Single cell analytics for nanobiology

Single cell analytics allows quantitative investigation of single biological cells from a structural, functional and proteomics point of view and opens possibilities to a novel unamplified cell analysis inherently insensitive to ensemble-averaging, cell-cycle or cell-population effects. We report on three different experimental methods and their application to cellular systems with single molecule sensitivity at the single cell level. Firstly, atomic force microscopy (AFM) can be used to elucidate the surface structure of living bacteria down to the nanometer scale where identification of irregular surface areas and 2D-arrays of regular protein s-layers is possible. Secondly, single cell manipulation and probing experiments with optical tweezers (OT) force spectroscopy allows quantitative identification of individual recognition events of membrane bound receptors. And thirdly, a novel, single cell analysis for protein fingerprinting in structured microfluidic device format will allow a future (label-free) on-chip electrophoretical protein separation of single cells without preamplification.     

Process analytics 4.0: A paradigm shift in rapid analytics for biologics development

Analytical testing of product quality attributes and process parameters during the biologics development (Process analytics) has been challenging due to the rapid growth of biomolecules with complex modalities to support unmet therapeutic needs. Thus, the expansion of the process analytics tool box for rapid analytics with the deployment of cutting-edge technologies and cyber-physical systems is a necessity. We introduce the term, Process Analytics 4.0; which entails not only technology aspects such as process analytical technology (PAT), assay automation, and high-throughput analytics, but also cyber-physical systems that enable data management, visualization, augmented reality, and internet of things (IoT) infrastructure for real time analytics in process development environment. This review is exclusively focused on dissecting high-level features of PAT, automation, and data management with some insights into the business aspects of implementing during process analytical testing in biologics process development. Significant technological and business advantages can be gained with the implementation of digitalization, automation, and real time testing. A systematic development and employment of PAT in process development workflows enable real time analytics for better process understanding, agility, and sustainability. Robotics and liquid handling workstations allow rapid assay and sample preparation automation to facilitate high-throughput testing of attributes and molecular properties which are otherwise challenging to monitor with PAT tools due to technological and business constraints. Cyber-physical systems for data management, visualization, and repository must be established as part of Process Analytics 4.0 framework. Furthermore, we review some of the challenges in implementing these technologies based on our expertise in process analytics for biopharmaceutical drug substance development.     

Multivariate Markov process models for the transmission of methicillin-resistant Staphylococcus aureus in a hospital ward

Summary .   Methicillin-resistant Staphylococcus Aureus (MRSA) is a pathogen that continues to be of major concern in hospitals. We develop models and computational schemes based on observed weekly incidence data to estimate MRSA transmission parameters. We extend the deterministic model of McBryde, Pettitt, and McElwain (2007, Journal of Theoretical Biology 245, 470–481) involving an underlying population of MRSA colonized patients and health-care workers that describes, among other processes, transmission between uncolonized patients and colonized health-care workers and vice versa. We develop new bivariate and trivariate Markov models to include incidence so that estimated transmission rates can be based directly on new colonizations rather than indirectly on prevalence. Imperfect sensitivity of pathogen detection is modeled using a hidden Markov process. The advantages of our approach include (i) a discrete valued assumption for the number of colonized health-care workers, (ii) two transmission parameters can be incorporated into the likelihood, (iii) the likelihood depends on the number of new cases to improve precision of inference, (iv) individual patient records are not required, and (v) the possibility of imperfect detection of colonization is incorporated. We compare our approach with that used by McBryde et al. (2007) based on an approximation that eliminates the health-care workers from the model, uses Markov chain Monte Carlo and individual patient data. We apply these models to MRSA colonization data collected in a small intensive care unit at the Princess Alexandra Hospital, Brisbane, Australia.     

Declarative platform for high-performance network traffic analytics

This paper presents Scalanytics, a declarative platform that supports high-performance application layer analysis of network traffic. Scalanytics uses (1) stateful network packet processing techniques for extracting application layer data from network packets, (2) a declarative rule-based language called Analog for compactly specifying analysis pipelines from reusable modules, and (3) a task-stealing architecture for processing network packets at high throughput within these pipelines, by leveraging multi-core processing capabilities in a load-balanced manner without the need for explicit performance profiling. In a cluster of machines, Scalanytics further improves throughput through the use of a consistent-hashing based load partitioning strategy. Our evaluation on a 16-core machine demonstrate that Scalanytics achieves up to 11.4 \(\times \) improvement in throughput compared with the best uniprocessor implementation. Moreover, Scalanytics outperforms the Bro intrusion detection system by an order of magnitude when used for analyzing SMTP traffic. We further observed increased throughput when running Scalanytics pipelines across multiple machines.     

Mayday - integrative analytics for expression data

Background  

DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files.     

Process analytics for purification of monoclonal antibodies

The application of appropriate analytical methods is an essential requirement for the purification of therapeutic antibodies. A range of analytical methods need to be employed to effectively determine the purity, identity, integrity and activity of these important class of pharmaceuticals. These include notably electrophoresis, high performance liquid chromatography and immunoassays. Regulatory and industry demands in recent years have brought the need for improvements and many have been successfully implemented. This article reviews the current analytical methods applied to support the purification of monoclonal antibodies.     

A controlled investigation of optimal internal medicine ward team structure at a teaching hospital

Background

The optimal structure of an internal medicine ward team at a teaching hospital is unknown. We hypothesized that increasing the ratio of attendings to housestaff would result in an enhanced perceived educational experience for residents.

Methods

Harbor-UCLA Medical Center (HUMC) is a tertiary care, public hospital in Los Angeles County. Standard ward teams at HUMC, with a housestaff∶attending ratio of 5∶1, were split by adding one attending and then dividing the teams into two experimental teams containing ratios of 3∶1 and 2∶1. Web-based Likert satisfaction surveys were completed by housestaff and attending physicians on the experimental and control teams at the end of their rotations, and objective healthcare outcomes (e.g., length of stay, hospital readmission, mortality) were compared.

Results

Nine hundred and ninety patients were admitted to the standard control teams and 184 were admitted to the experimental teams (81 to the one-intern team and 103 to the two-intern team). Patients admitted to the experimental and control teams had similar age and disease severity. Residents and attending physicians consistently indicated that the quality of the educational experience, time spent teaching, time devoted to patient care, and quality of life were superior on the experimental teams. Objective healthcare outcomes did not differ between experimental and control teams.

Conclusions

Altering internal medicine ward team structure to reduce the ratio of housestaff to attending physicians improved the perceived educational experience without altering objective healthcare outcomes.     

Identifying analytics for high throughput bioprocess development studies

In recent years, high throughput screening (HTS) studies have been increasingly employed as an integral element of bioprocess development activities. These studies are often limited by an analytical bottleneck; they generate multiple samples for analysis and the available analytical methods cannot always cope with the added analytical burden. A potential solution to this challenge is offered by the deployment of appropriate analytics. This article outlines features of analytical methods that affect their fit to high throughput (HT) applications. These are discussed for a range of analytics frequently used in bioprocess development studies of monoclonal antibodies. It then outlines how these features need to be considered in order to classify analytical methods in terms of their particular application in high throughput scenarios. Biotechnol. Bioeng. 2013; 110: 1924–1935. © 2013 Wiley Periodicals, Inc.     

A rehabilitation ward in a district general hospital: first three years' experience.

The experience of the first three years'' work on a ward for the rehabilitation of patients was reviewed. Adults with physical disabilities or mixed physical and psychological disabilities, including unstable or deteriorating conditions, were accepted for intensive rehabilitation. Most patients had neurological disorders. The ward policy was that each patient had considerable time with the therapist, maximum personal independence was encouraged, and multidisciplinary staff meetings were held to agree the goals of treatment. Much effort was spent helping patients and relatives to adapt to conditions of progressive disability, but the response to questionnaires suggested that these patients as well as those who did improve received some benefit from being on the ward. Overall the benefits of the intensive rehabilitation that was offered on this ward outweighed those from short stays on medical wards.     

Open ward management of acute alcoholism; experience with a pilot program

Sixty acutely alcoholic patients were treated in unsegregated rooms of two to twelve beds in a general hospital to determine the feasibility of open ward care. Personnel caring for them were first educated in the nature of alcoholism, the aberrations it produces and treatment with tranquilizing drugs. Fears and objections were overcome. Violent or unpredictable patients were excluded from the test, but those with alcoholic hallucinations or delirium suspectible to control were admitted. Preliminary physical examination was done to find out whether there was coincidental disease. In three patients, one with subarachnoid hemorrhage, one with severe anemia and one with pneumonia and shock, this examination followed by prompt treatment was probably life-saving. Tranquilizers, fluids and vitamins were given routinely, by mouth as soon as possible. Alcoholic patients were found to be no more unmanageable than others. If it were generally accepted that acutely alcoholic patients, diagnosed as such, could be admitted to open ward care in general hospitals, candor in diagnosis would be encouraged thereby, coincident disease probably would be promptly recognized if present, and long-term treatment for the alcoholic addiction could be begun early.     

Aptamers for pharmaceuticals and their application in environmental analytics

Aptamers are single-stranded DNA or RNA oligonucleotides, which are able to bind with high affinity and specificity to their target. This property is used for a multitude of applications, for instance as molecular recognition elements in biosensors and other assays. Biosensor application of aptamers offers the possibility for fast and easy detection of environmental relevant substances. Pharmaceutical residues, deriving from human or animal medical treatment, are found in surface, ground, and drinking water. At least the whole range of frequently administered drugs can be detected in noticeable concentrations. Biosensors and assays based on aptamers as specific recognition elements are very convenient for this application because aptamer development is possible for toxic targets. Commonly used biological receptors for biosensors like enzymes or antibodies are mostly unavailable for the detection of pharmaceuticals. This review describes the research activities of aptamer and sensor developments for pharmaceutical detection, with focus on environmental applications.     

Towards building data analytics benchmarks for IoT intrusion detection

Cluster Computing - Data analytics projects span all types of domains and applications. Researchers publish results using certain datasets and classification models. They present results with a...     

Current management of hypertension in hospital.

A study of the notes of 1784 patients new to two London hospitals found a blood-pressure recording in 1027 (58%). Only 423 (32%) of all outpatients had had a blood-pressure recording on their first visit. Of 144 patients with hypertension (systolic greater than or equal to 160 mm Hg or diastolic greater than or equal 100 mm Hg or both) a check recording was made in 89 (62%) and 18 (12%) were put on treatment. We conclude that the opportunity that a hospital visit provides for blood-pressure screening is being incompletely used, and that the discovery of hypertension often does not lead to further action.