The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

Subsequent Surgery After Revision Anterior Cruciate Ligament Reconstruction: Rates and Risk Factors From a Multicenter Cohort

BACKGROUND: While revision anterior cruciate ligament reconstruction (ACLR) can be performed to restore knee stability and improve patient activity levels, outcomes after this surgery are reported to be inferior to those after primary ACLR. Further reoperations after revision ACLR can have an even more profound effect on patient satisfaction and outcomes. However, there is a current lack of information regarding the rate and risk factors for subsequent surgery after revision ACLR. PURPOSE: To report the rate of reoperations, procedures performed, and risk factors for a reoperation 2 years after revision ACLR. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: A total of 1205 patients who underwent revision ACLR were enrolled in the Multicenter ACL Revision Study (MARS) between 2006 and 2011, composing the prospective cohort. Two-year questionnaire follow-up was obtained for 989 patients (82%), while telephone follow-up was obtained for 1112 patients (92%). If a patient reported having undergone subsequent surgery, operative reports detailing the subsequent procedure(s) were obtained and categorized. Multivariate regression analysis was performed to determine independent risk factors for a reoperation. RESULTS: Of the 1112 patients included in the analysis, 122 patients (11%) underwent a total of 172 subsequent procedures on the ipsilateral knee at 2-year follow-up. Of the reoperations, 27% were meniscal procedures (69% meniscectomy, 26% repair), 19% were subsequent revision ACLR, 17% were cartilage procedures (61% chondroplasty, 17% microfracture, 13% mosaicplasty), 11% were hardware removal, and 9% were procedures for arthrofibrosis. Multivariate analysis revealed that patients aged <20 years had twice the odds of patients aged 20 to 29 years to undergo a reoperation. The use of an allograft at the time of revision ACLR (odds ratio [OR], 1.79; P = .007) was a significant predictor for reoperations at 2 years, while staged revision (bone grafting of tunnels before revision ACLR) (OR, 1.93; P = .052) did not reach significance. Patients with grade 4 cartilage damage seen during revision ACLR were 78% less likely to undergo subsequent operations within 2 years. Sex, body mass index, smoking history, Marx activity score, technique for femoral tunnel placement, and meniscal tearing or meniscal treatment at the time of revision ACLR showed no significant effect on the reoperation rate. CONCLUSION: There was a significant reoperation rate after revision ACLR at 2 years (11%), with meniscal procedures most commonly involved. Independent risk factors for subsequent surgery on the ipsilateral knee included age <20 years and the use of allograft tissue at the time of revision ACLR

A Cryogenic Silicon Interferometer for Gravitational-wave Detection

The detection of gravitational waves from compact binary mergers by LIGO has opened the era of gravitational wave astronomy, revealing a previously hidden side of the cosmos. To maximize the reach of the existing LIGO observatory facilities, we have designed a new instrument able to detect gravitational waves at distances 5 times further away than possible with Advanced LIGO, or at greater than 100 times the event rate. Observations with this new instrument will make possible dramatic steps toward understanding the physics of the nearby Universe, as well as observing the Universe out to cosmological distances by the detection of binary black hole coalescences. This article presents the instrument design and a quantitative analysis of the anticipated noise floor

Effect of Graft Choice on the Outcome of Revision Anterior Cruciate Ligament Reconstruction in the Multicenter ACL Revision Study (MARS) Cohort

Most surgeons believe that graft choice for ACL reconstruction is an important factor related to outcome. Although graft choice may be limited in the revision setting based on previously used grafts, it is still felt to be important

A Cryogenic Silicon Interferometer for Gravitational-wave Detection

The detection of gravitational waves from compact binary mergers by LIGO has opened the era of gravitational wave astronomy, revealing a previously hidden side of the cosmos. To maximize the reach of the existing LIGO observatory facilities, we have designed a new instrument that will have 5 times the range of Advanced LIGO, or greater than 100 times the event rate. Observations with this new instrument will make possible dramatic steps toward understanding the physics of the nearby universe, as well as observing the universe out to cosmological distances by the detection of binary black hole coalescences. This article presents the instrument design and a quantitative analysis of the anticipated noise floor

Associations of self-reported physical activity types and levels with quality of life, depression symptoms, and mortality in hemodialysis patients: the DOPPS.

BACKGROUND AND OBJECTIVES Physical activity has been associated with better health status in diverse populations, but the association in patients on maintenance hemodialysis is less established. Patient-reported physical activities and associations with mortality, health-related quality of life, and depression symptoms in patients on maintenance hemodialysis in 12 countries were examined. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS In total, 5763 patients enrolled in phase 4 of the Dialysis Outcomes and Practice Patterns Study (2009-2011) were classified into five aerobic physical activity categories (never/rarely active to very active) and by muscle strength/flexibility activity using the Rapid Assessment of Physical Activity questionnaire. The Kidney Disease Quality of Life scale was used for health-related quality of life. The Center for Epidemiologic Studies Depression scale was used for depression symptoms. Linear regression was used for associations of physical activity with health-related quality of life and depression symptoms scores. Cox regression was used for association of physical activity with mortality. RESULTS The median (interquartile range) of follow-up was 1.6 (0.9-2.5) years; 29% of patients were classified as never/rarely active, 20% of patients were classified as very active, and 20.5% of patients reported strength/flexibility activities. Percentages of very active patients were greater in clinics offering exercise programs. Aerobic activity, but not strength/flexibility activity, was associated positively with health-related quality of life and inversely with depression symptoms and mortality (adjusted hazard ratio of death for very active versus never/rarely active, 0.60; 95% confidence interval, 0.47 to 0.77). Similar associations with aerobic activity were observed in strata of age, sex, time on dialysis, and diabetes status. CONCLUSIONS The findings are consistent with the health benefits of aerobic physical activity for patients on maintenance hemodialysis. Greater physical activity was observed in facilities providing exercise programs, suggesting a possible opportunity for improving patient outcomes

Development of a Data Fusion Framework to support the Analysis of Aviation Big Data

The Federal Aviation Administration (FAA) is primarily responsible for the advancement, safety, and regulation of civil aviation, as well as overseeing the development of the air traffic control system in the United States. As such, it is faced with tremendous amounts of data on a daily basis. This data, which comes in high volumes, in various formats, from disparate sources and at various frequencies, is used by FAA analysts and researchers to make accurate forecasts, improve the safety and operational performance of their operations, and streamline processes. However, by its very nature, aviation Big Data presents a number of challenges to analysts: it impedes their ability to get a real-time picture of the state of the system, identify trends and operational patterns, make real-time predictions, etc. As such, the overarching objective of the present effort is to support FAA through the development of a data fusion framework to support the analysis of aviation Big Data. For the purpose of this research, three datasets were considered: System-Wide Information Management (SWIM) Flight Publication Data Service (SFDPS), Traffic Flow Management System (TFMS), and Meteorological Terminal Aviation Routine (METAR). The equivalent of one day of data was retrieved from each dataset, parsed and fused. A use case was then used to illustrate how a data fusion framework could be used by FAA analysts and researchers. The use case focused on predicting the occurrence of weather-related Ground Delay Programs (GDP) at the Newark (EWR), La Guardia (LGA), and Boston Logan (BOS) International Airports. This involved developing a prediction model using the Decision Tree Machine Learning technique. Evaluation metrics such as Matthew’s Correlation Coefficient were then used to evaluate the model’s performance. It is expected that a data fusion framework, once integrated within the FAA’s Computing and Analytics Shared Services Integrated Environment (CASSIE) could be used by analysts and researchers alike to identify trends and patterns and develop efficient methods to ensure that the U.S. civil and general aviation remains the safest in the world

Predicting The Occurrence of Weather And Volume Related Ground Delay Program

Traffic Management Initiatives (TMI) such as Ground Delay Programs (GDP) are instituted by traffic management personnel to address and reduce the impacts of constraints in the National Airspace System. Ground Delay Programs are initiated whenever demand is projected to exceed an airport’s acceptance rate over a lengthy period of time. Such instances occur when an airport is affected by conditions such as inclement weather, aircraft congestion, runway-related incidents, equipment failures, and other causes that do not fall in these categories. Over the years, efforts have been made to reduce the impact of Ground Delay Programs on airports and flight operations by predicting their occurrence. However, these efforts have largely focused on weather-related Ground Delay Programs, primarily due to a lack of access to comprehensive Ground Delay Program data. There has also been limited benchmarking of Machine Learning algorithms to predict the occurrence of Ground Delay Programs. Consequently, this research 1)fused data from the Traffic Flow Management System (TFMS), Aviation System Performance Metrics (ASPM), and Automated Surface Observing Systems (ASOS) datasets, and 2) leveraged supervised Machine Learning algorithms to develop prediction models as a means to predict the occurrence of weather and volume-related Ground Delay Programs. The Kappa Statistic evaluation metric revealed that Boosting Ensemble was the best suited algorithm for predicting the occurrence of weather and volume-related Ground Delay Programs