Therapeutic opportunities and challenges in targeting the orphan G protein-coupled receptor GPR35

GPR35 is a class A, rhodopsin-like G protein-coupled receptor (GPCR) first identified more than 20 years ago. In the intervening period identification of strong expression in the lower intestine and colon, as well as in a variety of immune cells including monocytes and a variety of dendritic cells, and in dorsal root ganglia, has suggested potential therapeutic opportunities in targeting this receptor in a range of conditions. GPR35 is, however, unusual in a variety of ways that challenge routes to translation. These include that although a substantial range and diversity of endogenous ligands have been suggested as agonist partners for this receptor it officially remains defined as an ‘orphan’ GPCR; that humans express two distinct protein isoform sequences whilst rodents express only a single form, and that the pharmacology of the human and rodent orthologues of GPR35 is very distinct, with variation between rat and mouse GPR35 as marked as between either of these species and the human forms. 2 Herein we provide perspectives on each of the topics above as well as suggesting ways to overcome the challenges currently hindering potential translation. These include better understanding of the extent and molecular basis for species selective GPR35 pharmacology and the production of novel mouse models in which both ‘on-target’ and ‘off-target’ effects of presumptive GPR35 ligands can be better defined as well as clear understanding in human of isoform expression profile and its significance at both tissue and individual cell level

Autonomous Vehicles and Machines Conference, at IS&T Electronic Imaging

The performance of autonomous agents in both commercial and consumer applications increases along with their situational awareness. Tasks such as obstacle avoidance, agent to agent interaction, and path planning are directly dependent upon their ability to convert sensor readings into scene understanding. Central to this is the ability to detect and recognize objects. Many object detection methodologies operate on a single modality such as vision or LiDAR. Camera-based object detection models benefit from an abundance of feature-rich information for classifying different types of objects. LiDAR-based object detection models use sparse point clouds, where each point contains accurate 3D position of object surfaces. Camera-based methods lack accurate object to lens distance measurements, while LiDAR-based methods lack dense feature-rich details. By utilizing information from both camera and LiDAR sensors, advanced object detection and identification is possible. In this work, we introduce a deep learning framework for fusing these modalities and produce a robust real-time 3D bounding box object detection network. We demonstrate qualitative and quantitative analysis of the proposed fusion model on the popular KITTI dataset

KF-Loc: A Kalman Filter and Machine Learning Integrated Localization System Using Consumer-Grade Millimeter-wave Hardware

With the ever-increasing demands of e-commerce, the need for smarter warehousing is increasing exponentially. Such warehouses requires industry automation beyond Industry 4.0. In this work, we use consumer-grade millimeter-wave (mmWave) equipment to enable fast, and low-cost implementation of our localization system. However, the consumer-grade mmWave routers suffer from coarse-grained channel state information due to cost-effective antenna array design limiting the accuracy of localization systems. To address these challenges, we present a Machine Learning (ML) and Kalman Filter (KF) integrated localization system (KF-Loc). The ML model learns the complex wireless features for predicting the static position of the robot. When in dynamic motion, the static ML estimates suffer from position mispredictions, resulting in loss of accuracy. To overcome the loss in accuracy, we design and integrate a KF that learns the dynamics of the robot motion to provide highly accurate tracking. Our system achieves centimeter-level accuracy for the two aisles with RMSE of 0.35m and 0.37m, respectively. Further, compared with ML only localization systems, we achieve a significant reduction in RMSE by 28.5% and 54.3% within the two aisles

Interconnects for DNA, quantum, in-memory and optical computing: insights from a panel discussion

The computing world is witnessing a proverbial Cambrian explosion of emerging paradigms propelled by applications such as Artificial Intelligence, Big Data, and Cybersecurity. The recent advances in technology to store digital data inside a DNA strand, manipulate quantum bits (qubits), perform logical operations with photons, and perform computations inside memory systems are ushering in the era of emerging paradigms of DNA computing, quantum computing, optical computing, and in-memory computing. In an orthogonal direction, research on interconnect design using advanced electro-optic, wireless, and microfluidic technologies has shown promising solutions to the architectural limitations of traditional von-Neumann computers. In this article, experts present their comments on the role of interconnects in the emerging computing paradigms and discuss the potential use of chiplet-based architectures for the heterogeneous integration of such technologies.This work was supported in part by the US NSF CAREER Grant CNS-1553264 and EU H2020 research and innovation programme under Grant 863337.Peer ReviewedPostprint (author's final draft

Transient error resilience in network-on-chip communication fabrics

Network on chip (NoC) is emerging as a revolutionary methodology to integrate numerous Intellectual Property (IP) blocks in a single System-on-Chip (SoC). Only an extensively communication centric paradigm like NoC can ensure seamless integration of such a large number of cores. A major challenge that NoC design is expected to face is related to the intrinsic unreliability of the communication infrastructure under technology limitations. As the separation between the wires is reducing rapidly, any signal transition in a wire affects more than one neighbor. This phenomenon is commonly referred to as the crosstalk effect. Crosstalk is one of the sources of transient errors. Among other sources of transient noise, factors like electromagnetic interference, alpha particle hits, cosmic radiation, etc. can be enumerated. To protect the NoC architectures against all these varied sources of noise an embedded selfcorrecting design methodology and its corresponding circuit implementation in the NoC communication fabrics is proposed. This embedded intelligence will be achieved through simple joint crosstalk avoidance and error correction coding schemes. In this work many existing crosstalk avoidance coding schemes and joint crosstalk avoidance and single error correction coding schemes are implemented in a NoC interconnect architecture and are evaluated in terms of performance and gains in energy savings. Finally a novel joint crosstalk avoidance and double error correction scheme is developed. The performance of this novel code is compared with the other existing codes and is shown to deliver a higher savings in energy dissipation compared to the joint single error correction codes

Design technologies for green and sustainable computing systems

This book provides a comprehensive guide to the design of sustainable and green computing systems (GSC). Coverage includes important breakthroughs in various aspects of GSC, including multi-core architectures, interconnection technology, data centers, high-performance computing (HPC), and sensor networks. The authors address the challenges of power efficiency and sustainability in various contexts, including system design, computer architecture, programming languages, compilers and networking. ·         Offers readers a single-source reference for addressing the challenges of power efficiency and sustainability in embedded computing systems; ·         Provides in-depth coverage of the key underlying design technologies for green and sustainable computing; ·         Covers a wide range of topics, from chip-level design to architectures, computing systems, and networks