Kaplan Leon (deercomb96)

We present PALLAS, a practical method for gene regulatory network (GRN) inference from time series data, which employs penalized maximum likelihood and particle swarms for optimization. PALLAS is based on the Partially-Observable Boolean Dynamical System (POBDS) model and thus does not require ad-hoc binarization of the data. The penalty in the likelihood is a LASSO regularization term, which encourages the resulting network to be sparse. Tigecycline nmr PALLAS is able to scale to networks of realistic size under no prior knowledge, by virtue of a novel continuous-discrete Fish School Search particle swarm algorithm for efficient simultaneous maximization of the penalized likelihood over the discrete space of networks and the continuous space of observational parameters. The performance of PALLAS is demonstrated by a comprehensive set of experiments using synthetic data generated from real and artificial networks, as well as real time series microarray and RNA-seq data, where it is compared to several other well-known methods for gene regulatory network inference. The results show that PALLAS can infer GRNs more accurately than other methods, while being capable of working directly on gene expression data, without need of ad-hoc binarization. PALLAS is a fully-fledged program, written in python, and available on GitHub (https//github.com/yukuntan92/PALLAS).Although it seems intuitive to address the issue of reduced plantar cutaneous feedback by augmenting it, many approaches have adopted compensatory sensory cues, such as tactile input from another part of the body, for multiple reasons including easiness and accessibility. The efficacy of the compensatory approaches might be limited due to the cognitive involvement to interpret such compensatory sensory cues. The objective of this study is to test the hypothesis that the plantar cutaneous augmentation is more effective than providing compensatory sensory cues on improving postural regulation, when plantar cutaneous feedback is reduced. In our experiments, six healthy human subjects were asked to maintain their balance on a lateral balance board for as long as possible, until the balance board contacted the ground, for 240 trials with five interventions. During these experiments, subjects were instructed to close their eyes to increase dependency on plantar cutaneous feedback for balancing. Foam pad was also added on the board to emulate the condition of reduced plantar cutaneous feedback. The effects of tactile augmentation from the foot sole or the palm on standing balance were tested by applying transcutaneous electrical stimulation on calcaneal or ulnar nerve during the balance board tests, with and without a cognitively-challenging counting task. Experimental results indicate that the plantar cutaneous augmentation was effective on improving balance only with cognitive load, while the palmar cutaneous augmentation was effective only without cognitive load. This result suggests that the location of sensory augmentation should be carefully determined according to the attentional demands.Head-mounted loupes can increase the user's visual acuity to observe the details of an object. On the other hand, optical see-through head-mounted displays (OST-HMD) are able to provide virtual augmentations registered with real objects. In this paper, we propose AR-Loupe, combining the advantages of loupes and OST-HMDs, to offer augmented reality in the user's magnified field-of-vision. Specifically, AR-Loupe integrates a commercial OST-HMD, Magic Leap One, and binocular Galilean magnifying loupes, with customized 3D-printed attachments. We model the combination of user's eye, screen of OST-HMD, and the optical loupe as a pinhole camera. The calibration of AR-Loupe involves interactive view segmentation and an adapted version of stereo single point active alignment method (Stereo-SPAAM). We conducted a two-phase multi-user study to evaluate AR-Loupe. The users were able to achieve sub-m