Sub-pixel resolving optofluidic microscope for on-chip cell imaging

We report the implementation of a fully on-chip, lensless, sub-pixel resolving optofluidic microscope (SROFM). The device utilizes microfluidic flow to deliver specimens directly across a complementary metal oxide semiconductor (CMOS) sensor to generate a sequence of low-resolution (LR) projection images, where resolution is limited by the sensor's pixel size. This image sequence is then processed with a pixel super-resolution algorithm to reconstruct a single high resolution (HR) image, where features beyond the Nyquist rate of the LR images are resolved. We demonstrate the device's capabilities by imaging microspheres, protist Euglena gracilis, and Entamoeba invadens cysts with sub-cellular resolution and establish that our prototype has a resolution limit of 0.75 microns. Furthermore, we also apply the same pixel super-resolution algorithm to reconstruct HR videos in which the dynamic interaction between the fluid and the sample, including the in-plane and out-of-plane rotation of the sample within the flow, can be monitored in high resolution. We believe that the powerful combination of both the pixel super-resolution and optofluidic microscopy techniques within our SROFM is a significant step forwards toward a simple, cost-effective, high throughput and highly compact imaging solution for biomedical and bioscience needs

Leveraging RFID in hospitals: patient life cycle and mobility perspectives

The application of Radio Frequency Identification (RFID) to patient care in hospitals and healthcare facilities has only just begun to be accepted. This article develops a set of frameworks based on patient life cycle and time-and-motion perspectives for how RFID can be leveraged atop existing information systems to offer many benefits for patient care and hospital operations. It examines how patients are processed from admission to discharge, and considers where RFID can be applied. From a time-and-motion perspective, it shows how hospitals can apply RFID in three ways: fixed RFID readers interrogate mobile objects; mobile, handheld readers interrogate fixed objects; and mobile, handheld readers interrogate mobile objects. Implemented properly, RFID can significantly aid the medical staff in performing their duties. It can greatly reduce the need for manual entry of records, increase security for both patient and hospital, and reduce errors in administering medication. Hospitals are likely to encounter challenges, however, when integrating the technology into their day-to-day operations. What we present here can help hospital administrators determine where RFID can be deployed to add the most value

PATTERN: Pain Assessment for paTients who can't TEll using Restricted Boltzmann machiNe.

BackgroundAccurately assessing pain for those who cannot make self-report of pain, such as minimally responsive or severely brain-injured patients, is challenging. In this paper, we attempted to address this challenge by answering the following questions: (1) if the pain has dependency structures in electronic signals and if so, (2) how to apply this pattern in predicting the state of pain. To this end, we have been investigating and comparing the performance of several machine learning techniques.MethodsWe first adopted different strategies, in which the collected original n-dimensional numerical data were converted into binary data. Pain states are represented in binary format and bound with above binary features to construct (n + 1) -dimensional data. We then modeled the joint distribution over all variables in this data using the Restricted Boltzmann Machine (RBM).ResultsSeventy-eight pain data items were collected. Four individuals with the number of recorded labels larger than 1000 were used in the experiment. Number of avaliable data items for the four patients varied from 22 to 28. Discriminant RBM achieved better accuracy in all four experiments.ConclusionThe experimental results show that RBM models the distribution of our binary pain data well. We showed that discriminant RBM can be used in a classification task, and the initial result is advantageous over other classifiers such as support vector machine (SVM) using PCA representation and the LDA discriminant method

The Master’s Program in Information Systems: A Survey of Core Curricula in AACSB-Accredited Business Schools in the United States

This paper investigates the core curricula of Information Systems (IS) master’s programs. It examines all 532 AACSB-accredited business schools in the United States and identifies 74 IS master’s programs. MSIS 2016 and other curricular models and studies are used in a research framework to survey core courses. The top three required courses are Data, Information, and Content Management, Systems Development and Deployment, and Project and Change Management. One unexpected result is that Business Intelligence/Analytics/Data Mining is now the fourth most popular core course, while Business Continuity and Information Assurance is the fifth. The results are compared to those of a 2012 study to examine IS master curricula’ change over the last decade. Based on actual data on core courses being offered, a new IS master’s curriculum model is developed