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This study has demonstrated that higher relative humidity and wind speed, and lower atmospheric pressure, were associated with increased pain severity in people with long-term pain conditions

Explore this study

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There were 9695 hazard periods included in the analysis for the final 2658 participants, matched to 81,727 control periods in 6431 participant-months

Time spent outside did not have a significant interaction with relative humidity or wind speed , nor did it lead to significant associations for temperature when conducting analyses stratified by time spent outside (Supplementary Table 3 )

This study has demonstrated that higher relative humidity and wind speed , and lower atmospheric pressure, were associated with increased pain severity in people with long-term pain conditions

The ‘worst’ combination of weather variables would increase the odds of a pain event by just over 20% compared to an average day

We showed that Cloudy participants were largely representative of a population reporting chronic-pain symptoms,[13] proportionally fewer participants at both extremes of age were recruited

The odds of a pain event was 12% higher per one standard deviation increase in relative humidity (9 percentage points) ( OR 1.119 (1.084–1.154), compared to 4% lower for pressure ( OR 0.958 (0.930–0.989) and 4% higher for wind speed ( OR 1.041 (1.010–1.073) (11 mbar and 2 m s−1, respectively)

The analysis has demonstrated significant relationships between relative humidity , pressure, wind speed and pain, with correlations remaining even when accounting for mood and physical activity

This study used a smartphone app to collect data from 2658 patients with chronic pain over 15 months, finding significant relationships between pain and relative humidity, pressure, and wind speed, with correlations remaining even when accounting for mood and physical activity.

Key findings

The study found significant relationships between pain and relative humidity , pressure, and wind speed , with correlations remaining even when accounting for mood and physical activity . Relative humidity had the strongest association with pain, and temperature the least.

The study found significant relationships between relative humidity , pressure, wind speed , and pain, with correlations remaining even when accounting for mood and physical activity .

The objective of the study was to examine the relationship between local weather and daily pain in people living with long-term pain conditions using a smartphone app.

The study used a smartphone app to collect daily data from participants over 15 months, including pain symptoms, mood, physical activity , and weather data from nearby weather stations.

The study used a case-crossover design, where participants served as their own control, eliminating confounding by time-invariant factors. Weather data were obtained by linking hourly smartphone GPS data to the nearest of 154 possible United Kingdom Met Office weather stations.

The study uses a case-crossover design and statistical analysis, including the calculation of odds ratios using the following equation: n Odds Ratio = exp(βT(temperature - μT) + βRH( relative humidity - μRH) + βwsp( wind speed - μwsp) + βP(pressure - μp)).

The study found significant relationships between pain and relative humidity , pressure, and wind speed , with correlations remaining even when accounting for mood and physical activity . The odds of a pain event were higher with an increase in relative humidity and wind speed , and lower with an increase in atmospheric pressure.

The study found that the odds ratio for a pain event increased with relative humidity , pressure, and wind speed . The results remained significant even when accounting for mood and physical activity .

Conclusions

The study demonstrated that higher relative humidity and wind speed , and lower atmospheric pressure, were associated with increased pain severity in people with long-term pain conditions. The effect of weather on pain was not fully explained by its day-to-day effect on mood or physical activity .

The study concludes that there is a significant relationship between weather and pain, and that understanding this relationship is important for patients with chronic pain .

Front matter

How the weather affects the pain of citizen scientists using a smartphone app William G . Dixon 1,2,3*, Anna L . Beukenhorst 1, Belay B . Yimer[1], Louise Cook[1], Antonio Gasparrini 4,5, Tal El-Hay 6, Bruce Hellman 7, Ben James[7], Ana M. Vicedo-Cabrera[4], Malcolm Maclure[8], Ricardo Silva 9,10, John Ainsworth 2, Huai Leng Pisaniello1,[11], Thomas House 12,13, Mark Lunt 1, Carolyn Gamble3,[14,15], Caroline Sanders[14,15], David M. Schultz 16, Jamie C. Sergeant 1,3,17,18 and John McBeth1,[3,18]

Patients with chronic pain commonly believe their pain is related to the weather. Scientific evidence to support their beliefs is inconclusive, in part due to difficulties in getting a large dataset of patients frequently recording their pain symptoms during a variety of weather conditions. Smartphones allow the opportunity to collect data to overcome these difficulties. Our study Cloudy with a Chance of Pain analysed daily data from 2658 patients collected over a 15-month period . The analysis demonstrated significant yet modest relationships between pain and relative humidity, pressure and wind speed, with correlations remaining even when accounting for mood and physical activity . This research highlights how citizen-science experiments can collect large datasets on real-world populations to address long-standing health questions. These results will act as a starting point for a future system for patients to better manage their health through pain forecasts .

INTRODUCTION

Weather has been thought to affect symptoms in patients with chronic disease since the time of Hippocrates over 2000 years ago.[1] Around three-quarters of people living with arthritis believe their pain is affected by the weather .[2,3] Many report their pain is made worse by the cold, rain , and low atmospheric pressure. Others report that their pain is made worse by warmth and high humidity. Despite much research examining the existence and nature of the weather–pain relationship,[4] there remains no scientific consensus. Studies have failed to reach consensus in part due to their small sample sizes or short durations (commonly fewer than 100 participants or one month or less); by considering a limited range of weather conditions; and heterogeneity in study design (e.g. the populations studied, methods for assessing pain, assumptions to determine the weather exposure, and statistical analysis techniques).[5–11] Resolving this question requires collection of high-quality symptom and weather data on large numbers of individuals. Such data also need to include other factors potentially linked to daily pain variation and weather , such as mood and amount of physical activity . Collecting this kind of multi-faceted data in large populations over long periods of time, however, has been difficult.

The increasing uptake of smartphones offers new and significant opportunities for health research.[12] Smartphones allow the integration of data collection into daily life using applications (apps). Furthermore, embedded technologies within the smartphones, such as the Global Positioning System ( GPS ), can be used to link the data collection to specific locations. We created Cloudy with a Chance of Pain,[13,14] a national United Kingdom smartphone study, to collect a large dataset to examine the relationship between local weather and daily pain in people living with longterm pain conditions.

The study app was downloaded by 13,207 users over the 12month recruitment period (Figs 1 and 2a) with recruitment from all 124 UK postcode areas. A total of 10,584 participants had complete baseline information and at least one pain entry, with 6850 (65%) participants remaining in the study beyond their first week and 4692 (44%) beyond their first month (Fig. 2b) . Further description of engagement clusters is provided in Supplementary Table 2 and Supplementary Figs 1–3. A total of 2658 participants had at least one hazard period matched to a control period in the same month (Fig. 3) and were included in the final analysis. There were 9695 hazard periods included in the analysis for the final 2658 participants, matched to 81,727 control periods in 6431 participant-months. A total of 1235 participants contributed one month, and the remaining 1423 participants contributed 2–15 months.

The final cohort was active for a median of 165 days (interquartile range, IQR 84–245) with symptoms submitted on an average of 73% of all days. Cohort members were

How sƟff did you feel on waking No sƟffness Very severely this morning?

predominantly female (83%), had a mean age of 51 years (standard deviation 12.6), and had a range of different pain conditions, predominantly arthritis (Supplementary Table 1 ). The median number of weather stations associated with each participant during the course of their active data-collection period was 9 (IQR 4–14) with a maximum of 82 stations, indicating how mobile participants were during the course of the study and the importance of accounting for the weather at different locations over the course of the study. As an illustration of the structure of the data, the proportion of participants reporting a pain event was plotted as a heat map per calendar day for the study period (Fig. 4), aligned with the average United Kingdom weather data for the same time period. On any given day during the study, about 1–6% of participants had a pain event. At the start of the study, most participants believed in an association between weather and their pain (median score 8 out of 10, IQR 6–9). The demographics, health conditions and baseline beliefs of the 2658 participants included in the analysis were representative of the 10,584 participants who downloaded the app and provided baseline information (Supplementary Table 2 ).

The multivariable case-crossover analysis including the four state weather variables demonstrated that an increase in relative humidity was associated with a higher odds of a pain event with an OR of 1.139 (95% confidence interval 1.099–1.181) per 10 percentage point increase, as was an increase in wind speed with an OR of 1.02 (1.005–1.035) per 1 m s−1 increase ( Table 1 ). The odds of a pain event was lower with an increase in atmospheric pressure with an OR of 0.962 (0.937–0.987) per 10-mbar increase. Temperature did not have a significant association with pain ( OR 0.996 (0.985–1.007) per 1 °C increase). The odds of a pain event was 12% higher per one standard deviation increase in relative humidity (9 percentage points) (OR 1.119 (1.084–1.154), compared to 4% lower for pressure (OR 0.958 (0.930–0.989) and 4% higher for wind speed (OR 1.041 (1.010–1.073) (11 mbar and 2 m s−1, respectively) . Of the four weather variables, relative humidity had the strongest association with pain , and temperature the least, evidenced by the estimated relative importance of the variables and their standardized odds ratios ( Table 1 , Supplementary Table 4 ). Similar effect sizes were seen when each variable was examined in univariable analyses . Precipitation was not associated with an increased odds of a pain event ( OR 0.996 (0.989–1.003) per 1 mm daily rainfall amount) (Supplementary Table 5 ). Exploratory analyses considered time spent outside by including an interaction term with temperature, relative humidity , and wind speed . Time spent outside did not have a significant interaction with relative humidity or wind speed , nor did it lead to significant associations for temperature when conducting analyses stratified by time spent outside (Supplementary Table 3 ). It thus was not included in the final model.

The model was then expanded to include mood and physical activity on the day of interest, included as binary variables ( Table 1 ), resulting in a modest reduction in the point estimates for all weather variables. Mood was strongly and independently associated with pain events ( OR 4.083 (3.824–4.360) for low mood versus good mood), whereas there was no significant association with physical activity ( OR 0.939 (0.881–1.002) for high versus low activity).

This multivariable regression model output represents the effect of one weather variable while all else remains constant. In reality, a single weather variable rarely changes in isolation while others remain unchanged . To illustrate the composite effect of the weather variables on the odds of reporting pain, an OR was calculated for each day using the coefficients of our multivariable model and daily UK mean weather values. Figure 5 demonstrates there is significant variability in the odds of a pain event for any given value of each weather variable. For example, at a temperature of 8 °C, the odds of a pain event varied from around npj Digital Medicine (2019) 105

0.8–1.2, depending on the other state variables in the weather that day.

Other factors such as day of the week (Supplementary Table 6 ), lagged weather values (Supplementary Table 7 ) and changes in weather variables from the previous day were tested. Mondays, Thursdays, and Saturdays (ORs 1.14, 1.14, and 1.29, respectively) had higher odds of pain compared to Sundays, but adjusting for the day of the week did not alter the effect of the four main weather variables . Except for relative humidity (1-day lag and 2day lag), no significant associations were observed between lagged weather variables and pain events . Including change in weather from yesterday showed a minor effect of changing relative humidity ( OR 1.005 (1.001–1.009) per 10 percentage point increase), whereas the effects of today’s relative humidity and pressure remained unchanged (Supplementary Table 8 ). Stratification by disease led to a loss of statistical power and largely inconclusive results, although relative humidity appeared to have a stronger association with pain in patients with osteoarthritis (Supplementary Table 9 , Supplementary Fig. 4). Stratification by the number of pain sites also showed no meaningful difference (Supplementary Table 10 ). After stratifying by participants’ prior beliefs about their weather–pain relationship, relative humidity remained associated with pain in all participants although the association with pressure was only seen in those with a strong prior belief (Supplementary Table 11 ).

This study has demonstrated that higher relative humidity and wind speed , and lower atmospheric pressure, were associated with increased pain severity in people with long-term pain conditions . The most significant contribution was from relative humidity . The effect of weather on pain was not fully explained by its day-to-day effect on mood or physical activity . The overall effect sizes, while statistically significant, were modest. For example, the ‘worst’ combination of weather variables would increase the odds of a pain event by just over 20% compared to an average day . Nonetheless, such an increased risk may be meaningful to people living with chronic pain .

In addition to investigating the weather–pain relationship, we successfully conducted a national smartphone study that delivered on the promise of how consumer technology can support health research.[12,15] This study recruited over 10,000 participants throughout the United Kingdom, sustained daily self-reported data over many months,[13] and showcased the value of passively collected GPS data. Prior large smartphone studies have retained npj Digital Medicine (2019) 105

only around one in ten participants for seven days or less.[16,17] In contrast, our study retained 65% of participants for the first seven days, and 44% for the first month, with over 2600 participants contributing to the analysis having provided data for many months of the study.[13,14] An important success factor was strong public involvement in early setup and piloting, as well as participants’ interest in weather as a possible pain trigger.[14] The study design has resolved problems of prior weather–pain studies such as small populations,[5,7] short follow-up,[3,8] surrogate pain outcomes,[11] the absence of possible causal pathway variables such as mood, and assumptions about where participants were located and thus the weather to which they were exposed.[18,19]

Overcoming these obstacles produced a large dataset that allowed us to tease out subtle relationships between weather and pain .

There are potential limitations to this study. First, the reduction in participant numbers from over 10,000 with baseline data to the final 2658 participants with at least one within-month risk set raises questions about generalisability . Importantly, the characteristics of those included in the analysis were similar to the initial

10,000 participants, other than being slightly older (mean age 51 versus 48 years old). In a prior analysis, we showed that Cloudy participants were largely representative of a population reporting chronic-pain symptoms,[13] although proportionally fewer participants at both extremes of age were recruited. However, we would not expect middle-aged recruits to differ in their relationship between weather and pain from older or younger participants, and thus such selection factors would not invalidate our results. Second, the study was advertised to participants with a clear research question . It is possible that only people with a strong belief in a weather–pain relationship participated, generating an unrepresentative sample. However, the percentage of participants who believed in the weather –pain relationship was similar to prior studies,[20] and we did not see selective attrition of people who reported no weather–pain beliefs.[13] The within-person design would, regardless, mean that participants who drop out early would not introduce bias from time-invariant characteristics. Third, the lack of blinding raises possible information bias where observed weather could influence participants’ symptom reporting. Our baseline questionnaire demonstrated that rain and cold weather were the most common pre-existing beliefs. If a reporting bias were to exist, we would expect higher pain to be reported at times of colder weather . Our findings—including the absence of an association with either temperature or rainfall—cannot be explained by such a reporting bias. Fourth, pain reporting is subjective, meaning one participant’s “moderate” might equate to npj Digital Medicine (2019) 105

someone else’s “severe”. The within-person case-crossover analysis meant we compared moments when an individual’s score increased by a meaningful amount to a control period for that same person. Fifth, we chose to model the weather using daily averages. It is possible that other findings may be hidden if the association between weather and pain was with other metrics of weather , such as the daily maximum, minimum, or range, or even if the changes in weather on hourly time scales affect participants’ pain. Sixth, the findings from this United Kingdom study cannot necessarily be extrapolated to different climates where the weather is different . Seventh, our population-wide analysis assumed that all participants have the same weather –pain relationship. Different diseases may have different sensitivities to pain and, even within disease, participants may be affected differently. Our decision to use the whole chronic-pain population in our primary analysis means the overall associations with weather variables may be combinations of strong, weak and absent causal effects, thereby underestimating the most important associations. Notable differences were not seen after stratification by pain condition , although the power to detect any differences was reduced because of smaller sample sizes. Lastly, the inclusion of repeated events per person required us to consider within-subject dependence which, if not accounted for, would lead to bias.[21] Our outcome was based on changes in pain (a two or more category increase), which meant events rarely occurred on consecutive days , thereby ensuring a time gap between recurrent events and the avoidance of bias.

Understanding the relationship between weather and pain is important for several reasons . First, this study validates the perception of those who believe that their pain is associated with the weather . Second, given we can forecast the weather days in advance, understanding how weather relates to pain would allow pain forecasts . Patients could then plan activities and take greater control of their lives. Finally, understanding the relationship between weather and pain might also allow better understanding of the mechanisms for pain and thus allow the development of new and more effective interventions for those who suffer with pain .

In summary, our large national smartphone study has successfully supported the collection of daily symptoms and high-quality weather data , allowing examination of the relationship between weather and pain. The analysis has demonstrated significant relationships between relative humidity, pressure, wind speed and pain, with correlations remaining even when accounting for mood and physical activity .

Patient involvement has been important throughout the study , from inception to interpretation of the results. Co-author C.G. is a patient partner and co-applicant, while a patient and public involvement group of seven additional members has supported the study, meeting eight times in total . During the feasibility study,[14] patients positively influenced the wording and display of questions within the app . C.G. and other members of the Patient and Public Involvement Group were involved in media broadcasts at study launch and subsequent public engagement activities, explaining why the research question was important to them and relevant to patients with long-term pain conditions.[22] They have supported the interpretation of findings and the development of dissemination plans for the results, ensuring the results reach study participants, patient organizations and the general public.

We recruited participants through local and national media (television, radio, and press) and social media from 20 January 2016 to 20 January 2017. To participate in the study, participants needed to (i) be living with long-term (>3 months) pain conditions, (ii) be aged 17 years or older, (iii) be living in the United Kingdom, and (iv) own an Android or Apple iOS smartphone. Interested participants were directed to the study website (www.cloudywithachanceofpain.com) where they could check their eligibility, learn about the study, and download the uMotif app (Fig. 1). After downloading the study app, participants completed an electronic consent form and a baseline questionnaire including demographic information (sex, year of birth, first half of postcode), anatomical site(s) of pain, underlying pain condition(s), baseline medication use, and beliefs about the extent to which weather influenced their pain on a scale of 0–10, including which weather condition(s) were thought to be most associated with pain. Participants were then invited to collect daily symptoms for six months, or longer if willing. Each day, the app alerted participants to complete ten items at 6:24 p.m. (Fig. 1). The ten items were pain severity, fatigue, morning stiffness, impact of pain, sleep quality, time spent outside, waking up feeling tired, physical activity , mood, and well-being. Each data

Univariable (single weather variable only) Odds ratio (95% CI)

Multivariable (all weather variables only) Odds ratio (95% CI)

Multivariable (weather plus activity and mood) Odds ratio (95% CI)

Temperature Per 1 °C Per 1 s.d. (4.8 °C) Relative humidity Per 10% Per 1 s.d. (8.6%) Pressure Per 10 mbar Per 1 s.d. (11.1 mbar) Wind speed Per 1 m s–1 Per 1 s.d. (2.1 m s–1) High activity Low mood

1.011 (0.995–1.027) 1.022 (0.990–1.056) 0.939 (0.881–1.002) 4.083 (3.824–4.360)

High activity—Top three categories: 30 min or more of light or strenuous activity per day, or less than 30 min of strenuous activity Low mood—Bottom three categories: ‘depressed’, ‘feeling low’ or ‘not very happy’ s.d. standard deviation

Distribution of weather variables: Temperature: range −4.9 to 25.9 °C, s.d. 4.8 °C Relative humidity: range 43.8–100%, s.d. 8.6% Pressure: range 966–1044.8 mbar, s.d. 11.1 mbar Wind speed: range 0–21.5 m s−1, s.d. 2.1 m s−1 item had five possible labeled ordinal responses. For example, in response to the question “How severe was your pain today?”, possible responses were “no pain”, “mild pain”, “moderate pain”, “severe pain” or “very severe pain”. The data were analysed using a case-crossover design where, for each participant, exposure during days with a pain event (“hazard periods”) were compared to “control periods” without a pain event in the same month.[23] Pain events were defined as a two-or-more category increase in pain from the preceding day, consistent with more stringent definitions of a clinically important difference[24] (Fig. 3). Data collection ended on 20 April 2017.

Cohort selection Participants were included in the final cohort for analysis if they fulfilled the following criteria: (1) downloaded the app; (2) provided consent; (3) completed the baseline questionnaire; and (4) contributed at least one pain event and matched control period in the same month (see below). During exploratory analysis, it was apparent that people reported higher pain levels in the first ten days following recruitment (perhaps due to calibration or regression to the mean). Therefore, the first ten days were excluded from the formal analysis. However, even if the first ten days were included, they had a negligible effect on the results (Supplementary Table 12 ).

The total person-days in study was calculated for each participant as the number of days between their first and last day of entering pain data. The number of person-days on which a pain score was entered was summed per participant, presented as a proportion of the total person-days in study, and averaged across the population. The geographical distribution of recruitment was described as the number of UK postcode areas represented (out of a maximum of 124).[25] The movement of participants during the study was described as the median number of weather stations associated with each participant during their data-collection period.

Ethical approval

Ethical approval was obtained from the University of Manchester Research Ethics Committee (ref: ethics/15522) and from the NHS IRAS (ref: 23/NW/

0716). Participants were required to provide electronic consent for study inclusion. Further details are available elsewhere.[13,14]

Weather data were obtained by linking hourly smartphone GPS data to the nearest of 154 possible United Kingdom Met Office weather stations. Where GPS data were missing, we used significant location imputation. (For details, see supplement). Local hourly weather data were obtained from the Integrated Surface Database ( ISD ) of NOAA (http://www.ncdc. noaa.gov/isd), which includes hourly observations from UK Met Office weather stations.

Given the latitude–longitude coordinates of a participant location, the haversine distance to every Met Office weather station was calculated. The nearest station to the given location was selected, conditional on the distance being less than 100 km and the station having four weather variables (temperature, pressure, wind speed , and dewpoint temperature) available at that time. If all stations with the required weather data exceeded the maximum distance (100 km), the location was left unlinked and the observation was excluded from the analysis.

The significant location imputation approach for handling missing hourly GPS data had three stages.[26] First, the participant’s observed location data were ordered by the frequency that the locations were visited. Second, the locations were spatially clustered using Hartigan’s Leader Algorithm[27] with a threshold of 0.5 km. Third, missing locations during weekdays were replaced by the centroid of the participant’s most visited cluster for weekdays and missing locations during weekends were replaced by centroid of the participant’s most visited cluster for weekends.

Recruitment and duration of follow-up were presented as a graph of cumulative recruitment and active participants, with participation ending at the last symptom entry. Retention in the study was also presented as a survival probability against time since recruitment, with participants censored when they were no longer eligible for follow-up. Eligible follow-up time ranged from 90 days (for those recruited on 20 January 2017) to 456 days (for those recruited on 20 January 2016). Engagement of npj Digital Medicine (2019) 105

mbar. The day associated with the lowest estimated odds of a pain event was when the temperature was 7 °C, relative humidity was 67%, wind speed 4.5 m s−1 and pressure 1030 mbar participants was further described through clustering of engagement states, which has been described in detail elsewhere.[13] Following recruitment, individuals were labeled as engaged if they reported any of the ten symptoms on a given day. A first-order hidden Markov model was used to estimate the levels of engagement of participants by assuming three latent engagement states: high, low, and disengaged. Clusters were defined according to different probabilities of transitioning between high engagement, low engagement and disengagement during the study. Retention of active participants was also presented stratified by engagement cluster, and in the subset of participants who contributed to the final analysis.

Days without pain events were only control periods if they were eligible to have a two-or-more category increase (i.e. the preceding day’s pain was lower than “severe”), thus fulfilling the exchangeability assumption for the case-crossover study design.[28] With this design, participants serve as their own control, eliminating confounding by time-invariant factors. Each month per participant with at least one hazard and one control period formed a risk set. Conditional logistic regression was used to estimate the odds ratio ( OR ) for a pain event for four state weather variables (temperature, relative humidity , pressure, and wind speed ). The condition logistic regression model was implemented with the assumption that the possible recurrent events (hazard periods) within a person are independent conditional on the subject-specific variables and other observed timevarying covariates. Further, we make sure that there is no overlap between case and control periods. Our assumption is reasonable given the time gap between subsequent events.

Each weather variable was included in univariable models and then all four were included in a multivariable analysis. Each weather variable was represented as a daily average per participant for the hazard or control period, with results presented as an OR for a pain event in response to a one-unit increase for temperature and wind speed (°C and meter per second, respectively) or a ten-unit increase for relative humidity and pressure (percentage points and millibar, respectively). Standardized odds ratios of each weather variable were also calculated. The relative importance of the four state weather variables was estimated by summing the Akaike weights.[29] In all models, the preceding day’s pain score was included as it influenced the likelihood of a pain event the following day. The model was expanded to include mood and physical activity on the day of interest, included as binary variables. Time spent outside was considered as a possible effect modifier by including an interaction term with temperature, relative humidity , and wind speed . A directed acyclic graph is included in the supplementary material (Supplementary Fig. 5).

Sensitivity analyses were conducted to examine the effect of precipitation, day of the week, possible lag between weather and pain, change in weather from the day before the hazard or control day, disease type, sites of pain (single versus multiple sites) and prior beliefs in the weather–pain relationship. Respecting patients’ perspectives, we decided our primary analysis would focus on the whole chronic-pain population and our analyses of disease-specific associations would be secondary. We also reran the analysis including the first 10 days.

Daily pain-event estimates Estimated odds ratio for a pain event per day compared to the average weather day were calculated using the following equation: n

Odds Ratio 1⁄4 exp; βTðtemperature À μTÞ þ βRH 10 relative humidity À μRH

ÁÉ pressure À μp þ βwsp wind speed À μwsp þ βP 10 where βT = coefficient for temperature from final βRH = coefficient for relative humidity , βwsp = coefficient for wind speed , βP = coefficient for pressure, and μT = mean temperature, μRH = mean relative humidity , μwsp = mean wind speed , and μp = mean pressure model, of the daily UK means over the study period.

The predicted odds ratios of a pain event, relative to the average weather day, were plotted for all days within our study period for each of the four state weather variables. Statistical analyses were performed using R 3.3.0.30

22. Cloudy with a Chance of Pain on BBC North West Tonight. https://www.youtube.

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

com/watch?v=YUdtKGr49GY. Accessed 14 Oct 2019 (2016). 23. Maclure, M. The case-crossover design: a method for studying transient effects on the risk of acute events. Am. J. Epidemiol. 133, 144–153 (1991).

24. Olsen, M. F. et al Pain relief that matters to patients: systematic review of DATA AVAILABILITY

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

empirical studies assessing the minimum clinically important difference in acute pain. BMC Med. 15, 35. https://doi.org/10.1186/s12916-016-0775-3 (2017). 25. BPH postcodes. A brief guide to UK postcodes. https://www.bph-postcodes.co. uk/guidetopc.cgi (2018).

26. Isaacman, S., Becker, R., Martonosi, M., Rowland, J., & Varshavsky, A. Identifying

CODE AVAILABILITY important places in people’s lives from cellular network data sibren. Proc. of 9th Int. Conf. Pervasive Comput. 1–18 (2011).

Data management and analyses were performed in R 3.3.0. Code may be available on

27. Hartigan, J. A. Clustering Algorithms. (Wiley, New York, 1975).

reasonable request.

28. Mittleman, M. A. & Mostofsky, E. Exchangeability in the case-crossover design. Int.

J. Epidemiol. 43, 1645–1655 (2014).

Received: 22 May 2019; Accepted: 23 September 2019; 29. Burnham, K. P., Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed. (Springer-Verlag, New York, 2003).

30. R Core Team. R: A language and environment for statistical computing, https://www.r-project.org (2018).

ACKNOWLEDGEMENTS

We are grateful for the contributions of our patient and public involvement group throughout the study: Carolyn Gamble, Karen Staniland, Shanali Perara, Simon Stones, Rebecca Parris, Annmarie Lewis, Dorothy Slater and Susan Moore . The study app and website was provided by uMotif Limited (London, UK) . The unique flowerlike ‘motif’ symptom tracking interface is owned by uMotif Limited and protected through EU Design Registrations and a U.S Design Patent . We gratefully acknowledge the National Oceanic and Atmospheric Administration/National Climatic Data Center Integrated Surface Database (https://www.ncdc.noaa.gov/isd) for providing the weather data used in this study. The study was funded by Versus Arthritis (new name for Arthritis Research UK) (grant reference 21225), with additional support from the Centre for Epidemiology (grants 21755 and 20380). A.G. and A.M.V.C. are the recipients of Medical Research Council U.K. grants (MR/M022625/1 and MR/R013349/ 1). H.L.P. is the recipient of the Ken Muirden Overseas Training Fellowship from the Arthritis Australia, an educational research grant funded by the Australian Rheumatology Association. A.B. is supported by a Medical Research Council doctoral training partnership (grant MR/N013751/1). T.H. is supported by the Alan Turing Institute and the Royal Society (grant INF/R2/180067). D.M.S. is partially supported by the Natural Environment Research Council U.K. (grants NE/I005234/1, NE/I026545/1, and NE/N003918/1). R.S. is partially supported by the Alan Turing Institute (grant EP/ N510129/1).

W.G.D. designed the study, acquired funding, supervised and participated in datacollection and content analysis, and wrote the first draft of the manuscript. A.L.B., B.B.Y. and H.L.P. conducted the analysis. L.C. coordinated project management and participant support. A.G., T.E.L., A.V.M.C., M.M., R.S., T.H., M.L., D.M.S., J.C.S. and J. McB. contributed to analysis plans and supervised the analysis. B.H., B.J., J.A., C.G., C.S., D.M.S., J.C.S. and J.McB. contributed to study design. C.S. led qualitative research in the feasibility study and led patient and public involvement. All authors critically reviewed manuscript drafts and approved the final version of the manuscript. W.G.D. is responsible for the overall content as guarantor, and attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

COMPETING INTERESTS

W.G.D. has received consultancy fees from Bayer Pharmaceuticals and Google, unrelated to this study . B.J. and B.H. are co-founders of uMotif . All other authors declare no competing interests .

Supplementary information is available for this paper at https://doi.org/10.1038/ s41746-019-0180-3.

Correspondence and requests for materials should be addressed to W.G.D.

Reprints and permission information is available at http://www.nature.com/ reprints npj Digital Medicine (2019) 105

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Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative

Patient involvement has been important throughout the study , from inception to interpretation of the results. Co-author C.G. is a patient partner and co-applicant, while a patient and public involvement group of seven additional members has supported the study, meeting eight times in total. During the feasibility study,[14] patients positively influenced the wording and display of questions within the app. C.G. and other members of the Patient and Public Involvement Group were involved in media broadcasts at study launch and subsequent public engagement activities, explaining why the research question was important to them and relevant to patients with long-term pain conditions.[22] They have supported the interpretation of findings and the development of dissemination plans for the results, ensuring the results reach study participants, patient organizations and the general public.

Distribution of weather variables: Temperature: range −4.9 to 25.9 °C, s.d. 4.8 °C Relative humidity : range 43.8–100%, s.d. 8.6% Pressure: range 966–1044.8 mbar, s.d. 11.1 mbar Wind speed : range 0–21.5 m s−1, s.d. 2.1 m s−1 item had five possible labeled ordinal responses. For example, in response to the question “How severe was your pain today?”, possible responses were “no pain”, “mild pain”, “moderate pain”, “severe pain” or “very severe pain”. The data were analysed using a case-crossover design where, for each participant, exposure during days with a pain event (“hazard periods”) were compared to “control periods” without a pain event in the same month.[23] Pain events were defined as a two-or-more category increase in pain from the preceding day, consistent with more stringent definitions of a clinically important difference[24] (Fig. 3). Data collection ended on 20 April 2017.

The study app was downloaded by 13,207 users over the 12month recruitment period (Figs 1 and 2a) with recruitment from all 124 UK postcode areas. A total of 10,584 participants had complete baseline information and at least one pain entry, with 6850 (65%) participants remaining in the study beyond their first week and 4692 (44%) beyond their first month (Fig. 2b). Further description of engagement clusters is provided in Supplementary Table 2 and Supplementary Figs 1–3. A total of 2658 participants had at least one hazard period matched to a control period in the same month (Fig. 3) and were included in the final analysis. There were 9695 hazard periods included in the analysis for the final 2658 participants, matched to 81,727 control periods in 6431 participant-months. A total of 1235 participants contributed one month, and the remaining 1423 participants contributed 2–15 months.

The multivariable case-crossover analysis including the four state weather variables demonstrated that an increase in relative humidity was associated with a higher odds of a pain event with an OR of 1.139 (95% confidence interval 1.099–1.181) per 10 percentage point increase, as was an increase in wind speed with an OR of 1.02 (1.005–1.035) per 1 m s−1 increase ( Table 1 ). The odds of a pain event was lower with an increase in atmospheric pressure with an OR of 0.962 (0.937–0.987) per 10-mbar increase. Temperature did not have a significant association with pain ( OR 0.996 (0.985–1.007) per 1 °C increase). The odds of a pain event was 12% higher per one standard deviation increase in relative humidity (9 percentage points) ( OR 1.119 (1.084–1.154), compared to 4% lower for pressure ( OR 0.958 (0.930–0.989) and 4% higher for wind speed ( OR 1.041 (1.010–1.073) (11 mbar and 2 m s−1, respectively). Of the four weather variables, relative humidity had the strongest association with pain , and temperature the least, evidenced by the estimated relative importance of the variables and their standardized odds ratios ( Table 1 , Supplementary Table 4 ). Similar effect sizes were seen when each variable was examined in univariable analyses . Precipitation was not associated with an increased odds of a pain event ( OR 0.996 (0.989–1.003) per 1 mm daily rainfall amount) (Supplementary Table 5 ). Exploratory analyses considered time spent outside by including an interaction term with temperature, relative humidity , and wind speed . Time spent outside did not have a significant interaction with relative humidity or wind speed , nor did it lead to significant associations for temperature when conducting analyses stratified by time spent outside (Supplementary Table 3 ). It thus was not included in the final model.

Other factors such as day of the week (Supplementary Table 6 ), lagged weather values (Supplementary Table 7 ) and changes in weather variables from the previous day were tested. Mondays, Thursdays, and Saturdays ( ORs 1.14, 1.14, and 1.29, respectively) had higher odds of pain compared to Sundays, but adjusting for the day of the week did not alter the effect of the four main weather variables . Except for relative humidity (1-day lag and 2day lag), no significant associations were observed between lagged weather variables and pain events . Including change in weather from yesterday showed a minor effect of changing relative humidity ( OR 1.005 (1.001–1.009) per 10 percentage point increase), whereas the effects of today’s relative humidity and pressure remained unchanged (Supplementary Table 8 ). Stratification by disease led to a loss of statistical power and largely inconclusive results, although relative humidity appeared to have a stronger association with pain in patients with osteoarthritis (Supplementary Table 9 , Supplementary Fig. 4). Stratification by the number of pain sites also showed no meaningful difference (Supplementary Table 10 ). After stratifying by participants’ prior beliefs about their weather–pain relationship, relative humidity remained associated with pain in all participants although the association with pressure was only seen in those with a strong prior belief (Supplementary Table 11 ).

There are potential limitations to this study. First, the reduction in participant numbers from over 10,000 with baseline data to the final 2658 participants with at least one within-month risk set raises questions about generalisability. Importantly, the characteristics of those included in the analysis were similar to the initial

someone else’s “severe”. The within-person case-crossover analysis meant we compared moments when an individual’s score increased by a meaningful amount to a control period for that same person. Fifth, we chose to model the weather using daily averages. It is possible that other findings may be hidden if the association between weather and pain was with other metrics of weather , such as the daily maximum, minimum, or range, or even if the changes in weather on hourly time scales affect participants’ pain. Sixth, the findings from this United Kingdom study cannot necessarily be extrapolated to different climates where the weather is different. Seventh, our population-wide analysis assumed that all participants have the same weather –pain relationship. Different diseases may have different sensitivities to pain and, even within disease, participants may be affected differently. Our decision to use the whole chronic-pain population in our primary analysis means the overall associations with weather variables may be combinations of strong, weak and absent causal effects, thereby underestimating the most important associations. Notable differences were not seen after stratification by pain condition , although the power to detect any differences was reduced because of smaller sample sizes. Lastly, the inclusion of repeated events per person required us to consider within-subject dependence which, if not accounted for, would lead to bias.[21] Our outcome was based on changes in pain (a two or more category increase), which meant events rarely occurred on consecutive days , thereby ensuring a time gap between recurrent events and the avoidance of bias.

The reduction in participant numbers from over 10,000 with baseline data to the final 2658 participants with at least one within-month risk set raises questions about generalisability. The characteristics of those included in the analysis were similar to the initial

W.G.D. has received consultancy fees from Bayer Pharmaceuticals and Google, unrelated to this study. B.J. and B.H. are co-founders of uMotif. All other authors declare no competing interests

The study was funded by Versus Arthritis (new name for Arthritis Research UK) (grant reference 21225), with additional support from the Centre for Epidemiology (grants 21755 and 20380)

A.G. and A.M.V.C. are the recipients of Medical Research Council U.K. grants (MR/M022625/1 and MR/R013349/ 1)

H.L.P. is the recipient of the Ken Muirden Overseas Training Fellowship from the Arthritis Australia, an educational research grant funded by the Australian Rheumatology Association

A.B. is supported by a Medical Research Council doctoral training partnership (grant MR/N013751/1)

T.H. is supported by the Alan Turing Institute and the Royal Society (grant INF/R2/180067)

D.M.S. is partially supported by the Natural Environment Research Council U.K. (grants NE/I005234/1, NE/I026545/1, and NE/N003918/1)

R.S. is partially supported by the Alan Turing Institute (grant EP/ N510129/1)

Data and code

Further information on research design is available in the Nature Research Reporting Summary linked to this article

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request

Supplementary information is available for this paper at https://doi.org/10.1038/s41746-019-0180-3

Reprints and permission information is available at http://www.nature.com/reprints npj Digital Medicine (2019)[105]

Supplementary information is available for this paper at https://doi.org/10.1038/s41746-019-0180-3 .

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Term never explains itself. It is the first person narrative of Esther's perceptions. It's a slice of her life. It's a vivid picture of the inside of a mind through insanity as well as the precursor and the healing." Collaborative Sets Study Sessions Flashcard Pages About FlashcardMachine Support Form Privacy Policy Terms of Use Getting Started Apple App Store Google Play Amazon Apps Our Advantages Secure Payments For security reasons we do not store any credit card information. A complete guide to research papers writing Research the topic, research the sources, organise your information, craft the outline, compose the introduction, compose the body, compose the conclusion, how not to write a research paper. A research paper is an academic paper where you provide the results of your own investigation regarding a specific topic. It can be a master’s paper or even a dissertation. 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Most of the students are sure that they can manage cope with the task in one night before the deadline. And one more common mistake is using slangs and jargons. It’s an academic piece of writing, so mind you style. Research Guides Vanderbilt University Libraries Artificial Intelligence for Peabody College Ai research tools. AI @ Vanderbilt AI & Academic Integrity AI & Ethics AI Citation and Attribution About This Table The resources described in the table represent an incomplete list of tools specifically geared toward exploring and synthesizing research. As generative AI becomes more integrated into  online   search tools , even the early research and topic development stages could incorporate AI. If you have any questions about using these tools for your research, please email us at [email protected] Vanderbilt’s new private ChatGPT platform. It is a writing tool, idea generator, and code generator. 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Semantic Scholar Database Semantic Scholar is currently free. << Previous: AI & Academic Integrity Next: AI & Ethics >> Last Updated: Aug 28, 2024 2:31 PM URL: https://researchguides.library.vanderbilt.edu/aiforpeabody SUGGESTED TOPICS The Magazine Newsletters Managing Yourself Managing Teams Work-life Balance The Big Idea Data & Visuals Reading Lists Case Selections HBR Learning Topic Feeds Account Settings Email Preferences Research: How to Build Consensus Around a New Idea Devon Proudfoot Wayne Johnson Strategies for overcoming the disagreements that can stymie innovation. Previous research has found that new ideas are seen as risky and are often rejected. New research suggests that this rejection can be due to people’s lack of shared criteria or reference points when evaluating a potential innovation’s value. In a new paper, the authors find that the more novel the idea, the more people differ on their perception of its value. 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A credit line must be used when reproducing images; if one is not provided below, credit the images to "MIT." Previous image Next image Low-calorie diets and intermittent fasting have been shown to have numerous health benefits: They can delay the onset of some age-related diseases and lengthen lifespan, not only in humans but many other organisms. Many complex mechanisms underlie this phenomenon. Previous work from MIT has shown that one way fasting exerts its beneficial effects is by boosting the regenerative abilities of intestinal stem cells, which helps the intestine recover from injuries or inflammation. In a study of mice, MIT researchers have now identified the pathway that enables this enhanced regeneration, which is activated once the mice begin “refeeding” after the fast. They also found a downside to this regeneration: When cancerous mutations occurred during the regenerative period, the mice were more likely to develop early-stage intestinal tumors. “Having more stem cell activity is good for regeneration, but too much of a good thing over time can have less favorable consequences,” says Omer Yilmaz, an MIT associate professor of biology, a member of MIT’s Koch Institute for Integrative Cancer Research, and the senior author of the new study. Yilmaz adds that further studies are needed before forming any conclusion as to whether fasting has a similar effect in humans. “We still have a lot to learn, but it is interesting that being in either the state of fasting or refeeding when exposure to mutagen occurs can have a profound impact on the likelihood of developing a cancer in these well-defined mouse models,” he says. MIT postdocs Shinya Imada and Saleh Khawaled are the lead authors of the paper, which appears today in Nature . Driving regeneration For several years, Yilmaz’s lab has been investigating how fasting and low-calorie diets affect intestinal health. In a 2018 study , his team reported that during a fast, intestinal stem cells begin to use lipids as an energy source, instead of carbohydrates. They also showed that fasting led to a significant boost in stem cells’ regenerative ability. However, unanswered questions remained: How does fasting trigger this boost in regenerative ability, and when does the regeneration begin? “Since that paper, we’ve really been focused on understanding what is it about fasting that drives regeneration,” Yilmaz says. “Is it fasting itself that’s driving regeneration, or eating after the fast?” In their new study, the researchers found that stem cell regeneration is suppressed during fasting but then surges during the refeeding period. The researchers followed three groups of mice — one that fasted for 24 hours, another one that fasted for 24 hours and then was allowed to eat whatever they wanted during a 24-hour refeeding period, and a control group that ate whatever they wanted throughout the experiment. The researchers analyzed intestinal stem cells’ ability to proliferate at different time points and found that the stem cells showed the highest levels of proliferation at the end of the 24-hour refeeding period. These cells were also more proliferative than intestinal stem cells from mice that had not fasted at all. “We think that fasting and refeeding represent two distinct states,” Imada says. “In the fasted state, the ability of cells to use lipids and fatty acids as an energy source enables them to survive when nutrients are low. And then it’s the postfast refeeding state that really drives the regeneration. When nutrients become available, these stem cells and progenitor cells activate programs that enable them to build cellular mass and repopulate the intestinal lining.” Further studies revealed that these cells activate a cellular signaling pathway known as mTOR, which is involved in cell growth and metabolism. One of mTOR’s roles is to regulate the translation of messenger RNA into protein, so when it’s activated, cells produce more protein. This protein synthesis is essential for stem cells to proliferate. The researchers showed that mTOR activation in these stem cells also led to production of large quantities of polyamines — small molecules that help cells to grow and divide. “In the refed state, you’ve got more proliferation, and you need to build cellular mass. That requires more protein, to build new cells, and those stem cells go on to build more differentiated cells or specialized intestinal cell types that line the intestine,” Khawaled says. Too much of a good thing The researchers also found that when stem cells are in this highly regenerative state, they are more prone to become cancerous. Intestinal stem cells are among the most actively dividing cells in the body, as they help the lining of the intestine completely turn over every five to 10 days. Because they divide so frequently, these stem cells are the most common source of precancerous cells in the intestine. In this study, the researchers discovered that if they turned on a cancer-causing gene in the mice during the refeeding stage, they were much more likely to develop precancerous polyps than if the gene was turned on during the fasting state. Cancer-linked mutations that occurred during the refeeding state were also much more likely to produce polyps than mutations that occurred in mice that did not undergo the cycle of fasting and refeeding. “I want to emphasize that this was all done in mice, using very well-defined cancer mutations. In humans it’s going to be a much more complex state,” Yilmaz says. “But it does lead us to the following notion: Fasting is very healthy, but if you’re unlucky and you’re refeeding after a fasting, and you get exposed to a mutagen, like a charred steak or something, you might actually be increasing your chances of developing a lesion that can go on to give rise to cancer.” Yilmaz also noted that the regenerative benefits of fasting could be significant for people who undergo radiation treatment, which can damage the intestinal lining, or other types of intestinal injury. His lab is now studying whether polyamine supplements could help to stimulate this kind of regeneration, without the need to fast. “This fascinating study provides insights into the complex interplay between food consumption, stem cell biology, and cancer risk,” says Ophir Klein, a professor of medicine at the University of California at San Francisco and Cedars-Sinai Medical Center, who was not involved in the study. “Their work lays a foundation for testing polyamines as compounds that may augment intestinal repair after injuries, and it suggests that careful consideration is needed when planning diet-based strategies for regeneration to avoid increasing cancer risk.” The research was funded, in part, by a Pew-Stewart Trust Scholar award, the Marble Center for Cancer Nanomedicine, the Koch Institute-Dana Farber/Harvard Cancer Center Bridge Project, and the MIT Stem Cell Initiative. Share this news article on: Press mentions, medical news today. A new study led by researchers at MIT suggests that fasting and then refeeding stimulates cell regeneration in the intestines, reports Katharine Lang for Medical News Today . However, notes Lang, researchers also found that fasting “carries the risk of stimulating the formation of intestinal tumors.”  Prof. Ömer Yilmaz and his colleagues have discovered the potential health benefits and consequences of fasting, reports Max Kozlov for Nature . “There is so much emphasis on fasting and how long to be fasting that we’ve kind of overlooked this whole other side of the equation: what is going on in the refed state,” says Yilmaz. MIT researchers have discovered how fasting impacts the regenerative abilities of intestinal stem cells, reports Ed Cara for Gizmodo . “The major finding of our current study is that refeeding after fasting is a distinct state from fasting itself,” explain Prof. Ömer Yilmaz and postdocs Shinya Imada and Saleh Khawaled. “Post-fasting refeeding augments the ability of intestinal stem cells to, for example, repair the intestine after injury.”  Previous item Next item Related Links Omer Yilmaz Koch Institute Department of Biology Related Topics Related articles. How early-stage cancer cells hide from the immune system Study links certain metabolites to stem cell function in the intestine Fasting boosts stem cells’ regenerative capacity How diet influences colon cancer More mit news. A framework for solving parabolic partial differential equations Read full story → Designing better delivery for medical therapies Making a measurable economic impact Faces of MIT: Jessica Tam First AI + Education Summit is an international push for “AI fluency” President Kornbluth welcomes the Class of 2028 More news on MIT News homepage → Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA Map (opens in new window) Events (opens in new window) People (opens in new window) Careers (opens in new window) Accessibility Social Media Hub MIT on Facebook MIT on YouTube MIT on Instagram Artificial Intelligence Exclusive: New Research Finds Stark Global Divide in Ownership of Powerful AI Chips W hen we think of the “cloud,” we often imagine data floating invisibly in the ether. But the reality is far more tangible: the cloud is located in huge buildings called data centers, filled with powerful, energy-hungry computer chips. Those chips, particularly graphics processing units (GPUs), have become a critical piece of infrastructure for the world of AI, as they are required to build and run powerful chatbots like ChatGPT. As the number of things you can do with AI grows, so does the geopolitical importance of high-end chips—and where they are located in the world. The U.S. and China are competing to amass stockpiles, with Washington enacting sanctions aimed at preventing Beijing from buying the most cutting-edge varieties. But despite the stakes, there is a surprising lack of public data on where exactly the world’s AI chips are located. A new peer-reviewed paper , shared exclusively with TIME ahead of its publication, aims to fill that gap. “We set out to find: Where is AI?” says Vili Lehdonvirta, the lead author of the paper and a professor at Oxford University’s Internet Institute. Their findings were stark: GPUs are highly concentrated in only 30 countries in the world, with the U.S. and China far out ahead. Much of the world lies in what the authors call “Compute Deserts:” areas where there are no GPUs for hire at all. The finding has significant implications not only for the next generation of geopolitical competition, but for AI governance—or, which governments have the power to regulate how AI is built and deployed. “If the actual infrastructure that runs the AI, or on which the AI is trained, is on your territory, then you can enforce compliance,” says Lehdonvirta, who is also a professor of technology policy at Aalto University. Countries without jurisdiction over AI infrastructure have fewer legislative choices, he argues, leaving them subjected to a world shaped by others. “This has implications for which countries shape AI development as well as norms around what is good, safe, and beneficial AI,” says Boxi Wu, one of the paper’s authors. The paper maps the physical locations of “public cloud GPU compute”—essentially, GPU clusters that are accessible for hire via the cloud businesses of major tech companies. But the research has some big limitations: it doesn’t count GPUs that are held by governments, for example, or in the private hands of tech companies for their use alone. And it doesn’t factor in non-GPU varieties of chips that are increasingly being used to train and run advanced AI. Lastly, it doesn't count individual chips, but rather the number of compute “regions” (or groups of data centers containing those chips) that cloud businesses make available in each country. Read More: How ‘Friendshoring’ Made Southeast Asia Pivotal to the AI Revolution That’s not for want of trying. “GPU quantities and especially how they are distributed across [cloud] providers’ regions,” the paper notes, “are treated as highly confidential information.” Even with the paper’s limitations, its authors argue, the research is the closest up-to-date public estimate of where in the world the most advanced AI chips are located—and a good proxy for the elusive bigger picture. The paper finds that the U.S. and China have by far the most public GPU clusters in the world. China leads the U.S. on the number of GPU-enabled regions overall, however the most advanced GPUs are highly concentrated in the United States. The U.S. has eight “regions” where H100 GPUs—the kind that are the subject of U.S. government sanctions on China—are available to hire. China has none. This does not mean that China has no H100s; it only means that cloud companies say they do not have any H100 GPUs located in China. There is a burgeoning black market in China for the restricted chips, the New York Times reported in August, citing intelligence officials and vendors who said that many millions of dollars worth of chips had been smuggled into China despite the sanctions. The paper’s authors argue that the world can be divided into three categories: “Compute North,” where the most advanced chips are located; the “Compute South,” which has some older chips suited for running, but not training, AI systems; and “Compute Deserts,” where no chips are available for hire at all. The terms—which overlap to an extent with the fuzzy “Global North” and “Global South” concepts used by some development economists—are just an analogy intended to draw attention to the “global divisions” in AI compute, Lehdonvirta says.  The risk of chips being so concentrated in rich economies, says Wu, is that countries in the global south may become reliant on AIs developed in the global north without having a say in how they work.  It “mirrors existing patterns of global inequalities across the so-called Global North and South,” Wu says, and threatens to “entrench the economic, political and technological power of Compute North countries, with implications for Compute South countries’ agency in shaping AI research and development.” More Must-Reads from TIME Breaking Down the 2024 Election Calendar What if Ultra-Processed Foods Aren’t as Bad as You Think? How Ukraine Beat Russia in the Battle of the Black Sea The Reintroduction of Kamala Harris Long COVID Looks Different in Kids How Project 2025 Would Jeopardize Americans’ Health What a $129 Frying Pan Says About America’s Eating Habits The 32 Most Anticipated Books of Fall 2024 Write to Billy Perrigo at [email protected] Skip to main content Keyboard shortcuts for audio player Research News The physics behind the most annoying thing that could ever happen to you: a paper cut, the physics behind a very annoying thing that could ever happen to you: a paper cut. Scientists have figured out what type of paper is the most prone to cut skin. Kaare Jensen, associate professor of physics at the Technical University of Denmark, explains. Copyright © 2024 NPR. All rights reserved. Visit our website terms of use and permissions pages at www.npr.org for further information. NPR transcripts are created on a rush deadline by an NPR contractor. This text may not be in its final form and may be updated or revised in the future. Accuracy and availability may vary. The authoritative record of NPR’s programming is the audio record. Research shows our bodies go through rapid changes in our 40s and our 60s For many people, reaching their mid-40s may bring unpleasant signs the body isn’t working as well as it once did. Injuries seem to happen more frequently. Muscles may feel weaker. A new study, published Wednesday in Nature Aging , shows what may be causing the physical decline. Researchers have found that molecules and microorganisms both inside and outside our bodies are going through dramatic changes, first at about age 44 and then again when we hit 60. Those alterations may be causing significant differences in cardiovascular health and immune function. The findings come from Stanford scientists who analyzed blood and other biological samples of 108 volunteers ages 25 to 75, who continued to donate samples for several years.  “While it’s obvious that you’re aging throughout your entire life, there are two big periods where things really shift,” said the study’s senior author, Michael Snyder, a professor of genetics and director of the Center for Genomics and Personalized Medicine at Stanford Medicine. For example, “there’s a big shift in the metabolism of lipids when people are in their 40s and in the metabolism of carbohydrates when people are in their 60s.” Lipids are fatty substances, including LDL, HDL and triglycerides, that perform a host of functions in the body, but they can be harmful if they build up in the blood. The scientists tracked many kinds of molecules in the samples, including RNA and proteins, as well as the participants’ microbiomes. The metabolic changes the researchers discovered indicate not that people in their 40s are burning calories more slowly but rather that the body is breaking food down differently. The scientists aren’t sure exactly what impact those changes have on health. Previous research showed that resting energy use, or metabolic rate , didn’t change from ages 20 to 60. The new study’s findings don't contradict that. The changes in metabolism affect how the body reacts to alcohol or caffeine, although the health consequences aren’t yet clear. In the case of caffeine, it may result in higher sensitivity.  It’s also not known yet whether the shifts could be linked to lifestyle or behavioral factors. For example, the changes in alcohol metabolism might be because people are drinking more in their mid-40s, Snyder said. For now, Snyder suggests people in their 40s keep a close eye on their lipids, especially LDL cholesterol. “If they start going up, people might want to think about taking statins if that’s what their doctor recommends,” he said. Moreover, “knowing there’s a shift in the molecules that affect muscles and skin, you might want to warm up more before exercising so you don’t hurt yourself.” Until we know better what those changes mean, the best way to deal with them would be to eat healthy foods and to exercise regularly, Snyder said.Dr. Josef Coresh, founding director of the Optimal Aging Institute at the NYU Grossman School of Medicine, compared the new findings to the invention of the microscope. “The beauty of this type of paper is the level of detail we can see in molecular changes,” said Coresh, a professor of medicine at the school. “But it will take time to sort out what individual changes mean and how we can tailor medications to those changes. We do know that the origins of many diseases happen in midlife when people are in their 40s, though the disease may occur decades later.” The new study “is an important step forward,” said Dr. Lori Zeltser, a professor of pathology and cell biology at the Columbia University Vagelos College of Physicians and Surgeons. While we don’t know what the consequences of those metabolic changes are yet, “right now, we have to acknowledge that we metabolize food differently in our 40s, and that is something really new.” The shifts the researchers found might help explain numerous age-related health changes, such as muscle loss, because “your body is breaking down food differently,” Zeltser said. Linda Carroll is a regular health contributor to NBC News. She is coauthor of "The Concussion Crisis: Anatomy of a Silent Epidemic" and "Out of the Clouds: The Unlikely Horseman and the Unwanted Colt Who Conquered the Sport of Kings."  All Research Labs 3D Deep Learning Applied Research Autonomous Vehicles Deep Imagination New and Featured AI Art Gallery AI & Machine Learning Computer Vision Academic Collaborations Government Collaborations Graduate Fellowship Internships Research Openings Research Scientists Meet the Team Publications Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems that result in damaging extreme weather. By explicitly resolving convective dynamics within the atmosphere they afford meteorologists the nuance needed to provide outlook on hazard. Deep learning models have thus far not proven skilful at km-scale atmospheric simulation, despite being competitive at coarser resolution with state-of-the-art global, medium-range weather forecasting. We present a generative diffusion model called StormCast, which emulates the high-resolution rapid refresh (HRRR) model—NOAA’s state-of-the-art 3km operational CAM. StormCast autoregressively predicts 99 state variables at km scale using a 1-hour time step, with dense vertical resolution in the atmospheric boundary layer, conditioned on 26 synoptic variables. We present evidence of successfully learnt km-scale dynamics including competitive 1-6 hour forecast skill for composite radar reflectivity alongside physically realistic convective cluster evolution, moist updrafts, and cold pool morphology. StormCast predictions maintain realistic power spectra for multiple predicted variables across multi-hour forecasts. Together, these results establish the potential for autoregressive ML to emulate CAMs – opening up new km-scale frontiers for regional ML weather prediction and future climate hazard dynamical downscaling. Publication Date Research area, uploaded files. 449 COMMENTS Research Paper Flashcards A research paper is the culmination and final product of an involved process of research, critical thinking, source evaluation, organization, and composition. Study with Quizlet and memorize flashcards containing terms like Title, Abstract, Introduction and SOP and more. Article Summarizer Scholarcy's AI summarization tool is designed to generate accurate, reliable article summaries. Our summarizer tool is trained to identify key terms, claims, and findings in academic papers. These insights are turned into digestible Summary Flashcards. Scroll in the box below to see the magic ⤸. The knowledge extraction and summarization ... 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Research Finds US, China Drive Global Divide in AI Chip Power The paper finds that the U.S. and China have by far the most public GPU clusters in the world. China leads the U.S. on the number of GPU-enabled regions overall, however the most advanced GPUs are ... The physics behind the most annoying thing that could ever happen to Scientists have figured out what type of paper is the most prone to cut skin. Kaare Jensen, associate professor of physics at the Technical University of Denmark, explains. Elon professor wins award for AI and consumer behavior research Assistant Professor Smaraki Mohanty at the Martha and Spencer Love School of Business received two awards, Best Overall Paper and Best in Track Paper, at the AMA Summer Academic Conference in August 2024 for her co-authored research. The paper titled, "Political Ideology and Acceptance of AI-Based Recommendations," explores how political beliefs could influence the acceptance of AI-driven ... 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