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Virtual Event May 10 - 13, Authors will pre-record their poster talk minutes and will it to the virtual conference platform site along with a PDF of their poster beginning July 19 and no later than July All registered conference participants will have access to the poster and presentation through the conference and content until June 30, Short Abstract: Languages can be classified as tonal and non-tonal. Tonal languages are those in which the intonation change the meaning of the words. Although there is no concrete answer to the development of tonal language, there is evidence of genetic influence.

However, which genes are implicated in this linguistic feature have not been fully elucidated. The aim of this work is to identify candidate genes of tonal language development. We retrieved a total of genes, which serve as the input for the network analysis. We used String v. We excluded the text mining and neighborhood interaction sources. The next steps were performed in the Cytoscape v. We used the Cytohubba v. The gene set analysis was performed with ClueGO v. DLBCL affects the B-lymphocytes, which are cells of the immune system that create antibodies to fight disease.

However, only 2 out of 3 people can be treated with chemotherapy, meaning finding more effective ways to limit the proliferation of the cancer is imperative to help patients. Because there are no existing graph theoretic models for lymphoma, this analysis provides a unique take on the protein interactions that drive DLBCL proliferation. We utilized Cytoscape v 3. Finally, we computed the Betweenness and Rank Centrality of each protein to pinpoint the most influential proteins in the system.

We thoroughly investigate these proteins and their direct links to confirm their ificance in the system, indicating that these proteins are likely the backbone of DLBCL proliferation. With this data, scientists will be better equipped to inhibit DLBCL by targeting the proteins we identified. In early stages, the of infected individuals grows exponentially if the basic reproduction which represents the of secondary infections caused by a single infected individual inserted in a fully susceptible population for the duration of the disease is bigger than one.

Instead, the pandemic is considered under control when the basic reproduction is below one. Both these regimes emerge by a simple first order approximation of the evolution curve of the pandemic around its initial time.

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For example, you can think about Taylor-approximating at first order the solution of the SIR Susceptible-Infected-Removed compartmental model, but a similar insight can be taken from many other models as well. If the basic reproduction is approximately one, the growth rate might not be exactly exponential anymore and the approximation just mentioned is inconclusive unless conducted to a higher order. In this latter scenario, the higher order terms become fundamental and will be the main contributors to the evolution of the disease.

The basic reproduction depends on the contact rate and so space-time interactions must be taken into to have a more precise description of the diffusion of the disease.

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Note that lockdowns, social distancing, and air travel restriction were the most common non-pharmaceutical interventions implemented by governments to try to counteract the coronavirus. Therefore, any accurate model describing the dynamics of COVID must take into a spatial component. Of course, how COVID spre is important for multiple public health reasons, such as avoiding strain on the healthcare system.

Having a clear picture of the spatial and temporal characteristics of the transmission of COVID during the first stages of the pandemic can also potentially help predict the dynamics of early stages of subsequent waves or novel mutations. In this work, we are interested in understanding if higher order terms play a role in the spatio-temporal diffusion of COVID We model the of cases and deaths on a logarithmic scale as a linear combination of functions, which are themselves products of two functions, one of time only and one of space only, that are allowed to be random, deterministic, and nonlinear, and capture increasing model complexity.

For the sake of this presentation, we will concentrate on the case-study of Ohio in its first three weeks following the first COVID case in each county. In our analysis, we scale the time variable of each county to the first case of COVID in Ohio in order to for the time-lag between different counties. Our analysis suggests that the linear model seems preferable in most of the regimes that we tested our models on.

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This fact has positive implications for decision makers as nonlinear terms are a source of irregularity with respect to initial data. Irregularity can cause uncertainty even in the short-term evolution of a pandemic, which in turn challenges public health policies. Although in the cases where the basic reproduction is around one, higher order terms still play an important role in understanding the dynamics of COVID, we did not find strong evidence that for our setting Ohio had basic reproduction over onenonlinearities played an important role in the initial phases of the pandemic.

Our study is ongoing and we are adding more complex models and more general situations eg. Short Abstract: Around the world, coral reefs serve as an invaluable resource for more than million people, acting as sources of food, income, and coastal protection. However, despite their overwhelming necessity, the overharvesting of corals through coral mining has contributed greatly to their depletion—with overpieces of live coral mined and exported each year from the U.

The purpose of this research was to train image classification machine learning models specifically convolutional neural networks or CNNs to identify threatened coral species. A total of 4 CNNs were constructed using the transfer learning process a process in which existing complex machine learning algorithms are repurposed to conduct a different taskwith each one trained on a different dataset consisting of either curated or crowdsourced images.

This variety enabled their corresponding CNNs to have unique coral classification abilities. Ultimately, the 4 models trained in this research project present the capabilities of machine learning to identify corals based on structure, texture, and colony features. Future work includes validating the models against collections of new images towards the ultimate goal of facilitating reductions in coral mining and increasing coral preservation awareness by giving a larger audience the means to identify threatened and endangered corals species.

Short Abstract: Candida albicans is an opportunistic fungal pathogen that can lead to deadly infections in humans, especially in immunocompromised individuals. Understanding which genes are essential for growth of this organism would provide opportunities for developing more effective therapeutics. We identified several relevant features including average gene expression level and variance across a large compendium of conditions, degree of co-expression, codon adaptation index CAIthe of SNPs per nucleotide for each gene across a set of sequenced C.

We additionally incorporated six features from a recent transposon mutagenesis TnSeq study in a stable haploid genetic background.

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Through cross-validation analysis on our random forest model, we estimated an AUC of 0. Our machine learning approach is an effective strategy for efficient discovery of essential genes, and a similar approach may also be useful in other species. However, simultaneous quantification of these stereoisomeric lipids is difficult due to their virtually identical structures. Where synthetic standards do not exist, method development is not possible. We describe here a supervised learning approach that predicts instrument responses to different lipid stereoisomers at various machine parameter settings.

This in silico optimization method replaces manual, empirical parameter optimization and eliminates the dependency on available synthetic lipid standards. Our approach promises to greatly accelerate the development of assays for the detection of lipid stereoisomers in biological samples. Short Abstract: In the past few decades, many statistical methods have been developed to identify rare variants associated with a complex trait or a disease.

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Recently, rare variant association studies with multiple phenotypes have drawn a lot of attentions because association als can be boosted when rare variants are related with more than one phenotype. Most of existing statistical methods to identify rare variants associated with multiple phenotypes are based on a group test, where a gene or a genetic region is tested one at a time. However, these methods are not deed to locate individual rare variants within a gene or a genetic region.

In this article, we propose a unified standardized selection probability to locate rare variants associated with highly correlated multiple phenotypes. The proposed method basically combined weighted selection probability of elastic-net and selection probability of mgaussian according to z-scores.

We then select top ranked rare variants that have relatively large z-scores. In our simulation study, we demonstrated that the propose method outperforms the existing selection methods in terms of true positive rate, when phenotype outcomes are highly correlated with each other. We also applied the proposed method to our wild bean data set that consists of 10, rare variants and 13 correlated amino acids.

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Short Abstract: An essential survival skill of bacteria is quick adaptability to environmental shifts, through gene expression regulation [1]. Next, we performed flow-cytometry to characterize their single-cell statistics of protein s, prior and after a shift in growth phase. For control, RNA-seq was performed in the two growth phases.

From the data, we found ificant correlation between promoter sequences and fold-changes due to the growth phase shift. Finally, we propose a generalized analytical model of the dynamical changes of any gene, as cells shift from the exponential to the stationary phase. We expect our model to be useful in the future de of genetic circuits robust to shifts in cell growth phase.

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References: [1] R. Phillips, N. Belliveau, G. Chure, H. Garcia, M. Razo-Mejia, and C. Kandavalli, H. Tran, and A.

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