A dataset of human body tracking of walking actions captured using two Azure Kinect sensors.

A dataset of body tracking information is presented. The dataset consists of 315 captured walking sequences. Each sequence is simultaneously captured by two Azure Kinect devices. The two captures are interleaved to effectively double the frame rate. Fifteen participants partook in this experiment. Each experiment consists of seven walking actions, and having three predefined trajectories per experiment. That results in 21 sequences per participant. The data were collected using the Azure Kinect Sensor SDK. They were later processed using the official tools and libraries provided by Microsoft. For each sequence and trajectory, the positions and orientations of thirty-two tracked joints were obtained and saved. The dataset is structured as follows. The experiments from each subject are saved in a single directory. Each directory contains multiple JSON files of timestamped body tracking information to enable the fusion of the two device streams. A calibration file is also provided, enabling the mapping of the coordinates between the two Azure Kinect devices capturing the data (mapping the coordinates of the device known as the Subordinate device to the Master device coordinate system). This data can be used to train neural networks for human motion prediction tasks or test pre-existing algorithms on Azure Kinect data. This dataset could also aid in gait recognition and analysis, as well as in performing action recognition and other surveillance activities. The dataset can be found at https://zenodo.org/record/7997856.

Multi-sensor, multi-device smart building indoor environmental dataset.

A dataset of sensor measurements is presented. Our dataset contains discrete measurements of 8 IoT devices located in various places in a research lab at the University of Bristol. Nordic nRF52840 DK IoT devices periodically collects environmental data, such as temperature, humidity, pressure, gas, room light intensity, accelerometer; including also a measurement quality indicator. The measurements were taken every 10 seconds over a six-month period between February and September 2022. In addition, we provide Received Signal Strength Indicator (RSSI) of the IoT devices. The data files are formatted as CSV files. There are various software libraries available to access and read this file format. We provide "README.txt" file which explains the repository and how to use dataset. Each data file is named according to its creation date and, once it reaches a size of 1MB, it is compressed and archived. A new folder is created every week to store all the data files from that week automatically. The dataset can be used for drift detection such as malicious or anomaly detection algorithms. It can also be used for smart building applications like occupation detection. The dataset can be found at https://data.bris.ac.uk/data/dataset/fwlmb11wni392kodtyljkw4n2.

A dataset of images of public streetlights with operational monitoring using computer vision techniques.

A dataset of street light images is presented. Our dataset consists of ∼350 k images, taken from 140 UMBRELLA nodes installed in the South Gloucestershire region in the UK. Each UMBRELLA node is installed on the pole of a lamppost and is equipped with a Raspberry Pi Camera Module v1 facing upwards towards the sky and lamppost light bulb. Each node collects an image at hourly intervals for 24 h every day. The data collection spans for a period of six months. Each image taken is logged as a single entry in the dataset along with the Global Positioning System (GPS) coordinates of the lamppost. All entries in the dataset have been post-processed and labelled based on the operation of the lamppost, i.e., whether the lamppost is switched ON or OFF. The dataset can be used to train deep neural networks and generate pre-trained models providing feature representations for smart city CCTV applications, smart weather detection algorithms, or street infrastructure monitoring. The dataset can be found at 10.5281/zenodo.6046758.

A dataset of full-stack ITS-G5 DSRC communications over licensed and unlicensed bands using a large-scale urban testbed.

A dataset of measurements of ETSI ITS-G5 Dedicated Short Range Communications (DSRC) is presented. Our dataset consists of network interactions happening between two On-Board Units (OBUs) and four Road Side Units (RSUs). Each OBU was fitted onto a vehicle driven across the FLOURISH Test Track in Bristol, UK. Each RSU and OBU was equipped with two transceivers operating at different frequencies. During our experiments, each transceiver broadcasts Cooperative Awareness Messages (CAMs) over the licensed DSRC band, and over the unlicensed Industrial, Scientific, and Medical radio (ISM) bands 2.4 GHz-2.5 GHz and 5.725 GHz-5.875 GHz. Each transmitted and received CAM is logged along with its Received Signal Strength Indicator (RSSI) value and accurate positioning information. The Media Access Control layer (MAC) layer Packet Delivery Rates (PDRs) and RSSI values are also empirically calculated across the whole length of the track for any transceiver. The dataset can be used to derive realistic approximations of the PDR as a function of RSSI values under urban environments and for both the DSRC and ISM bands - thus, the dataset is suitable to calibrate (simplified) physical layers of full-stack vehicular simulators where the MAC layer PDR is a direct function of the RSSI. The dataset is not intended to be used for signal propagation modelling. The dataset can be found at https://doi.org/10.5523/bris.eupowp7h3jl525yxhm3521f57, and it has been analyzed in the following paper: I. Mavromatis, A. Tassi, and R. J. Piechocki, "Operating ITS-G5 DSRC over Unlicensed Bands: A City-Scale Performance Evaluation," IEEE PIMRC 2019. [Online]. Available: https://arxiv.org/abs/1904.00464.

Clinical characteristics of Parkinson's disease patients in Greece: a multicenter, nation-wide, cross-sectional study.

Parkinson's disease is a neurodegenerative disease, with a constantly increasing prevalence and a high global financial impact arising from direct and indirect costs. Large-scale, observational studies provide data that support the better comprehension of disease aspects, constitute a baseline reference for future studies and assist comparisons among different patient populations, allowing the recognition of distinctive characteristics and special needs. The present study is the first to depict the clinical characteristics and their interplay in a large sample of Parkinson's disease (PD) patients in Greece. Nine hundred eighty six consecutive PD outpatients were recruited from 17 centers around Greece in the time period from 8/2007 to 7/2009 and were examined and interviewed by movement disorders experts. Multiple clinical characteristics were recorded including age at diagnosis, disease severity, patients' self classification of PD symptoms and their relevance to physician's global clinical impression, smoking, alcohol consumption, presence of family history for PD, dementia, depression, hypertension, cancer and other comorbidities. Associations of high clinical significance were found between certain clinical characteristics.

Controlled ovarian hyperstimulation for IVF: impact on ovarian, endometrial and cervical cancer--a systematic review and meta-analysis.

BACKGROUND: In response to the ongoing debate on the long-term effects of assisted reproduction technologies, such as IVF, we systematically reviewed and meta-analyzed available evidence on the association between controlled ovarian hyperstimulation for IVF and risk of ovarian, endometrial and cervical cancer. METHODS: Eligible studies were identified and pooled effect estimates for relative risk (RR) were calculated by cancer type among two reference groups (general population or infertile women), through fixed- or random-effects models as appropriate. RESULTS: Nine cohort studies were synthesized, corresponding to a total size of 109 969 women exposed to IVF, among whom 76 incident cases of ovarian, 18 of endometrial and 207 cases of cervical cancer were studied. The synthesis of studies with general population as the reference group pointed to a statistically significant positive association between IVF and increased risk for ovarian (RR = 1.50, 95% confidence interval (CI): 1.17-1.92) and endometrial (RR = 2.04, 95% CI: 1.22-3.43), but not cervical (RR = 0.86, 95% CI: 0.49-1.49) cancers. On the contrary, when infertile women were used as the reference group, no significant associations with ovarian, endometrial or cervical cancer types were noted (RR = 1.26, 95% CI: 0.62-2.55 RR = 0.45, 95% CI: 0.18-1.14 and RR = 5.70, 95% CI: 0.28-117.20, respectively). CONCLUSIONS: IVF does not seem to be associated with elevated cervical cancer risk, nor with ovarian or endometrial cancer when the confounding effect of infertility was neutralized in studies allowing such comparisons. Of note, only one study provided follow-up longer than 10 years for the group exposed to IVF. Future cohort studies should preferably use infertile women as the reference group, rely on IVF-registered valid exposure data, adjust for a variety of meaningful confounders and adopt relatively longer follow-up periods before sound conclusions are drawn.

Validity of intraoperative gross examination of myometrial invasion in patients with endometrial cancer: a meta-analysis.

OBJECTIVE: The current recommended endometrial cancer surgical treatment is abdominal extrafascial total hysterectomy with bilateral salpingo-oophorectomy followed by pelvic lymphadenectomy if >50% myometrial invasion is estimated by intraoperative gross examination (IGE). This meta-analysis aims to quantify evidence regarding the validity/predictive value of IGE staging compared with final histology. DESIGN: Meta-analysis of studies published until October 2011. SETTING: Systematic search, according to PRISMA guidelines, of the six major medical literature databases - Medline, Scopus, EMBASE, Google Scholar, Ovid, Cochrane. POPULATION: Sixteen eligible studies including 2567 endometrial cancer patients. METHODS: Pooled sensitivity/specificity, accuracy, negative/positive predictive value (NPV/PPV) and diagnostic odds ratio (DOR) of IGE were calculated and the summary receiver operator characteristic (sROC) curve was constructed. A meta-regression analysis was used to explore the role of potential modifiers of sensitivity and specificity. MAIN OUTCOME MEASURES: Pooled diagnostic measures of IGE indices. Results. Sixteen studies (15 retrospective, one prospective) meeting the inclusion criteria were qualitatively analyzed. Pooled IGE estimates were: sensitivity = 0.75 (95%CI: 0.72-0.78), specificity = 0.92 (95%CI 0.90-0.94), accuracy = 0.87 (95%CI 0.86-0.88), NPV = 0.89 (95%CI 0.87-0.92), PPV = 0.80 (95%CI 0.76-0.84) and DOR = 36.9 (95%CI 28.7-47.4). No significant modifiers were identified for sensitivity or specificity. CONCLUSIONS: The synthesized measures presented here for the first time showed that accuracy, sensitivity and specificity of IGE were 87, 75 and 92%, respectively, which indicates that IGE is useful for estimating depth of myometrial invasion and staging of endometrial cancer in clinical practice. The degree to which the relatively low values of some of its performance indicators could be improved remains to be elucidated in order for the values to be comparable with those from frozen section biopsies.

Administration of 2-arachidonoylglycerol ameliorates both acute and chronic experimental autoimmune encephalomyelitis.

BACKGROUND AND PURPOSE: Experimental autoimmune encephalomyelitis (EAE) is a widely used model of multiple sclerosis (MS) and both conditions have been reported to exhibit reduced endocannabinoid activity. The purpose of this study was to address the effect of exogenously administered 2-arachidonoylglycerol (2AG), an endocannabinoid receptor ligand, on acute phase and chronic disability in EAE. EXPERIMENTAL APPROACH: Acute and chronic EAE models were induced in susceptible mice and 2AG-treatment was applied for 14 days from day of disease induction. KEY RESULTS: 2AG-treatment ameliorated acute phase of disease with delay of disease onset in both EAE models and reduced disease mortality and long-term (70 days post-induction) clinical disability in chronic EAE. Reduced axonal pathology in the chronic EAE- (p<0.0001) and increased activation and ramification of microglia in the 2AG-treated acute EAE- (p<0.05) model were noticed. The latter was accompanied by a 2- to 4-fold increase of the M2-macrophages in the perivascular infiltrations (p<0.001) of the 2AG-treated animals in the acute (day 22), although not the chronic (day 70), EAE model. Expression of cannabinoid receptors 1 (CB1R) and 2 (CB2R) was increased in 2AG-treated animals of acute EAE vs. controls (p<0.05). In addition, ex vivo viability assays exhibited reduced proliferation of activated lymph node cells when extracted from 2AG-treated EAE animals, whereas a dose-dependent response of activated lymphocytes to 2AG-treatment in vitro was noticed. CONCLUSION AND IMPLICATIONS: Our data indicate for the first time that 2AG treatment may provide direct (via CBRs) and immune (via M2 macrophages) mediated neuroprotection in EAE.

Animal models of central nervous system immune-mediated diseases: therapeutic interventions with bioactive peptides and mimetics.

Experimental allergic encephalomyelitis (EAE) is a T helper 1 (Th1) mediated autoimmune disease and the principal animal model for multiple sclerosis (MS). Like MS, EAE is characterized by a coordinated inflammatory attack on the myelin sheath in the central nervous system (CNS), with damage to axons. No matter whether the ideal animal model is not yet available, much knowledge concerning the pathogenesis of MS has been achieved through studies on EAE. Dissecting the underlying immune mechanisms provided recognition of several myelin antigens that are vulnerable in autoimmune attack. The beneficial effect and the mechanism of action of a number of the currently used immunomodulating agents in MS therapy were first indicated in EAE. Altered peptide ligands (APL) can modulate T-cell responses to native peptide antigens implicated in the pathogenesis of autoimmune diseases such as MS and EAE. However, peptide therapy is hindered due to the sensitivity of peptides to proteolytic enzymes as well as due to some immune-mediated side effects. A number of cyclic myelin peptide analogs seem to be potential candidates in maintaining the biological function of the original peptide and effective in controlling inflammation in EAE. Additional data regarding the immunomodulating and neuroprotective effect of these much promising agents is required. Based on the data from studies on EAE models, clinical trials should also be designed in order to elucidate the impact of such APL-induced immune responses in MS disease activity. These clinical trials should carefully incorporate monitoring of both clinical, neuroimaging and immunological parameters.