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Abstract The negative membrane potential within bacterial cells is crucial in various essential cellular processes. Sustaining a hyperpolarised membrane could offer a novel strategy to combat antimicrobial resistance. However, it remains uncertain which molecules are responsible for inducing hyperpolarization and what the underlying molecular mechanisms are. Here, we demonstrate that chemically diverse antimicrobial peptides (AMPs) trigger hyperpolarization of the bacterial cytosolic membrane when applied at subinhibitory concentrations. Specifically, these AMPs adopt a membrane-induced amphipathic structure and, thereby, generate hyperpolarization in Escherichia coli without damaging the cell membrane. These AMPs act as selective ionophores for K+ (over Na+) or Cl− (over H2PO4 − and NO3 −) ions, generating diffusion potential across the membrane. At lower dosages of AMPs, a quasi-steady-state membrane polarisation value is achieved. Our findings highlight the potential of AMPs as a valuable tool for chemically hyperpolarising bacteria, with implications for antimicrobial research and bacterial electrophysiology.

Irreversible demagnetization processes in high-performance Fe-Nd-B magnets were investigated using a novel test rig. Designed to capture local magnetic field distributions and integral average magnetization in situ, the rig operates under field and temperature conditions similar to those found in electric motors. Validation against established techniques such as the hysteresisgraph and Hall mapper confirmed its accuracy. Furthermore, we observed the ability to detect even small variations of less than 2.5% in coercive field strength across the sample volume using field scans. The system significantly reduces measurement times from days to hours, enabling efficient in situ detection of magnetic field distributions during the whole demagnetization process.

Abstract The tumor microenvironment (TME) and the cellular interactions within it can be critical to tumor progression and treatment response. Although technologies to generate multiplex images of the TME are advancing, the many ways in which TME imaging data can be mined to elucidate cellular interactions are only beginning to be realized. Here, we present a novel approach for multipronged computational immune synapse analysis (CISA) that reveals T-cell synaptic interactions from multiplex images. CISA enables automated discovery and quantification of immune synapse interactions based on the localization of proteins on cell membranes. We first demonstrate the ability of CISA to detect T-cell:APC (antigen presenting cell) synaptic interactions in two independent human melanoma imaging mass cytometry (IMC) tissue microarray datasets. We then verify CISA’s applicability across data modalities with melanoma histocytometry whole slide images, revealing that T-cell:macrophage synapse formation correlates with T-cell proliferation. We next show the generality of CISA by extending it to breast cancer IMC images, finding that CISA quantifications of T-cell:B-cell synapses are predictive of improved patient survival. Our work demonstrates the biological and clinical significance of spatially resolving cell-cell synaptic interactions in the TME and provides a robust method to do so across imaging modalities and cancer types.

Bark beetles (Curculionidae: Scolytinae) represent an important insect group including many significant pest species, but in some cases, the distribution and ecology of many bark beetles are still not well known. Maple bark beetle Scolytus koenigi Schyverew, 1890 is one of the mentioned group of species. The following study presents i) the first data of the occurrence of S. koenigi in Bohemia, ii) the first data of the species occurrence in an urban environment (large cities – Prague and Brno), iii) the distribution of the species in the Czech Republic since its first record (in 2013), and iv) new data about the bionomy of the species (i.e. a new host tree – Acer pseudoplatanus L. and refinement of the colonized substrate range diameter).

The accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks—one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.

Abstract Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

Abstract Microscale thermophoresis (MST) is a well-established method to quantify protein-RNA interactions. In this study, we employed MST to analyze the RNA binding properties of glycine-rich RNA binding protein 7 (GRP7), which is known to have multiple biological functions related to its ability to bind different types of RNA. However, the exact mechanism of GRP7’s RNA binding is not fully understood. While the RNA-recognition motif of GRP7 is known to be involved in RNA binding, the glycine-rich region (known as arginine-glycine-glycine-domain or RGG-domain) also influences this interaction. To investigate to which extend the RGG-domain of GRP7 is involved in RNA binding, mutation studies on putative RNA interacting or modulating sites were performed. In addition to MST experiments, we examined liquid–liquid phase separation of GRP7 and its mutants, both with and without RNA. Furthermore, we systemically investigated factors that might affect RNA binding selectivity of GRP7 by testing RNAs of different sizes, structures, and modifications. Consequently, our study revealed that GRP7 exhibits a high affinity for a variety of RNAs, indicating a lack of pronounced selectivity. Moreover, we established that the RGG-domain plays a crucial role in binding longer RNAs and promoting phase separation.

Background RNA-binding proteins (RBPs) play a critical role in regulating gene expression by binding to specific sites on RNA molecules. Identifying these binding sites is crucial for understanding the many functions of RBPs in cellular function, development and disease. Current experimental methods for identifying RBP binding sites, such as ultra-violet (UV) crosslinking and immunoprecipitation (CLIP), and especially the enhanced CLIP (eCLIP) protocol, were developed to identify authentic RBP binding sites experimentally. Methods To make this data more accessible to the scientific community, we have developed RBP-Tar ( https://ncbr.muni.cz/RBP-Tar ), a web server and database that utilises eCLIP data for 167 RBPs mapped on the human genome. The web server allows researchers to easily search and retrieve binding site information by genomic location and RBP name. Use case Researchers can produce lists of all known RBP binding sites on a gene of interest, or produce lists of binding sites for one RBP on different genomic loci. Conclusions Our future goal is to continue to populate the web server with additional experimental datasets from CLIP experiments as they become available and processed, making it an increasingly valuable resource for the scientific community.

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. Various data integration methodologies have been developed and used in various domains, processing heterogeneous data using various approaches. This review article aims to provide an overview of heterogeneous data integration research focusing on methodology and approaches. It surveys existing publications, highlighting key trends, challenges, and open research topics. The main findings are: (i) Research has been conducted in various domains. However, most focus on big data rather than specific study domains; (ii) researchers primarily focus on semantics challenges, and (iii) gaps still need to be addressed and related to integration issues involving semantics and unstructured data formats that must be thoroughly addressed. Furthermore, considering elements of cutting-edge technology, such as machine learning and data integration, about privacy concerns provides a chance for additional investigation. Finally, we provide insight into the potential for a broader review of integration challenges based on case studies.

Optical biosensors that consist of a light source, optical elements, and a photodetector are used to detect chemical and biological species and pollutants. This Tutorial discusses the fundamental details of optical biosensing techniques that include materials, working principle, components, sensor configurations, parameters, and future prospects. Optical biosensing techniques include plasmonic [surface plasmon resonance (SPR) and localized SPR], fluorescence, luminescence, Raman scattering, colorimetric, and interferometric methods. Bioreceptor elements play a significant role in detecting the specific analyte that can be synthetic or natural. Surface functionalization techniques to bind the bioreceptor elements on the surface, to control the bioreceptor orientation, have been discussed in detail. The possibility of integration of techniques on a chip, to develop wearable, implantable sensors, and the associated challenges have been fully demonstrated. This Tutorial provides valuable insights into the present state and future directions of optical biosensors for various applications.

The durability of LED bulbs ensures that the lights can operate for a long time without needing to be replaced or maintained too often. The brightness of high-power LED bulbs provides a strong and efficient light source, saves energy, and has a long lifespan. The traditional method of testing LED bulb life span has limitations such as long testing time, limited accuracy, high cost, inability to predict accurately, and failure to respond quickly. Therefore, the development of new and improved testing methods is necessary to effectively and accurately evaluate and ensure the durability of LED bulbs. This research paper proposes a hybrid Six Sigma method based on the recursive Kalman filter and Weibull distribution to estimate the lifespan of Bulb LEDs. The goal of the new method for evaluating LED bulb life is to provide accurate, reliable, and multi-dimensional information about the life of LED lamps, meeting the requirements of accuracy, diversity, time-saving, resources, credibility, and the ability to evaluate from a variety of perspectives. As a result of the research, the recursive Kalman filter method has strengths such as high accuracy, flexibility, saving computational resources, the ability to handle inaccurate data, and scalability. When applied to LED bulb life assessment, it provides an accurate and reliable estimate of LED lamp life. Research results on longevity testing were reduced from 1650 h to 710 h. The end-of-life test time is 710 h, and the aging time per life test cycle is 135 h. The cost of life testing is reduced from 2100.17 USD to 410.12 USD. This LED bulb lifespan testing method can be applied to similar types of LED products to improve the company's productivity and business efficiency.

Background: RNA-binding proteins (RBPs) play a critical role in regulating gene expression by binding to specific sites on RNA molecules. Identifying these binding sites is crucial for understanding the many functions of RBPs in cellular function, development and disease. Current experimental methods for identifying RBP binding sites, such as ultra-violet (UV) crosslinking and immunoprecipitation (CLIP), and especially the enhanced CLIP (eCLIP) protocol, were developed to identify authentic RBP binding sites experimentally. Methods: To make this data more accessible to the scientific community, we have developed RBP-Tar ( https://ncbr.muni.cz/RBP-Tar), a web server and database that utilises eCLIP data for 167 RBPs mapped on the human genome. The web server allows researchers to easily search and retrieve binding site information by genomic location and RBP name. Use case: Researchers can produce lists of all known RBP binding sites on a gene of interest, or produce lists of binding sites for one RBP on different genomic loci. Conclusions: Our future goal is to continue to populate the web server with additional experimental datasets from CLIP experiments as they become available and processed, making it an increasingly valuable resource for the scientific community.

Next-generation concentrating solar thermal power (CSP) technologies target a wide spectrum of applications including electricity generation, thermochemical processes, and industrial process heat for broad decarbonization potential. Many of these applications require higher temperatures than those of current commercial nitrate salt systems. Particulate materials are promising candidates for next-generation high-temperature heat transfer and low-cost storage media and can facilitate operation over a wide temperature range. However these materials necessitate novel receiver configurations to accept the high incident flux concentrations that enable high solar-to-thermal receiver efficiency. One option is a novel light-trapping planar cavity receiver configuration in which small cavity-like structures are formed from opaque planar surfaces such that a high incident flux concentration at the cavity aperture is reduced to a wall-absorbed solar flux concentration that is manageable within limited wall-to-particle heat transfer rates. The paper introduces the receiver configuration and provides calculated optical performance for a preliminary 50 MWt receiver design.

Hydrogen has been identified as a leading sustainable contender to replace fossil fuels for transportation or electricity generation, and hydrogen generated from renewable sources can be an energy carrier for a carbon-free economy. Several hydrogen production methods are under development or deployment with various technical readiness levels and technoeconomic potentials. This study focuses on integrating concentrating solar thermal power (CSP) with high temperature electrolysis (HTE) using solid oxide electrolysis cells (SOEC). The CSP-HTE integration approach provides the benefits of thermal energy storage for continuous operation, improved capacity, and reduced thermal cycling for improved SOEC life. The CSP-HTE system analysis utilizes a Python-based system modeling program in connection with solar receiver thermal output derived from the NREL System Advisor Model (SAM) software. The system model facilitates component sizing, performance simulation, and evaluation of operation modes on an annual basis for various CSP-HTE configurations including CSP with thermal energy storage (TES). The SOEC operation conditions were simulated to assess component sizing and performance and to derive system capacity factors.

The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing long-lasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management. This multi-criteria decision making (MCDM) challenge is tackled by applying a dynamic fuzzy hypersoft set-based Method for the evaluation of currently deployed Renewable Energy systems and providing a decision support system for the installation of new ones based on the factors mentioned above for Turkey. As the installation of new renewable energy projects and the evaluation of old ones is significantly influenced by human judgment, it leaves great room for uncertainty primarily because of the psychological factors of the expert. The novel concept of Fuzzy Hypersoft Sets (FHSs) and their Entropy (EN) and TOPSIS-based operations are first discussed with reference to the problem at hand. The presented structure is superior to the ones in the literature by allowing access to data parameters as sub-parametric values while utilizing the versatility of Fuzzy structures to deal with uncertainty. The technique has great potential to serve as a potential decision support system in any setting. For now, hypothetical expert ratings are used to illustrate the working of the dynamic structure along with a sensitivity analysis to investigate the primary criterion weights in sorting. The evaluation of currently deployed renewable energy systems using our methodology revealed an average improvement in system performance compared to traditional methods. Furthermore, the decision support system for the installation of new projects based on geographical, political, social, and natural factors exhibited a potential increase in overall system efficiency. These numeric outcomes highlight the effectiveness and practical applicability of our approach in optimizing resource management and fostering sustainable energy practices.