastructures, and a BIM standard. Higher levels of BIM maturity signified the importance of BIM internal issues such as liability, licensing and maintenance issues among other adoption barriers. Female participants tended not to consider intangibility of BIM benefits as a major barrier towards BIM adoption compared to male participants. Age of the participants turned out to be the least important factor in their prioritization of BIM perceived adoption barriers.Originality/valueWhile many research studies have explored BIM adoption barriers in various countries around the world, none to the best of the authors' knowledge have attempted to develop a model to highlight the impact of individuals' personal/professional attributes on their perception of adoption barriers within their community which can help with prioritizing the barriers that are deemed to be more important given the characteristics of the community under study. Our result indicated the importance of BIM maturity level and project complexity in prioritizing barriers associated with BIM adoption within Ethiopia's construction industry.

Emerging applications of radio frequency (RF) vision sensors for security and gesture recognition primarily target single individual scenarios which restricts potential applications. In this article, we present the design of a cyber-physical framework that analyzes RF micro-Doppler signatures for individual anomaly detection, such as a hidden rifle among multiple individuals. RF avoids certain limitations of video surveillance, such as recognizing concealed objects and privacy concerns. Current RF-based approaches for human activity detection or gesture recognition usually consider single individual scenarios, and the features extracted for such scenarios are not applicable for multi-person cases. From a machine learning perspective, the RF sensor spectrogram images are conducible for training using deep convolutional neural networks. However, generating a large labeled training dataset with an exhaustive variety of multi-person scenarios is extremely time consuming and nearly impossible due to the wide range of combinations possible. We present approaches for multi-person spectrogram generation based on individual person spectrograms that can augment the training dataset and increase the accuracy of prediction. Our results show that the spectrogram generated by RF sensors can be harnessed by artificial intelligence algorithms to detect anomalies such as a concealed weapon for single and multiple people scenarios. The proposed system can aid as a standalone tool, or be complemented by video surveillance for anomaly detection, in scenarios involving single or multiple individuals.

Predicting protein properties from amino acid sequences is an important problem in biology and pharmacology. Protein–protein interactions among SARS-CoV-2 spike protein, human receptors and antibodies are key determinants of the potency of this virus and its ability to evade the human immune response. As a rapidly evolving virus, SARS-CoV-2 has already developed into many variants with considerable variation in virulence among these variants. Utilizing the proteomic data of SARS-CoV-2 to predict its viral characteristics will, therefore, greatly aid in disease control and prevention. In this paper, we review and compare recent successful prediction methods based on long short-term memory (LSTM), transformer, convolutional neural network (CNN) and a similarity-based topological regression (TR) model and offer recommendations about appropriate predictive methodology depending on the similarity between training and test datasets. We compare the effectiveness of these models in predicting the binding affinity and expression of SARS-CoV-2 spike protein sequences. We also explore how effective these predictive methods are when trained on laboratory-created data and are tasked with predicting the binding affinity of the in-the-wild SARS-CoV-2 spike protein sequences obtained from the GISAID datasets. We observe that TR is a better method when the sample size is small and test protein sequences are sufficiently similar to the training sequence. However, when the training sample size is sufficiently large and prediction requires extrapolation, LSTM embedding and CNN-based predictive model show superior performance.

The complex connectivity of the mammalian brain underlies its function, but understanding how interconnected brain regions interact in neural processing remains a formidable challenge. Here we address this problem by introducing a genetic probe that permits selective functional imaging of distributed neural populations defined by viral labeling techniques. The probe is an engineered enzyme that transduces cytosolic calcium dynamics of probe-expressing cells into localized hemodynamic responses that can be specifically visualized by functional magnetic resonance imaging. Using a viral vector that undergoes retrograde transport, we apply the probe to characterize a brain-wide network of presynaptic inputs to the striatum activated in a deep brain stimulation paradigm in rats. The results reveal engagement of surprisingly diverse projection sources and inform an integrated model of striatal function relevant to reward behavior and therapeutic neurostimulation approaches. Our work thus establishes a strategy for mechanistic analysis of multiregional neural systems in the mammalian brain.

Signaling circuits crucial to systemic physiology are widespread, yet uncovering their molecular underpinnings remains a barrier to understanding the etiology of many metabolic disorders. Here, we identify a copper-linked signaling circuit activated by disruption of mitochondrial function in the murine liver or heart that results in atrophy of the spleen and thymus and causes a peripheral white blood cell deficiency. We demonstrate that the leukopenia is caused by α-fetoprotein, which requires copper and the cell surface receptor CCR5 to promote white blood cell death. We further show that α-fetoprotein expression is upregulated in several cell types upon inhibition of oxidative phosphorylation, including a muscle cell model of Barth syndrome. Collectively, our data argue that α-fetoprotein secreted by bioenergetically stressed tissue suppresses the immune system, an effect which may explain the recurrent infections that are observed in a subset of mitochondrial diseases or in other disorders with mitochondrial involvement.

This study introduces a multi-step methodology for analyzing social media data during the post-disaster recovery phase of Hurricane Sandy. Its outputs include identification of the people who experienced the disaster, estimates of their physical location, assessments of the topics they discussed post-disaster, analysis of the tract-level relationships between the topics people discussed and tract-level internal attributes, and a comparison of these outputs to those of people who did not experience the disaster. Faith-based, community, assets, and financial topics emerged as major topics of discussion within the context of the disaster experience. The differences between predictors of these topics compared to those of people who did not experience the disaster were investigated in depth, revealing considerable differences among vulnerable populations. The use of this methodology as a new Machine Learning Algorithm to analyze large volumes of social media data is advocated in the conclusion.

The complex connectivity of the mammalian brain underlies its function, but understanding how interconnected brain regions interact in neural processing remains a formidable challenge. Here we address this problem by introducing a genetic probe that permits selective functional imaging of neural circuit elements defined by their synaptic interrelationships throughout the brain. The probe is an engineered enzyme that transduces cytosolic calcium dynamics of probe-expressing cells into localized hemodynamic responses that can be selectively visualized by functional magnetic resonance imaging. Using a viral vector that undergoes retrograde transport, we apply the probe to characterize a brain-wide network of monosynaptic inputs to the striatum activated in a deep brain stimulation paradigm in rats. The results reveal engagement of surprisingly diverse projection sources and inform an integrated model of striatal function relevant to reward behavior and therapeutic neurostimulation approaches. Our work thus establishes a potent strategy for mechanistic analysis of distributed neural systems.

Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) to arrange high-dimensional vectors in a compact image form conducible for CNN-based deep learning. We consider the similarities between features to generate a concise feature map in the form of a two-dimensional image by minimizing the pairwise distance values following a Bayesian Metric Multidimensional Scaling Approach. We hypothesize that this approach enables embedded feature extraction and, integrated with CNN-based deep learning, can boost the predictive accuracy. We illustrate the superior predictive capabilities of the proposed framework as compared to state-of-the-art methodologies in drug sensitivity prediction scenarios using synthetic datasets, drug chemical descriptors as predictors from NCI60, and both transcriptomic information and drug descriptors as predictors from GDSC., Convolutional Neural Networks (CNN) are often unsuitable for predictive modeling involving nonimage based biological features. Here, the authors present a mapping termed REFINED to represent high dimensional vectors as compact images with spatial correlation that makes it compatible with CNN based learning.

In recent decades computer-aided technologies have become prevalent in medicine, however, cancer drugs are often only tested on in vitro cell lines from biopsies. We derive a full three-dimensional model of inhomogeneous -anisotropic diffusion in a tumor region coupled to a binary population model, which simulates in vivo scenarios faster than traditional cell-line tests. The diffusion tensors are acquired using diffusion tensor magnetic resonance imaging from a patient diagnosed with glioblastoma multiform. Then we numerically simulate the full model with finite element methods and produce drug concentration heat maps, apoptosis hotspots, and dose-response curves. Finally, predictions are made about optimal injection locations and volumes, which are presented in a form that can be employed by doctors and oncologists.

Dark matter search experiments demand a low to ultralow radiation background to operate. It is very important to understand the nature of the radiation background including knowledge about the sources contributing to it. Sometimes, evaluation of the background becomes very specific to the site chosen for the experiment, and also to the experimental configuration. A dark matter search experiment is proposed to be set up at the Jaduguda Underground Science Laboratory (JUSL) in India. The laboratory will be located inside an existing mine with 555 m of vertical rock overburden. Neutrons produced from (α,n) reactions, spontaneous fission of natural radioactive impurities in the rocks, and also from cosmic muon-induced reactions are considered as the main background which can affect the sensitivity and outcome of the experiment. In this work, simulations based on GEANT4 are done to understand both the radiogenic neutron background caused by natural radioactivity of the surrounding rock and the cosmogenic neutron background due to interactions of the deeply penetrating cosmic muons with the rock material. The muon flux in the cavern is obtained to be 4.49(±0.25)×10-7 cm-2s-1 and the fluxes of radiogenic and cosmogenic neutrons above an energy threshold of 1 MeV in the cavern are obtained to be 5.75(±0.69)×10-6cm-2s-1 and 7.25(±0.40)× 10-9cm-2 s-1 respectively. The values obtained are comparable with estimates and measurements from DAMA, WIPP, and dark matter experiments at Boulby mine. The effectiveness of different shielding materials is also investigated to obtain the best possible neutron background reduction for a dark matter search experiment at JUSL. We also estimate the sensitivity of a CsI based detector for Weakly Interacting Massive Particle (WIMP) dark matter search at JUSL considering the estimated neutron background.

Copyright © 2020 American Chemical Society. Detection of nitric oxide (NO) in biological systems is challenging due to both physicochemical properties of NO and limitations of current imaging modalities and probes. Magnetic resonance imaging (MRI) could be applied for studying NO in living tissue with high spatiotemporal resolution, but there is still a need for chemical agents that effectively sensitize MRI to biological NO production. To develop a suitable probe, we studied the interactions between NO and a library of manganese complexes with various oxidation states and molecular structures. Among this set, the manganese(III) complex with N,N′-(1,2-phenylene)bis(5-fluoro-2-hydroxybenzamide) showed favorable changes in longitudinal relaxivity upon addition of NO-releasing chemicals in vitro while also maintaining selectivity against other biologically relevant reactive nitrogen and oxygen species, making it a suitable NO-responsive contrast agent for T1-weighted MRI. When loaded with this compound, cells ectopically expressing nitric oxide synthase (NOS) isoforms showed MRI signal decreases of over 20% compared to control cells and were also responsive to NOS inhibition or calcium-dependent activation. The sensor could also detect endogenous NOS activity in antigen-stimulated macrophages and in a rat model of neuroinflammation in vivo. Given the key role of NO and associated reactive nitrogen species in numerous physiological and pathological processes, MRI approaches based on the new probe could be broadly beneficial for studies of NO-related signaling in living subjects.

Reestablishment of housing is a crucial component of the recovery process and has a domino effect on the overall timing of recovery. Anchors of social networks, such as schools and churches...

Background Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model the temporal behaviors at each dose without capturing the evolution of time curves across dosage. Results In this article, we propose a parametric model for dose-time responses that follows Gompertz law in time and Hill equation across dose approximately. We derive a recursion relation for dose-response curves over time capturing the temporal evolution and then specify a regression model connecting the parameters controlling the dose-time responses with individual level proteomic data. The resultant joint model allows us to predict the dose-response curves over time for new individuals. Conclusion We have compared the efficacy of our proposed Recursive Hybrid model with individual dose-response predictive models at desired time points. We note that our proposed model exhibits a superior performance compared to the individual ones for both synthetic data and actual pharmacological data. For the desired dose-time varying genetic characterization and drug response values, we have used the HMS-LINCS database and demonstrated the effectiveness of our model for all available anticancer compounds. Electronic supplementary material The online version of this article (10.1186/s12859-019-2831-4) contains supplementary material, which is available to authorized users.

This article presents a robust predictive model using parametric copula-based regression. We show that copula selection test procedures and predictive conditional distributions can be used to assess model adequacy and predictive validity. We offer simulation experiments to demonstrate the ability of our diagnostic procedure to correctly identify the true data generating process. Finally, we apply our methodology on a well-known insurance claims dataset to produce the distribution profile of allocated loss adjustment expense for given pre-specified indemnity payments information. The availability of this entire expense distribution will provide greater insight to the decision-makers before allocating resources for a given insurance claim.

Drug sensitivity prediction for individual tumors is a significant challenge in personalized medicine. Current modeling approaches consider prediction of a single metric of the drug response curve such as AUC or IC50. However, the single summary metric of a dose-response curve fails to provide the entire drug sensitivity profile which can be used to design the optimal dose for a patient. In this article, we assess the problem of predicting the complete dose-response curve based on genetic characterizations. We propose an enhancement to the popular ensemble-based Random Forests approach that can directly predict the entire functional profile of a dose-response curve rather than a single summary metric. We design functional regression trees with node costs modified based on dose/response region dependence methodologies and response distribution based approaches. Our results relative to large pharmacological databases such as CCLE and GDSC show a higher accuracy in predicting dose-response curves of the proposed functional framework in contrast to univariate or multivariate Random Forest predicting sensitivities at different dose levels. Furthermore, we also considered the problem of predicting functional responses from functional predictors i.e., estimating the dose-response curves with a model built on dose-dependent expression data. The superior performance of Functional Random Forest using functional data as compared to existing approaches have been shown using the HMS-LINCS dataset. In summary, Functional Random Forest presents an enhanced predictive modeling framework to predict the entire functional response profile considering both static and functional predictors instead of predicting the summary metrics of the response curves.

It has been shown recently that changing the fluidic properties of a drug can improve its efficacy in ablating solid tumors. We develop a modeling framework for tumor ablation, and present the simplest possible model for drug diffusion in a spherical tumor with leaky boundaries and assuming cell death eventually leads to ablation of that cell effectively making the two quantities numerically equivalent. The death of a cell after a given exposure time depends on both the concentration of the drug and the amount of oxygen available to the cell. Higher oxygen availability leads to cell death at lower drug concentrations. It can be assumed that a minimum concentration is required for a cell to die, effectively connecting diffusion with efficacy. The concentration threshold decreases as exposure time increases, which allows us to compute dose-response curves. Furthermore, these curves can be plotted at much finer time intervals compared to that of experiments, which is used to produce a dose-threshold-response surface giving an observer a complete picture of the drug's efficacy for an individual. In addition, since the diffusion, leak coefficients, and the availability of oxygen is different for different individuals and tumors, we produce artificial replication data through bootstrapping to simulate error. While the usual data-driven model with Sigmoidal curves use 12 free parameters, our mechanistic model only has two free parameters, allowing it to be open to scrutiny rather than forcing agreement with data. Even so, the simplest model in our framework, derived here, shows close agreement with the bootstrapped curves, and reproduces well established relations, such as Haber's rule., Comment: 25 pages, 65 figures, 2 tables

Background In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the issue. However, one cannot directly employ data from multiple sources together due to the existing distribution shift in data. One way to solve this problem is to utilize the transfer learning methodologies tailored to fit in this specific context. Results In this paper, we present two novel approaches for incorporating information from a secondary database for improving the prediction in a target database. The first approach is based on latent variable cost optimization and the second approach considers polynomial mapping between the two databases. Utilizing CCLE and GDSC databases, we illustrate that the proposed approaches accomplish a better prediction of drug sensitivities for different scenarios as compared to the existing approaches. Conclusion We have compared the performance of the proposed predictive models with database-specific individual models as well as existing transfer learning approaches. We note that our proposed approaches exhibit superior performance compared to the abovementioned alternative techniques for predicting sensitivity for different anti-cancer compounds, particularly the nonlinear mapping model shows the best overall performance. Electronic supplementary material The online version of this article (10.1186/s12859-018-2465-y) contains supplementary material, which is available to authorized users.

Interaction of apolipoprotein E (apoE) isoforms with amyloid-β (Aβ) peptides is considered a critical determinant of the progression of Alzheimer’s disease. However, molecular mechanism of the apoE-Aβ interaction is poorly understood. Here we characterize the nature of the apoE-Aβ complexes and identify the region of apoE that interacts with Aβ. We have prepared three distinct fragments of apoE4,viz., the N-terminal fragment (NTF), hinge domain fragment (HDF) and C-terminal fragment (CTF) to compare its interactions with Aβ. Kinetics of aggregation of Aβ is delayed dramatically in presence of low, substoichiometric concentrations of both NTF and CTF in lipid-free, as well as, in lipidated forms. Effect of HDF is found to be small. Strong inhibition by NTF and CTF at substoichiometric concentrations indicate interactions with the ‘intermediates’ or the oligomers of Aβ. Kinetics of Forster Resonance Energy Transfer (FRET) between full-length apoE4 labeled with EDANS at positions 62, 139, 210, 247, and 276 and tetramethylrhodamine (TMR)-labeled Aβ further support involvement of multiple regions of apoE in the interactions. Since the interactions involve intermediates of Aβ quantitative evaluation of the binding affinities are not feasible. Hence we employed a competitive binding assay to examine whether the N- and C-terminal domains interact cooperatively. Addition of unlabeled full-length apoE eliminates the FRET between EDANS-NTF + EDANS-CTF and TMR-Aβ almost completely but not vice versa. Furthermore, full-length apoE but not the equimolar mixture of the fragments could displace the already bound EDANS-apoE molecules from the complexes. Therefore, binding affinity of the Aβ oligomers to the intact full-length apoE is much higher than the affinity to the domains when mixed together as fragments. Thus, our results indicate that apoE-Aβ complex formation is mediated by positively cooperative multivalent binding between the multiple sites on apoE and the oligomeric forms of Aβ.

Integrating multiple databases of similar tasks is a significant problem in biological data analysis. In this paper, we consider whether feature selection in a single database can benefit from incorporating similar databases. We report that by using adaptive multi-task elastic net for feature selection and Random Forest for prediction, the prediction performance can be improved for pharmacogenomics databases. We also present a simulation study to explain the robust feature selection benefit of adaptive multi task elastic net while dealing with noisy features.