Versatile Interface Research Articles

Simona: a comprehensive R package for semantic similarity analysis on bio-ontologies

BackgroundBio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between biological concepts based on the semantics encoded in ontologies. It plays an important role in structured and meaningful interpretations and integration of complex data from multiple biological domains.ResultsWe present simona, a novel R package for semantic similarity analysis on general bio-ontologies. Simona implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. Moreover, it provides a robust toolbox supporting over 70 methods for semantic similarity analysis. With simona, we conducted a benchmark against current semantic similarity methods. The results demonstrate methods are clustered based on their mathematical methodologies, thus guiding researchers in the selection of appropriate methods. Additionally, we explored annotation-based versus topology-based methods, revealing that semantic similarities solely based on ontology topology can efficiently reveal semantic similarity structures, facilitating analysis on less-studied organisms and other ontologies.ConclusionsSimona offers a versatile interface and efficient implementation for processing, visualization, and semantic similarity analysis on bio-ontologies. We believe that simona will serve as a robust tool for uncovering relationships and enhancing the interoperability of biological knowledge systems.

Design of an AI Chatbot Using Generative AI to Access Local Database

The "College Chatbot" project aims to create an advanced conversational agent tailored for college-related inquiries. This innovative chatbot incorporates a versatile interface, capable of seamlessly processing both text and voice inputs, enhancing user accessibility. Its distinctive feature includes multilingual output capabilities, enabling responses in local languages to cater to a diverse user base. The primary function of the chatbot is to comprehensively address a wide array of college-related queries, providing students and users with accurate and timely information on various aspects of college life, academic affairs, and related topics. By combining a user-friendly interface with linguistic flexibility, the College Chatbot seeks to revolutionize the way students interact with and retrieve information from their educational institutions. Keywords- AI chatbot, Generative AI, Local database, Natural Language processing, Data Integrarion,User Intraction, Query mechanism, Model training.

Triblock PolyA-Mediated Protein Biosensor Based on a Size-Matching Proximity Hybridization Analysis.

The antibodies in the natural biological world utilize bivalency/multivalency to achieve a higher affinity for antigen capture. However, mimicking this mechanism on the electrochemical sensing interface and enhancing biological affinity through precise spatial arrangement of bivalent aptamer probes still pose a challenge. In this study, we have developed a novel self-assembly layer (SAM) incorporating triblock polyA DNA to enable accurate organization of the aptamer probes on the interface, constructing a "lock-and-key-like" proximity hybridization assay (PHA) biosensor. The polyA fragment acts as an anchoring block with a strong affinity for the gold surface. Importantly, it connects the two DNA probes, facilitating one-to-one spatial proximity and enabling a controllable surface arrangement. By precisely adjusting the length of the polyA fragment, we can tailor the distance between the probes to match the molecular dimensions of the target protein. This design effectively enhances the affinity of the aptamers. Notably, our biosensor demonstrates exceptional specificity and sensitivity in detecting PDGF-BB, as confirmed through successful validation using human serum samples. Overall, our biosensor presents a novel and versatile interface for proximity assays, offering a significantly improved surface arrangement and detection performance.

Design and Construction of a Multi-Tiered Minimal Actin Cortex for Structural Support in Lipid Bilayer Applications.

Artificial lipid bilayers have revolutionized biochemical and biophysical research by providing a versatile interface to study aspects of cell membranes and membrane-bound processes in a controlled environment. Artificial bilayers also play a central role in numerous biosensing applications, form the foundational interface for liposomal drug delivery, and provide a vital structure for the development of synthetic cells. But unlike the envelope in many living cells, artificial bilayers can be mechanically fragile. Here, we develop prototype scaffolds for artificial bilayers made from multiple chemically linked tiers of actin filaments that can be bonded to lipid headgroups. We call the interlinked and layered assembly a multiple minimal actin cortex (multi-MAC). Construction of multi-MACs has the potential to significantly increase the bilayer's resistance to applied stress while retaining many desirable physical and chemical properties that are characteristic of lipid bilayers. Furthermore, the linking chemistry of multi-MACs is generalizable and can be applied almost anywhere lipid bilayers are important. This work describes a filament-by-filament approach to multi-MAC assembly that produces distinct 2D and 3D architectures. The nature of the structure depends on a combination of the underlying chemical conditions. Using fluorescence imaging techniques in model planar bilayers, we explore how multi-MACs vary with electrostatic charge, assembly time, ionic strength, and type of chemical linker. We also assess how the presence of a multi-MAC alters the underlying lateral diffusion of lipids and investigate the ability of multi-MACs to withstand exposure to shear stress.

Virtual Mouse System using Hand Gesture recognition with Open CV

The use of hand gesture recognition for controlling virtual devices has gained popularity with the advancement of artificial intelligence technology. In this paper, we propose a hand gesture-controlled virtual mouse system that employs AI algorithms to identify and interpret hand gestures, translating them into mouse movements. The primary objective of this system is to provide an alternative interface, particularly beneficial for individuals facing challenges with traditional mouse or keyboard usage.Our proposed system utilizes a camera to capture images of the user's hand. To enable gesture recognition, the system undergoes training using a dataset comprising various hand gestures. Once a gesture is identified, it is translated into a corresponding mouse movement, subsequently executed on the virtual screen. An essential feature of our system is its scalability and adaptability, allowing seamless integration into diverse environments and devices. To enhance user interaction, our system incorporates dynamic/static hand gestures along with a voice assistant for virtually controlling all input operations. Notably, our approach employs Machine Learning (ML) and Computer Vision algorithms to recognize hand gestures and voice commands, eliminating the need for additional hardware requirements. The model is implemented using the MediaPipe framework, demonstrating the feasibility of our proposed system. In conclusion, our hand gesture-controlled virtual mouse system presents a promising avenue for improving user experience and accessibility through human-computer interaction. By leveraging AI, ML, and Computer Vision, we aim to create an intuitive and versatile interface that can be easily adapted to different environments, ultimately enhancing accessibility for a wider range of users. Keywords: Computer vision, hand gesture recognition, Media-pipe, virtual mouse.

"Clickable" graphene nanoribbons for biosensor interfaces.

We report on the synthesis of "clickable" graphene nanoribbons (GNRs) and their application as a versatile interface for electrochemical biosensors. GNRs are successfully deposited on gold-coated working electrodes and serve as a platform for the covalent anchoring of a bioreceptor (i.e., a DNA aptamer), enabling selective and sensitive detection of Interleukin 6 (IL6). Moreover, when applied as the intermediate linker on reduced graphene oxide (rGO)-based field-effect transistors (FETs), the GNRs provide improved robustness compared to conventional aromatic bi-functional linker molecules. GNRs enable an orthogonal and covalent attachment of a recognition unit with a considerably higher probe density than previously established methods. Interestingly, we demonstrate that GNRs introduce photoluminescence (PL) when applied to rGO-based FETs, paving the way toward the simultaneous optical and electronic probing of the attached biointerface.

Application of Deep Learning for Studying NMDA Receptors.

Artificial intelligence underwent remarkable advancement in the past decade, revolutionizing our way of thinking and unlocking unprecedented opportunities across various fields, including drug development. The emergence of large pretrained models, such as ChatGPT, has even begun to demonstrate human-level performance in certain tasks.However, the difficulties of deploying and utilizing AI and pretrained model for nonexpert limited its practical use. To overcome this challenge, here we presented three highly accessible online tools based on a large pretrained model for chemistry, the Uni-Mol, for drug development against CNS diseases, including those targeting NMDA receptor: the blood-brain barrier (BBB) permeability prediction, the quantitative structure-activity relationship (QSAR) analysis system, and a versatile interface of the AI-based molecule generation model named VD-gen. We believe that these resources will effectively bridge the gap between cutting-edge AI technology and NMDAR experts, facilitating rapid and rational drug development.

Anki Flashcards for Radiology Education.

From online textbooks to educational social media posts, digital learning tools are transforming medical education. Spaced repetition programs are a popular tool, with 70% of first-year medical students reporting using the free, open-source flashcard application Anki.1 The Anki program pairs active recall and spaced repetition learning principles with a simple but versatile user interface.2 Spaced repetition flashcard programs such as Anki are uniquely suited to radiology education because they strengthen image-based recall, a necessary skill for radiology trainees to develop. In this review, we will describe the science behind active recall and spaced repetition programs, introduce the Anki platform, and describe future directions for its potential as an educational resource for radiology.

Strain-tunable Berry curvature in quasi-two-dimensional chromium telluride

Magnetic transition metal chalcogenides form an emerging platform for exploring spin-orbit driven Berry phase phenomena owing to the nontrivial interplay between topology and magnetism. Here we show that the anomalous Hall effect in pristine Cr2Te3 thin films manifests a unique temperature-dependent sign reversal at nonzero magnetization, resulting from the momentum-space Berry curvature as established by first-principles simulations. The sign change is strain tunable, enabled by the sharp and well-defined substrate/film interface in the quasi-two-dimensional Cr2Te3 epitaxial films, revealed by scanning transmission electron microscopy and depth-sensitive polarized neutron reflectometry. This Berry phase effect further introduces hump-shaped Hall peaks in pristine Cr2Te3 near the coercive field during the magnetization switching process, owing to the presence of strain-modulated magnetic layers/domains. The versatile interface tunability of Berry curvature in Cr2Te3 thin films offers new opportunities for topological electronics.

URANOS v1.0 – the Ultra Rapid Adaptable Neutron-Only Simulation for Environmental Research

Abstract. The understanding of neutron transport by Monte Carlo simulations led to major advancements towards precise interpretation of measurements. URANOS (Ultra Rapid Neutron-Only Simulation) is a free software package which has been developed in the last few years in cooperation with particle physics and environmental sciences, specifically for the purposes of cosmic-ray neutron sensing (CRNS). Its versatile user interface and input/output scheme tailored for CRNS applications offers hydrologists straightforward access to model individual scenarios and to directly perform advanced neutron transport calculations. The geometry can be modeled layer-wise, whereas in each layer a voxel geometry is extruded using a two-dimensional map from pixel images representing predefined materials and allowing for the construction of objects on the basis of pixel graphics without a three-dimensional editor. It furthermore features predefined cosmic-ray neutron spectra and detector configurations and also allows for a replication of important site characteristics of study areas – from a small pond to the catchment scale. The simulation thereby gives precise answers to questions like from which location do neutrons originate? How do they propagate to the sensor? What is the neutron's response to certain environmental changes? In recent years, URANOS has been successfully employed by a number of studies, for example, to calculate the cosmic-ray neutron footprint, signals in complex geometries like mobile applications on roads, urban environments and snow patterns.

National Institute of Malaria Research-Malaria Dashboard (NIMR-MDB): A digital platform for analysis and visualization of epidemiological data

A digital dashboard can be an invaluable resource for the research community and can help analyze and visualize data as per the inputs provided by the user. In India, large data sets on malaria are available though presently no digital dashboard is being used to monitor and analyze the malaria data. We have developed a dashboard (National Institute of Malaria Research-Malaria Dashboard (NIMR-MDB)) in R software using 19 different packages within which shiny and ggplot2 are used more intensively. NIMR-MDB can be used offline by running the app on any R software installed computer. Furthermore, NIMR-MDB can be accessed across different computers within an organization using a local server, or it may be made publicly available by publishing it online with secured access. There are two options for publishing the shiny dashboard online 1) via a personal Linux server for hosting this application or 2) by hosting the application on a certified online platform such as ‘shinyapps.io’ at a reasonable cost without setting up a server. The NIMR-MDB is a versatile interface that allows prompt and interactive analyses of malaria epidemiological data. The primary interface of NIMR-MDB is like a web page with 14 tabs (or pages) where each Tab corresponds to a particular set of analysis. Users may move from one Tab to another via icons. Each Tab allows flexibility in correlating various epidemiological parameters like SPR, API, AFI, ABER, RT, malaria cases, deaths, BSC and BSE. The malaria epidemiological data can be analyzed in the required granularity (national level, state level or district level), and its enhanced visualization allows for facile usage and extensive analysis. This NIMR-MDB developed here will play an important role in the analysis of epidemiological data and in strategizing malaria control in India. Researchers and policymakers may use it as a prototype for developing other dashboards for various diseases globally. There is no specific grant received from any funding agency for this work yet.

Constructing a Superior Interfacial Microstructure on Carbon Fiber for High Interfacial and Mechanical Properties of Epoxy Composites.

The interface quality is crucial for the properties of carbon fiber-reinforced polymer- matrix composites (CFRPs). In order to improve the interfacial and mechanical properties of CFRPs, a superior gradient modulus interfacial microstructure is constructed on the carbon fiber (CF) surface by chemically grafting a self-assembly carboxyl-terminated hyperbranched polymer (HP-COOH). A monofilament debonding test, a short beam shear test, an impact test and a dynamic mechanical thermal analysis (DMTA) were conducted to investigate the properties of the modified composite. Prominent improvements of 79.6% for the interfacial shear strength, 51.5% for the interlaminar shear strength, and 49.2% for the impact strength, as well as superior heat-resistance properties are achieved for composites with the gradient modulus interface over those of the untreated CF composites. The mechanism for performance improvement is mainly attributed to the enhanced CF surface energy, mechanical interlocking, and chemical bonding interactions. In particular, an atomic force microscopy (AFM) test proved that the gradient modulus interfacial microstructure formed by HP–COOH could widen the interface layer thickness and buffer the sharp variations in the modulus from CF to resin, thereby transmitting an external force and reducing the stress concentration. This work provides a facile and efficient strategy for constructing a superior and versatile interface for high- performance composites.

Music: a versatile interface for explorations in art & science

ABSTRACT The article traces the author's research into auditory and temporal perception to probe the strategies useful to a composer for portraying illusions of multiple layers in music, then to a secondary exploration of cross-disciplinary terminologies and strategies, and subsequently to a focus on variables in the listener profile as influencing the potential discernment of meaning in music. Further exploration, tracing the millennia of primitive sound-processing strategies as well as the many contexts which involve body movements and rhythms, leads to the realization that a composer who ventures outside traditional stylistic conventions of music-making will benefit from attention to these factors. In conclusion, the author points out that music is created not only for portraying emotions and moods, but, like all art forms, with an infinite variety of manifestations, subject matter, and research modes.

Open AR-Sandbox: A haptic interface for geoscience education and outreach

Abstract Virtual reality concepts have been widely adapted to teach geoscientific content, most notably in virtual field trips—with increased developments due to recent travel restrictions and challenges of field access. On the spectrum between real and fully virtual environments are also combinations of digital and real content in mixed-reality environments. In this category, augmented-reality (AR) sandboxes have been used as a valuable tool for science outreach and teaching due to their intuitive and haptic interaction-enhancing operation. Most of the common AR-sandboxes are limited to the visualization of topography with contour lines and colors, as well as water simulations on the digital terrain surface. We show here how we can get beyond this limitation, through an open-source implementation of an AR-sandbox system with a versatile interface written in the free and cross-platform programming language Python. This implementation allows for creative and novel applications in geosciences education and outreach in general. With a link to a 3-D geomodelling system, we show how we can display geologic subsurface information such as the outcropping lithology, creating an interactive geological map for structural geology classes. The relations of subsurface structures, topography, and outcrop can be explored in a playful and comprehensible way. Additional examples include the visualizations of geophysical fields and the propagation of seismic waves, as well as simulations of Earth surface processes. We further extended the functionality with ArUco-marker detection to enable more precise and flexible interaction with the projected content. In combination, with these developments, we aim to make AR-sandbox systems, with the additional dimension of haptic interactions, accessible to a wider range of geoscientific applications for education and outreach.

Coupled hydro-mechanical interfaces to enable uplift modelling in offshore engineering

In many fields of geotechnical engineering, the numerical modelling of structure-soil interaction remains a fundamental but challenging topic. A key problem is the requirement of a versatile interface to account for the contributions from both the soil skeleton and the pore fluid between the contacting structure and soil. This is especially relevant for modelling uplift problems, where separation occurs and a gap is eventually formed, with suction sustained in the short term and dissipated gradually. Both the effective stresses of the soil skeleton and the pore fluid pressures must be modelled properly, including flow within the developing gap. There is a demand for coupled hydro-mechanical interfaces that address the mechanical and suction separation. This paper presents two coupled hydro-mechanical interfaces. For one, fluid flow is defined only in the transversal direction, while for the other, both longitudinal and transversal flows are accounted for. After verifying the two interfaces against established solutions, a systematic study of the uplift of a surface footing sitting on clay soil (modelled as Modified Cam Clay) for a range of uplift rates is described. The two interfaces have the potential to model many problems in offshore geotechnical engineering that involve lifting off the seabed.

Thermoelectric performance of p-type (Bi,Sb)2Te3 incorporating amorphous Sb2S3 nanospheres

Tremendous efforts have been focusing on the improvement of p-type (Bi, Sb)2Te3-based thermoelectric materials for commercial applications. In this study, we achieve versatile interface engineering through a surface decoration of Bi0.5Sb1.5Te3 by amorphous Sb2S3 combining with spark plasma sintering, which introduces semi-coherent Sb/Bi0.5Sb1.5Te3 interfaces and dopes S into Bi0.5Sb1.5Te3. Semi-coherent Sb/Bi0.5Sb1.5Te3 interfaces strongly scatter phonons and lower energy carriers, leading to decreased thermal conductivity and increased Seebeck coefficient, while the electrical conductivity is not sacrificed due to the compromise of the slightly reduced carrier mobility by interfacial scattering and the increased carrier concentration by S doping. Benefited from the decoupled thermoelectric properties, a significantly enhanced power factor of 3345.40 μW m−1 K−2 and a low thermal conductivity of 0.78 W m−1 K−1 is obtained in Bi0.5Sb1.5Te3-0.4%Sb2S3, leading to a high peak zT of ∼ 1.31 at 330 K, which shows a 54% enhancement compared with pristine Bi0.5Sb1.5Te3. Moreover, a conversion efficiency of ∼ 7.6% can be predicted in a single leg Bi0.5Sb1.5Te3-0.4%Sb2S3-based module under a cold side temperature of 300 K and hot side temperature of 480 K. This study paves a facile amorphous Sb2S3 induced interface engineering strategy for the development of high performance (Bi,Sb)2Te3-based thermoelectric materials.

Control of substrate strain transfer to thin film photovoltaics via interface design

Brittle thin film photovoltaics (PV) that are integrated with load-bearing structures can be subjected to large strains that lead to fragmentation and performance degradation. Such adverse effects can be mitigated, or altogether eliminated, by mechanically isolating a thin PV module from the underlying load-bearing structure. We developed an effective and efficient strain attenuation strategy to eliminate strain transfer from a stiff unidirectional Carbon Fiber Reinforced Polymer (CFRP) laminate to an integrated thin amorphous Si PV module. An analytical model of the three-layer material system (CFRP laminate, interface layer and PV module) was employed to create strain attenuation maps that depend on the length of the PV module and capture the coupled effects of the shear modulus and the thickness of the interface layer (G2, t2), and the Young’s modulus and the thickness of the PV module (E3, t3) through the terms G2/t2 and E3t3, respectively. Based on these strain attenuation maps, polydimethylsiloxane (PDMS) was identified as an effective and versatile interface material, which provided up to 100% strain attenuation between the CFRP laminate and the PV module. After accounting for confinement effects on the effective modulus of the PDMS interface layer, a very good agreement was achieved between the measured and the predicted strain attenuation. Importantly, the PDMS interfacial layer preserved the initial fill factor of the PV module until CFRP laminate failure at 1.7% strain, while in the absence of the PDMS interface the fill factor decreased when the CFRP laminate strain exceeded 0.80%.

Probing machine-learning classifiers using noise, bubbles, and reverse correlation

BackgroundMany scientific fields now use machine-learning tools to assist with complex classification tasks. In neuroscience, automatic classifiers may be useful to diagnose medical images, monitor electrophysiological signals, or decode perceptual and cognitive states from neural signals. However, such tools often remain black-boxes: they lack interpretability. A lack of interpretability has obvious ethical implications for clinical applications, but it also limits the usefulness of these tools to formulate new theoretical hypotheses. New methodWe propose a simple and versatile method to help characterize the information used by a classifier to perform its task. Specifically, noisy versions of training samples or, when the training set is unavailable, custom-generated noisy samples, are fed to the classifier. Multiplicative noise, so-called “bubbles”, or additive noise are applied to the input representation. Reverse correlation techniques are then adapted to extract either the discriminative information, defined as the parts of the input dataset that have the most weight in the classification decision, and represented information, which correspond to the input features most representative of each category. ResultsThe method is illustrated for the classification of written numbers by a convolutional deep neural network; for the classification of speech versus music by a support vector machine; and for the classification of sleep stages from neurophysiological recordings by a random forest classifier. In all cases, the features extracted are readily interpretable. Comparison with existing methodsQuantitative comparisons show that the present method can match state-of-the art interpretation methods for convolutional neural networks. Moreover, our method uses an intuitive and well-established framework in neuroscience, reverse correlation. It is also generic: it can be applied to any kind of classifier and any kind of input data. ConclusionsWe suggest that the method could provide an intuitive and versatile interface between neuroscientists and machine-learning tools.

Guiding Stem Cell Differentiation and Proliferation Activities Based on Nanometer-Thick Functionalized Poly-p-xylylene Coatings

Modifications of biomaterials based on the combination of physical, chemical, and biological cues for manipulating stem cell growth are needed for modern regenerative medicine. The exploitation of these sophisticated modifications remains a challenge, including substrate limitation, biocompatibility, and versatile and general cues for stem cell activities. In this report, a vapor-phase coating technique based on the functionalization of poly-p-xylylene (PPX) was used to generate a surface modification for use with stem cells in culture. The coating provided the ability for covalent conjugation that immobilized bone morphogenetic protein 2 (BMP-2) and fibroblast growth factor 2 (FGF-2), and the modified coating surfaces enabled direct stem cell differentiation and controlled proliferation because of the specific activities. The ligations were realized between the growth factors and the maleimide-modified surface, and the conjugation reactions proceeded with high specificity and rapid kinetics under mild conditions. The conjugation densities were approximately 140 ng·cm−2 for BMP-2 and 155 ng·cm−2 for FGF-2. Guiding the activities of the human adipose-derived stem cells (hADSCs) was achieved by modifying surfaces to promote the hADSC differentiation capacity and proliferation rate. The reported coating system demonstrated biocompatibility, substrate-independent conformity, and stability, and it could provide an effective and versatile interface platform for further use in biomedical applications.

Biochemical insights into structure and function of arrestins.

Arrestins (arr) are multifunctional cytosolic adaptors that bind to active and phosphorylated G protein-coupled receptors (GPCRs) via a highly versatile interface. Arrestins stop G protein signaling and trigger other signaling pathways. Recently, 3D structures of arr-GPCR complexes have been solved, which provide a bulk of structural information for understanding the mechanism of arr recruitment and activation. However, many questions about the functional consequences of structural details and the dynamics of the arr-GPCR interaction remain open. A wealth of information about key determinants for the arr-GPCR interaction and their functional relevance, and dynamic insights into the process of arr binding and the functional outcomes of different binding modes have been provided by a series of biochemical methods which we review here. Importantly, most of these methods provide information from the live cell, which is a necessary validation and complement for structural data. With the main focus on the most recent research, we will highlight major findings about arr structure, function, and dynamics derived from mutagenesis studies, cross-linking studies, conformational probes, and sensors, and we summarize available systems to detect arr recruitment. Furthermore, we discuss recent findings and directions of insilico investigations in arr-GPCR complexes.