All patients with acute, traumatic ACL disruption sustained a chondral injury at the time of initial impact with subsequent longitudinal chondral degradation in compartments unaffected by the initial "bone bruise," a process that is accelerated at 5 to 7 years' follow-up.

The mean cartilage T(2) values, their SD, and their entropy were greater in OA patients than in controls, indicating that the T(2) values in osteoarthritic cartilage are not only elevated, but also more heterogeneous than those in healthy cartilage. The longitudinal results demonstrate that changes in texture parameters of cartilage T(2) may precede morphological changes in thickness and volume in the progression of OA.

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Massive data analysis in cloud-scale data centers plays a crucial role in making critical business decisions. Highlevel scripting languages free developers from understanding various system trade-offs, but introduce new challenges for query optimization. One key optimization challenge is missing accurate data statistics, typically due to massive data volumes and their distributed nature, complex computation logic, and frequent usage of user-defined functions. In this paper we propose novel techniques to adapt query processing in the Scope system, the cloud-scale computation environment in Microsoft Online Services. We continuously monitor query execution, collect actual runtime statistics, and adapt parallel execution plans as the query executes. We discuss similarities and differences between our approach and alternatives proposed in the context of traditional centralized systems. Experiments on large-scale Scope production clusters show that the proposed techniques systematically solve the challenge of missing/inaccurate data statistics, detect and resolve partition skew and plan structure, and improve query latency by a few folds for real workloads. Although we focus on optimizing high-level languages, the same ideas are also applicable for MapReduce systems.

Covid-19 pandemic has adversely affected all the aspects of life in adverse manner; however, a significant improvement has been observed in the air quality, due to restricted human activities amidst lockdown. Present study reports a comparison of air quality between the lockdown duration and before the lockdown duration in seven selected cities (Ajmer, Alwar, Bhiwadi, Jaipur, Jodhpur, Kota, and Udaipur) of Rajasthan (India). The period of analysis is 10 March 2020 to 20 March 2020 (before lockdown period) versus 25 March to 17 May 2020 (during lockdown period divided into three phases). In order to understand the variations in the level of pollutant accumulation amid the lockdown period, a trend analysis is performed for 24 h daily average data for five pollutants (PM 2.5 , PM 10 , NO 2 , SO 2 , and ozone). Fig. a Graphical abstract