When Algorithms Recommend What’s New(s): New Dynamics of Decision-Making and Autonomy in Newsgathering

Newsroom innovation labs have been created over the last ten years to develop algorithmic news recommenders (ANR) that suggest and summarise what news is. Although these ANRs are still in an early stage and have not yet been implemented in the entire newsroom, they have the potential to change how newsworkers fulfil their daily decisions (gatekeeping) and autonomy in setting the agenda (agenda-setting). First, this study focuses on the new dynamics of the ANR and how it potentially influences the newsworkers’ role of gatekeeping within the newsgathering process. Second, this study investigates how the dynamics of an ANR could influence the autonomy of the newsworkers’ role as media agenda setters. In order to advance our understanding of the changing dynamics of gatekeeping and agenda-setting in the newsroom, this study conducts expert interviews with 16 members of newsroom innovation labs of<em> The Washington Post</em>,<em> The Wall Street Journal</em>, <em>Der Spiegel</em>, the BBC, and the Bayerische Rundfunk (BR) radio station. The results show that when newsworkers interact with ANRs, they rely on suggestions and summaries to evaluate what is newsworthy, especially when there is a “news peak” (elections, a worldwide pandemic, etc.). With regard to the agenda-setting role, the newsworker still has full autonomy, but the ANR creates a “positive acceleration effect” on how certain topics are put on the agenda.

Shoulder Kinematics and Symmetry at Different Load Intensities during Bench Press Exercise

This study aimed to analyze between-shoulder kinematics symmetry at different load intensities considering full range of movement (ROM), mean and maximum velocities (VMEAN, VMAX), and accelerations (AMEAN, AMAX) of shoulders during phases 2 (characterized by positive acceleration and negative velocity, eccentric) and 3 (characterized by positive acceleration and velocity, concentric) of bench press exercise (BP); as well as to compare unilateral kinematics variables between the different load intensity intervals. Twenty-seven participants were evaluated during phases 2 and 3 of BP at different load intervals: interval 1 (55–75% 1-repetition maximum: 1RM), interval 2 (75–85% 1RM) and interval 3 (85–100% 1RM). Kinematics variables were determined using the Xsens MVN Link System. Results showed that full ROM was higher in left than right shoulder at all intensities (p = 0.008–0.035). VMEAN, VMAX, AMEAN, and AMAX were different in both shoulders for interval 3 during phase 2 and were lower as load intensity increased in both shoulders (p = 0.001–0.029). During phase 3, only VMAX on interval 2 was different between shoulders. Moreover, VMEAN, VMAX, AMEAN, and AMAX were greater during interval 1 compared with the others in both shoulders (p = 0.001–0.029). Therefore, there exists a kinematics asymmetry between both shoulders during phases 2 and 3 of bench press, although the acceleration was similar during both phases at all load intensities. Moreover, kinematic parameters differ between loads of 55–75% RM compared to 75–100% RM loads.

Analysis of Driving Dynamics Considering Driving Resistances in On-Road Driving

Internal combustion engine emissions are a serious worldwide problem. To combat this, emission regulations have become stricter with the goal of reducing the proportion of transportation emissions in global air pollution. In addition, the European Commission passed the real driving emissions–light-duty vehicles (RDE-LDV) regulation that evaluates vehicle emissions by driving on real roads. The RDE test is significantly dependent on driving conditions such as traffic or drivers. Thus, the RDE regulation has the means to evaluate driving dynamics such as the vehicle speed per acceleration (v·apos) and the relative positive acceleration (RPA) to determine whether the driving during these tests is normal or abnormal. However, this is not an appropriate way to assess the driving dynamics because the v⋅apos and the RPA do not represent engine load, which is directly related to exhaust emissions. Therefore, in the present study, new driving dynamic variables are proposed. These variables use engine acceleration calculated from wheel force instead of the acceleration calculated from the vehicle speed, so they are proportional to the engine load. In addition, a variable of driving dynamics during braking is calculated using the negative wheel force. This variable can be used to improve the accuracy of the emission assessment by analyzing the braking pattern.

The Effect of Footwear, Running Speed, and Location on the Validity of Two Commercially Available Inertial Measurement Units During Running

Introduction: Most running-related injuries are believed to be caused by abrupt changes in training load, compounded by biomechanical movement patterns. Wearable technology has made it possible for runners to quantify biomechanical loads (e.g., peak positive acceleration; PPA) using commercially available inertial measurement units (IMUs). However, few devices have established criterion validity. The aim of this study was to assess the validity of two commercially available IMUs during running. Secondary aims were to determine the effect of footwear, running speed, and IMU location on PPA.Materials and Methods: Healthy runners underwent a biomechanical running analysis on an instrumented treadmill. Participants ran at their preferred speed in three footwear conditions (neutral, minimalist, and maximalist), and at three speeds (preferred, +10%, −10%) in the neutral running shoes. Four IMUs were affixed at the distal tibia (IMeasureU-Tibia), shoelaces (RunScribe and IMeasureU-Shoe), and insole (Plantiga) of the right shoe. Pearson correlations were calculated for average vertical loading rate (AVLR) and PPA at each IMU location.Results: The AVLR had a high positive association with PPA (IMeasureU-Tibia) in the neutral and maximalist (r = 0.70–0.72; p ≤ 0.001) shoes and in all running speed conditions (r = 0.71–0.83; p ≤ 0.001), but low positive association in the minimalist (r = 0.47; p &lt; 0.05) footwear condition. Conversely, the relationship between AVLR and PPA (Plantiga) was high in the minimalist (r = 0.75; p ≤ 0.001) condition and moderate in the neutral (r = 0.50; p &lt; 0.05) and maximalist (r = 0.57; p &lt; 0.01) footwear. The RunScribe metrics demonstrated low to moderate positive associations (r = 0.40–0.62; p &lt; 0.05) with AVLR across most footwear and speed conditions.Discussion: Our findings indicate that the commercially available Plantiga IMU is comparable to a tibia-mounted IMU when acting as a surrogate for AVLR. However, these results vary between different levels of footwear and running speeds. The shoe-mounted RunScribe IMU exhibited slightly lower positive associations with AVLR. In general, the relationship with AVLR improved for the RunScribe sensor at slower speeds and improved for the Plantiga and tibia-mounted IMeasureU sensors at faster speeds.

Surveillance Metrics of SARS-CoV-2 Transmission in Central Asia: Longitudinal Trend Analysis

Background SARS-CoV-2, the virus that caused the global COVID-19 pandemic, has severely impacted Central Asia; in spring 2020, high numbers of cases and deaths were reported in this region. The second wave of the COVID-19 pandemic is currently breaching the borders of Central Asia. Public health surveillance is necessary to inform policy and guide leaders; however, existing surveillance explains past transmissions while obscuring shifts in the pandemic, increases in infection rates, and the persistence of the transmission of COVID-19. Objective The goal of this study is to provide enhanced surveillance metrics for SARS-CoV-2 transmission that account for weekly shifts in the pandemic, including speed, acceleration, jerk, and persistence, to better understand the risk of explosive growth in each country and which countries are managing the pandemic successfully. Methods Using a longitudinal trend analysis study design, we extracted 60 days of COVID-19–related data from public health registries. We used an empirical difference equation to measure the daily number of cases in the Central Asia region as a function of the prior number of cases, level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results COVID-19 transmission rates were tracked for the weeks of September 30 to October 6 and October 7-13, 2020, in Central Asia. The region averaged 11,730 new cases per day for the first week and 14,514 for the second week. Infection rates increased across the region from 4.74 per 100,000 persons to 5.66. Russia and Turkey had the highest 7-day moving averages in the region, with 9836 and 1469, respectively, for the week of October 6 and 12,501 and 1603, respectively, for the week of October 13. Russia has the fourth highest speed in the region and continues to have positive acceleration, driving the negative trend for the entire region as the largest country by population. Armenia is experiencing explosive growth of COVID-19; its infection rate of 13.73 for the week of October 6 quickly jumped to 25.19, the highest in the region, the following week. The region overall is experiencing increases in its 7-day moving average of new cases, infection, rate, and speed, with continued positive acceleration and no sign of a reversal in sight. Conclusions The rapidly evolving COVID-19 pandemic requires novel dynamic surveillance metrics in addition to static metrics to effectively analyze the pandemic trajectory and control spread. Policy makers need to know the magnitude of transmission rates, how quickly they are accelerating, and how previous cases are impacting current caseload due to a lag effect. These metrics applied to Central Asia suggest that the region is trending negatively, primarily due to minimal restrictions in Russia.

Asymmetric Rainband Processes Leading to Secondary Eyewall Formation in a Model Simulation of Hurricane Matthew (2016)

The dynamics of an asymmetric rainband complex leading into secondary eyewall formation (SEF) are examined in a simulation of Hurricane Matthew (2016), with particular focus on the tangential wind field evolution. Prior to SEF, the storm experiences an axisymmetric broadening of the tangential wind field as a stationary rainband complex in the downshear quadrants intensifies. The axisymmetric acceleration pattern that causes this broadening is an inward-descending structure of positive acceleration nearly 100 km wide in radial extent and maximizes in the low levels near 50 km radius. Vertical advection from convective updrafts in the downshear-right quadrant largely contributes to the low-level acceleration maximum, while the broader inward-descending pattern is due to horizontal advection within stratiform precipitation in the downshear-left quadrant. This broad slantwise pattern of positive acceleration is due to a mesoscale descending inflow (MDI) that is driven by midlevel cooling within the stratiform regions and draws absolute angular momentum inward. The MDI is further revealed by examining the irrotational component of the radial velocity, which shows the MDI extending downwind into the upshear-left quadrant. Here, the MDI connects with the boundary layer, where new convective updrafts are triggered along its inner edge; these new upshear-left updrafts are found to be important to the subsequent axisymmetrization of the low-level tangential wind maximum within the incipient secondary eyewall.

Central Asia SARS-CoV-2 Surveillance: A Longitudinal Trend Analysis (Preprint)

BACKGROUND SARS-CoV-2, the virus that caused the COVID-19 global pandemic, has severely impacted Central Asia, resulting in a high caseload and deaths that varied by country in Spring 2020. The varying severity of the pandemic is explained by differences in prevention efforts in the form of public health policy, adherence to those guidelines, as well as socio-cultural, climate, and population characteristics. The second wave of the COVID-19 currently is breaching the borders of Europe. Public health surveillance is necessary to inform policy and guide leaders; however, existing surveillance explains past transmissions obscuring shifts in the pandemic, increases in infection rates, and the persistence in the transmission of COVID-19. OBJECTIVE The goal of this study is to provide enhanced surveillance metrics for COVID-19 transmission that account for shifts in the pandemic, week over week, speed, acceleration, jerk and persistence, to better understand country risk for explosive growth and those who are managing the pandemic successfully. Existing surveillance, coupled with our dynamic metrics of transmission, will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed and provides novel metrics to measure the transmission of disease. METHODS Using a longitudinal trend analysis study design, we extracted 60 days of COVID data from public health registries. We use an empirical difference equation to measure the daily number of cases in the Central Asia region as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments (GMM) approach by implementing the Arellano-Bond estimator in R. RESULTS COVID-19 transmission rates were tracked for the weeks of 9/30-10/06 and 10/07 to 10/13 in Central Asia. The region averaged 11,730 new cases per day for the week ending in 10/06 and 14,514 for the week ending in 10/13. Infection rates increased across the region from 4.74 per 100,000 in the population to 5.66. Infection rates varied by country. Russia and Turkey had the highest seven-day moving averages in the region, at 9,836 and 1,469 respectively for the week of 10/06 and 12,501 and 1,603 respectively for the week of 10/13. Russia has the fourth highest speed in the region and continues to have positive acceleration, driving the negative trend for the entire region as the largest country by population. Armenia is experiencing explosive growth of COVID-19, with an infection rate of 13.73 for the week of 10/06 quickly jumping to 25.19, the highest in the region, the following week. The pandemic speed in Armenia, consistent with the infection rate trajectory, increased from 15.4 to 21.7, with an acceleration increase from 0.4 to 1.6 meaning acceleration has increased fourfold. The region overall is experiencing increases in seven-day moving average of new cases, infection, rate and speed, with continued positive acceleration and no sign of a reversal in sight. CONCLUSIONS The rapidly evolving COVID-19 pandemic requires novel dynamic surveillance metrics in addition to static metrics to effectively analyze pandemic trajectory and control spread. Policymakers need to know the magnitude of transmission rates, how quickly they are accelerating, and how previous cases are impacting current caseload due to a lag effect. These metrics applied to Central Asia suggest that the region is trending negatively, primarily due to minimal restrictions in Russia. Russia already has the fourth highest number of cases in the world and current metrics suggest Russia will continue on that trajectory. CLINICALTRIAL NA

Canada SARS-CoV-2 Surveillance System: Longitudinal Trend Analysis (Preprint)

BACKGROUND The COVID-19 global pandemic has disrupted structures and communities across the globe. Numerous regions of the world have had varying responses in their attempts to contain the spread of the virus. Varying factors such as public health policies, governance, and sociopolitical factors, have led to differential levels of success at controlling the spread of SARS-CoV-2. Ultimately, a more advanced surveillance metric for COVID-19 transmission is necessary to help government systems and national leaders understand which responses have been effective and gauge where outbreaks occur. OBJECTIVE The goal of this study is to provide advanced Canadian surveillance metrics at the Province level for COVID-19 transmission that account for shifts in the pandemic, week over week, speed, acceleration, jerk and persistence, to better understand country risk for explosive growth and those who are managing the pandemic successfully. Existing surveillance coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed and provides novel metrics to measure the transmission of disease. METHODS Using a longitudinal trend analysis study design, we extracted 52 days of COVID data from public health registries for 14 Provinces and Territories. We use an empirical difference equation to measure the daily number of cases in Canada as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments (GMM) approach by implementing the Arellano-Bond estimator in R. RESULTS We compare the week of October 11-17 with the week of October 18-24. Canada, as a whole, had an increase in 7-day average COVID-19 cases from 1965 per 100,000 population during October 11-17 to 2043 per 100,000 population during October 18-24. Evaluating Canada’s COVID-19 at the subnational level is necessary to identify where the novel coronavirus is transmitting to prevent future outbreaks. Alberta, BC, Ontario and Manitoba had positive acceleration of cases for October 11-17 at 2.21, 1.23, .97, and .71 respectively per 100,000 population, however, these same provinces experienced deceleration one week later at -2.06, -1.06, -.71, and -.16.; Moreover, the positive jerk experienced during October 11-17 at 2.18, 1.19, 2.15 and 1.57 reversed course and jerked downward during October 18 to 24 at -4.96, -2.44, -1.39, and -.19 respectively. CONCLUSIONS While Canada maintained good COVID-19 control policies that resulted in fewer transmissions, the first week of this study on October 11-17 resulted in increases in new cases, increased rates of infections, increased acceleration and jerk in infections for the most populated provinces. These same provinces reversed course whereby the number of new cases decreased, the speed of new infections decelerated, and experienced a negative jerk in COVID-19 per 100,000 population during the week of October 12-24. The surge followed by a significant decrease is consistent with Canadians celebrating Thanksgiving on October 12, 2020. While no Province or Territory has exceeded 1000 cases per day, new sources of COVID-19 expected from the pending Wave 2 of COVID-19 transmissions could result in novel outbreaks. It is not time for Canada to declare victory over COVID-19 transmissions or to be complacent just because there were decreases this past week. To that end, Canada must remain vigilant and continue implementing those policies that caused the Canadian outbreak to reverse course and decrease. CLINICALTRIAL NA

Latin America and the Caribbean SARS-Cov-2 Surveillance: Longitudinal Trend Analysis (Preprint)

BACKGROUND SARS-CoV-19, the virus that causes COVID-19, is a global pandemic that has placed unprecedented stress on national economies, food systems and healthcare resources in Latin America and the Caribbean (LAC). This region has become an epicenter for the coronavirus, with Brazil and Mexico leading the globe in deaths following the U.S. in death count. Existing surveillance provides a proxy on COVID-19 caseload and deaths; however, these measures make it difficult to identify shifts to the pandemic and changes in the speed and acceleration in COVID-19. Accordingly, we provide an enhanced surveillance system to complement static metrics with dynamic ones that inform hen there are shifts and where explosive growth is likely to occur in LAC. OBJECTIVE This study aims to provide additional surveillance metrics for SARS-Cov-2 transmission that more accurately tracks shifts in the pandemic, speed, acceleration, jerk, and persistence in transmission than existing metrics. Enhanced surveillance will inform policy and COVID-19 outbreaks for leaders in LAC. METHODS Using a longitudinal trend analysis study design, we extracted 45 days of COVID data from public health registries. We use an empirical difference equation to measure the daily number of cases in the Latin America and Caribbean region as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments (GMM) approach by implementing the Arellano-Bond estimator in R. RESULTS COVID transmission rates were tracked for Latin America and the Caribbean during the weeks of 9/30-10/06 and 10/07-10/13. New cases in the region totaled 79,053 on 10/06 and 42,837 on 10/13. The 7-day moving average of new cases for the week of 10/6 was 56,106 and for the week of 10/13 was 47,276. Total infection rate decreased from 12.42 to 6.73 accompanied by a death rate decrease from 0.33 to 0.24. Within the region, on 9/30, Brazil had the largest number of new cases at 41,906 followed by Argentina at 14,740, Colombia at 7,650, and Mexico at 4,828. On 10/07, Argentina had the largest number of new cases in the region at 13,305, followed by Brazil at 10,220, Colombia at 5,014, and Mexico at 4,295. For both weeks, Brazil had the highest 7-day moving average, followed by Argentina. The region as a whole saw a decrease in speed, acceleration, and jerk for the week of 10/13 compared to the week of 10/6, accompanied by a decrease in new cases and 7-day moving average. For the week of 10/6, Belize had the highest acceleration and jerk in the region, at 1.7 and 1.8 respectively, which is particularly concerning given the high death rate in the country. The Bahamas also had a high acceleration at 1.5. 11 countries had a positive acceleration during the week of 10/6 whereas only six countries had a positive acceleration for the week of 10/13. The region overall is trending positively, with a speed of 10.40, an acceleration of 0.27, and a jerk of -0.31 all decreasing the subsequent week to 9.04, -0.81 and -0.03 respectively. CONCLUSIONS 1) Metrics such as new cases, cumulative cases, deaths, and 7-day moving averages provide a static view of the pandemic but fail to identify where and the speed at which SARS-CoV-19 is infecting new persons, the rate at which the speed is accelerating or decelerating and comparing this week to last week, how the rate of acceleration is increasing or decreasing indicate pending explosive growth or control of the pandemic; and 2) Although Latin America and the Caribbean saw an overall decrease in speed, acceleration, and jerk for the week of 10/13 compared to the week of 10/6, accompanied by a decrease in new cases and 7-day moving average, this is largely due to decreases in infections in Brazil and Mexico, the two countries containing over 50% of the population in the region. However, Brazil continues to have the highest 7-day moving average in the region, more than two times that of Argentina, the next highest in the region. CLINICALTRIAL NA

The Association between Prostate-Specific Antigen Velocity (PSAV), Value and Acceleration, and of the Free PSA/Total PSA Index or Ratio, with Prostate Conditions

Introduction: Prostate-specific antigen velocity (PSAV) is used to monitor men with clinical suspicion of prostate cancer (PCa), with a normal cut-off point of 0.3–0.5 ng/mL/year. The aim of the study is to establish the predictive capacity of PSAV (value and acceleration) and of the free PSA/total PSA index or ratio. Method: Prospective multicentre observational study in 2035 men of over 47 years of age. Inclusion criteria: men who wished to be informed on the health of their prostate. Exclusion criteria: men with a previously diagnosed prostate condition. Groups: GA: (n = 518): men with serum PSA equal to or greater than 2.01 ng/mL. GB: (n = 775): men with serum PSA greater than or equal to 0.78 ng/mL and less than 2.01 ng/mL. GC: (n = 742): men with serum PSA less than 0.78 ng/mL. Variables: prostate-specific antigen (PSA); age; body mass index (BMI); PSA velocity (PSAV) (ng/mL per year); free PSA/total PSA index (iPSA); PSAV acceleration (increasing: positive, or decreasing: negative); prostate diagnosis (benign prostatic hyperplasia (BPH), prostatic intraepithelial neoplasia (PIN), or infectious and non-infectious prostatitis and prostatic adenocarcinoma (PCa)); de novo diagnoses of urinary tract diseases or conditions; concomitant treatments, diseases and conditions; final diagnosis of prostate health. Results: Mean age 62.35 years (SD 8.12), median 61 (47–94); age was lowest in GC. Mean BMI was 27.89 kg/m2 (SD 3.96), median 27.58 (18.56–57.13); no differences between groups. Mean PSAV was 0.69, SD 2.16, median 0.13 (0.001–34.46); PSAV was lowest in GC. Mean iPSA was 27.39 u/L (SD 14.25), median 24.29 (3.7–115); iPSA was lowest in GA. PSAV had more positive acceleration in GA and more negative acceleration in GC. There were 1600 (78.62%) cases of normal prostate or BPH, 322 (15.82%) cases of PIN or non-infectious prostatitis, and 113 (5.55%) cases of PCa. There were more cases of BPH in GC and more cases of PIN or prostatitis and cancer in GA (p = 0.00001). De novo diagnoses: 15 cases of urinary incontinence (UI), 16 discomfort/pain in LUT, 112 cases of voiding disorders, 12 urethral strictures, 19 hematuria, 51 cystitis, 3 pyelonephritis, 4 pelvic inflammatory disease; no differences were found between groups. In the multivariate analysis, PSAV and the direction of PSAV acceleration (positive or negative) were the variables which were correlated most strongly with prostate health. iPSA was associated with the presence of prostatitis, PCa, and BPH. Men in GA had more prostatitis, PCa, treatment with alpha blockers, and history of previous smoking. GB had more cases of BPH and more positive acceleration of PSAV. GC had more normal prostates, more BPH, more use of ranitidine, and more PSAV with negative acceleration. Conclusions: PSAV, direction of PSAV acceleration, and iPSA in PSA cut-off points of 0.78 ng/mL and 2.01 ng/mL in a priori healthy men over 47 predict the probability of benign or malignant pathology of the prostate.