Faster Versions Research Articles

CRUMB: a shiny-based app to analyze rhythmic feeding in Drosophila using the FLIC system.

Rhythmic feeding activity has become an important research area for circadian biologists as it is now clear that metabolic input is critical for regulating circadian rhythms, and chrononutrition has been shown to promote health span. In contrast to locomotor activity rhythm, studies conducting high throughput analysis of Drosophila rhythmic food intake have been limited and few monitoring system options are available. One monitoring system, the Fly Liquid-Food Interaction Counter (FLIC) has become popular, but there is a lack of efficient analysis toolkits to facilitate scalability and ensure reproducibility by using unified parameters for data analysis. Here, we developed Circadian Rhythm Using Mealtime Behavior (CRUMB), a user-friendly Shiny app to analyze data collected using the FLIC system. CRUMB leverages the 'plotly' and 'DT' packages to enable interactive raw data review as well as the generation of easily manipulable graphs and data tables. We used the main features of the FLIC master code provided with the system to retrieve feeding events and provide a simplified pipeline to conduct circadian analysis. We also replaced the use of base functions in time-consuming processes such as 'rle' and 'read.csv' with faster versions available from other packages to optimize computing time. We expect CRUMB to facilitate analysis of feeding-fasting rhythm as a robust output of the circadian clock.

CRUMB: a shiny-based app to analyze rhythmic feeding in Drosophila using the FLIC system

Rhythmic feeding activity has become an important research area for circadian biologists as it is now clear that metabolic input is critical for regulating circadian rhythms, and chrononutrition has been shown to promote health span. In contrast to locomotor activity rhythm, studies conducting high throughput analysis of Drosophila rhythmic food intake have been limited and few monitoring system options are available. One monitoring system, the Fly Liquid-Food Interaction Counter (FLIC) has become popular, but there is a lack of efficient analysis toolkits to facilitate scalability and ensure reproducibility by using unified parameters for data analysis. Here, we developed Circadian Rhythm Using Mealtime Behavior (CRUMB), a user-friendly Shiny app to analyze data collected using the FLIC system. CRUMB leverages the ‘plotly’ and ‘DT’ packages to enable interactive raw data review as well as the generation of easily manipulable graphs and data tables. We used the main features of the FLIC master code provided with the system to retrieve feeding events and provide a simplified pipeline to conduct circadian analysis. We also replaced the use of base functions in time-consuming processes such as ‘rle’ and ‘read.csv’ with faster versions available from other packages to optimize computing time. We expect CRUMB to facilitate analysis of feeding-fasting rhythm as a robust output of the circadian clock.

Sham: A DSL for Fast DSLs

Domain-specific languages (DSLs) are touted as both easy to embed in programs and easy to optimize. Yet these goals are often in tension. Embedded or internal DSLs fit naturally with a host language, while inheriting the host's performance characteristics. External DSLs can use external optimizers and languages but sit apart from the host. We present Sham, a toolkit designed to enable internal DSLs with high performance. Sham provides a domain-specific language (embedded in Racket) for implementing other high-performance DSLs, with transparent compilation to assembly code at runtime. Sham is well suited as both a compilation target for other embedded DSLs and for transparently replacing DSL support code with faster versions. Sham provides seamless inter-operation with its host language without requiring any additional effort from its users. Sham also provides a framework for defining language syntax which implements Sham's own language interface as well. We validate Sham's design on a series of case studies, ranging from Krishnamurthi's classic automata DSL to a sound synthesis DSL and a probabilistic programming language. All of these are existing DSLs where we replaced the backend using Sham, resulting in major performance gains. We present an example-driven description of how Sham can smoothly enhance an existing DSL into a high-performance one. When compared to existing approaches for implementing high-performance DSLs, Sham's design aims for both simplicity and programmer control. This makes it easier to port our techniques to other languages and frameworks, or borrow Sham's innovations "\`a la carte" without adopting the whole approach. Sham builds a sophisticated and powerful DSL construction toolkit atop fundamental language features including higher-order functions, data structures, and a foreign-function interface (FFI), all readily available in other languages. Furthermore, Sham's approach allows DSL developers to simply write functions, either using Sham or generating Sham, without needing to work through complex staging or partial evaluation systems.

Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN

Object detection has boomed in areas like image processing in accordance with the unparalleled development of CNN (Convolutional Neural Networks) over the last decade. The CNN family which includes R-CNN has advanced to much faster versions like Fast-RCNN which have mean average precision(Map) of up to 76.4 but their frames per second(fps) still remain between 5 to 18 and that is comparatively moderate to problem-solving time. Therefore, there is an urgent need to increase speed in the advancements of object detection. In accordance with the broad initiation of CNN and its features, this paper discusses YOLO (You only look once), a strong representative of CNN which comes up with an entirely different method of interpreting the task of detecting the objects. YOLO has attained fast speeds with fps of 155 and map of about 78.6, thereby surpassing the performances of other CNN versions appreciably. Furthermore, in comparison with the latest advancements, YOLOv2 attains an outstanding trade-off between accuracy and speed and also as a detector possessing powerful generalization capabilities of representing an entire image

Applications of Next-Generation Sequencing in Cancer

The completion of the Human Genome Project established a baseline for human genome reference sequence allowing characterization of various alterations underlying several human diseases, including cancer and has brought the field of genomics to this unprecedented moment of a great scientific ferment. It has also sparked a concomitant revolution in sequencing technologies that have become a fundamental tool for genome analysis with potential to transform medical practice. As an emerging field, the next-generation sequencing (NGS) technology has stimulated rapid cataloguing of all alterations in cancer genomes and has enabled researchers to look at large-scale genome events such as chromosomal lesions and copy-number variations as well as small-scale aberrations represented by point mutations, small insertions and deletions. Several advancements with smaller and faster versions of available technologies have recently been introduced enabling more democratic usage of the technology. Here, we review the application of NGS technology in understanding the underlying goal to catalogue human cancer-causing somatic mutations.

Self consistency grouping: a stringent clustering method

BackgroundNumerous types of clustering like single linkage and K-means have been widely studied and applied to a variety of scientific problems. However, the existing methods are not readily applicable for the problems that demand high stringency.MethodsOur method, self consistency grouping, i.e. SCG, yields clusters whose members are closer in rank to each other than to any member outside the cluster. We do not define a distance metric; we use the best known distance metric and presume that it measures the correct distance. SCG does not impose any restriction on the size or the number of the clusters that it finds. The boundaries of clusters are determined by the inconsistencies in the ranks. In addition to the direct implementation that finds the complete structure of the (sub)clusters we implemented two faster versions. The fastest version is guaranteed to find only the clusters that are not subclusters of any other clusters and the other version yields the same output as the direct implementation but does so more efficiently.ResultsOur tests have demonstrated that SCG yields very few false positives. This was accomplished by introducing errors in the distance measurement. Clustering of protein domain representatives by structural similarity showed that SCG could recover homologous groups with high precision.ConclusionsSCG has potential for finding biological relationships under stringent conditions.

A new ADC circuit for nuclear spectroscopy based on digital signal processing techniques

A 12-bit analog-to-digital converter (ADC) circuit for nuclear spectroscopy applications was developed using a digital signal processor (DSP) as the central processing element. The DSP runs a program that builds the distribution function of data collected by the ADC (the multichannel analyzer algorithm) and simultaneously corrects the ADC differential nonlinearity (DNL) by the sliding scale method. The acquisition routine runs in ≃4 μs. The conversion time, when the faster versions of the ADC are used, is well below 10 μs. The resulting DNL is better than 0.4% and the integral nonlinearity <0.002%.

Transient Waveform Recorder Using Stripline Technology

Transient waveform recorders that sample and digitize a waveform at 1 Gigasample/second (Gs/s) and 10 bits resolution have been implemented in stripline technology. The prototype unit records 32 samples with an accuracy of 0.5%. A second unit will record 500 samples at 1 Gs/s with 12 bit resolution. These digitizers are suitable for recording single transient wave forms with frequency content up to 200 MHz. Faster versions with sample rates of 10 Gs/s are under development.