Protein-protein interaction (PPI) data is an important type of data used in functional genomics. However, high-throughput experiments are often insufficient to complete the PPI interactome of ...different organisms. Computational techniques are thus used to infer missing data, with link prediction being one such approach that uses the structure of the network of PPIs known so far to identify non-edges whose addition to the network would make it more sound, according to some underlying assumptions. Recently, a new idea called the L3 principle introduced biological motivation into PPI link predictions, yielding predictors that are superior to general-purpose link predictors for complex networks. Interestingly, the L3 principle can be interpreted in another way, so that other signatures of PPI networks can also be characterized for PPI predictions. This alternative interpretation uncovers candidate PPIs that the current L3-based link predictors may not be able to fully capture, underutilizing the L3 principle.
In this article, we propose a formulation of link predictors that we call NormalizedL3 (L3N) which addresses certain missing elements within L3 predictors in the perspective of network modeling. Our computational validations show that the L3N predictors are able to find missing PPIs more accurately (in terms of true positives among the predicted PPIs) than the previously proposed methods on several datasets from the literature, including BioGRID, STRING, MINT, and HuRI, at the cost of using more computation time in some of the cases. In addition, we found that L3-based link predictors (including L3N) ranked a different pool of PPIs higher than the general-purpose link predictors did. This suggests that different types of PPIs can be predicted based on different topological assumptions, and that even better PPI link predictors may be obtained in the future by improved network modeling.
The concentric circles (CC) map design is an alternative approach for schematically representing metro systems. Compared with traditional octo-linear maps, CC maps can effectively simplify the ...perception of a network by visually accenting circular line patterns. This design offers new insights into the schematic drawing of metro systems that can improve map readability and engagement. Automated mapping studies in the literature have mostly applied the traditional octo-linear design using optimization methods, where design criteria are modeled as constraints and/or objective functions in a constrained mixed-integer optimization program, whereas the automated CC map drawing approach has received less attention. In this article, we develop an automatic CC map drawing method by adopting map design criteria as a mixed-integer programming problem. Numerical experiments are conducted using (a) a simple network to illustrate the model procedure in detail, (b) two real-world metro networks in Vienna and Montréal to analyze the effects of the selected map center and parameter settings and (c) the Beijing subway to analyze the applicability of the proposed approach to large-scale metro networks.
Solutions of proteins and other molecules exhibit puzzling, mesoscopically sized inclusions of a solute-rich liquid, well outside the region of stability of the solute-rich phase. This mesoscopic ...size is in conflict with existing views on heterophase fluctuations. Here we systematically work out a microscopic mechanism by which a metastable solute-rich phase can readily nucleate in a liquid solution. A requisite component of the mechanism is that the solute form long-lived complexes with itself or other molecules. After nucleated in this non-classical fashion, individual droplets grow until becoming mechanically unstable because of a concomitant drop in the internal pressure, the drop caused by the metastability of the solute-rich phase. The ensemble of the droplets is steady-state. In a freshly prepared solution, the ensemble is predicted to evolve in a way similar to the conventional Ostwald ripening, during which larger droplets grow at the expense of smaller droplets.
Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, ...car-sharing models are being adopted on a mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond the significant potential to reduce car ownership, car sharing shows promise in supporting the adoption of fuel-efficient vehicles, such as electric vehicles (EVs), because of these vehicles’ special cost structure with high purchase but low operating costs. Recently, key players in the car-sharing business, such as Autolib’, car2go, and DriveNow, have begun to employ EVs in an operations model that accommodates one-way trips. On the one hand (and particularly in free-floating car sharing), the one-way model results in significant improvements in coverage of travel needs and therefore in adoption potential compared with the conventional round-trip-only model (advocated by Zipcar, for example). On the other hand, this model poses tremendous planning and operational challenges. In this work, we study the planning problem faced by service providers in designing a geographical service region in which to operate the service. This decision entails trade-offs between maximizing customer catchment by covering travel needs and controlling fleet operation costs. We develop a mathematical programming model that incorporates details of both customer adoption behavior and fleet management (including EV repositioning and charging) under imbalanced travel patterns. To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally robust optimization framework that informs robust decisions to overcome possible ambiguity (or lacking) of data. Mathematically, the problem can be approximated by a mixed integer second-order cone program, which is computationally tractable with practical scale data. Applying this approach to the case of car2go’s service with real operations data, we address a number of planning questions and suggest that there is potential for the future development of this service.
The online appendix is available at
https://doi.org/10.1287/msom.2016.0611
.
Novel reassortant avian influenza H7N9 virus and pandemic 2009 H1N1 (H1N1pdm) virus cause human infections, while avian H7N2 and swine H1N1 virus mainly infect birds and pigs, respectively. There is ...no robust in vitro model for assessing the infectivity of emerging viruses in humans. Based on a recently established method, we generated long-term expanding 3D human airway organoids which accommodate four types of airway epithelial cells: ciliated, goblet, club, and basal cells. We report differentiation conditions which increase ciliated cell numbers to a nearly physiological level with synchronously beating cilia readily discernible in every organoid. In addition, the differentiation conditions induce elevated levels of serine proteases, which are essential for productive infection of human influenza viruses and low-pathogenic avian influenza viruses. We also established improved 2D monolayer culture conditions for the differentiated airway organoids. To demonstrate the ability of differentiated airway organoids to identify human-infective virus, 3D and 2D differentiated airway organoids are applied to evaluate two pairs of viruses with known distinct infectivity in humans, H7N9/Ah versus H7N2 and H1N1pdm versus an H1N1 strain isolated from swine (H1N1sw). The human-infective H7N9/Ah virus replicated more robustly than the poorly human-infective H7N2 virus; the highly human-infective H1N1pdm virus replicated to a higher titer than the counterpart H1N1sw. Collectively, we developed differentiated human airway organoids which can morphologically and functionally simulate human airway epithelium. These differentiated airway organoids can be applied for rapid assessment of the infectivity of emerging respiratory viruses to human.
CTNNB1-related disorder is an autosomal dominant neurodevelopmental disorder characterized by a variable degree of cognitive impairment, microcephaly, truncal hypotonia, peripheral spasticity, visual ...defects, and dysmorphic features. In this case series, we report the clinical and molecular findings of nine Chinese patients affected by CTNNB1-related disorders. The facial features of these affected individuals appear to resemble what had been previously described, with thin upper lip (77.8%) and hypoplastic alae nasi (77.8%) being the most common. Frequently reported clinical characteristics in our cohort include developmental delay (100%), peripheral spasticity (88.9%), truncal hypotonia (66.7%), microcephaly (66.7%), and dystonia (44.4%). While various eye manifestations were reported, two affected individuals (22.2%) in our cohort had familial exudative vitreoretinopathy. One of the affected individuals had craniosynostosis, a feature not reported in the literature before. To our knowledge, this is the first reported Chinese case series of CTNNB1-related neurodevelopmental disorders. Further studies are required to look into whether ethnic differences play a role in phenotypic variations.
Excessive accumulation of sodium in plants causes toxicity. No mutation that greatly diminishes sodium (Na+) influx into plant roots has been isolated. The OsHKT2;1 (previously named OsHKT1) ...transporter from rice functions as a relatively Na+‐selective transporter in heterologous expression systems, but the in vivo function of OsHKT2;1 remains unknown. Here, we analyzed transposon‐insertion rice lines disrupted in OsHKT2;1. Interestingly, three independent oshkt2;1‐null alleles exhibited significantly reduced growth compared with wild‐type plants under low Na+ and K+ starvation conditions. The mutant alleles accumulated less Na+, but not less K+, in roots and shoots. OsHKT2;1 was mainly expressed in the cortex and endodermis of roots. 22Na+ tracer influx experiments revealed that Na+ influx into oshkt2;1‐null roots was dramatically reduced compared with wild‐type plants. A rapid repression of OsHKT2;1‐mediated Na+ influx and mRNA reduction were found when wild‐type plants were exposed to 30 mM NaCl. These analyses demonstrate that Na+ can enhance growth of rice under K+ starvation conditions, and that OsHKT2;1 is the central transporter for nutritional Na+ uptake into K+‐starved rice roots.
The role of programming in computing education for children has grown rapidly in recent years with the proliferation of specially designed programming tools, which is grounded on Seymour Papert's ...theoretical work in Constructionism. Studies show that some children can develop computational thinking skills and practices with programming activities when learning with the tools through a well-design curriculum in elementary education (or primary education). However, existing studies may not completely address whether programming skills and computational thinking can be connected to the development of other generic skills, which are considered important to the learning and cognitive development of children. This study investigates the impact of programming on three learning competencies (creative thinking, critical thinking and problem solving) known as twenty-first century skills. The conceptual mapping between programming, computational thinking and the three learning competencies is presented. In a one-year intervention in a primary school, students were taught how to build interactive games through programming, and thus explored some basic computational thinking concepts in class. Our results show that children perceived a significant impact of programming on their learning competencies. Yet, the transferability of twenty-first century skills developed through computational thinking and programming may require a further study. Our study provides insights from children as primary respondents to help direct future research in the field of programming and computational thinking education and its potential impact on twenty-first century skills.
Lignin-first fractionation has become a new biorefinery target to obtain valuable lignin monomers toward the complete utilization of lignocellulosic biomass, but increasing delignification through ...conventional pretreatment approaches often affects the structural integrity of the dissociated lignin. We discovered a new reactive lignin with a great solvent solubility and preserved β-O-4 linkages from eucalyptus after a modified organosolv pretreatment using 1,4-butanediol (1,4-BDO). Unlike conventional organosolv pretreatment using ethanol, lignin deposition was not observed in 1,4-BDO pretreatment. Meanwhile, 2D HSQC NMR analysis revealed that the residual lignin obtained from 1,4-BDO pretreated eucalypts contained a relatively higher amount of β-O-4 interunit linkages, indicating its higher integrity than ethanol pretreated lignin. This result agreed with the lower content of phenolic hydroxyl groups in dissolved lignin
via
31
P NMR analysis. With increasing pretreatment severity, the amount of aliphatic hydroxyl groups decreased in ethanol pretreated lignin while it remained at a higher level in 1,4-BDO pretreated lignin. These results suggested that 1,4-BDO quenched the benzyl carbocation intermediate and formed ether linkages with a hydroxyl tail at the α position of the lignin. Solubility tests revealed that grafting aliphatic hydroxyl groups on 1,4-BDO lignin increased its dissolution. This phenomenon was further demonstrated for four different diols with similar structures. In addition, more than 90% cellulose conversion was obtained for all the diol pretreated eucalyptus after 48 h of enzymatic hydrolysis with cellulase at a loading of 7.5 FPU per gram of glucan. Diol pretreatment thus offers a promising reaction pathway to coincide with three pillars of biorefinery for lignin fractionation, lignin structural integrity, and cellulose hydrolysis.
α-Etherification in diol pretreatment quenched lignin intermediate and produced a reactive lignin with hydroxyl tails. Hydroxyl tails increased lignin solubilization without cleavage of β-O-4 ether linkages.
Objective
To obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network.
Methods
Brain PET images from 129 patients were ...evaluated. The network was designed to map non-attenuation-corrected (NAC) images to pixel-wise continuously valued measured attenuation-corrected (MAC) PET images via an encoder-decoder architecture. Image quality was evaluated using various evaluation metrics. Image quantification was assessed for 19 radiomic features in 83 brain regions as delineated using the Hammersmith atlas (n30r83). Reliability of measurements was determined using pixel-wise relative errors (RE; %) for radiomic feature values in reference MAC PET images.
Results
Peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) values were 39.2 ± 3.65 and 0.989 ± 0.006 for the external validation set, respectively. RE (%) of SUV
mean
was − 0.10 ± 2.14 for all regions, and only 3 of 83 regions depicted significant differences. However, the mean RE (%) of this region was 0.02 (range, − 0.83 to 1.18). SUV
max
had mean RE (%) of − 3.87 ± 2.84 for all brain regions, and 17 regions in the brain depicted significant differences with respect to MAC images with a mean RE of − 3.99 ± 2.11 (range, − 8.46 to 0.76). Homogeneity amongst Haralick-based radiomic features had the highest number (20) of regions with significant differences with a mean RE (%) of 7.22 ± 2.99.
Conclusions
Direct AC of PET images using deep convolutional encoder-decoder networks is a promising technique for brain PET images. The proposed deep learning method shows significant potential for emission-based AC in PET images with applications in PET/MRI and dedicated brain PET scanners.
Key Points
• We demonstrate direct emission-based attenuation correction of PET images without using anatomical information.
• We performed radiomics analysis of 83 brain regions to show robustness of direct attenuation correction of PET images.
• Deep learning methods have significant promise for emission-based attenuation correction in PET images with potential applications in PET/MRI and dedicated brain PET scanners.
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