scholarly journals Sensing Bluetooth Mobility Data: Potentials and Applications

Author(s):  
João Filgueiras ◽  
Rosaldo J. F. Rossetti ◽  
Zafeiris Kokkinogenis ◽  
Michel Ferreira ◽  
Cristina Olaverri-Monreal ◽  
...  
Keyword(s):  
1960 ◽  
Vol 4 (01) ◽  
pp. 031-044
Author(s):  
George Y. Shinowara ◽  
E. Mary Ruth

SummaryFour primary fractions comprising at least 97 per cent of the plasma proteins have been critically appraised for evidence of denaturation arising from a low temperature—low ionic strength fractionation system. The results in addition to those referable to the recovery of mass and biological activity include the following: The high solubilities of these fractions at pH 7.3 and low ionic strengths; the compatibility of the electrophoretic and ultracentrifugal data of the individual fractions with those of the original plasma; and the recovery of hemoglobin, not hematin, in fraction III obtained from specimens contaminated with this pigment. However, the most significant evidence for minimum alterations of native proteins was that the S20, w and the electrophoretic mobility data on the physically recombined fractions were identical to those found on whole plasma.The fractionation procedure examined here quantitatively isolates fibrinogen, prothrombin and antithrombin in primary fractions. Results have been obtained demonstrating its significance in other biological systems. These include the following: The finding of 5 S20, w classes in the 4 primary fractions; the occurrence of more than 90 per cent of the plasma gamma globulins in fraction III; the 98 per cent pure albumin in fraction IV; and, finally, the high concentration of beta lipoproteins in fraction II.


2020 ◽  
Author(s):  
Elizabeth Neumann ◽  
Lukasz Migas ◽  
Jamie L. Allen ◽  
Richard Caprioli ◽  
Raf Van de Plas ◽  
...  

<div> <div> <p>Small metabolites are essential for normal and diseased biological function but are difficult to study because of their inherent structural complexity. MALDI imaging mass spectrometry (IMS) of small metabolites is particularly challenging as MALDI matrix clusters are often isobaric with metabolite ions, requiring high resolving power instrumentation or derivatization to circumvent this issue. An alternative to this is to perform ion mobility separation before ion detection, enabling the visualization of metabolites without the interference of matrix ions. Here, we use MALDI timsTOF IMS to image small metabolites at high spatial resolution within the human kidney. Through this, we have found metabolites, such as arginic acid, acetylcarnitine, and choline that localize to the cortex, medulla, and renal pelvis, respectively. We have also demonstrated that trapped ion mobility spectrometry (TIMS) can resolve matrix peaks from metabolite signal and separate both isobaric and isomeric metabolites with different localizations within the kidney. The added ion mobility data dimension dramatically increased the peak capacity for molecular imaging experiments. Future work will involve further exploring the small metabolite profiles of human kidneys as a function of age, gender, and ethnicity.</p></div></div>


2020 ◽  
Vol 13 (1) ◽  
pp. 5
Author(s):  
William Straka ◽  
Shobha Kondragunta ◽  
Zigang Wei ◽  
Hai Zhang ◽  
Steven D. Miller ◽  
...  

The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.


Author(s):  
Mark David Walker ◽  
Mihály Sulyok

Abstract Background Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's “Mobility Trends” (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists. Objective The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany? Methods AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response. Results Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with “driving,” 2511. “transit,” 2513. “walking,” 2508). Conclusion These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Pierre Nouvellet ◽  
Sangeeta Bhatia ◽  
Anne Cori ◽  
Kylie E. C. Ainslie ◽  
Marc Baguelin ◽  
...  

AbstractIn response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27–77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49–91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12–48%]) post-relaxation. In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.


2021 ◽  
pp. 1-37
Author(s):  
Michał Burzyński ◽  
Frédéric Docquier ◽  
Hendrik Scheewel

Abstract In this paper, we investigate the long-term effects of climate change on the mobility of working-age people. We use a world economy model that covers almost all the countries around the world, and distinguishes between rural and urban regions as well as between flooded and unflooded areas. The model is calibrated to match international and internal mobility data by education level for the last 30 years, and is then simulated under climate change variants. We endogenize the size, dyadic, and skill structure of climate migration. When considering moderate climate scenarios, we predict mobility responses in the range of 70–108 million workers over the course of the twenty-first century. Most of these movements are local or inter-regional. South–South international migration responses are smaller, while the South–North migration response is of the “brain drain” type and induces a permanent increase in the number of foreigners in OECD countries in the range of 6–9% only. Changes in the sea level mainly translate into forced local movements. By contrast, inter-regional and international movements are sensitive to temperature-related changes in productivity. Lastly, we show that relaxing international migration restrictions may exacerbate the poverty effect of climate change at origin if policymakers are unable to select/screen individuals in extreme poverty.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaobin Wang ◽  
Yun Tong ◽  
Yupeng Fan ◽  
Haimeng Liu ◽  
Jun Wu ◽  
...  

AbstractSince spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.


2021 ◽  
Vol 11 (7) ◽  
pp. 3059
Author(s):  
Myeong-Hun Jeong ◽  
Tae-Young Lee ◽  
Seung-Bae Jeon ◽  
Minkyo Youm

Movement analytics and mobility insights play a crucial role in urban planning and transportation management. The plethora of mobility data sources, such as GPS trajectories, poses new challenges and opportunities for understanding and predicting movement patterns. In this study, we predict highway speed using a gated recurrent unit (GRU) neural network. Based on statistical models, previous approaches suffer from the inherited features of traffic data, such as nonlinear problems. The proposed method predicts highway speed based on the GRU method after training on digital tachograph data (DTG). The DTG data were recorded in one month, giving approximately 300 million records. These data included the velocity and locations of vehicles on the highway. Experimental results demonstrate that the GRU-based deep learning approach outperformed the state-of-the-art alternatives, the autoregressive integrated moving average model, and the long short-term neural network (LSTM) model, in terms of prediction accuracy. Further, the computational cost of the GRU model was lower than that of the LSTM. The proposed method can be applied to traffic prediction and intelligent transportation systems.


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