Terminal++ for Robot Researcher Using C#

Purpose: Sometimes our robot researcher needs a terminal program to exchange the data with the robot or automation device. Nevertheless, the readily available terminal program lacks some functionality that is most relevant to the researcher. We feel that a featured rich terminal program can handle lots of communication overhead for the researcher and relieve them from repetitive and time-consuming tasks. In mind for this, we researched and developed a utility program. We added extra features like automatic send, change dynamic data, etc., so our robot researcher can test the system communication better. In this paper, we demonstrated the utility program in detail. It is built using C#, which is under the Microsoft dot net framework. The code is uploaded to GitHub. Anyone can download and use it. It can be customized for their need. All used classes are available in .cs format. Design/Methodology/Approach: This is the software utility program built by the dot net framework of Microsoft visual studio. It has a graphical user interface (GUI) and some object classes. It has a serial and ethernet interface to test the channel. Once the medium is selected, the application will send whatever is written in the input text box. The Data sending may be an automatic or manual process. In manual mode, after typing the command, we need to press the “Enter” key to send the data. In automatic mode, it will send automatically within the preset interval. The transmit and receive content is displayed inside the list box. Findings/results: sometimes, our project goes into a critical phase. We need to have good tools to overcome the situation immediately. This is a helpful tool to trace the communication-related issue. Using this tool, we can observe the outgoing and incoming data traffic. The robot researcher can use it for their communication-related debug purposes. Originality/Value: Using this terminal program, our robot researcher will get lots of benefits where readily available utility programs cannot provide them. It has some unique features like automatic sending, changing dynamic content, etc. It has a serial and ethernet interface channel so that most of the device communication can be debugged through this interface software. It is entirely free and open source. Anyone can download and use it for personal as well as commercial purposes. Paper Type: Experiment-based Research.

Recent trends on the development of machine learning approaches for the prediction of lysine acetylation sites

: Acetylation on lysine residues is considered as one of the most potent protein post-translational modifications owing to its crucial role in cellular metabolism and regulatory processes. Recent advances in experimental techniques has unraveled several lysine acetylation substrates and sites. However, towing to its cost-ineffectiveness, cumbersome process, time-consumption, and labor-intensiveness, several efforts have geared towards the development of computational tools. In particular, machine learning (ML)-based approaches hold great promise in the rapid discovery of lysine acetylation modification sites, which could be witnessed by the growing number of prediction tools. Recently, several ML methods have been developed for the prediction of lysine acetylation sites owing to their time- and cost-effectiveness. In this review, we present a complete survey of the state-of-the-art ML predictors for lysine acetylation. We discuss about a variety of key aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, validation techniques, and software utility. Initially, we review about lysine acetylation site databases, current ML approaches, working principles, and their performances. Lastly, we discuss the shortcomings and future directions of ML approaches in the prediction of lysine acetylation sites. This review may act as a useful guide for the experimentalists in choosing a right ML tool for their research. Moreover, it may help bioinformaticians in the development of more accurate and advanced ML-based predictors in protein research.

The Automatization of a New Thermography Method Using Invasive Nociceptive Stimulation to Confirm an Autonomic Phenomenon within a Trigger Point Referred Pain Zone

The trigger points (TrPs) related to chronic low back pain that mimic sciatica have been lately recognized and included in the International Classification of Diseases, 11th Revision. This study examined the MATLAB software utility for the objective stratification of low back pain patients using the Minimally Invasive Procedure (MIP). The two diagnostic MIP parameters were: average temperature (ΔTavr) and autonomic referred pain (AURP). Chronic sciatica patients with TrPs (n = 20) and without TrPs (n = 20) were examined using the MIP. A significant increase in both parameters was confirmed for the thigh ROI of the TrP-positive patients, with ΔTavr being the leading parameter (p = 0.016, Exp(β) = 2.603). A continued significance of both parameters was confirmed from 6′00″ to 15′30″ (p < 0.05). The maximum AURP value was confirmed at 13′30″ (p < 0.05) (TrPs(+) 20.4 ± 19.9% vs. TrPs(-) 3.77 ± 9.14%; p = 0.000; CI (0.347,0.348)).

HemoDownloader: Open source software utility to extract data from HemoCue HbA1c 501 devices in epidemiological studies of diabetes mellitus

Diabetes mellitus is a serious disease with increasing global prevalence. Point-of-care analysis of glycated hemoglobin A (HbA1c) holds promise as a diagnostic test for diabetes mellitus in epidemiological studies in challenging environments with limited access to centralized biochemical labs. The HemoCue HbA1c 501 device can be used for point-of-care determination of HbA1c, but its usability in epidemiological studies is limited by its inability to export results in digital format. We have developed the open source HemoDownloader software to overcome this limitation of the device. HemoDownloader has an easy-to-use graphical user interface and can export data from HemoCue HbA1c 501 to standard spreadsheet file formats. The program has the potential to improve data collection and management in epidemiological studies of diabetes mellitus.

Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites

A variety of protein post-translational modifications has been identified that control many cellular functions. Phosphorylation studies in mycobacterial organisms have shown critical importance in diverse biological processes, such as intercellular communication and cell division. Recent technical advances in high-precision mass spectrometry have determined a large number of microbial phosphorylated proteins and phosphorylation sites throughout the proteome analysis. Identification of phosphorylated proteins with specific modified residues through experimentation is often laborintensive, costly and time-consuming. All these limitations could be overcome through the application of machine learning (ML) approaches. However, only a limited number of computational phosphorylation site prediction tools have been developed so far. This work aims to present a complete survey of the existing ML-predictors for microbial phosphorylation. We cover a variety of important aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, window size, and software utility. Initially, we review the currently available phosphorylation site databases of the microbiome, the state-of-the-art ML approaches, working principles, and their performances. Lastly, we discuss the limitations and future directions of the computational ML methods for the prediction of phosphorylation.

A Fusion Method for Combining Low-Cost IMU/Magnetometer Outputs for Use in Applications on Mobile Devices

This paper presents a fusion method for combining outputs acquired by low-cost inertial measurement units and electronic magnetic compasses. Specifically, measurements of inertial accelerometer and gyroscope sensors are combined with no-inertial magnetometer sensor measurements to provide the optimal three-dimensional (3D) orientation of the sensors’ axis systems in real time. The method combines Euler–Cardan angles and rotation matrix for attitude and heading representation estimation and deals with the “gimbal lock” problem. The mathematical formulation of the method is based on Kalman filter and takes into account the computational cost required for operation on mobile devices as well as the characteristics of the low-cost microelectromechanical sensors. The method was implemented, debugged, and evaluated in a desktop software utility by using a low-cost sensor system, and it was tested in an augmented reality application on an Android mobile device, while its efficiency was evaluated experimentally.

The Importance of a Collaboratory

Researchers have identified active collaborative learning and membership in learning communities as factors that facilitate the engagement of learners. In the reported student engagement study, a commercially available software utility was used to establish such an environment in a computer laboratory. This chapter addresses the following issues: will collaborative learning result in more student engagement and what type of software will support such activity? The collected data includes anonymous survey responses, mean marks for assessable components, and tutorial attendance figures for 2011 (pre-intervention) and 2012 (post-intervention) and 2013 (modified content with post-intervention style delivery). Freeform responses in the anonymous student survey were positive towards the experience. In 2012 with respect to those of 2011, mean assignment and tutorial participation marks for the students improved, while those for other assessable components appeared to have worsened. Student engagement, as reflected in tutorial attendance and assignment marks, were better in 2012. In 2013, the study was repeated with the focus of the tutorials changed to group problem solving with the tutorial participation reflecting student contributions during such sessions. The mean marks for the exam, the laboratory participation and for the course improved over those for 2012 and 2011, respectively. The other means slightly improved over those for 2011 but were slightly worse for those for 2012.

A Power Monitoring System Based on a Multi-Component Power Model

As the increasing IT energy consumption emerged as a prominent issue, computer system energy consumption monitoring and optimization has gradually become a significant research forefront. However, most existing energy monitoring methods are limited to hardware-based measurement or coarse-grained energy consumption estimation. They cannot provide fine-grained energy consumption data (i.e., component energy consumption) and high-scalability for distributed cloud environments. In this article, the authors first study widely-used power models of CPUs, memory and hard disks. Then, following an investigation into disk power behaviors in sequential I/O and random I/O, they propose an improved I/O-mode aware disk power model with multiple variables and thresholds. They developed EnergyMeter, a monitoring software utility that can provide accurate power estimate by exploiting a multi-component power model. Experiments based on PCMark prove that the average error of EnergyMeter is merely 5% under a variety of workloads

Development of tools to automate quantitative analysis of radiation damage in SAXS experiments

Biological small-angle X-ray scattering (SAXS) is an increasingly popular technique used to obtain nanoscale structural information on macromolecules in solution. However, radiation damage to the samples limits the amount of useful data that can be collected from a single sample. In contrast to the extensive analytical resources available for macromolecular crystallography (MX), there are relatively few tools to quantitate radiation damage for SAXS, some of which require a significant level of manual characterization, with the potential of leading to conflicting results from different studies. Here, computational tools have been developed to automate and standardize radiation damage analysis for SAXS data. RADDOSE-3D, a dose calculation software utility originally written for MX experiments, has been extended to account for the cylindrical geometry of the capillary tube, the liquid composition of the sample and the attenuation of the beam by the capillary material to allow doses to be calculated for many SAXS experiments. Furthermore, a library has been written to visualize and explore the pairwise similarity of frames. The calculated dose for the frame at which three subsequent frames are determined to be dissimilar is defined as the radiation damage onset threshold (RDOT). Analysis of RDOTs has been used to compare the efficacy of radioprotectant compounds to extend the useful lifetime of SAXS samples. Comparison of the RDOTs shows that, for radioprotectant compounds at 5 and 10 mM concentration, glycerol is the most effective compound. However, at 1 and 2 mM concentrations, dithiothreitol (DTT) appears to be most effective. Our newly developed visualization library contains methods that highlight the unusual radiation damage results given by SAXS data collected using higher concentrations of DTT: these observations should pave the way to the development of more sophisticated frame merging strategies.