Smart medical report: efficient detection of common and rare diseases on common blood tests.

Introduction: The integration of AI into healthcare is widely anticipated to revolutionize medical diagnostics, enabling earlier, more accurate disease detection and personalized care.

Methods: In this study, we developed and validated an AI-assisted diagnostic support tool using only routinely ordered and broadly available blood tests to predict the presence of major chronic and acute diseases as well as rare disorders.

Results: Our model was tested on both retrospective and prospective datasets comprising over one million patients.

Generative modeling and augmentation of EEG signals using improved diffusion probabilistic models.

. The development of deep learning models for electroencephalography (EEG) signal processing is often constrained by the limited availability of high-quality data. Data augmentation techniques are among the solutions to overcome these challenges, and deep neural generative models, with their data synthesis capabilities, are potential candidates.

Assessing parkinsonism & cerebellar dysfunction with spiral & line drawings.

Background And Purpose: Recognising neurological diseases is challenging without accurate diagnostic tools. Therefore, many approaches have been taken to recognise and evaluate these diseases through speech, movement, or drawing modalities.
The purpose of the study is to compare the recognition of Parkinson’s and cerebellar symptoms using spiral and line drawings recorded from the same subjects.

BRAF Modulates the Interplay Between Cell-Cell and Cell-Extracellular Matrix Adhesions in PECAM-1-Mediated Mechanotransduction.

Mechanotransduction, the process of how cells sense and convert mechanical stimuli into biochemical response, is crucial in the migration of leukocytes or cancer cells through the endothelium during inflammation or metastasis. Migrating cells exert forces on the endothelium through cell surface adhesion molecules, such as platelet endothelial adhesion molecule PECAM-1, and this is essential for a successful transmigration. To study PECAM-1-mediated mechanotransduction, we applied PECAM-1-antibody-coated magnetic beads and exerted about 40 pN force on the endothelial monolayer.

Effects of ZrO Nano-Particles' Incorporation into SnAgCu Solder Alloys: An Experimental and Theoretical Study.

This study investigates the mechanism and effects of incorporating different ZrO nano-particles into SAC0307 solder alloys. ZrO nano-powder and nano-fibers in 0.25-0.

New Parameter for Benchmarking Plasmonic Gas Sensors Demonstrated with Densely Packed Au Nanoparticle Layers.

Localized surface plasmon resonance (LSPR) gas sensitivity is introduced as a new parameter to evaluate the performance of plasmonic gas sensors. A model is proposed to consider the plasmonic sensors' surface sensitivity and plasmon decay length and correlate the LSPR response, measured upon gas exchange, with an equivalent refractive index change consistent with adsorbed gas layers. To demonstrate the applicability of this new parameter, ellipsoidal gold nanoparticles (NPs) arranged in densely packed hexagonal lattices were fabricated.

Machine learning-based real-time anomaly detection using data pre-processing in the telemetry of server farms.

Fast and accurate anomaly detection is critical in telemetry systems because it helps operators take appropriate actions in response to abnormal behaviours. However, recent techniques are accurate but not fast enough to deal with real-time data. There is a need to reduce the anomaly detection time, which motivates us to propose two new algorithms called AnDePeD (Anomaly Detector on Periodic Data) and AnDePed Pro.

Heterogeneous data integration: Challenges and opportunities.

Integrating multiple data source technologies is essential for organizations to respond to highly dynamic market needs. Although physical data integration systems have been considered to have better query processing systems, they pose higher implementation and maintenance costs. Meanwhile, virtual data integration has become an alternative topic that is increasingly attracting the attention of researchers in the current era of big data.

User Engagement with the User Manuals of Smart Medical Wearable Technologies and Relevant Questions of User Behavior.

In this paper, engagement with smart medical wearables and with their user manuals, as well as related user behavior are studied. A research questionnaire containing 15 single-choice questions was completed by 1381 test participants to address relevant topics of the investigated area, including trust in measured medical data, device calibration, technical terminologies and function discovery in the documentation, information sources beyond the documentation, and wearing such devices to bed. The questionnaire particularly focused on device functionalities and characteristics that initially led to the purchase of the smart medical wearable.

EEG based depression detection by machine learning: Does inner or overt speech condition provide better biomarkers when using emotion words as experimental cues?

Background: Objective diagnostic approaches need to be tested to enhance the efficacy of depression detection. Non-invasive EEG-based identification represents a promising area.

Aims: The present EEG study addresses two central questions: 1) whether inner or overt speech condition result in higher diagnositc accuracy of depression detection; and 2) does the affective nature of the presented emotion words count in such diagnostic approach.

Label-Free Single-Cell Cancer Classification from the Spatial Distribution of Adhesion Contact Kinetics.

There is an increasing need for simple-to-use, noninvasive, and rapid tools to identify and separate various cell types or subtypes at the single-cell level with sufficient throughput. Often, the selection of cells based on their direct biological activity would be advantageous. These steps are critical in immune therapy, regenerative medicine, cancer diagnostics, and effective treatment.

Surface-Enhanced Raman Spectroscopy (SERS)-Based Sensors for Deoxyribonucleic Acid (DNA) Detection.

Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful technique for the detection and analysis of biomolecules due to its high sensitivity and selectivity. In recent years, SERS-based sensors have received significant attention for the detection of deoxyribonucleic acid (DNA) molecules, offering promising applications in fields such as medical diagnostics, forensic analysis, and environmental monitoring. This paper provides a concise overview of the principles, advancements, and potential of SERS-based sensors for DNA detection.

Probing Beat Perception with Event-Related Potentials (ERPs) in Human Adults, Newborns, and Nonhuman Primates.

The aim of this chapter is to give an overview of how the perception of rhythmic temporal regularity such as a regular beat in music can be studied in human adults, human newborns, and nonhuman primates using event-related brain potentials (ERPs). First, we discuss different aspects of temporal structure in general, and musical rhythm in particular, and we discuss the possible mechanisms underlying the perception of regularity (e.g.

Football Movement Profile-Based Creatine-Kinase Prediction Performs Similarly to Global Positioning System-Derived Machine Learning Models in National-Team Soccer Players.

Purpose: The relationship between external load and creatine-kinase (CK) response at the team/position or individual level using Global Positioning Systems (GPS) has been studied. This study aimed to compare GPS-derived and Football Movement Profile (FMP) -derived CK-prediction models for national-team soccer players. The second aim was to compare the performance of general and individualized CK prediction models.

The effects of spatial leakage correction on the reliability of EEG-based functional connectivity networks.

Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero-lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject-level identifiability.

Single-cell classification based on label-free high-resolution optical data of cell adhesion kinetics.

Selecting and isolating various cell types is a critical procedure in many applications, including immune therapy, regenerative medicine, and cancer research. Usually, these selection processes involve some labeling or another invasive step potentially affecting cellular functionality or damaging the cell. In the current proof of principle study, we first introduce an optical biosensor-based method capable of classification between healthy and numerous cancerous cell types in a label-free setup.

Recent Advances of VO in Sensors and Actuators.

Vanadium dioxide (VO) stands out for its versatility in numerous applications, thanks to its unique reversible insulator-to-metal phase transition. This transition can be initiated by various stimuli, leading to significant alterations in the material's characteristics, including its resistivity and optical properties. As the interest in the material is growing year by year, the purpose of this review is to explore the trends and current state of progress on some of the applications proposed for VO in the field of sensors and actuators using literature review methods.

Hydrodynamic function and spring constant calibration of FluidFM micropipette cantilevers.

Fluidic force microscopy (FluidFM) fuses the force sensitivity of atomic force microscopy with the manipulation capabilities of microfluidics by using microfabricated cantilevers with embedded fluidic channels. This innovation initiated new research and development directions in biology, biophysics, and material science. To acquire reliable and reproducible data, the calibration of the force sensor is crucial.

Investigating the kinetics of layer development during the color etching of low-carbon steel with in-situ spectroscopic ellipsometry.

Color etching is a useful corrosive process, widely applied in metallography to study the microstructure of metals. To prove the existence of the previously hypothesized steady-state etching rate, in-situ investigations were performed with spectroscopic ellipsometry during the color etching of ferritic materials. Kinetic information regarding the refractive index, extinction coefficient, and layer thickness were used to calculate the steady-state layer buildup rate, which was 1.

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities.

Newborn infants have been shown to extract temporal regularities from sound sequences, both in the form of learning regular sequential properties, and extracting periodicity in the input, commonly referred to as a regular pulse or the 'beat'. However, these two types of regularities are often indistinguishable in isochronous sequences, as both statistical learning and beat perception can be elicited by the regular alternation of accented and unaccented sounds. Here, we manipulated the isochrony of sound sequences in order to disentangle statistical learning from beat perception in sleeping newborn infants in an EEG experiment, as previously done in adults and macaque monkeys.

Experimental Analysis and Optimization Approach of Self-Clocked Rate Adaptation for Multimedia Congestion Control Algorithm in Emulated 5G Environment.

The congestion problem has driven many researchers to address it, among other networking issues. In a packet-switched network, congestion is essential; it leads to a high response time to deliver packets due to heavy traffic, which eventually causes packet loss. Hence, congestion control mechanisms are utilized to prevent such cases.

Joint Power and Channel Allocation for Non-Orthogonal Multiple Access in 5G Networks and Beyond.

Spectral efficiency is a crucial metric in wireless communication systems, as it defines how much information can be transmitted over a given amount of spectrum resources. Non-orthogonal multiple access (NOMA) is a promising technology that has captured the interest of the wireless research community because of its capacity to enhance spectral efficiency. NOMA allows multiple users to share the same frequency band and time slot by assigning different power levels and modulation schemes to different users.

Feasibility of a novel neurofeedback system: a parallel randomized single-blinded pilot study.

Neurocognitive assessment tools have been proposed to optimize, maintain, and improve perceptual-cognitive performance. Here, we investigated the feasibility and efficacy of a novel neurofeedback system, neuroMoon (nM), on cognitive abilities compared with one of the most popular perceptual-cognitive training (PCT) tools both in sports and rehabilitation called NeuroTracker (NT). Thirty-one young athletes performed a comprehensive battery of cognitive tests from the Vienna Test System before and after a 12-session computer-based cognitive training program using nM (n = 11, age 22.

Implementation of MEC-Assisted Collective Perception in an Integrated Artery/Simu5G Simulation Framework.

Advanced vehicle-to-everything (V2X) safety applications must operate with ultra-low latency and be highly reliable. Therefore, they require sophisticated supporting technologies. This is especially true for cooperative applications, such as Collective Perception (CP), where a large amount of data constantly flows among vehicles and between vehicles and a network intelligence server.

Plasmonic Effect of Gold-Patchy Silica Nanoparticles on Green Light-Photopolymerizable Dental Resin.

A low ratio of polymerization is a major problem in resin-based composites. In this paper, the plasmonic effect of gold-covered silica nanoparticles on the physicochemical and mechanical properties of bisphenol A diglycidyl dimethacrylate (Bis-GMA), triethylene glycol dimethacrylate (TEGDMA) and urethane dimethacrylate (UDMA) green light-photopolymerizable dental resin was investigated at an intensity of 1.4 mW/cm for 40 s.