Neutrophils Enable Local and Non-Invasive Liposome Delivery to Inflamed Skeletal Muscle and Ischemic Heart

Uncontrolled inflammation is a major pathological factor underlying a range of diseases including autoimmune conditions, cardiovascular disease, and cancer. Improving localized delivery of immunosuppressive drugs to inflamed tissue in a non-invasive manner offers significant promise to reduce severe side effects caused by systemic administration. Here, a neutrophil-mediated delivery system able to transport drug-loaded nanocarriers to inflamed tissue by exploiting the inherent ability of neutrophils to migrate to inflammatory tissue is reported. This hybrid system (neutrophils loaded with liposomes ex vivo) efficiently migrates in vitro following an inflammatory chemokine gradient. Furthermore, the triggered release of loaded liposomes and reuptake by target macrophages is studied. The migratory behavior of liposome-loaded neutrophils is confirmed in vivo by demonstrating the delivery of drug-loaded liposomes to an inflamed skeletal muscle in mice. A single low-dose injection of the hybrid system locally reduces inflammatory cytokine levels. Biodistribution of liposome-loaded neutrophils in a human-disease-relevant myocardial ischemia reperfusion injury mouse model after i.v. injection confirms the ability of injected neutrophils to carry loaded liposomes to inflammation sites. This strategy shows the potential of nanocarrier-loaded neutrophils as a universal platform to deliver anti-inflammatory drugs to promote tissue regeneration in inflammatory diseases.     

Open Sesame? Open access priorities,incentives, and policies among higher education institutions in the United Arab Emirates

Scientometrics - Higher education institutions (HEIs) have an instrumental role in the move towards Open Access (OA) by shaping the national strategies, policies, and agendas. This study sets out...     

Detection,localisation and tracking of pallets using machine learning techniques and 2D range data

The problem of autonomous transportation in industrial scenarios is receiving a renewed interest due to the way it can revolutionise internal logistics, especially in unstructured environments. This paper presents a novel architecture allowing a robot to detect, localise, and track (possibly multiple) pallets using machine learning techniques based on an on-board 2D laser rangefinder only. The architecture is composed of two main components: the first stage is a pallet detector employing a Faster Region-Based Convolutional Neural Network (Faster R-CNN) detector cascaded with a CNN-based classifier; the second stage is a Kalman filter for localising and tracking detected pallets, which we also use to defer commitment to a pallet detected in the first stage until sufficient confidence has been acquired via a sequential data acquisition process. For fine-tuning the CNNs, the architecture has been systematically evaluated using a real-world dataset containing 340 labelled 2D scans, which have been made freely available in an online repository. Detection performance has been assessed on the basis of the average accuracy over k-fold cross-validation, and it scored 99.58% in our tests. Concerning pallet localisation and tracking, experiments have been performed in a scenario where the robot is approaching the pallet to fork. Although data have been originally acquired by considering only one pallet as per specification of the use case we consider, artificial data have been generated as well to mimic the presence of multiple pallets in the robot workspace. Our experimental results confirm that the system is capable of identifying, localising and tracking pallets with a high success rate while being robust to false positives.

     

On thermoelectric materials with memory-dependent derivative and subjected to a moving heat source

We develop a model of generalized thermoelasticity with memory-dependent derivative (MDD) heat conduction law for a thermoelectric half-space. Some urgent theories take after as most remote point cases. The Laplace transform and state-space procedures are utilized to urge the overall account for any arrangement of limit conditions. The general solution acquired is connected to the particular issue of a half-space exposed to a uniform magnetic field, a moving heat source with consistent speed and ramp-type heating. The inverse Laplace transforms are registered numerically. The impacts of various estimations of the figure-of-merit quantity, heat source speed, MDD parameters, the magnetic number and the ramping time parameter are thought about.

     

Semi-blind channel estimation based on modified CMA and unitary scrambling for massive MIMO systems

Pilot contamination is one of the main impairments in multi-cell massive Multiple-Input Multiple-Output systems. In order to improve the channel estimation in this context, we propose to use a semi-blind channel estimator based on the constant modulus algorithm (CMA). We consider an enhanced version of the CMA namely the Modified CMA which modifies the cost function of the CMA algorithm to the sum of cost functions for real and imaginary parts. Due to pilot contamination, the channel estimator may estimate the channel of a contaminating user instead of that of the user of interest (the user for which the Base Station wants to estimate the channel and then the data). To avoid this, we propose to scramble the users sequences before transmission. We consider different methods to perform unitary scrambling based on rotating the transmitted symbols (one Dimensional (1-D) scrambling) and using unitary matrices (two-Dimensional (2-D) scrambling). At the base station, the received sequence of the user of interest is descrambled leading to a better convergence of the channel estimator. We also consider the case where the Automatic Repeat reQuest protocol is used. In this case, using scrambling leads to a significant gain in terms of BLock Error Rate due to the change of the contaminating users data from one transmission to another induced by scrambling.