A Mobility Model for Synthetic Travel Demand from Sparse Individual Traces

Knowing how much people travel is essential for lowering carbon emissions in the transport sector. Empirical mobility traces collected from call detail records (CDRs), location-based social networks (LBSNs), and social media data have been used widely to study mobility patterns. However, these traces suffer from sparsity, an issue that has largely been overlooked. In order to extend the use of these low-cost and easy-to-access data, this study proposes an individual-based mobility model to fill the gaps in sparse mobility traces. The proposed model applies the fundamental mechanisms of exploration and preferential return to synthesise mobility to generate trips, designed to accommodate the sparse individual traces of geolocated social media data. We validated our model and found good agreement on origin-destination matrices and trip distance distributions for Sweden, the Netherlands, and S\ue3o Paulo, Brazil. The proposed model can be used to synthesise mobility at any geographic scale, and the results can later be applied to modelling travel demand. We further apply the model to characterise domestic trip distances for a mixture of cities and countries globally. The trip distance distributions from the model-synthesised trips using sparse geolocations from 22 regions largely follow lognormal distributions and they reflect reasonable characteristics of regional heterogeneity. Further exploration is needed to understand the regional differences between the 22 cities and countries tested

A Mobility Model for Synthetic Travel Demand from Sparse Traces

Knowing how much people travel is essential for transport planning. Empirical mobility traces collected from call detail records (CDRs), location-based social networks (LBSNs), and social media data have been used widely to study mobility patterns. However, these data suffer from sparsity, an issue that has largely been overlooked. In order to extend the use of these low-cost and accessible data, this study proposes a mobility model that fills the gaps in sparse mobility traces from which one can later synthesise travel demand. The proposed model extends the fundamental mechanisms of exploration and preferential return to synthesise mobility trips. The model is tested on sparse mobility traces from Twitter. We validate our model and find good agreement on origin-destination matrices and trip distance distributions for Sweden, the Netherlands, and Sa\uf5 Paulo, Brazil, compared with a benchmark model using a heuristic method, especially for the most frequent trip distance range (1-40 km). Moreover, the learned model parameters are found to be transferable from one region to another. Using the proposed model, reasonable travel demand values can be synthesised from a dataset covering a large enough population of very sparse individual geolocations (around 1.5 geolocations per day covering 100 days on average)

North American Land Data Assimilation System: A Framework for Merging Model and Satellite Data for Improved Drought Monitoring

Drought is a pervasive natural climate hazard that has widespread impacts on human activity and the environment. In the United States, droughts are billion-dollar disasters, comparable to hurricanes and tropical storms and with greater economic impacts than extratropical storms, wildfires, blizzards, and ice storms combined (NCDC, 2009). Reduction of the impacts and increased preparedness for drought requires the use and improvement of monitoring and prediction tools. These tools are reliant on the availability of spatially extensive and accurate data for representing the occurrence and characteristics (such as duration and severity) of drought and their related forcing mechanisms. It is increasingly recognized that the utility of drought data is highly dependent on the application (e.g., agricultural monitoring versus water resource management) and time (e.g., short- versus long-term dryness) and space (e.g., local versus national) scales involved. A comprehensive set of drought indices that considers all components of the hydrological–ecological–human system is necessary. Because of the dearth of near-real-time in situ hydrologic data collected over large regions, modeled data are often useful surrogates, especially when combined with observations from remote sensing and in situ sources. This chapter provides an overview of drought-related activities associated with the North American Land Data Assimilation System (NLDAS), which purports to provide an incremental step toward improved drought monitoring and forecasting. The NLDAS was originally conceived to improve short-term weather forecasting by providing better land surface initial conditions for operational weather forecast models. This reflects increased recognition of the role of land surface water and energy states, such as surface temperature, soil moisture, and snowpack, to atmospheric processes via feedbacks through the coupling of the water and energy cycles. Phase I of the NLDAS (NLDAS-1; Mitchell et al., 2004) made tremendous progress toward developing an operational system that gave high-resolution land hydrologic products in near real time. The system consists of multiple land surface models (LSMs) that are driven by an observation-based meteorological data set both in real time and retrospectively. This work resulted in a series of scientific papers that evaluated the retrospective data (meteorology and model output) in terms of their ability to reflect observations of the water and energy cycles and the uncertainties in the simulations as measured by the spread among individual models (Pan et al., 2003; Robock et al., 2003; Sheffield et al., 2003; Lohmann et al., 2004; Mitchell et al., 2004; Schaake et al., 2004). These evaluations led to the implementation of significant improvements to the LSMs in the form of new model physics and adjustments to parameter values and to the methods and input meteorological data (Xia et al., 2012). The system has since expanded in scope to include model intercomparison studies, real-time monitoring, and hydrologic prediction and has inspired other activities such as high-resolution land surface modeling and global land data assimilation systems (e.g., the Global Land Data Assimilation System [GLDAS], Rodell et al., 2004; the Land Information System [LIS], Kumar et al., 2006)

North American Land Data Assimilation System: A Framework for Merging Model and Satellite Data for Improved Drought Monitoring

Drought is a pervasive natural climate hazard that has widespread impacts on human activity and the environment. In the United States, droughts are billion-dollar disasters, comparable to hurricanes and tropical storms and with greater economic impacts than extratropical storms, wildfires, blizzards, and ice storms combined (NCDC, 2009). Reduction of the impacts and increased preparedness for drought requires the use and improvement of monitoring and prediction tools. These tools are reliant on the availability of spatially extensive and accurate data for representing the occurrence and characteristics (such as duration and severity) of drought and their related forcing mechanisms. It is increasingly recognized that the utility of drought data is highly dependent on the application (e.g., agricultural monitoring versus water resource management) and time (e.g., short- versus long-term dryness) and space (e.g., local versus national) scales involved. A comprehensive set of drought indices that considers all components of the hydrological–ecological–human system is necessary. Because of the dearth of near-real-time in situ hydrologic data collected over large regions, modeled data are often useful surrogates, especially when combined with observations from remote sensing and in situ sources. This chapter provides an overview of drought-related activities associated with the North American Land Data Assimilation System (NLDAS), which purports to provide an incremental step toward improved drought monitoring and forecasting. The NLDAS was originally conceived to improve short-term weather forecasting by providing better land surface initial conditions for operational weather forecast models. This reflects increased recognition of the role of land surface water and energy states, such as surface temperature, soil moisture, and snowpack, to atmospheric processes via feedbacks through the coupling of the water and energy cycles. Phase I of the NLDAS (NLDAS-1; Mitchell et al., 2004) made tremendous progress toward developing an operational system that gave high-resolution land hydrologic products in near real time. The system consists of multiple land surface models (LSMs) that are driven by an observation-based meteorological data set both in real time and retrospectively. This work resulted in a series of scientific papers that evaluated the retrospective data (meteorology and model output) in terms of their ability to reflect observations of the water and energy cycles and the uncertainties in the simulations as measured by the spread among individual models (Pan et al., 2003; Robock et al., 2003; Sheffield et al., 2003; Lohmann et al., 2004; Mitchell et al., 2004; Schaake et al., 2004). These evaluations led to the implementation of significant improvements to the LSMs in the form of new model physics and adjustments to parameter values and to the methods and input meteorological data (Xia et al., 2012). The system has since expanded in scope to include model intercomparison studies, real-time monitoring, and hydrologic prediction and has inspired other activities such as high-resolution land surface modeling and global land data assimilation systems (e.g., the Global Land Data Assimilation System [GLDAS], Rodell et al., 2004; the Land Information System [LIS], Kumar et al., 2006)

Measurement of cortisol in saliva: a comparison of measurement error within and between international academic-research laboratories

Objective: Hundreds of scientific publications are produced annually that involve the measurement of cortisol in saliva. Intra- and inter-laboratory variation in salivary cortisol results has the potential to contribute to cross- study inconsistencies in findings, and the perception that salivary cortisol results are unreliable. This study rigor- ously estimates sources of measurement variability in the assay of salivary cortisol within and between established international academic-based laboratories that specialize in saliva analyses. One hundred young adults (Mean age: 23.10 years; 62 females) donated 2 mL of whole saliva by passive drool. Each sample was split into multiple- 100 µL aliquots and immediately frozen. One aliquot of each of the 100 participants’ saliva was transported to academic laboratories (N = 9) in the United States, Canada, UK, and Germany and assayed for cortisol by the same commercially available immunoassay. Results: 1.76% of the variance in salivary cortisol levels was attributable to differences between duplicate assays of the same sample within laboratories, 7.93% of the variance was associated with differences between laboratories, and 90.31% to differences between samples. In established-qualified laboratories, measurement error of salivary cortisol is minimal, and inter-laboratory differences in measurement are unlikely to have a major influence on the determined values

Forensic pregnancy diagnostics with placental mRNA markers

Current methods for pregnancy diagnostics are based on immunodetection of pregnancy-specific proteins and in a forensic context suffer from sensitivity and specificity issues. Here, we applied reverse transcriptase polymerase chain reaction (RT-PCR) technology to 11 genes previously reported with placental mRNA circulating in maternal blood. We found two genes, hPL and βhCG, with pregnancy-specific expression in whole blood samples. RT-PCR detection of hPL was positive in all samples tested throughout the pregnancy, whereas βhCG was detectable until half of the second trimester but not at later gestation ages. For hPL, in vitro stability of the transcript was demonstrated until 2 months of age, and the hPL-specific RT-PCR assay applied was highly sensitive with reliable detection from down to 0.25 cm2 dried bloodstain. We therefore suggest hPL-specific RT-PCR as a new molecular tool for forensic pregnancy diagnostics from dried blood stains. Moreover, our results indicate that the time-wise reverse expression of hPL and βhCG during pregnancy may allow an RT-PCR-based estimation of the gestational age from blood stains, adding to the value of forensic pregnancy diagnosis for crime scene investigations

The clinical significance of incidental intra-abdominal findings on positron emission tomography performed to investigate pulmonary nodules

<p>Abstract</p> <p>Background</p> <p>Lung cancer is a common cause of cancer-related death. Staging typically includes positron emission tomography (PET) scanning, in which¹⁸F-fluoro-2-dexoy-D-glucose (FDG) is taken up by cells proportional to metabolic activity, thus aiding in differentiating benign and malignant pulmonary nodules. Uptake of FDG can also occur in the abdomen. The clinical significance of incidental intraabdominal FDG uptake in the setting of pulmonary nodules is not well established. Our objective was to report on the clinical significance of incidental intra-abdominal FDG activity in the setting of lung cancer.</p> <p>Methods</p> <p>Fifteen hundred FDG-PET reports for studies performed for lung cancer were retrospectively reviewed for the presence of incidental FDG-positive intraabdominal findings. Patient charts with positive findings were then reviewed and information extracted.</p> <p>Results</p> <p>Twenty-five patients (25/1500) demonstrated incidental intraabdominal FDG uptake thought to be significant (1.7%) with a mean patient age of 71 years. Colonic uptake was most common (n = 17) with 9 (52%) being investigated further. Of these 9 cases, a diagnosis of malignancy was made in 3 patients, pre-malignant adenomas in 2 patients, a benign lipoma in 1 patient and no abnormal findings in the remaining patients. 8 patients were not investigated further (3 diagnosed with metastatic lung cancer and 2 were of advanced age) secondary to poor prognosis.</p> <p>Conclusion</p> <p>Incidental abdominal findings in the colon on FDG-PET scan for work-up of pulmonary nodules need to be further investigated by colonoscopy.</p

NT2 Derived Neuronal and Astrocytic Network Signalling

A major focus of stem cell research is the generation of neurons that may then be implanted to treat neurodegenerative diseases. However, a picture is emerging where astrocytes are partners to neurons in sustaining and modulating brain function. We therefore investigated the functional properties of NT2 derived astrocytes and neurons using electrophysiological and calcium imaging approaches. NT2 neurons (NT2Ns) expressed sodium dependent action potentials, as well as responses to depolarisation and the neurotransmitter glutamate. NT2Ns exhibited spontaneous and coordinated calcium elevations in clusters and in extended processes, indicating local and long distance signalling. Tetrodotoxin sensitive network activity could also be evoked by electrical stimulation. Similarly, NT2 astrocytes (NT2As) exhibited morphology and functional properties consistent with this glial cell type. NT2As responded to neuronal activity and to exogenously applied neurotransmitters with calcium elevations, and in contrast to neurons, also exhibited spontaneous rhythmic calcium oscillations. NT2As also generated propagating calcium waves that were gap junction and purinergic signalling dependent. Our results show that NT2 derived astrocytes exhibit appropriate functionality and that NT2N networks interact with NT2A networks in co-culture. These findings underline the utility of such cultures to investigate human brain cell type signalling under controlled conditions. Furthermore, since stem cell derived neuron function and survival is of great importance therapeutically, our findings suggest that the presence of complementary astrocytes may be valuable in supporting stem cell derived neuronal networks. Indeed, this also supports the intriguing possibility of selective therapeutic replacement of astrocytes in diseases where these cells are either lost or lose functionality

Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition