An example of a method to wirelessly transfer measurement data from cows in a free stall barn

Here we describe a wireless data measurement and transfer system that operates within a free stall barn. We report also the reliability of the system. This system was designed and built in Very Intelligent Cow Barn project in 2006-2007

Computational challenges in deriving dairy cows' action patterns from accelerometer data

We describe an attempt to build a computational model for deriving dairy cows' action patterns automatically from accelerometer data

Fuel poverty in the cost of living crisis

Access to energy is being severely restricted by the cost of living crisis, with almost a quarter of households experiencing fuel poverty, despite existing interventions. Here we draw on a large body of evidence on fuel poverty to summarise likely effects on households this winter. We find there is urgent need for further bill support, energy efficiency and targeted advice for vulnerable households

Microparticle - based platform for point-of-care immunoassays

There is a need for quantitative and sensitive, yet simple point-of-care immunoassays: We have developed a point-of-care microparticle-based immunoassay platform which combines the performance of a microliter well based assay with the usability of a rapid assay. The platform contained a separate reaction and detection chambers and microparticles for the solid-phase. Photoluminescent up-converting nanoparticles (UCNPs) were used as labels. The platform was tested with a cardiac troponin I assay, and a limit of detection of 19.7 ng/L was obtained. This study demonstrates the feasibility of developing point-of-care assays on the new platform for various analytes of interests

Policy prescriptions to address energy and transport poverty in the United Kingdom

Tens of millions of households across Europe struggle to afford adequate electricity and heating services and reliable transportation, while recent high fuel prices could lead to an increase in excess winter deaths. Tackling energy and transport poverty is thus of paramount policy importance. Here we document the drivers and lived experiences of energy and transport poverty in the United Kingdom, based on public focus groups and expert interviews. We find a set of policies that resonate with both expert planners and members of the public, implying they have a level of political and social acceptability that other measures may be lacking, notably: mandatory landlord energy efficiency upgrades, increasing the extent of financial assistance to households, cheaper (or even free) bus and train fares and restarting and expanding bus services. We buttress these findings with further suggestions for energy and transport system redesign that better meets emerging principles of energy and social justice

Eating, heating or taking the bus? Lived experiences at the intersection of energy and transport poverty

Experiences of poverty can manifest in multiple aspects of everyday life, often in interlinking ways. One example is ‘double energy vulnerability’, where a household faces both energy poverty and transport poverty simultaneously. This can result in trade-offs, where prioritising one essential need (e.g., transport) makes accessing another impossible (e.g., heating). Such decisions are not easily made, and they can have distinct spatio-temporal characteristics. They can vary between space and time and across different household members, and result in stark inter- as well as intra-household differences. People with socio-demographic and contextual vulnerabilities are particularly at risk of experiencing double energy vulnerability. Based on 59 household interviews across the four nations of the United Kingdom, we provide novel, multi-nation empirical evidence on the lived experiences of double energy vulnerability, drawing on our themes; ‘being locked into infrastructure’, ‘facing high costs and low incomes’, ‘choosing between energy and transport’, and ‘missing out’. A cross-national lived-experiences approach sheds light on double energy vulnerability as a relational, contingent and ongoing phenomena, attending to everyday experiences and capacities. We provide suggestions for further research, such as further study of double energy vulnerability amongst refugees and migrants. We also highlight that the study of lived experiences can aid the recognition of how different forms of poverty intersect and how they need to be taken into account in the design of Net Zero policies

Smart homes and their users:a systematic analysis and key challenges

Published research on smart homes and their users is growing exponentially, yet a clear understanding of who these users are and how they might use smart home technologies is missing from a field being overwhelmingly pushed by technology developers. Through a systematic analysis of peer-reviewed literature on smart homes and their users, this paper takes stock of the dominant research themes and the linkages and disconnects between them. Key findings within each of nine themes are analysed, grouped into three: (1) views of the smart home-functional, instrumental, socio-technical; (2) users and the use of the smart home-prospective users, interactions and decisions, using technologies in the home; and (3) challenges for realising the smart home-hardware and software, design, domestication. These themes are integrated into an organising framework for future research that identifies the presence or absence of cross-cutting relationships between different understandings of smart homes and their users. The usefulness of the organising framework is illustrated in relation to two major concerns-privacy and control-that have been narrowly interpreted to date, precluding deeper insights and potential solutions. Future research on smart homes and their users can benefit by exploring and developing cross-cutting relationships between the research themes identified

DHCR24 exerts neuroprotection upon inflammation-induced neuronal death

Abstract Background DHCR24, involved in the de novo synthesis of cholesterol and protection of neuronal cells against different stress conditions, has been shown to be selectively downregulated in neurons of the affected brain areas in Alzheimer’s disease. Methods Here, we investigated whether the overexpression of DHCR24 protects neurons against inflammation-induced neuronal death using co-cultures of mouse embryonic primary cortical neurons and BV2 microglial cells upon acute neuroinflammation. Moreover, the effects of DHCR24 overexpression on dendritic spine density and morphology in cultured mature mouse hippocampal neurons and on the outcome measures of ischemia-induced brain damage in vivo in mice were assessed. Results Overexpression of DHCR24 reduced the loss of neurons under inflammation elicited by LPS and IFN-γ treatment in co-cultures of mouse neurons and BV2 microglial cells but did not affect the production of neuroinflammatory mediators, total cellular cholesterol levels, or the activity of proteins linked with neuroprotective signaling. Conversely, the levels of post-synaptic cell adhesion protein neuroligin-1 were significantly increased upon the overexpression of DHCR24 in basal growth conditions. Augmentation of DHCR24 also increased the total number of dendritic spines and the proportion of mushroom spines in mature mouse hippocampal neurons. In vivo, overexpression of DHCR24 in striatum reduced the lesion size measured by MRI in a mouse model of transient focal ischemia. Conclusions These results suggest that the augmentation of DHCR24 levels provides neuroprotection in acute stress conditions, which lead to neuronal loss in vitro and in vivo

Upconverting nanoparticle reporter–based highly sensitive rapid lateral flow immunoassay for hepatitis B virus surface antigen

Detection of hepatitis B Virus surface antigen (HBsAg) is an established method for diagnosing both acute and chronic hepatitis B virus (HBV) infection. In addition to enzyme immunoassays (EIAs), rapid diagnostic tests (RDTs) are available for the detection of HBsAg in resource-poor settings. However, the available RDTs have inadequate sensitivity and therefore are not suitable for diagnosis of patients with low levels of HBsAg and for blood screening. To provide a high-sensitivity RDT, we developed a lateral flow immunoassay (LFIA) for HBsAg utilizing upconverting nanoparticle (UCNP) reporter. The UCNP-LFIA can use whole blood, serum, or plasma and the results can be read in 30 min using a reader device. When compared with a commercial conventional visually read LFIA, the developed UCNP-LFIA had a Limit of Detection (LoD) of 0.1 IU HBsAg/ml in spiked serum, whereas the LoD of the conventional LFIA was 3.2 IU HBsAg/ml. The developed UCNP-LFIA fulfills the WHO criterion for blood screening (LoD ≤ 0.13 IU HBsAg/ml) in terms of LoD. The UCNP-LFIA and conventional LFIA were evaluated with well-characterized sample panels. The UCNP-LFIA detected 20/24 HBsAg-positive samples within the HBsAg Performance Panel and 8/10 samples within the Mixed Titer Performance Panel, whereas the conventional LFIA detected 8/24 and 4/10 samples in these panels, respectively. The performance of the assays was further evaluated with HBsAg-positive (n = 108) and HBsAg-negative (n = 315) patient samples. In comparison with a central laboratory test, UCNP-LFIA showed 95.4% (95% CI: 89.5–98.5%) sensitivity whereas sensitivity of the conventional LFIA was 87.7% (95%CI: 79.9–93.3%).</p

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep