bluuebunny/crossref_metadata_2025_split huggingface dataset:
An open-source software for the simulation of fixational eye movements
Authors: Allgeier Stephan , Anzlinger Fabian , Bohn Sebastian , Mikut Ralf , Neumann Oliver , Reichert Klaus-Martin , Stachs Oliver , Sperlich Karsten 
Published: December 2024

Abstract
Laser-scanning confocal microscopy of the human cornea acquires in vivoimages of corneal tissues with cellular resolution, which offers diagnostic potential for a variety of diseases. However, involuntary fixational eye movements induce motion artefacts that pose a challenge for accurate morphometric analysis and particularly for preceding image data fusion steps. Different image registration algorithms promise to eliminate such artefacts, but objective, quantitative evaluation of the registration results is difficult because of the lack of ground truth data. Simulation approaches offer a possibility to close this gap. Existing open-source software for the simulation of fixational eye movements is currently limited to creating drift movement. The present contribution proposes complementary models for microsaccade and tremor components to provide complete simulated fixational eye movement paths. In addition, it reports results from an extensive examination of different model parameter combinations. It is shown that the microsaccade model is capable of reproducing intermicrosaccade intervals that closely resemble experimental data. A software implementation is provided as open-source Python modules.
ER-WGAN: Prediction of Cell Painted Endoplasmic Reticulum from Brightfield Images
Authors: Anthiyur Aravindan Abhinav , Palanisamy Rohini 
Published: December 2024

Abstract
Prediction of cell painting facilitates highthroughput screening of cellular biology and disease mechanisms, accelerating the discovery of novel therapeutic targets or drugs. This study explores the prediction of cell painted Endoplasmic Reticulum (ER) images from transmitted light brightfield images employing deep learning techniques. A conditional Generative Adversarial Network (cGAN) based framework incorporating Wasserstein loss and Gradient Penalty with a modified UNet++ based generator and a patch discriminator is used to predict cell-painting images from brightfield images captured at varying focal planes. Evaluation of the GAN network reveals promising results with a Mean Absolute Error (MAE) of 0.20, Multi-Scale Structural Similarity Index (MS-SSIM) of 0.80, and Peak Signal-to- Noise Ratio (PSNR) of 56.52. The results showcase the effectiveness of the proposed approach in accurately predicting the ER cell painting channel from transmitted light microscopy. Consequently, the study proposes the ER-WGAN network for predicting cell painting using brightfield images.
Comparison of proximal and distal BCG measurements for blood pressure estimation
Authors: Becker Niels , Strotmann Niko , Abusaleh Islam , Kurka Yannick , Wiede Christian , Seidl Karsten 
Published: December 2024

Abstract
This paper presents a comparison of the blood pressure estimation based on Ballistocardiography (BCG) measurements, acquired at two different body locations. The first BCG signal being the proximally measured BCG at the carotid artery, the second being the distally measured BCG at the foot. Parallel to the BCG measurement, the reference blood pressure is measured. Visual inspection of both BCG signals shows a decreased signal quality for the distal measurement. Moreover, in order to estimate the blood pressure based on BCG derived features for beat-to-beat intervals two neural networks are deployed. It is shown that Mean Absolut Error (MAE) is twice as large for the distal blood pressure estimation as for the proximal estimation and that the estimated pressure values of the proximal model show less variation when comparing the reference pressure and estimated pressure visually.
Dry resist laser patterning of flexible neural probes with fluidic functionality
Authors: Bucherer Andreas , Klein Eric , Paul Oliver , Ruther Patrick 
Published: December 2024

Abstract
Optogenetics enables insights into the development of neural diseases. Custom-designed neural probes are necessary to access targeted brain regions. Multifunctional and mechanically flexible probes with minimal footprint, comprising fluidic channels for drug delivery, light sources for optical stimulation, and micro electrodes for electro-physiological readout are beneficial due to reduced tissue trauma and consequently increased long-term stability. This study introduces a mechanically compliant neural probe that provides microfluidic channels and electrodes arranged close to fluidic outlet ports. The key challenge is to open the fluidic inlet and outlet ports avoiding potential channel clogging. A novel laserbased patterning process is demonstrated using a 248 nm KrF excimer laser. It offers high design flexibility in positioning the fluidic ports along the fluidic channels, as well their contamination-free opening under dry conditions. The laser patterning process applies reflective metal layers inside the fluidic channels, confining the laser ablation to the channel cover, thus minimizing potential damage of the channel substrate. Based on its high reflectivity and correspondingly low absorption and transmission at the applied laser wavelength, aluminium is determined to be the best choice for this protective layer.
Identification of Mind Awareness from EDA signals using Wavelet based ResNet50 model
Authors: Chappidi Sriram Kalyan , Palanisamy Rohini 
Published: December 2024

Abstract
The analysis of spontaneous mind wandering is crucial for comprehending an individual mental state and holds the potential to enhance performance and productivity. This paper proposes a framework using Continuous Wavelet Transform (CWT) based ResNet model to analyze Electrodermal Activity (EDA) signals for mind wandering detection. In this analysis, EDA signals are sourced from an openly accessible database and preprocessed for artifact and noise removal. Time-frequency analysis generates CWT spectrogram images, which are classified using a modified ResNet50 model that is custom built to classify the spectrogram images corresponding the mind wandering and awareness. Hyperparameter tuning is carried to obtain the optimal network parameters that provides the best accuracy. Results indicate that batch size of 32, learning rate 1e-5 provides better results. This hyperparameter tuned model achieved an accuracy of 64% in differentiating between the two classes. This paper proposes an adapted ResNet50 model that could be employed in wearable devices as a potential application of knowing the mind awareness of an individual.
Electrospinning of annatto-loaded cellulose acetate scaffolds using acetone/DMSO as solvent
Authors: Dreier Tim , Neukirch Florian , Priebe Hannes , Seitz Hermann 
Published: December 2024

Abstract
Electrospinning has emerged as a versatile technique for producing nanofibers for diverse applications. The fibers produced are highly regarded for their biocompatibility, exceptional surface-to-volume ratios, porosity, and adjustable composition properties, making them promising scaffolds for tissue engineering. In the literature, annatto-loaded cellulose acetate has already been successfully used for electrospinning. However, N,N-dimethylformamide (DMF) was utilized as a solvent in the experiments, which is considered a carcinogenic and mutagenic substance. The aim of this study is to analyze whether comparable results can be achieved with a low toxicity solvent consisting of acetone and dimethyl sulfoxide (DMSO). In this study, we investigate the electrospinning process of cellulose acetate (CA) and cellulose acetate combined with annatto extract (CA/A). Initially, various polymer concentrations ranging from 12% to 16% (w/v) were characterized by rheological measurements to determine their suitability for electrospinning. The morphology and diameter distribution of the electrospun fibers were analyzed using scanning electron microscopy (SEM). Contact angle measurements were carried out to determine the wettability of the electrospun meshes. The rheological investigations conducted revealed that increasing the polymer content of CA raises viscosity, while the incorporation of annatto decreases it. Uniform fibers are achieved at polymer concentrations of 14% and above. The electrospun nanofibers exhibit uniform morphology and diameters in the nanometer range, indicating the successful fabrication of annatto-loaded CA nanofibers. The contact angle measurements showed hydrophilic behavior on all investigated meshes. The incorporation of annatto extract in the electrospun fibers holds promise for various applications, including biomedical scaffolds, scaffolds for cultured meat, filtration membranes and food packaging materials. This study provides valuable insights into optimizing electrospinning parameters for the fabrication of functional nanofibers with enhanced properties.
Autonomy and Use of Flexible Endoscopy Robots in Gastroenterology – A Review
Authors: Giacoppo Giuliano A. , Pott Peter P. 
Published: December 2024

Abstract
The integration of mechatronic systems to improve the control and maneuverability of endoscopes in complex gastroenterology procedures is necessary since manual handling often lacks precision. The result are flexible endoscopy robots. Therefore, the question arises as to how far these robots have evolved. This basically depends on their degree of autonomy and the typical interventions they are designed to perform. In this review, we provide an overview of existing flexible endoscopy robotic systems, categorizing their levels of autonomy and intended applications. Of the 28 systems reviewed, most operate at autonomy levels 0 (no autonomy) and level 1 (robotic assistance), with some progress toward level 2 (task automation) in colonoscopy. For more complex procedures, efforts are made to establish master-slave systems. Advances in flexible endoscopic robots are aimed at increasing autonomy, particularly for autonomous navigation within hollow organs. The integration of advanced sensor technologies is critical to achieving precise control and steering in future developments, and thus higher levels of autonomy for these systems.
Novel axial oxygenator with bevelled shape of priming volume
Authors: Goubergrits Leonid , Franke Benedikt 
Published: December 2024

Abstract
Oxygenators are a lifesaving technology used for blood oxygenation and decarboxylation in case of acute respiratory failure, chronic lung disease, and during open-heart surgery. Devices typically consist of a bundle of thousands of fibre membranes in a housing, with gas flowing inside the fibres and blood flowing in the opposite direction outside the fibres. Both ends of the fibre-membranes are fixed with an adhesive to separate gas and blood. The shape of the volume through which the blood flows is determined by the housing of the oxygenator and the internal end surfaces of the fixed ends of the fibre-membrane bundle. The traditional potting process results in a volume shape that is associated with stagnation zones, which are known to promote thrombus formation. In this study, an adapted potting process is proposed which results in a blood compartment with bevelled end faces of the glued bundle parts. Using a numerical study, we have demonstrated that the novel oxygenator design results in optimized flow conditions.
A New Receiver Design for Biomedical Magnetic Induction Tomography with a Deep Learning Method
Authors: Hofmann Anna , Schledewitz Tatiana , Rueter Dirk , Sauer Andreas 
Published: December 2024

Abstract
Medical imaging is an essential aspect of modern medicine, providing a non-invasive technique for the diagnosis, monitoring and treatment of a wide range of conditions. However, there is a constant need for advancements to reduce radiation exposure, enhance patient comfort, and increase accessibility. As a cheap, contactless and non-hazardous imaging method, Magnetic Induction Tomography (MIT) could offer a new alternative to established imaging methods. First introduced in the early 1990s, MIT is still at the basic research stage and under constant change in the technical requirements. The focus of this study is a recently developed planar setting that offers several advantages, particularly in the image reconstruction of voluminous bodies in a biomedical setting. Due to the novelty of this structure, it is necessary to fundamentally reexamine both the forward and inverse problem of MIT. Here, a deep neural network is used to compare an established receiver setting with a new receiver design to improve reconstruction quality. Additionally, a testing method for new receiver setups is introduced.
Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare Monitoring
Authors: Kalashtari Niloofar , Huhs Niklas , Kraitl Jens , Hornberger Christoph , Simanski Olaf 
Published: December 2024

Abstract
Continuous patient monitoring is a critical component in healthcare systems to ensure patient safety and well-being. Traditionally, this monitoring requires significant oversight by healthcare professionals, making it resourceintensive. Common methods, such as cameras, often compromise patient privacy by capturing detailed visual information. LiDAR technology presents a non-intrusive alternative for continuous monitoring, offering effectiveness across various lighting conditions due to its use of laser light. It provides high-resolution depth information encoded as intensity, making it an ideal solution for maintaining patient privacy while delivering precise monitoring capabilities. LiDAR was selected as a reference method because of its precision and ability to capture detailed depth information, which are crucial for accurate monitoring. In this project, a solution that leverages LiDAR technology to enhance patient monitoring systems was introduced. By training a YOLOv5 deep learning model using transfer learning, a method for accurate human detection and tracking within rooms using data collected from a digital LiDAR sensor was used. The high accuracy of the LiDAR sensor enables precise human tracking, which is critical for timely interventions and ensuring patient safety. This approach accelerates response times, preserves privacy, ensures safety during disease outbreaks, addresses healthcare worker shortages, and facilitates efficient monitoring of multiple patients simultaneously without invasive sensors. The ultimate goal of this project is to integrate this solution into Ambient Assisted Living (AAL) systems for elderly individuals, providing them with a safer and more autonomous living environment. Our trained YOLOv5 model has demonstrated exceptional performance, achieving a Mean Average Precision (mAP) of 99.4% at an Intersection over Union (IoU) threshold of 0.5, with a Precision of 99.6% and a Recall of 99.4%.
Electrical Impedance Tomography for Hip Stem Implant Monitoring
Authors: Krukewitt Lisa , Spors Sascha 
Published: December 2024

Abstract
As a first step towards a new method for noninvasive hip-stem implant monitoring, this work aims to detect bone defects from time- and frequency-difference electrical impedance tomography. As a proof-of concept, an in-silico model of a thigh with bone and implant is used, and convolutional neural networks are applied to predict size and position of areas where conductivity of the bone is increased. From both, time- and frequency-difference voltages, defect size and position can be predicted with high accuracy if signal-to-noise ratios are sufficiently high. This shows the potential for external impedance measurements using electrodes on a patient’s thigh to detect bone loss in the femur.
Degradation und mechanical behavior of nanofiber membranes for timed release of antibacterial additives
Authors: Luckow Pauline , Eickner Thomas , Teske Michael , Götz Andreas , Senz Volkmar , Grabow Niels , Illner Sabine 
Published: December 2024

Abstract
Ultrafine membranes produced via electrospinning exhibit multidirectional strength and elasticity. In contrast to compact polymer films or coatings, they also have an exceptional surface-to-volume ratio, which makes them attractive carrier structures for many biomedical applications, also with regard to the release of bioactive substances. Within the scope of an orientation study we investigated the mechanical properties of biodegradable nanofibrous carrier materials using tensile specimens made of nanoparticle loaded polydioxanone (PDO), polylactide (PLA), polyglycolide (PGA) and polybutylene succinate (PBS). Mass loss and tensile strength as well as pH changes during incubation at 37°C in an unbuffered isotonic saline solution with 10% ethanol were examined within defined time periods up to 33 days. Initial tests with several antibacterial additives, especially TiO2 and Ag nanoparticles and octenidine were performed.
Passive control strategy of inductive power transfer to microimplants based on ferroelectric dielectrics
Authors: Olsommer Yves , Ihmig Frank R. 
Published: December 2024

Abstract
In this study, a passive control strategy of inductive power transfer (IPT) using ferroelectric multilayer ceramic chip capacitors (MLCCs) is presented. The required system parameters, i.e., ferroelectric hysteresis, frequency of the IPT, and voltage range across the MLCCs are reported. The receiver circuit consists only of a parallel resonant circuit, a halfwave rectifier and a load; the passive control of the IPT is achieved exclusively by the nonlinear properties of the ferroelectric MLCCs. The stabilization of the secondary output voltage ULoad at constant load is experimentally evaluated for an inductive coupling factor k between 10 % and 30 % for three nonlinear MLCCs #1, #2 and #3. With our proposed passive control strategy ULoad is maintained at -1.2 % and +0.6 % around a median value of 17.3 V (17.1 - 17.4 V) for k ∈ [20 %, 30 %] using MLCC #1, ±0.9 % around a median value of 11.2 V (11.1 - 11.3 V) for k ∈ [10 %, 18 %] using MLCC #2, and -1.6% and +2.6 % around a median value of 5.03 V (4.95 - 5.16 V) for k ∈ [16 %, 30 %] using MLCC #3. The proposed control principle is particularly advantageous for highly miniaturized microimplants, as it allows IPT control without additional semiconductors, sensors and vulnerable communication channels.
Applications of pre- and intraoperative eAEP like eABR in cochlear implant candidacy
Authors: Polterauer Daniel , Neuling Maike , Mandruzzato Giacomo , Polak Marek , Müller Joachim , Hempel John-Martin 
Published: December 2024

Abstract
Introduction: Preoperative electrically auditory evoked potentials (= eAEP, i.e., eABR for brainstem, eAMLR for midbrain, and eALR for cortex) can be performed under local anesthesia, achieving subjective sensation and hearing in patients having a functional auditory nerve. For intraoperative eAEP, we stimulate the auditory nerve with an intra-cochlear test electrode like a cochlear implant (= CI) and stimulate basally when using an extra-cochlear electrode. These measurements aim to confirm preoperative findings (e.g. uncertain preoperative eABR) or intraoperative decisions (e.g. vestibular schwannoma resection). Methods: Preoperative eAEP can be performed with the trans-tympanic stimulation electrode and a neurological EP system for recording the responses. In addition, eAEP under local anesthesia allows for preoperative diagnostics of subjective sensation. For intraoperative eAEP, the intra-cochlear auditory nerve test system (= ANTS) system is preferable showing similar results to the gold standard CI-eABR (i.e., eABR using a CI for stimulation). The eAMLR can be used only preoperatively in case of ambiguous EABR or for extended analysis of the auditory pathway. Results: During preoperative eAEP in local anesthesia, an eABR is essential. eAMLR and eALR are optional. The subjective sensation can be recorded in parallel. A reliable response often avoids eAEP under general anesthesia. eAEP under general anesthesia should be performed as rarely as possible preoperatively to avoid unnecessary intraoperative eAEP, performed usually directly before cochlear implantation. Extra-cochlear stimulation avoids mechanical trauma and possible intra-cochlear infections. However, intra-cochlear stimulation is usually recommended due to larger response amplitudes; in fact, the ANTS electrode provides more stable monitoring. Conclusion: eAEP can be diagnostically useful preoperatively and intraoperatively with the concept presented here. Since not all clinics are familiar with these methods, a learning curve may be necessary.
Biomarker identification and gene-drug interaction prediction for breast cancer using machine learning algorithms
Authors: Raja Abhavya , Pragya Pragya , Sabitha R. , Kumar Brijesh , Agastinose Ronickom Jac Fredo 
Published: December 2024

Abstract
Breast cancer (BC) poses a significant worldwide health challenge, necessitating the identification of its molecular origins and the development of possible treatment approaches. In this investigation, we introduce a pioneering methodology integrating genomic data and machine learning algorithms to identify genes associated with BC and their corresponding drugs. Initially, RNA-sequencing data of normal and malignant BC tissues publicly available in the NCBI GEO database were pre-processed using a standard pipeline. Further, machine learning algorithms, such as logistic regression, support vector machine, and random forest, were used to identify the differentially expressed genes (DEGs). The results were validated based on accuracy, sensitivity, specificity, precision, and F-score. Moreover, we identified the drugs corresponding to DEGs using the DepMap database. Our results revealed that genes such as OC90, KLK9, CXCL10, CDRT1, LCN6, GOLGA7B, and ZNF223 were commonly identified by all three machine learning algorithms. We found that the drugs DIHYDROROTENONE, 4-IODO-6-PHENYLPYRIMIDINE, BMS-754807, ARRY- 886, ESTRAMUSTINE-PHOSPHATE, FR-122047, and PIK 93 produced high correlation values on gene-drug interaction. The present study emphasizes the significance of utilizing genetic data and powerful machine learning algorithms to decipher the intricacies of BC biology and expedite the creation of tailored therapeutic approaches.
Using Large Language Models for Extracting Structured Information From Scientific Texts
Authors: Rettenberger Luca , Münker Marc F. , Schutera Mark , Niemeyer Christof M. , Rabe Kersten S. , Reischl Markus 
Published: December 2024

Abstract
Extracting structured information from scientific works is challenging as sought parameters or properties are often scattered across lengthy texts. We introduce a novel iterative approach using Large Language Models (LLMs) to automate this process. Our method first condenses scientific literature, preserving essential information in a dense format, then retrieves predefined attributes. As a biomedical application example, our concept is employed to extract experimental parameters for preparing Metal-Organic Frameworks (MOFs) from scientific work to enable complex and information-rich applications in the biotechnology-oriented life sciences. Our open-source method automates extracting information from verbose texts, converting them into structured and easily navigable data. This considerably improves scientific literature research by utilizing the power of LLMs and paves the way for enhanced and faster information extraction from extensive scientific texts.