Identification of Alternative Transcription Start Sites that Generate Neuron-Specific nhsl1b Isoform that Regulates Neuron Migration

Identification of Alternative Transcription Start Sites that Generate Neuron-Specific nhsl1b Isoform that Regulates Neuron Migration Abanoub Bector, Depts. of Biology and Chemistry, with Dr. Sarah Golding, Dept. of Biology A recently identified novel gene, nhsl1b, has been shown to be necessary for the caudal tangential migration of facial branchiomotor neurons (FBMNs) as an effector of planar cell polarity (PCP) signaling. The role of nhsl1b in regulating neuron migration remains unknown. Nhsl1b has six variants, termed ex1nhsl1b, ex1anhsl1b, ex1bnhsl1b, ex1cnhsl1b, ex1dnhsl1b, and ex1enhsl1b in exon 1 that then splices into the common portion of the transcript (exon 2 to exon 8). Each isoform has its own transcriptional start site (TSS) and 5-prime UTR region. Here we examined the spatial expression patterns for the six nhsl1b variants and investigated their role in FBMN migration. In order to determine the spatial expression pattern for each isoform, we performed whole mount in-situ hybridization. We found that all nhsl1b variants were expressed generally throughout the developing nervous system, including neural progenitor cells. An exception was the ex1dnhsl1b that exhibited an enrichment in FBMNs, suggesting that ex1dnhsl1b is a neuron-specific isoform of nhsl1b. To determine whether ex1dnhsl1b was required for FBMN migration, we generated de novo mutations in ex1dnhsl1b using CRISPR/-Cas9 genome editing. We found that an ex1dnhsl1b mutation can lead to a severe migration defect of motor neurons consistent with the idea that ex1dNhsl1b is a neuron-specific isoform.https://scholarscompass.vcu.edu/uresposters/1297/thumbnail.jp

Adaptive Discounting of Training Time Attacks

Among the most insidious attacks on Reinforcement Learning (RL) solutions are training-time attacks (TTAs) that create loopholes and backdoors in the learned behaviour. Not limited to a simple disruption, constructive TTAs (C-TTAs) are now available, where the attacker forces a specific, target behaviour upon a training RL agent (victim). However, even state-of-the-art C-TTAs focus on target behaviours that could be naturally adopted by the victim if not for a particular feature of the environment dynamics, which C-TTAs exploit. In this work, we show that a C-TTA is possible even when the target behaviour is un-adoptable due to both environment dynamics as well as non-optimality with respect to the victim objective(s). To find efficient attacks in this context, we develop a specialised flavour of the DDPG algorithm, which we term gammaDDPG, that learns this stronger version of C-TTA. gammaDDPG dynamically alters the attack policy planning horizon based on the victim's current behaviour. This improves effort distribution throughout the attack timeline and reduces the effect of uncertainty the attacker has about the victim. To demonstrate the features of our method and better relate the results to prior research, we borrow a 3D grid domain from a state-of-the-art C-TTA for our experiments. Code is available at "bit.ly/github-rb-gDDPG".Comment: 19 pages, 7 figure

Yield prediction of maize crop (Zea Mays) by integrating NDVI with yield monitor data

Monitoring of crop growth and forecasting its yield well before harvest is very important for better crop and food management. Unmanned aerial vehicle (UAV) installed with near infrared camera (NIR camera) is a potentially important for acquisition of data to provide spatial and temporal data for site specific crop management. Hence, the study has been carried out to develop the empirical relationship for Infrared camera and N-Tester data at different crop growth stages with yield data for maize crop. Infrared camera and N-Tester were used to collect data at different growth stages of the crop to develop relationship with the yield monitor data. The near infrared (NIR) camera was mounted on parrot AR. Drone 2.0 frame for image acquisition. Based on aerial images of the plots the Normalized Difference Vegetation Index (NDVI) was calculated. Maize field was harvested by the combine harvester mounted with yield monitor to generate the yield map of the field. Yield is the measure for quantifying the agricultural input and crop management. Yield map is vital for site specific crop management. Statistical linear regression models were used to develop empirical relationship between the NDVI and N-Tester data and yield at different growth stages of maize crop. The yield prediction equations have maximum ) coefficient of determination (R20.84 for N-Tester and 0.86 for NDVI (NIR camera) at silking stage (R1).NDVI and N-tester values were positively correlated with yield data at all growth stages of maize

Recent developments in volatility modeling and applications

In financial modeling, it has been constantly pointed out that volatility clustering and conditional nonnormality induced leptokurtosis observed in high frequency data. Financial time series data are not adequately modeled by normal distribution, and empirical evidence on the non-normality assumption is well documented in the financial literature (details are illustrated by Engle (1982) and Bollerslev (1986)). An ARMA representation has been used by Thavaneswaran et al., in 2005, to derive the kurtosis of the various class of GARCH models such as power GARCH, non-Gaussian GARCH, nonstationary and random coefficient GARCH. Several empirical studies have shown that mixture distributions are more likely to capture heteroskedasticity observed in high frequency data than normal distribution. In this paper, some results on moment properties are generalized to stationary ARMA process with GARCH errors. Application to volatility forecasts and option pricing are also discussed in some detail.Peer Reviewe

A global optimization approach to fractional optimal control

In this paper, we consider a fractional optimal control problem governed by system of linear differential equations, where its cost function is expressed as the ratio of convex and concave functions. The problem is a hard nonconvex optimal control problem and application of Pontriyagin's principle does not always guarantee finding a global optimal control. Even this type of problems in a finite dimensional space is known as NP hard. This optimal control problem can, in principle, be solved by Dinkhelbach algorithm [10]. However, it leads to solving a sequence of hard D.C programming problems in its finite dimensional analogy. To overcome this difficulty, we introduce a reachable set for the linear system. In this way, the problem is reduced to a quasiconvex maximization problem in a finite dimensional space. Based on a global optimality condition, we propose an algorithm for solving this fractional optimal control problem and we show that the algorithm generates a sequence of local optimal controls with improved cost values. The proposed algorithm is then applied to several test problems, where the global optimal cost value is obtained for each case

UNBOUND

Featured here, are the extraordinary works of our graduating Fanshawe Design class. This accomplishment is truly a celebration of the three years of passion, hard work, and dedication put forth by our students. It is our greatest hope that family, friends and the fashion industry will enjoy the creative endeavors of these emerging designers from the Fashion Design program at Fanshawe College in London, Ontario.https://first.fanshawec.ca/famd_design_fashiondesign_unbound/1001/thumbnail.jp