A prospective observational study of the use of desflurane anesthesia in Indian adult inpatients undergoing surgery: The Registry in India on Suprane Emergence registry

Background and Aims: Limited registry studies are available on the use of anesthetic agents. This registry was conducted to evaluate emergence outcomes in Indian adult patients undergoing surgery with desflurane anesthesia. Material and Methods: This multicenter, prospective, non-interventional, observational study (Registry in India on Suprane Emergence [RISE] registry) included adult inpatients who received desflurane as general anesthetic for surgical procedure of ≥2 h. Patients were stratified by age into three groups: ≥18–40 years, ≥41–65 years, and >65 years. Data on patients' demographics, practice, and usage pattern of medications were collected. The primary efficacy outcomes were time to extubation, time to response to verbal command, and time to orientation. Results: Of 236 patients screened, 201 (≥18–40 years, n = 70; ≥41–65 years, n = 65; >65 years, n = 66) were enrolled in the study. Mean time to extubation observed in ≥18–40 years group was 7.2 ± 4.1 min, ≥41–65 years was 11.6 ± 8.99 min, and >65 years was 12.0 ± 10.5 min. Mean time to response to verbal command was 7.4 ± 4.3 min for ≥18–40 years, 10.9 ± 8.5 min for ≥41–65 years, and 10.0 ± 5.4 min for >65 years. Mean time to orientation was 13.0 ± 7.0 min for ≥18–40 years, 16.1 ± 12.0 min for ≥41–65 years, and 17.0 ± 8.6 min for >65 years. Incidence of nausea and retching/vomiting was observed in 8% of patients each in the postoperative period, and these complications were seen more in the >65 years age group. Overall, desflurane treatment maintained hemodynamic stability and no major airway events were reported. Conclusions: The RISE registry data suggest that desflurane-based anesthesia provides early recovery with stable hemodynamics without any airway adverse events, in a wide variety of surgical procedures

Estimates of interseismic deformation in northeast india from GPS measurements

Estimates of interseismic deformation in northeastern India based on GPS measurements at eight permanent stations (2003-2006) and six campaign sites (1997-2006) are presented here. The Euler pole of rotation of Indian tectonic plate in ITRF2000 determined from the present data set is located at 51.7 ± 0.5 °N, - 15.1 ± 1.5 °E with angular velocity of 0.469 ± 0.01 Myr- 1. The results show that there is a statistically insignificant present-day active deformation within the Shillong Plateau and in the foreland spur north of the plateau in the Brahmaputra valley. Convergence rate of the northeastern GPS sites with respect to the IGS station Lhasa along baselines that are normal to the Himalayan arc in this region is 16 ± 0.5 mm/yr. This represents the arc-normal Indo-Eurasian convergence rate across the northeastern boundary, similar to arc-normal convergence rates determined in central Nepal along the Himalayan arc. However, unlike central Nepal, in the Arunachal Himalaya the 16 mm/yr shortening is distributed between the Lesser as well as Higher and Tethyan Himalayas. Baselines between sites on the Indo-Burmese Fold and Thrust Belt (IBFTB) and Shillong Plateau indicate variations in the shortening rate from 1.5 mm/yr on the Tripura-Mizoram salient (TRS) south of the plateau, to 6 mm/yr in the Imphal Recess (IR) to the east and 8 mm/yr in Naga salient (NS) to the northeast. This suggests that the deformation in the IBFTB is segmented into N-S blocks along E-W transverse zones exhibiting dextral slip between NS-IR and sinistral slip between IR and TRS. Baselines between the IBFTB sites also show 10 ± 0.6 mm/yr convergence pointing to the existence of an active transverse zone between Aizawl and Imphal

Delta lake: high-performance ACID table storage over cloud object stores

Cloud object stores such as Amazon S3 are some of the largest and most cost-effective storage systems on the planet, making them an attractive target to store large data warehouses and data lakes. Unfortunately, their implementation as key-value stores makes it difficult to achieve ACID transactions and high performance: metadata operations such as listing objects are expensive, and consistency guarantees are limited. In this paper, we present Delta Lake, an open source ACID table storage layer over cloud object stores initially developed at Databricks. Delta Lake uses a transaction log that is compacted into Apache Parquet format to provide ACID properties, time travel, and significantly faster metadata operations for large tabular datasets (e.g., the ability to quickly search billions of table partitions for those relevant to a query). It also leverages this design to provide high-level features such as automatic data layout optimization, upserts, caching, and audit logs. Delta Lake tables can be accessed from Apache Spark, Hive, Presto, Redshift and other systems. Delta Lake is deployed at thousands of Databricks customers that process exabytes of data per day, with the largest instances managing exabyte-scale datasets and billions of objects

Delta lake: high-performance ACID table storage over cloud object stores

Cloud object stores such as Amazon S3 are some of the largest and most cost-effective storage systems on the planet, making them an attractive target to store large data warehouses and data lakes. Unfortunately, their implementation as key-value stores makes it difficult to achieve ACID transactions and high performance: metadata operations such as listing objects are expensive, and consistency guarantees are limited. In this paper, we present Delta Lake, an open source ACID table storage layer over cloud object stores initially developed at Databricks. Delta Lake uses a transaction log that is compacted into Apache Parquet format to provide ACID properties, time travel, and significantly faster metadata operations for large tabular datasets (e.g., the ability to quickly search billions of table partitions for those relevant to a query). It also leverages this design to provide high-level features such as automatic data layout optimization, upserts, caching, and audit logs. Delta Lake tables can be accessed from Apache Spark, Hive, Presto, Redshift and other systems. Delta Lake is deployed at thousands of Databricks customers that process exabytes of data per day, with the largest instances managing exabyte-scale datasets and billions of objects