A multi-disciplinary commentary on preclinical research to investigate vascular contributions to dementia

Although dementia research has been dominated by Alzheimer's disease (AD), most dementia in older people is now recognised to be due to mixed pathologies, usually combining vascular and AD brain pathology. Vascular cognitive impairment (VCI), which encompasses vascular dementia (VaD) is the second most common type of dementia. Models of VCI have been delayed by limited understanding of the underlying aetiology and pathogenesis. This review by a multidisciplinary, diverse (in terms of sex, geography and career stage), cross-institute team provides a perspective on limitations to current VCI models and recommendations for improving translation and reproducibility. We discuss reproducibility, clinical features of VCI and corresponding assessments in models, human pathology, bioinformatics approaches, and data sharing. We offer recommendations for future research, particularly focusing on small vessel disease as a main underpinning disorder

A Multi-disciplinary Commentary on Preclinical Research to investigate Vascular Contributions to Dementia

Although dementia research has been dominated by Alzheimer's disease (AD), most dementia in older people is now recognised to be due to mixed pathologies, usually combining vascular and AD brain pathology. Vascular cognitive impairment (VCI), which encompasses vascular dementia (VaD) is the second most common type of dementia. Models of VCI have been delayed by limited understanding of the underlying aetiology and pathogenesis. This review by a multidisciplinary, diverse (in terms of sex, geography and career stage), cross-institute team provides a perspective on limitations to current VCI models and recommendations for improving translation and reproducibility. We discuss reproducibility, clinical features of VCI and corresponding assessments in models, human pathology, bioinformatics approaches, and data sharing. We offer recommendations for future research, particularly focusing on small vessel disease as a main underpinning disorder.</p

Fabrication Principles and Their Contribution to the Superior In Vivo Therapeutic Efficacy of Nano-Liposomes Remote Loaded with Glucocorticoids

We report here the design, development and performance of a novel formulation of liposome- encapsulated glucocorticoids (GCs). A highly efficient (>90%) and stable GC encapsulation was obtained based on a transmembrane calcium acetate gradient driving the active accumulation of an amphipathic weak acid GC pro-drug into the intraliposome aqueous compartment, where it forms a GC-calcium precipitate. We demonstrate fabrication principles that derive from the physicochemical properties of the GC and the liposomal lipids, which play a crucial role in GC release rate and kinetics. These principles allow fabrication of formulations that exhibit either a fast, second-order (t1/2 ∼1 h), or a slow, zero-order release rate (t1/2 ∼ 50 h) kinetics. A high therapeutic efficacy was found in murine models of experimental autoimmune encephalomyelitis (EAE) and hematological malignancies

Collecting High-Rate Data Over Low-Rate Sensor Network Radios

Embedded systems can already capture data produced at high rates, and embedded CPU and sensor performance are still rapidly improving. Radio technology, however, cannot keep pace, and will not in the future due to known physical limits of shared communication channels. This leads to a fundamental gap between the data a sensor network node can collect and the data it can transmit back for analysis. VanGo, our software system for data collection, uses flexible transcoding to narrow this gap. To make effective use of channel bandwidth, data reduction software must run on sensor nodes. However, to calibrate how data reduction software should run, that same software should be capable of running on the back end on real data received from the network. In VanGo, users decide where data processing occurs. To show that transcoding helps, we evaluate two radically different applications: acoustic collection and the measurement of neural activity. Among our findings is that in bandwidth-limited environments, proactive filtering of some of our signal can result in collecting three times the signal energy than we could by removing silent periods alone

Collecting High-Rate Data Over Low-Rate Sensor Network Radios

Embedded systems can already capture data produced at high rates, and embedded CPU and sensor performance are still rapidly improving. Radio technology, however, can not keep pace, and will not in the future due to known physical limits of shared communication channels. This leads to a fundamental gap between the data a sensor network node can collect and the data it can transmit back for analysis. VanGo, our software system for data collection, uses flexible transcoding to narrow this gap. To make effective use of channel bandwidth, data reduction software must run on sensor nodes. However, to calibrate how data reduction software should run, that same software should be capable of running on the back end on real data received from the network. In VanGo, users decide where data processing occurs. To show that transcoding helps, we evaluate two radically different applications: acoustic collection and the measurement of neural activity. Among our findings is that in bandwidth-limited environments, proactive filtering of some of our signal can result in collecting three times the signal energy than we could by removing silent periods alone

Capturing highfrequency phenomena using a bandwidth-limited sensor network. Sensys

Small-form-factor, low-power wireless sensors—motes—are convenient to deploy, but lack the bandwidth to capture and transmit raw high-frequency data, such as human voices or neural signals, in real time. Local filtering can help, but we show that the right filter settings depend on changing ambient conditions and network effects such as congestion, which makes them dynamic and unpredictable. Mote collection systems for high-frequency data must support iteratively-tuned, deployment-specific filter settings as well as fast sampling. VANGO, our software system for high-frequency data collection, achieves these goals via integrated processing across network tiers. Bandwidth-limited sensor nodes reduce data in network but rely on microservers, which have greater computational capabilities and a wider scope of observation, to plan how. VANGO provides a cross-platform library for data transformation, measurement, and classification; a fast and low-jitter data acquisition system for motes; and a mechanism to control mote and microserver signal processing. With VANGO we have developed new applications: the first acoustic collection system for motes responsive to changing environmental conditions and user interests, and the first neural spike acquisition application capable of supporting a network of nodes. Categories and Subject Descriptors

Collecting High-Rate Data Over Low-Rate Sensor Network Radios

Embedded systems can already capture data produced at high rates, and embedded CPU and sensor performance are still rapidly improving. Radio technology, however, cannot keep pace, and will not in the future due to known physical limits of shared communication channels. This leads to a fundamental gap between the data a sensor network node can collect and the data it can transmit back for analysis. VanGo, our software system for data collection, uses flexible transcoding to narrow this gap. To make effective use of channel bandwidth, data reduction software must run on sensor nodes. However, to calibrate how data reduction software should run, that same software should be capable of running on the back end on real data received from the network. In VanGo, users decide where data processing occurs. To show that transcoding helps, we evaluate two radically different applications: acoustic collection and the measurement of neural activity. Among our findings is that in bandwidth-limited environments, proactive filtering of some of our signal can result in collecting three times the signal energy than we could by removing silent periods alone

Collecting High-Rate Data Over Low-Rate Sensor Network Radios

Embedded systems can already capture data produced at high rates, and embedded CPU and sensor performance are still rapidly improving. Radio technology, however, can not keep pace, and will not in the future due to known physical limits of shared communication channels. This leads to a fundamental gap between the data a sensor network node can collect and the data it can transmit back for analysis. VanGo, our software system for data collection, uses flexible transcoding to narrow this gap. To make effective use of channel bandwidth, data reduction software must run on sensor nodes. However, to calibrate how data reduction software should run, that same software should be capable of running on the back end on real data received from the network. In VanGo, users decide where data processing occurs. To show that transcoding helps, we evaluate two radically different applications: acoustic collection and the measurement of neural activity. Among our findings is that in bandwidth-limited environments, proactive filtering of some of our signal can result in collecting three times the signal energy than we could by removing silent periods alone