Transistor counting rate meters.

The use of transistors in counting-rate meters has so far been restricted mainly to portable health and survey type monitors. This report points out that transistors may be used sucessfully in rate-meters of high accuracy and gives examples of practical circuits which may be designed to attain accuracies and linearities of better than plus or minus 1%

Psychiatric symptoms in glioma patients: from diagnosis to management

Patients with primary intrinsic brain tumors can experience neurological, cognitive, and psychiatric symptoms that greatly affect daily life. In this review, we focus on changes in personality and behavior, mood issues, hallucinations, and psychosis, because these are either difficult to recognize, to treat, or are understudied in scientific literature. Neurobehavioral symptoms are common, often multiple, and causation can be multifactorial. Although different symptoms sometimes require a different treatment approach, we advise a comprehensive treatment approach, including pharmacological treatment and/or psychotherapy where appropriate. Further research is needed to obtain a better estimate of the prevalence of psychiatric symptoms in glioma patients, and the extent to which these affect everyday functioning and family life

A Recurrent Neural Network Survival Model: Predicting Web User Return Time

The size of a website's active user base directly affects its value. Thus, it is important to monitor and influence a user's likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation.Comment: Accepted into ECML PKDD 2018; 8 figures and 1 tabl

Measurement of extravascular lung water to diagnose severe reperfusion lung injury following pulmonary endarterectomy: a prospective cohort clinical validation study

The measurement of extravascular lung water is a relatively new technology which has not yet been well validated as a clinically useful tool. We studied its utility in patients undergoing pulmonary endarterectomy as they frequently suffer reperfusion lung injury and associated oedematous lungs. Such patients are therefore ideal for evaluating this new monitor. We performed a prospective observational cohort study during which extravascular lung water index measurements were taken before and immediately after surgery and postoperatively in intensive care. Data were analysed for 57 patients; 21 patients (37%) experienced severe reperfusion lung injury. The first extravascular lung water index measurement after cardiopulmonary bypass failed to predict severe reperfusion lung injury, area under the receiver operating characteristic curve 0.59 (95%CI 0.44–0.74). On intensive care, extravascular lung water index correlated most strongly at 36 h, area under the receiver operating characteristic curve 0.90 (95%CI 0.80–1.00). Peri‐operative extravascular lung water index is not a useful measure to predict severe reperfusion lung injury after pulmonary endarterectomy, however, it does allow monitoring and measurement during the postoperative period. This study implies that extravascular lung water index can be used to directly assess pulmonary fluid overload and that monitoring patients by measuring extravascular lung water index during their intensive care stay is useful and correlates with their clinical course. This may allow directed, pre‐empted therapy to attenuate the effects and improve patient outcomes and should prompt further studies

Studies on the Toxicity and Efficacy of a New Amino Acid Solution in Pediatric Parenteral Nutrition

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142223/1/jpen0368.pd

Increasing negotiation performance at the edge of the network

Automated negotiation has been used in a variety of distributed settings, such as privacy in the Internet of Things (IoT) devices and power distribution in Smart Grids. The most common protocol under which these agents negotiate is the Alternating Offers Protocol (AOP). Under this protocol, agents cannot express any additional information to each other besides a counter offer. This can lead to unnecessarily long negotiations when, for example, negotiations are impossible, risking to waste bandwidth that is a precious resource at the edge of the network. While alternative protocols exist which alleviate this problem, these solutions are too complex for low power devices, such as IoT sensors operating at the edge of the network. To improve this bottleneck, we introduce an extension to AOP called Alternating Constrained Offers Protocol (ACOP), in which agents can also express constraints to each other. This allows agents to both search the possibility space more efficiently and recognise impossible situations sooner. We empirically show that agents using ACOP can significantly reduce the number of messages a negotiation takes, independently of the strategy agents choose. In particular, we show our method significantly reduces the number of messages when an agreement is not possible. Furthermore, when an agreement is possible it reaches this agreement sooner with no negative effect on the utility.Comment: Accepted for presentation at The 7th International Conference on Agreement Technologies (AT 2020

Characterisation of silent and active genes for a variable large protein of Borrelia recurrentis

BACKGROUND: We report the characterisation of the variable large protein (vlp) gene expressed by clinical isolate A1 of Borrelia recurrentis; the agent of the life-threatening disease louse-borne relapsing fever. METHODS: The major vlp protein of this isolate was characterised and a DNA probe created. Use of this together with standard molecular methods was used to determine the location of the vlp1(B. recurrentis A1) gene in both this and other isolates. RESULTS: This isolate was found to carry silent and expressed copies of the vlp1(B. recurrentis A1) gene on plasmids of 54 kbp and 24 kbp respectively, whereas a different isolate, A17, had only the silent vlp1(B. recurrentis A17) on a 54 kbp plasmid. Silent and expressed vlp1 have identical mature protein coding regions but have different 5' regions, both containing different potential lipoprotein leader sequences. Only one form of vlp1 is transcribed in the A1 isolate of B. recurrentis, yet both 5' upstream sequences of this vlp1 gene possess features of bacterial promoters. CONCLUSION: Taken together these results suggest that antigenic variation in B. recurrentis may result from recombination of variable large and small protein genes at the junction between lipoprotein leader sequence and mature protein coding region. However, this hypothetical model needs to be validated by further identification of expressed and silent variant protein genes in other B. recurrentis isolates