आँध्रप्रदेश के समुद्री मात्स्यिकी विकास के लिए एन आई ऍफ़ पि टी टी

आँध्रप्रदेश के समुद्री मात्स्यिकी विकास के लिए एन आई ऍफ़ पि टी ट

Functional interpretation of single cell similarity maps.

We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration

Patient satisfaction of outpatient department at ESIS hospital, Nagpur, India

Background: Employee state insurance scheme (ESIS) is a multidimensional social security system providing medical facilities to the insured persons and their family through large network of hospitals and dispensaries all over India. The present study was done to measure the satisfaction of OPD (Outpatient Department) patients.Methods: A descriptive cross-sectional study was conducted in ESI Hospital, Nagpur, Maharashtra, India on 500 patients attending OPD. Patients were approached at the end of their OPD visits to know their perceptions towards the public health facilities, during the months of September and October 2017. Data collected was analyzed.Results: In present study, out of 500 patients, 210 (42%) said that crowd was average, 270 (54%) were satisfied with queue system, 390 (78%) were satisfied with the availability of drugs, 380 (76%) were satisfied with the behaviour of pharmacist.205 (41%) patients said it was not overcrowded, 310 (62%) patients were happy with the behaviour of registration clerk, 330 (66%) were satisfied with the seating arrangements for the patients and attendants, 265 (53%) were satisfied with the cleanliness, 205 (41%) were satisfied with the condition of toilets.390 (78%) patients said that doctor was available, 270 (54%) said that waiting time was less, 325 (65%) said that doctor listened to the problem attentively, 435 (87%) said that the doctor explained nicely about the disease while 425 (85%) were satisfied with the time given by the doctor.Conclusions: Almost half of the patients were satisfied with the registration facilities, basic amenities, service by doctor and pharmacy services. Mostly, patients chose this hospital as it was free for them due to their insurance and as it was near their house. Still, there is scope for improvement

The Parallel Persistent Memory Model

We consider a parallel computational model that consists of $P$ processors, each with a fast local ephemeral memory of limited size, and sharing a large persistent memory. The model allows for each processor to fault with bounded probability, and possibly restart. On faulting all processor state and local ephemeral memory are lost, but the persistent memory remains. This model is motivated by upcoming non-volatile memories that are as fast as existing random access memory, are accessible at the granularity of cache lines, and have the capability of surviving power outages. It is further motivated by the observation that in large parallel systems, failure of processors and their caches is not unusual. Within the model we develop a framework for developing locality efficient parallel algorithms that are resilient to failures. There are several challenges, including the need to recover from failures, the desire to do this in an asynchronous setting (i.e., not blocking other processors when one fails), and the need for synchronization primitives that are robust to failures. We describe approaches to solve these challenges based on breaking computations into what we call capsules, which have certain properties, and developing a work-stealing scheduler that functions properly within the context of failures. The scheduler guarantees a time bound of $O(W/P_A + D(P/P_A) \lceil\log_{1/f} W\rceil)$ in expectation, where $W$ and $D$ are the work and depth of the computation (in the absence of failures), $P_A$ is the average number of processors available during the computation, and $f \le 1/2$ is the probability that a capsule fails. Within the model and using the proposed methods, we develop efficient algorithms for parallel sorting and other primitives.Comment: This paper is the full version of a paper at SPAA 2018 with the same nam