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Levodopa-induced dyskinesias (LIDs) are the most common and disabling adverse motor effect of therapy in Parkinson's disease (PD) patients. In this study, we investigated serotonergic mechanisms in LIDs development in PD patients using 11C-DASB PET to evaluate serotonin terminal function and 11C-raclopride PET to evaluate dopamine release. PD patients with LIDs showed relative preservation of serotonergic terminals throughout their disease. Identical levodopa doses induced markedly higher striatal synaptic dopamine concentrations in PD patients with LIDs compared with PD patients with stable responses to levodopa. Oral administration of the serotonin receptor type 1A agonist buspirone prior to levodopa reduced levodopa-evoked striatal synaptic dopamine increases and attenuated LIDs. PD patients with LIDs that exhibited greater decreases in synaptic dopamine after buspirone pretreatment had higher levels of serotonergic terminal functional integrity. Buspirone-associated modulation of dopamine levels was greater in PD patients with mild LIDs compared with those with more severe LIDs. These findings indicate that striatal serotonergic terminals contribute to LIDs pathophysiology via aberrant processing of exogenous levodopa and release of dopamine as false neurotransmitter in the denervated striatum of PD patients with LIDs. Our results also support the development of selective serotonin receptor type 1A agonists for use as antidyskinetic agents in PD.

Fluctuation-related pain (FRP) affects more than one third of people with Parkinson’s disease (PwP, PD) and has a harmful effect on health-related quality of life (HRQoL), but often remains under-reported by patients and neglected by clinicians. The National Institute for Health and Care Excellence (NICE) recommends The Parkinson KinetiGraph TM (the PKG TM ) for remote monitoring of motor symptoms. We investigated potential links between the PKG TM -obtained parameters and clinical rating scores for FRP in PwP in an exploratory, cross-sectional analysis of two prospective studies: “ The Non-motor International Longitudinal , Real-Life Study in PD—NILS ” and “ An observational-based registry of baseline PKG™ in PD—PKGReg ”. 63 PwP (41.3% female; age: 64.24±9.88 years; disease duration, DD: 6.83±5.63 years; Hoehn and Yahr Stage, H&Y: 2 (1–4); Levodopa Equivalent Daily Dose 535 (0–3230) mg) were included. PwP with FRP (n = 23) had longer DD (8.88 (1.29–19.05) vs. 3.16 (0.34–28.92), p = 0.001), higher severity of motor symptoms (H&Y 3 (1–4) vs. 2 (1–4), p = 0.015; SCOPA Motor total score 21.35±10.19 vs. 13.65±8.99, p = 0.003), more dyskinesia (SCOPA Motor Item 18 ≥1 60.9% vs. 7.5%, p <0.001), and worse HRQoL (PDQ-8 Total Score 10.74±5.98 vs. 6.78±5.13, p = 0.007) then PwP without FRP (n = 40). In the multivariate logistic regression, after the adjustment for DD, H&Y and SCOPA-Motor total score, the presence of FRP was significantly associated with the PKG TM -derived Fluctuation-dyskinesia score (Exp (B) = 1.305, 95% CI for Exp (B) 1.012–1.683, p = 0.040) and the Bradykinesia score (Exp (B) = 0.917, 95% CI for Exp (B) 0.842–0.999, p = 0.048). The PKG TM system may potentially advance the way we screen for, assess, and treat FRP in clinical practice.

In smart cities and smart industry, a Battery Management System (BMS) focuses on the intelligent supervision of the status (e.g., state of charge, temperature) of batteries (e.g., lithium battery, lead battery). Internet of Things (IoT) integration enhances the system’s intelligence and convenience, making it a Smart BMS (SBMS). However, this also raises concerns regarding evaluating the SBMS in the wireless context in which these systems are installed. Considering the battery application, in particular, the SBMS will depend on several wireless communication characteristics, such as mobility, latency, fading, etc., necessitating a tailored evaluation strategy. This study proposes an IEEE P2668-Compatible SBMS Evaluation Strategy (SBMS-ES) to overcome this issue. The SBMS-ES is based on the IEEE P2668 worldwide standard, which aims to assess IoT solutions’ maturity. It evaluates the characteristics of the wireless environment for SBMS while considering battery factors. The SBMS-ES scores the candidates under numerous scenarios with various characteristics. A final score between 0 and 5 is given to indicate the performance of the SBMS regarding the application demands. The disadvantages of the SBMS solution and the most desired candidate can be found with the evaluated score. SBMS-ES provides guidance to avoid potential risks and mitigates the issues posed by an inadequate or unsatisfactory SBMS solution. A case study is depicted for illustration.

Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human’s biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.

Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by providing guidance for early warning methods of potential poor tree health. In PTA development, tree health is defined and evaluated based on proximity environmental features (PEFs). The PEF takes into consideration the seven surrounding ambient features that strongly impact tree health. The PEFs were measured by the deployed smart sensors surrounding trees. A database composed of tree health and relative PEFs was established for further analysis. An adaptive data identifying (ADI) algorithm is applied to exclude the influence of interference factors in the database. Finally, the radial basis function (RBF) neural network (NN), a machine leaning algorithm, has been identified as the appropriate tool with which to correlate tree health and PEFs to establish the PTA algorithm. One of the salient features of PTA is that the algorithm can evaluate, and thus monitor, tree health remotely and automatically from smart sensor data by taking advantage of the well-established internet of things (IoT) network and machine learning algorithm.

Water distribution infrastructure (WDI) is well-established and significantly improves living quality. Nonetheless, aging WDI has posed an awkward worldwide problem, wasting natural resources and leading to direct and indirect economic losses. The total losses due to leaks are valued at USD 7 billion per year. In this paper, a multi-classification multi-leak identification (MC-MLI) scheme is developed to combat the captioned problem. In the MC-MLI, a novel adaptive kernel (AK) scheme is developed to adapt to different WDI scenarios. The AK improves the overall identification capability by customizing a weighting vector into the extracted feature vector. Afterwards, a multi-classification (MC) scheme is designed to facilitate efficient adaptation to potentially hostile inhomogeneous WDI scenarios. The MC comprises multiple classifiers for customizing to different pipelines. Each classifier is characterized by the feature vector and corresponding weighting vector and weighting vector pertinent to system requirements, thus rendering the developed scheme strongly adaptive to ever-changing operating environments. Hence, the MC scheme facilitates low-cost, efficient, and accurate water leak detection and provides high practical value to the commercial market. Additionally, graph theory is utilized to model the realistic WDIs, and the experimental results verify that the developed MC-MLI achieves 96% accuracy, 96% sensitivity, and 95% specificity. The average detection time is about 5 s.

Measurement of health indicators in the blood is a commonly performed diagnostic procedure. Two blood studies one involving extended observations on the health of an individual by integrative Personal Omics Profiling (iPOP) and the other tracking the impact of Left Ventricular Assist Device (LVAD) placement on nine heart failure patients were examined for the association of change in health status with change in microbial RNA species. Decrease in RNA expression ratios of human to bacteria and viruses accompanying deteriorated conditions was evident in both studies. Despite large between-subject variations in bacterial composition before LVAD implantation among all the patients, on day 180 after the implantation they manifested apparent between-subject bacterial similarity. In the iPOP study three periods, namely, pre-respiratory syncytial virus (RSV) infection with normal blood glucose level, RSV infection with normal blood glucose level and post-RSV infection with high blood glucose level could be defined. The upsurge of Enterobacteria phage PhiX 174 sensu lato and Escherichia coli gene expression, in which membrane transporters, membrane receptors for environment signalling, carbohydrate catabolic genes and carbohydrate-active enzymes were enriched only throughout the second period, which suggests a potentially overlooked microbial response to or modulation of the host blood glucose level.

Schizophrenia (SCZ) is a heterogeneous psychiatric disorder, with significant contribution from genetic factors particularly for chronic cases with negative symptoms and cognitive deficits. To date, Genome Wide Association Studies (GWAS) and exome sequencing have associated SCZ with a number of single nucleotide polymorphisms (SNPs) and copy number variants (CNVs), but there is still missing heritability. Medium-sized structural variants (SVs) are difficult to detect using SNP arrays or second generation sequencing, and may account for part of the missing heritability of SCZ. To identify SVs associated with severe chronic SCZ across the whole genome. 10 multiplex families with probands suffering from chronic SCZ with negative symptoms and cognitive deficits were recruited, with all their affected members demonstrating uni-lineal inheritance. Control subjects comprised one affected member from the affected lineage, and unaffected members from each paternal and maternal lineage. Third generation sequencing was applied to peripheral blood samples from 10 probands and 5 unaffected controls. Bioinformatic tools were used to identify SVs from the long sequencing reads, with confirmation of findings in probands by short-read Illumina sequencing, Sanger sequencing and visual manual validation with Integrated Genome Browser. In the 10 probands, we identified and validated 88 SVs (mostly in introns and medium-sized), within 79 genes, which were absent in the 5 unaffected control subjects. These 79 genes were enriched in 20 biological pathways which were related to brain development, neuronal migration, neurogenesis, neuronal/synaptic function, learning/memory, and hearing. These identified SVs also showed evidence for enrichment of genes that are highly expressed in the adolescent striatum. A substantial part of the missing heritability in SCZ may be explained by medium-sized SVs detectable only by third generation sequencing. We have identified a number of such SVs potentially conferring risk for SCZ, which implicate multiple brain-related genes and pathways. In addition to previously-identified pathways involved in SCZ such as neurodevelopment and neuronal/synaptic functioning, we also found novel evidence for enrichment in hearing-related pathways and genes expressed in the adolescent striatum.

BackgroundEvidence suggests there is variation in the diagnosis, management, and support for people with epilepsy (PWE). To improve equity of service, in line with the aspirations of public policy across the UK, it is imperative to understand how sociodemographic factors affect management and care of PWE.MethodsWe conducted a focused review of published literature between 2000-2021 to outline the evidence describing the relationship between the nine protected characteristics of the Equality Act 2010 and the epidemiology, diagnosis, management and outcomes of PWE. Other factors such as depriva- tion, settlement status, education and employment were also explored.ResultsThere is an abundance of published literature on the relationship between age, disability, pregnancy and socioeconomic factors and epilepsy epidemiology and management. There is little evidence examining the relationship between epilepsy and gender reassignment, sexual orientation, religion, immigration and settlement status. Further work should also build on the data from the NASH audits, to examine the reasons for the geographical variation in epilepsy care in planned outpatient as well as unplanned emergency care settings.DiscussionBetter understanding of sociodemographic factors and their relationship with epilepsy epide- miology and management is key to improving equity of care. This review has identified gaps in knowledge that require further research.

BackgroundTo improve equity of service, in line with the aspirations of public policy across the UK, it is imperative that we understand how sociodemographic factors affect the management and care of stroke.AimsTo describe the relationship between sociodemographic factors and the epidemiology, clinical factors, diagnosis, treatment, patient experience and outcomes for people with stroke.MethodsWe conducted a focused review of published literature from 2000 to 2021 looking at stroke and each of the patient protected characteristics, as outlined in the Equality Act 2010, and the relationship to epidemiology, treatment and patient outcomes. Additional patient factors such as socioeconomic status, education and employment were also explored.ResultsThere is plenty of evidence published exploring the relationship of stroke with age, pregnancy, race, sex and gender. There is a lack of evidence exploring that relationship with disability, gender reas- signment, marriage and civil partnership, religion and beliefs, sexual orientation, geographical variation, socioeconomic status, settlement status, education and employment.DiscussionCertain areas within stroke medicine are well researched, with cause and effect relationships established. However, there are certain protected patient characteristics with minimal/no evidence which require further research to ensure equal access for all patients to stroke care.