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2022 ◽  
Author(s):  
Meng Mao ◽  
Michael R. Strand ◽  
Gaelen R. Burke

Bracoviruses (BVs) are endogenized nudiviruses in parasitoid wasps of the microgastroid complex (family Braconidae). Microgastroid wasps have coopted nudivirus genes to produce replication-defective virions that females use to transfer virulence genes to parasitized hosts. The microgastroid complex further consists of six subfamilies and ∼50,000 species but current understanding of BV gene inventories and organization primarily derives from analysis of two wasp species in the subfamily Microgastrinae ( Microplitis demolitor and Cotesia congregata ) that produce M. demolitor BV (MdBV) and C. congregata BV (CcBV). Notably, several genomic features of MdBV and CcBV remain conserved since divergence of M. demolitor and C. congregata ∼53 million years ago (MYA). However, it is unknown whether these conserved traits more broadly reflect BV evolution, because no complete genomes exist for any microgastroid wasps outside of the Microgastrinae. In this regard, the subfamily Cheloninae is of greatest interest because it diverged earliest from the Microgastrinae (∼85 MYA) after endogenization of the nudivirus ancestor. Here, we present the complete genome of Chelonus insularis , which is an egg-larval parasitoid in the Cheloninae that produces C. insularis BV (CinsBV). We report that the inventory of nudivirus genes in C. insularis is conserved but are dissimilarly organized when compared to M. demolitor and C. congregata . Reciprocally, CinsBV proviral segments share organizational features with MdBV and CcBV but virulence gene inventories exhibit almost no overlap. Altogether, our results point to the functional importance of a conserved inventory of nudivirus genes and a dynamic set of virulence genes for the successful parasitism of hosts. Our results also suggest organizational features previously identified in MdBV and CcBV are likely not essential for BV virion formation. Significance Bracoviruses are a remarkable example of virus endogenization, because large sets of genes from a nudivirus ancestor continue to produce virions that thousands of wasp species rely upon to parasitize hosts. Understanding how these genes interact and have been coopted by wasps for novel functions is of broad interest in the study of virus evolution. This manuscript characterizes bracovirus genome components in the parasitoid wasp Chelonus insularis , which together with existing wasp genomes captures a large portion of the diversity among wasp species that produce bracoviruses. Results provide new information about how bracovirus genome components are organized in different wasps while also providing additional insights on key features required for function.


2022 ◽  
Author(s):  
Lauren Marazzi ◽  
Milan Shah ◽  
Shreedula Balakrishnan ◽  
Ananya Patil ◽  
Paola Vera-Licona

The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. NETISCE identifies reprogramming targets through the innovative use of control theory within a dynamical systems framework. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system that are relevant for the desired reprogramming task.


Author(s):  
Debani Prasad Mishra ◽  
Sanhita Mishra ◽  
Rakesh Kumar Yadav ◽  
Rishabh Vishnoi ◽  
Surender Reddy Salkuti

For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as a large amount of electrical energy cannot be stored. For the proper functioning of a power supply system, an adequate model for predicting load is a necessity. In the present world, in almost every industry, whether it be healthcare, agriculture, and consulting, growing digitization and automation is a prominent feature. As a result, large sets of data related to these industries are being generated, which when subjected to rigorous analysis, yield out-of-the-box methods to optimize the business and services offered. This paper aims to ascertain the viability of long short term memory (LSTM) neural networks, a recurrent neural network capable of handling both long-term and short-term dependencies of data sets, for predicting load that is to be met by a Dispatch Center located in a major city. The result shows appreciable accuracy in forecasting future demand.


2022 ◽  
Vol 12 (1) ◽  
pp. 409
Author(s):  
Tomasz Maria Boiński ◽  
Julian Szymański ◽  
Agata Krauzewicz

The paper proposes a crowdsourcing-based approach for annotated data acquisition and means to support Active Learning training approach. In the proposed solution, aimed at data engineers, the knowledge of the crowd serves as an oracle that is able to judge whether the given sample is informative or not. The proposed solution reduces the amount of work needed to annotate large sets of data. Furthermore, it allows a perpetual increase in the trained network quality by the inclusion of new samples, gathered after network deployment. The paper also discusses means of limiting network training times, especially in the post-deployment stage, where the size of the training set can increase dramatically. This is done by the introduction of the fourth set composed of samples gather during network actual usage.


2021 ◽  
Author(s):  
Li Ding ◽  
Tony Kang ◽  
Ajay E. Kuriyan ◽  
Rajeev S. Ramchandran ◽  
Charles C. Wykoff ◽  
...  

<div>We propose a novel hybrid framework for registering retinal images in the presence of extreme geometric distortions that are commonly encountered in ultra-widefield (UWF) fluorescein angiography. Our approach consists of two stages: a feature-based global registration and a vessel-based local refinement. For the global registration, we introduce a modified RANSAC algorithm that jointly identifies robust matches between feature keypoints in reference and target images and estimates a polynomial geometric transformation consistent with the identified correspondences. Our RANSAC modification particularly improves feature point matching and the registration in peripheral regions that are most severely impacted by the geometric distortions. The second local refinement stage is formulated in our framework as a parametric chamfer alignment for vessel maps obtained using a deep neural network. Because the complete vessel maps contribute to the chamfer alignment, this approach not only improves registration accuracy but also aligns with clinical practice, where vessels are typically a key focus of examinations. We validate the effectiveness of the proposed framework on a new UWF fluorescein angiography (FA) dataset and on the existing narrow-field FIRE (fundus image registration) dataset and demonstrate that it significantly outperforms prior retinal image registration methods. The proposed approach enhances the utility of large sets of longitudinal UWF images by enabling: (a) automatic computation of vessel change metrics and (b) standardized and co-registered examination that can better highlight changes of clinical interest to physicians.</div>


2021 ◽  
Author(s):  
Li Ding ◽  
Tony Kang ◽  
Ajay E. Kuriyan ◽  
Rajeev S. Ramchandran ◽  
Charles C. Wykoff ◽  
...  

<div>We propose a novel hybrid framework for registering retinal images in the presence of extreme geometric distortions that are commonly encountered in ultra-widefield (UWF) fluorescein angiography. Our approach consists of two stages: a feature-based global registration and a vessel-based local refinement. For the global registration, we introduce a modified RANSAC algorithm that jointly identifies robust matches between feature keypoints in reference and target images and estimates a polynomial geometric transformation consistent with the identified correspondences. Our RANSAC modification particularly improves feature point matching and the registration in peripheral regions that are most severely impacted by the geometric distortions. The second local refinement stage is formulated in our framework as a parametric chamfer alignment for vessel maps obtained using a deep neural network. Because the complete vessel maps contribute to the chamfer alignment, this approach not only improves registration accuracy but also aligns with clinical practice, where vessels are typically a key focus of examinations. We validate the effectiveness of the proposed framework on a new UWF fluorescein angiography (FA) dataset and on the existing narrow-field FIRE (fundus image registration) dataset and demonstrate that it significantly outperforms prior retinal image registration methods. The proposed approach enhances the utility of large sets of longitudinal UWF images by enabling: (a) automatic computation of vessel change metrics and (b) standardized and co-registered examination that can better highlight changes of clinical interest to physicians.</div>


2021 ◽  
Author(s):  
Pierina Cheung ◽  
Mary Toomey ◽  
Yahao Jiang ◽  
Tawni Stoop ◽  
Anna Shusterman

Studies on children’s understanding of counting examine when and how children acquire the cardinal principle: the idea that the last word in a counted set reflects the cardinal value of the set. Using Wynn’s (1990) Give-N Task, researchers classify children who can count to generate large sets as having acquired the cardinal principle (cardinal-principle-knowers) and those who cannot as lacking knowledge of it (subset-knowers). However, recent studies have provided a more nuanced view of number word acquisition. Here, we explore this view by examining the developmental progression of the counting principles with an aim to elucidate the gradual elements that lead to children successfully generating sets and being classified as CP-knowers on the Give-N Task. Specifically, we test the claim that subset-knowers lack cardinal principle knowledge by separating children’s understanding of the cardinal principle from their ability to apply and implement counting procedures. We also ask when knowledge of Gelman &amp; Gallistel’s (1978) other how-to-count principles emerge in development. We analyzed how often children violated the three how-to-count principles in a secondary analysis of Give-N data (N = 86). We found that children already have knowledge of the cardinal principle prior to becoming CP-knowers, and that understanding of the stable-order and word-object correspondence principles likely emerged earlier. These results suggest that gradual development may best characterize children’s acquisition of the counting principles, and that learning to coordinate all three principles represents an additional step beyond learning them individually.


2021 ◽  
Vol 1 ◽  
Author(s):  
Karl Gemayel ◽  
Alexandre Lomsadze ◽  
Mark Borodovsky

State-of-the-art algorithms of ab initio gene prediction for prokaryotic genomes were shown to be sufficiently accurate. A pair of algorithms would agree on predictions of gene 3′ends. Nonetheless, predictions of gene starts would not match for 15–25% of genes in a genome. This discrepancy is a serious issue that is difficult to be resolved due to the absence of sufficiently large sets of genes with experimentally verified starts. We have introduced StartLink that infers gene starts from conservation patterns revealed by multiple alignments of homologous nucleotide sequences. We also have introduced StartLink+ combining both ab initio and alignment-based methods. The ability of StartLink to predict the start of a given gene is restricted by the availability of homologs in a database. We observed that StartLink made predictions for 85% of genes per genome on average. The StartLink+ accuracy was shown to be 98–99% on the sets of genes with experimentally verified starts. In comparison with database annotations, we observed that the annotated gene starts deviated from the StartLink+ predictions for ∼5% of genes in AT-rich genomes and for 10–15% of genes in GC-rich genomes on average. The use of StartLink+ has a potential to significantly improve gene start annotation in genomic databases.


2021 ◽  
Vol 10 (1) ◽  
pp. 6-19
Author(s):  
Alireza Navid M G

This paper aimed to study the metacognitive awareness of reading strategies between field-dependent (FD)and field-independent (FI) Turkish EFL university students who are learning English as a foreign language. To this end, 270 students from Istanbul (Cerrahpasa) University were chosen.First, Group Embedded Figure Test was used to appoint the participants into either FD or FI groups.After this, participants’ metacognitive awareness of reading strategy was assessed by using MARSI-R (Metacognitive Awareness of reading Strategies Inventory-Revised). Recently revised by Mokhtari et al., the MARSI-R instrument contains 15 items and measures three large sets of strategies including: Global Reading Strategies (GRS), Problem-Solving Strategies (PSS) and Support Reading Strategies (SRS).The results showed that the students reported using the 3 categories of strategies almost at a high-frequency level and they were aware of their metacognitive strategies. And statistically significant difference was found between FI and FD students regarding their use of GRS and SRS, hence, the use of students’ metacognitive reading strategies was affected by their different FI/FD cognitive styles.


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