Laptop Research Articles

Unlocking biomedical data sharing: A structured approach with digital twins and artificial intelligence (AI) for open health sciences.

Data sharing promotes the scientific progress. However, not all data can be shared freely due to privacy issues. This work is intended to foster FAIR sharing of sensitive data exemplary in the biomedical domain, via an integrated computational approach for utilizing and enriching individual datasets by scientists without coding experience. We present an in silico pipeline for openly sharing controlled materials by generating synthetic data. Additionally, it addresses the issue of inexperience to computational methods in a non-IT-affine domain by making use of a cyberinfrastructure that runs and enables sharing of computational notebooks without the need of local software installation. The use of a digital twin based on cancer datasets serves as exemplary use case for making biomedical data openly available. Quantitative and qualitative validation of model output as well as a study on user experience are conducted. The metadata approach describes generalizable descriptors for computational models, and outlines how to profit from existing data resources for validating computational models. The use of a virtual lab book cooperatively developed using a cloud-based data management and analysis system functions as showcase enabling easy interaction between users. Qualitative testing revealed a necessity for comprehensive guidelines furthering acceptance by various users. The introduced framework presents an integrated approach for data generation and interpolating incomplete data, promoting Open Science through reproducibility of results and methods. The system can be expanded from the biomedical to any other domain while future studies integrating an enhanced graphical user interface could increase interdisciplinary applicability.

АСИМЕТРИЧНИЙ АНАЛІЗ ТОНАЛЬНОСТІ ДІЛОВИХ НОВИН В СИСТЕМАХ ПРОГНОЗУВАННЯ ДАНИХ НА ФІНАНСОВИХ РИНКАХ

Forecasting data in financial markets is a pertinent task in the modern world. The ability to predict market movements helps investors avoid obvious risks and spare themselves additional expenses. Numerous trading platforms have been developed to quickly access extensive historical data, enabling real-time analysis of the financial market from any corner of the planet using only a laptop or personal computer. Such platforms allow the development of unique strategies and approaches based on fundamental or technical analysis, taking into account news about a particular company, its earnings, capitalization, and the amount of dividends it is expected to pay on time. Business news is a crucial source of information about the state of the economy and markets. They can be used for forecasting future events. One method of forecasting based on business news is sentiment analysis. Sentiment analysis allows assessing the positivity or negativity of business news.Traditional sentiment analysis methods employ a symmetric approach. This means that positive and negative news are equally considered in forecasting. However, in the real world, positive news may have a greater impact on markets than negative news. This is because positive news can stimulate economic activity, while negative news may hinder it. The article explores the application of asymmetric sentiment analysis of business news in financial data forecasting systems. Various methods of sentiment analysis of business news, their advantages, and disadvantages are analyzed. A new approach to sentiment analysis of business news is proposed, which comprehensively utilizes artificial neural networks and principal component analysis.

(Invited) Grain Texture and Transport in Sintered Lithium Cobaltite

Reversible exchange of lithium from lithium cobaltite (LCO) with the layered rock-salt structure was first reported by Goodenough in 1980 [1]. It has since become a critical component in the cathode of lithium-ion batteries that power portable electronics like cell phones, laptop computers, and cordless power tools. The rate of lithium diffusivity in LCO particles ranges from 10-7 to 10-11 cm²/s depending upon the state of charge and the method of measurement [2]. Lithium diffusivity in LCO because of its layered structure is also dependent upon crystallographic direction. In thin-film, micro-batteries (TFMB’s), the crystallographic orientation of LCO is controlled through choice of conditions for deposition and crystallization [3]. The diffusivity of lithium is significantly greater within rather than across the basal plane [4]. Orientations of (104) and (110) are preferred for rate performance.Continuous, roll-to-roll sintering of LCO ribbon with thicknesses down to 10 µm was recently demonstrated [5]. The LCO ribbon because it is sintered and self-supporting can in principle serve as a mechanical support to reduce the proportions of inactive battery components to dramatically increase energy density. A possible way of creating a solid-state battery with LCO ribbon is to capitalize on the LiPON solid electrolyte from TFMB’s. Both composite and dense cathodes can be envisioned. Energy densities greater than 1500 Wh/L are possible. The practicality and design of a cathode-supported battery depends upon lithium diffusion in the LCO and charge transfer resistance at the interface with the separator. Ideally, transport is sufficiently facile that acceptable rate performance in a battery is realized with cathode thickness of greater than 20 µm.In this work, we report on characterization of lithium diffusivity and ohmic losses in sintered LCO with controlled grain textures. LCO cathodes with thicknesses of 15-30 µm with random and (003) grain textures were prepared by tape casting and rapid sintering. The (hk0) texture was made by taking advantage of the (003) textured green tape; approximately 200 layers were stacked and cold-welded to form a block with a thickness of ~8 mm. The sintered block was diced to expose the edge with the favorable (hk0) orientation. Electron backscatter diffraction maps and pole figures that illustrate the grain structure and texture are shown in Fig. 1. The texture is so strong that material is orthotropic in character.Lithium diffusivity and ohmic cell resistances were determined as a function of state of charge by galvanostatic intermittent titration [7]. An average diffusivity to use as a single figure of merit was also determined by fitting of capacities measured as a function of rate of discharge from a cut-off potential of 4.3 V. Rates of lithium diffusion in sintered LCO cathodes with the (hk0) texture is 0.09 µm²/s and approximately twelve-fold greater than for the (003) texture, 0.0076 µm²/s. Ohmic loss was also found to depend upon grain texture. It was approximately 60-100 Ωcm² for (hk0) texture and >500 Ωcm² for (003). A reasonable explanation for the elevated resistance is that charge transfer occurs more easily with the (hk0) texture.[1] K. Mizushima, P.C. Jones, P.J. Wiseman, and J.B. Goodenough, Mat. Res. Bull., 15 (1980) 783-789, https://doi.org/10.1016/0025-5408(80)90012-4.[2] K. Dokko, M. Mohamedi, Y. Fujita, T. Itoh, M. Nishizawa, M. Umeda, and I. Uchida, J. Electrochem. Soc., 148 (2001) A422-A426, https://doi.org/10.1149/1.1359197.[3] J. Trask, A. Anapolsky, B. Cardozo, E. Januar, K. Kumar, M. Miller, R. Brown, and R. Bhardwaj, J. Power Sources, 350 (2017) 56-64, https://dx.doi.org/10.1016/j.jpowsour.2017.03.017.[4] J. Xie, N. Imanishi, T. Matsumura, A. Hirano, Y. Takeda, and O. Yamamoto, Solid State Ionics, 179 2008) 362-370, https://doi.org/10.1016/j.ssi.2008.02.051.[5] C. Tanner and J. Lorenzo, ECS Meet. Abstr. MA2022-02 (2022) 2482, https://doi.org/10.1149/MA2022-0272482mtgabs.[6]J.B. Bates, N.J. Dudney, G.R. Gruzalski, R.A. Zuhr, A. Choudhury, C.F. Luck, and J.D. Robertson, Solid State Ionics, 53-56 (1992) 647-654, https://doi.org/10.1016/0167-2738(92)90442-R.[7] C.J. Wen, B.A. Boukamp, R.A. Huggins, and W. Weppner, J. Electrochem. Soc., 126 (1979) 2258-2266, https://doi.org/10.1149/1.2128939. Figure 1

Variational autoencoder–based neural electrocardiogram synthesis trained by FEM-based heart simulator

BackgroundFor comprehensive electrocardiogram (ECG) synthesis, a recent promising approach has been based on a heart model with physical and chemical cardiac parameters. However, the problem is that such approach requires a high-cost and limited environment using supercomputers due to the massive computation. ObjectiveThe purpose of this study is to develop an efficient method for synthesizing 12-lead ECG signals from cardiac parameters. MethodsThe proposed method is based on a variational autoencoder (VAE). The encoder and decoder of the VAE are conditioned by the cardiac parameters so that it can model the relationship between the ECG signals and the cardiac parameters. The training data are produced by a comprehensive, Finite Element Method (FEM)-based heart simulator. New ECG signals can then be synthesized by inputting the cardiac parameters into the trained VAE decoder without relying on enormous computational resources. We used two metrics to evaluate the quality of ECG signals synthesized by the proposed model. ResultsExperimental results showed that the proposed model synthesized adequate ECG signals while preserving empirically important feature points and the overall signal shapes. We also explored the optimal model by varying the number of layers and the size of latent variables in the proposed model that balances the model complexity and the simulation accuracy. ConclusionThe proposed method has the potential to become an alternative to computationally-expensive FEM-based heart simulators. It is able to synthesize ECGs from various cardiac parameters within seconds on a personal laptop computer.

Agile Deflectometry

Concentrating Solar Power (CSP) systems use mirorrs to focus light onto a receiver, and high optical slope accuracy is required to achieve high concentration. Deflectometry is a technique for measuring optical slope directly, at fine spatial resolution across a miror surface. In this paper we report techniques for making an existing deflectometry system, SOFAST, more flexible and easier to deploy across a wide range of application scenarios. Example applications include mirror prototype development, high-volume manufacturing, outdoor in situ heliostat inspection, and education. We report using the resulting system to measure mirrors ranging in diameter from 0.13 m to 9.3 m, a range of nearly two orders of magnitude. Hardware implementations vary from a simple laptop computer to a system combining a computer, projector, screen, and camera. These efforts pursue a goal of making deflectometry easier to use, applicable to a wider range of problems, and widely available to the CSP community.

Numerical and Experimental Investigation of a Volumetric Resistance Blower Performance and Its Optimization for Portable Computing Device Applications

Abstract In this paper, we present our results on a relatively new kind of blower called Volumetric Resistance Blower (VRB) for cooling of portable computing platforms like laptop computers. The VRB performance was modeled numerically and compared to traditional bladed blowers. The sources of noise, dominant in bladed blower, are absent in case of VRB, because it uses a continuous porous disk instead of discrete blades. Thus, even though at iso-rpm, VRB yielded lower flowrate, its iso-acoustic performance could be superior. Hence, further analysis was crucial to quantify the potential benefit. The acoustics experiments for bladed blower and VRB were conducted in a hemi-anechoic chamber in accordance with ECMA-74 and ECMA TR/99 standards. Iso-acoustics pressure versus volume flowrate plot for both bladed blower and VRB are compared. VRB was found to have superior performance as compared to bladed blower. The volume flowrate at open flow condition for bladed blower and VRB are comparable, but as back pressure increased the flowrate yielded by VRB kept increasing and at stagnation condition, VRB showed around 79% higher static pressure. In the second part of the work, the experimentally validated numerical model for VRB was used for numerical optimization using a design of experiments (DOE) approach and varying the geometrical parameters. Rotor distance (minimum distance from the axis of rotation of impeller to the cutwater surface) was found to be the most important parameter, and an optimum value was found. A second DOE elucidated the optimal rotor hub center location in the two-dimensional space inside the casing as when the rotor is tucked back into the casing as much as possible and when the rotor distance is above 20.15 mm. A partial P–Q curve is generated (up to 20 Pa) for optimal geometry configuration. Based on the numerical and experimental evidence, VRB is found to have the potential to replace traditional bladed design in portable computing devices. In addition, due to absence of blades, it creates lower tonal noise, giving a much more comfortable experience to the end user.

A novel cell phone localization solution for trapped victims based on compressed RSSI fluctuation range and PSO-BP neural network

With the frequent occurrence of global earthquake disasters in recent years, quickly and accurately locating trapped people is the key to reducing the mortality rate of earthquake disasters. This paper proposes a cell phone localization solution for trapped people that can be rapidly deployed after an earthquake. This solution locates the buried cell phone by collecting its received WiFi signal strength indication (RSSI) data. Reducing the fluctuation of RSSI value is crucial to accurately locating the buried cell phone. We filter RSSI data outliers by hybrid filtering and propose a fluctuation range compression factor Q to reduce the fluctuation range of RSSI data. In order to adapt to the compressed RSSI data fluctuation range, the processed RSSI data were grouped. The grouped data are then imported into the PSO-BP neural network training to build the ranging model. So that there is a mapping relationship between RSSI data and trapped distance. The established ranging model is denoted as the HFQBP ranging model. After establishing the ranging model, the cell phone is localized by an Optimized Multilateration (OM) algorithm. For simplicity, this algorithm is named HFQBP-OM. In the experimental phase, we combined the method proposed in this paper with other methods, resulting in three different localization algorithms: HFQBP-WSC, PL-OM, and PL-WSC. And compared with the algorithms in this paper. The results indicate that the algorithm HFQBP-OM proposed in this paper has improved the total average accuracy of the nine localization points by 34.7%, 54.8%, and 58.9% compared to HFQBP-WSC, PL-OM, and PL-WSC, respectively. The HFQBP-OM algorithm has higher accuracy and stability. Also, it should be noted that the localization solution proposed in this paper uses smartphones connected to WiFi probes to collect RSSI data of the WiFi signal of the buried cell phone. Use a laptop computer to solve the localization coordinates. Smartphones and laptops are so popular that WiFi probes are the only special equipment needed. WiFi probe is inexpensive. It reduces the difficulty of implementing the localization solution in this paper and increases the generalizability of the solution.

X-ray image analysis for explosive circuit detection using deep learning algorithms

X-ray imaging technologies find applications across various domains, including medical imaging in health institutions or security in military facilities and public institutions. X-ray images acquired from diverse sources necessitate analysis by either trained human experts or automated systems. In cases where concealed electronic cards potentially pose threats, such as in laptops harboring explosive triggering circuits, conventional analysis methods are challenging to detect, even when scrutinized by skilled. The present investigation is centered on the utilization of deep learning algorithms for the analysis of X-ray images of laptop computers, with the aim of identifying concealed hazardous components. To construct the dataset, some control cards such as Arduino, Raspberry Pi and Bluetooth circuits were hidden inside the 60 distinct laptop computers and were subjected to X-ray imaging, yielding a total of 5094 X-ray images. The primary objective of this study is to distinguish laptops based on the presence or absence of concealed electronic cards. To this end, a suite of deep learning models, including EfficientNet, DenseNet, DarkNet19, DarkNet53, Inception, MobileNet, ResNet18, ResNet50, ResNet101, ShuffleNet and Xception were subjected to training, testing, and comparative evaluation. The performance of these models was assessed utilizing a range of metrics, encompassing accuracy, sensitivity, specificity, precision, f-measure, and g-mean. Among the various models examined, the ShuffleNet model emerged as the top-performing one, yielding superior results in terms of accuracy (0.8355), sensitivity (0.8199), specificity (0.8530), precision (0.8490), f-measure (0.8322), and g-mean (0.8352).

Digital-movie-based flow colorimetry for pH measurement with universal indicators

A continuous, simple, and versatile pH monitoring method based on digital-movie-based colorimetry is proposed. A constructed flow system was of a two-channel configuration mainly composed of two peristaltic pumps, a digital microscope-based detector, a pH meter with a flow-through combination pH-reference electrode, and a laptop computer. While the total flow rate (FT) was held constant, the flow rate (FB) of a base solution for Britton-Robinson buffer containing a universal indicator (Yamada's indicator or Van Urik's indicator) was changed in proportion to the control signal (Vc) from the computer. An acid solution for the buffer containing the indicator was aspirated to the confluence point at the flow rate of FT – FB and mixed with the base solution. Thus, buffer solutions with arbitrary pH could be easily prepared. The image of the mixed solution was captured with the microscope downstream; the pH of the solution was measured with the pH meter at the most downstream. An in-house program written in Visual Basic .NET was developed to control the system, acquire and analyze the signals (image data and pH), and display the results automatically; the color of the image was expressed as tristimulus values (i.e., R, G, B), hue, and luminance. The relationships between these color-specific values and pH were analyzed after optimizing the Vc scan rate. Van Uik's indicator was superior to Yamada's regarding the applicable pH range (ca. 2.5 – 10). A sigmoid-like calibration curve was established between hue and pH, which was used to determine sample pH. The proposed method was validated by measuring the pH of different drugs and vinegar samples.

Notebooks Now! The Future of Reproducible Research

AbstractIn this paper, we introduce a new ambitious project led by AGU in collaboration with Wiley and Curvenote and a team of geoscientists, computer and data scientists. The project, aptly called Notebooks Now!, aims to develop an end‐to‐end workflow for the preparation, submission, peer‐review and publication of Earth and space science papers, based on computational notebooks, specifically Jupyter Notebooks or R Markdown. The idea, first proposed by Dr. Peter Fox, Editor in Chief of Earth and Space Science from 2019 to early 2021, is to revolutionize the way data‐ and computation‐rich scientific research is performed and published. The vision is to establish the computational notebook as the centerpiece of the scientific work, around which the manuscript is then written. In this model, the notebook is the primary publication, accompanied by a static “traditional” paper as the enduring archive. Advantages of this model include: ease of editing of data and code during manuscript writing and revision; open access to the same by reviewers and readers; prompt reusability and repurposing of research outputs by other researchers; access to sophisticated computational tools by the global community; “multiplier effect” of the research. The model thus fulfills the findable, accessible, interoperable, and reusable principles of scientific publishing, and commits to Open Science. This is part of AGU's strategic initiative to accelerate openly accessible scientific contributions to sustainable development.

The NeuGRID Workflow: a “Swiss Army knife” for MRI processing

AbstractBackgroundThe advent of big data and artificial intelligence has energized the neuroimaging community with powerful processing tools and the need for large‐scale computational resources for data analysis has increased. Not all neuroscientists have access to extensive resources or have sufficient expertise to configure these libraries, making them difficult to use.MethodA collection of popular MRI scan processing software, (i.e.: Fsl, FreeSurfer, Ants, SPM, Adaboost, Neuromorphometrics, LPA, Tracula) was selected as a proof of concept to be included in the NeuGRID Workflow to produce an advanced tool for group analysis and biomarkers assessment.ResultThe NeuGRID Workflow was designed to allow the user to build a custom MRI processing pipeline by means of a layered structure (Fig.1) available for users at the URL https://neugrid2.eu/index.php/workflowgroup. In the first layer, the user must upload a compressed file containing T13D, FLAIR, and DWI scans of subjects. In the second layer, the user selects which kind of MRI sequence to process. If DICOMs were uploaded, the third layer provides tools to convert DICOMs into NIFTI, the default format for all subsequent workflow processes. All DICOMs are anonymized upon upload. The fourth layer allows the user to choose how images are preprocessed, and the preprocessing pipelines can be customized for different modalities. In the final layer, the user can choose how to analyze the images to calculate volume, thickness, area and curvature of brain regions, quantify white‐matter lesions and reconstruct major white‐matter pathways. The triggered job is efficiently parallelized using a Sun Grid Engine scheduler to reduce computation time. After the scans are processed in the NeuGRID cluster (450 CPUs, 2.5TB memory, 400TB storage), the user is notified by email that the job is finished and the results can be downloaded.ConclusionThe NeuGRID Workflow is a practical tool that allows researchers without computational resources and/or expertise to design a custom pipeline to process large amounts of data from anywhere using a traditional laptop or personal computer. The collection of heterogeneous tools provides a high degree of freedom in pipeline design, making NeuGRID Workflow a Swiss Army knife for MRI processing

Real-Time Embedded Control of Electrical Drives: A New Approach for Bringing Hands-On Experience Into the Classroom

This article presents a novel educational platform for teaching advanced electric drive systems to students using embedded controllers. The developed platform is based on a portable motor test stand with a quadrotor motor interfaced with the Texas Instruments (TI) LaunchPad F28069 and TI BOOST-DRV8301 inverter. The platform is combined with Altair Embed educational software, allowing for direct access from personal laptop computers. We also present a three-phase methodology of teaching electric drives, which relies on simulation (SIM), co-SIM (COSIM), and hardware-in-the-loop (HIL) testing. The proposed approach would allow students to learn the control theory and techniques of motor drives with hands-on experience and without the need for advanced programming skills.

Modelling the conjugate heat transfer during the fast-filling of high-pressure hydrogen vessels for vehicular transport

Compressed gas in cylinders is currently the preferred solution for storing hydrogen on board vehicles. Fast-filling combined with high storage pressures is required to meet competitive targets of long driving ranges and short refuelling times. Experiments and CFD models have shown that the fast-filling leads to significant rise in temperature within the hydrogen cylinder, which can lead to its structural failure. Thus, controlling the rise in temperature is vital during the refuelling process. This paper describes the implementation of a universal thermodynamic model that determines the gas and structural temperature during the fast-filling of hydrogen cylinders. It includes the computation of conjugate heat transfer from the gas to the cylinder structure. The thermodynamic model requires negligible computational time without compromising accuracy and can used to implement different fast-filling scenarios on a laptop or personal computer. The flexibility and robustness of the model is shown as it is capable of modelling the fast-filling of cylinders while varying different key parameters such as fill time, structural material, cylinder volume, final pressure, filling rate, initial temperatures and pressures.

A Greek Pediatric Word Recognition Test by Picture Identification.

(1) Background: The study aimed to construct a clinically valuable closet-set WRS test with a picture identification task for young Greek-speaking children. (2) Methods: The test material was meticulously designed based on specific criteria. To determine which parts of speech are used more frequently by preschool children, a spontaneous speech sample (250 words per child) was acquired from three hundred children aged 3 to 6 years (M = 4.56, SD = 0.90). The study involved the development and application of two phonemically balanced 50-word lists suitable for young children, as well as the creation of picture representations for each response set. All testing was accomplished in an audiometric booth that exceeded the audiometric rooms' ambient noise level standards. The speech signal was routed from a laptop computer to a GSI 61 audiometer, and all test items were delivered from the audiometer to the subject. (3) Results: The results indicated that materials for a WRS test for young children are developed with high face validity and are applicable for children as young as three years old. The test satisfies the essential components needed for a WRS test. It consists of two phonemically balanced 50-word lists with low-redundancy bisyllabic words, with each list containing 227 phonemes. (5) Conclusions: This novel closed-set WRS test presents a valuable tool for assessing speech perception skills in young Greek-speaking children. The test results have various applications, including diagnosis, research, and (re)habilitation.

Research shows Americans with disabilities are less likely to own digital devices

Research from the Pew Research Center suggests that there is still a digital divide between individuals who have a disability and individuals who do not have a disability. Pew found that 81% of adults without a disability report owning a desktop or laptop computer. However, only 62% of adults with disabilities report computer ownership. Additionally, there was a 16% gap in smartphone ownership (72% of individuals with disabilities versus 88% of individuals without disabilities).

A novel bionic impeller for laptop cooling fan system

The performance and stability of laptop computers can be adversely affected by overheating, which, in turn, can result in hardware failure due to low performance and stability. The purpose of this study is to develop a blower fan impeller that will solve the problems of overheating and noise production in laptop computers as a result of overheating. This proposed impeller was tested both numerically and experimentally using a Lenovo ThinkPad Carbon X1 notebook for performance analysis. Using the revolutionary novel impeller and adding an airfoil between each adjacent blades demonstrated an improved airflow distribution and a resistance reduction. According to the results of the free air simulation, there was a performance improvement of 6 %. Additionally, there was a significant increase of around 20.1 % in the volumetric flow rate of air and a significant increase in static pressure by 20 % respectively in the experimental results. Lastly, even at high speeds, blade peak noise generated by blades is extremely low in terms of sound pressure level (SPL). This work has patented under patent No. TW202248538A, CN216009004U, and CN115419611A.

SurfaceCast: Ubiquitous, Cross-Device Surface Sharing

Real-time online interaction is the norm today. Tabletops and other dedicated interactive surface devices with direct input and tangible interaction can enhance remote collaboration, and open up new interaction scenarios based on mixed physical/virtual components. However, they are only available to a small subset of users, as they usually require identical bespoke hardware for every participant, are complex to setup, and need custom scenario-specific applications. We present SurfaceCast, a software toolkit designed to merge multiple distributed, heterogeneous end-user devices into a single, shared mixed-reality surface. Supported devices include regular desktop and laptop computers, tablets, and mixed-reality headsets, as well as projector-camera setups and dedicated interactive tabletop systems. This device-agnostic approach provides a fundamental building block for exploration of a far wider range of usage scenarios than previously feasible, including future clients using our provided API. In this paper, we discuss the software architecture of SurfaceCast, present a formative user study and a quantitative performance analysis of our framework, and introduce five example application scenarios which we enhance through the multi-user and multi-device features of the framework. Our results show that the hardware- and content-agnostic architecture of SurfaceCast can run on a wide variety of devices with sufficient performance and fidelity for real-time interaction.

Yclon: Ultrafast clustering of B cell clones from high-throughput immunoglobulin repertoire sequencing data

MotivationThe next-generation sequencing technologies have transformed our understanding of immunoglobulin (Ig) profiles in various immune states. Clonotyping, which groups Ig sequences into B cell clones, is crucial in investigating the diversity of repertoires and changes in antigen exposure. Despite its importance, there is no widely accepted method for clonotyping, and existing methods are computationally intensive for large sequencing datasets. ResultsTo address this challenge, we introduce YClon, a fast and efficient approach for clonotyping Ig repertoire data. YClon uses a hierarchical clustering approach, similar to other methods, to group Ig sequences into B cell clones in a highly sensitive and specific manner. Notably, our approach outperforms other methods by being more than 30 to 5000 times faster in processing the repertoires analyzed. Astonishingly, YClon can effortlessly handle up to 2 million Ig sequences on a standard laptop computer. This enables in-depth analysis of large and numerous antibody repertoires. Availability and implementationYClon was implemented in Python3 and is freely available on GitHub.

Benchmarking Space Mission Applications on the Snapdragon Processor Onboard the ISS

Future space missions will process and analyze imagery on board as well as plan and act more autonomously, placing greater demands on flight computing. Traditional flight hardware provides modest computing power, even when compared to common laptop and desktop computers. A new generation of commercial-off-the-shelf (COTS) processors designed for commercial electronics such as cell phones and tablets, such as the Qualcomm Snapdragon, deliver significant compute in a small size, weight, and power; and they offer hardware acceleration in the form of graphics processing units and digital signal processors. We benchmark a variety of instrument processing and mission planning software on a Qualcomm Snapdragon system on a chip currently hosted by Hewlett Packard Enterprise’s Spaceborne Computer-2 on board the International Space Station to highlight the potential of using embedded COTS processors on future space missions.

Novel Instrument for Clinical Evaluations of Active Extraocular Muscle Tension

Strabismus can be caused by abnormal tension of the extraocular muscles (EOMs) attached to the eyeball in superior, inferior, lateral, medial, superior oblique, and inferior oblique positions. Evaluating the tension in each EOM is crucial for surgical planning in strabismus, which is conducted by adjusting the tension on the EOM. The purpose of this study was to develop a compact measuring device to non-invasively evaluate the active EOM tension. The proposed device employed a cotton-tipped medical swab to transfer the EOM tension connected to the force sensor as a non-invasive medium. The tilting angle of the swab and the force of active EOM tension were wirelessly transferred to a laptop computer for recording and real-time displaying of the measured values. The active EOM tensions for the four recti muscles were 101.7 ± 15.0 g (mean ± SD) for the lateral rectus; 88.0 ± 15.4 g for the medial rectus; 61.3 ± 6.8 g for the inferior rectus; and 121.3 ± 38.5 g for the superior rectus. These values were higher than the reported values of 45–60 g measured in previous studies. In the previous studies, however, the EOM was detached from the globe and attached to a strain gauge, and, thus, there were no passive elastic forces from ocular connective tissue, resulting in lower values compared with the current study. The previous methods were also complex and not suitable for clinical measurement. Thus, the proposed method, which is non-invasive and mimics the conventional force generation test with a cotton-tipped swab, could facilitate the evaluation of active EOM tension, both clinically in strabismus management and in research into understanding its pathophysiology.