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Kinase inhibitors are potent therapeutic agents in cancer treatment, but their effectiveness is frequently restricted by the inability to image the tumor microenvironment. To address this constraint, kinase inhibitor-fluorophore conjugates have emerged as promising theranostic agents, allowing for simultaneous cancer diagnosis and treatment. These conjugates are gaining attention for their ability to visualize malignant tissues and concurrently enhance therapeutic interventions. This review explores the design principles governing the development of multimodal inhibitors, highlighting their potential as platforms for kinase tracking and inhibition via bioimaging. The structural aspects of constructing such theranostic agents are critically analyzed. This work could shed light on this intriguing field and provide adequate impetus for developing novel theranostic compounds based on small molecule inhibitors and fluorophores.

Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models across decentralized devices while maintaining data privacy. In the healthcare domain, FL enables collaborative training among diverse medical devices and institutions, enhancing model robustness and generalizability without compromising patient privacy. In this paper, we propose DPS-GAT, a novel approach integrating graph attention networks (GATs) with differentially private client selection and resource allocation strategies in FL. Our methodology addresses the challenges of data heterogeneity and limited communication resources inherent in medical applications. By employing graph neural networks (GNNs), we effectively capture the relational structures among clients, optimizing the selection process and ensuring efficient resource distribution. Differential privacy mechanisms are incorporated, to safeguard sensitive information throughout the training process. Our extensive experiments, based on the Regensburg pediatric appendicitis open dataset, demonstrated the superiority of our approach, in terms of model accuracy, privacy preservation, and resource efficiency, compared to traditional FL methods. The ability of DPS-GAT to maintain a high and stable number of client selections across various rounds and differential privacy budgets has significant practical implications, indicating that FL systems can achieve strong privacy guarantees without compromising client engagement and model performance. This balance is essential for real-world applications where both privacy and performance are paramount. This study suggests a promising direction for more secure and efficient FL medical applications, which could improve patient care through enhanced predictive models and collaborative data utilization.

Random matrix series are a significant component of random matrix theory, offering rich theoretical content and broad application prospects. In this paper, we propose modified versions of tail bounds for random matrix series, including matrix Gaussian (or Rademacher) and sub-Gaussian and infinitely divisible (i.d.) series. Unlike present studies, our results depend on the intrinsic dimension instead of ambient dimension. In some cases, the intrinsic dimension is much smaller than ambient dimension, which makes the modified versions suitable for high-dimensional or infinite-dimensional setting possible. In addition, we obtain the expectation bounds for random matrix series based on the intrinsic dimension.

The spline reconstruction technique (SRT) is a fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The purpose of this study was to compare the SRT, filtered back-projection (FBP), and the Tera-Tomo 3D algorithm for various iteration numbers, using small-animal dynamic PET data obtained from a Mediso nanoScan ® PET/CT scanner. For this purpose, Patlak graphical kinetic analysis was employed to noninvasively quantify the myocardial metabolic rate of glucose (MRGlu) in seven male C57BL/6 mice (n=7). All analytic reconstructions were performed via software for tomographic image reconstruction. The analysis of all PET-reconstructed images was conducted with PMOD software (version 3.506, PMOD Technologies LLC, Fällanden, Switzerland) using the inferior vena cava as the image-derived input function. Statistical significance was determined by employing the one-way analysis of variance test. The results revealed that the differences between the values of MRGlu obtained via SRT versus FBP, and the variants of he Tera-Tomo 3D algorithm were not statistically significant ( p > 0.05). Overall, the SRT appears to perform similarly to the other algorithms investigated, providing a valid alternative analytic method for preclinical dynamic PET studies.

We consider, in this paper, the problem of state estimation for a class of dynamical systems governed via continuous-time McKean-Vlasov stochastic differential equations. The estimation problem is stated and solved under an H2 norm setting. We adopt a Riccati-based approach in order to solve the optimal estimation problem.

Immunotherapy with checkpoint inhibitors, albeit commonly used against tumors, is still at its infancy against chronic virus infections. It relies on the reinvigoration of exhausted T lymphocytes to eliminate virus-infected cells. Since T cell exhaustion is a physiological process to reduce immunopathology, the reinvigoration of these cells might be associated with an augmentation of pathological changes. To test this possibility, we here analyzed in the model system of chronic lymphocytic choriomeningitis virus (LCMV)-infected mice whether treatment with the checkpoint inhibitor anti-PD-L1 antibody would increase CD8 T cell-dependent fibrosis. We show that pre-existing spleen fibrosis did not worsen under conditions that increase CD8 T cell functionality and reduce virus loads suggesting that the CD8 T cell functionality increase remained below its pathogenicity threshold. These promising findings should further encourage immunotherapeutic trials against chronic virus infections.

Knowledge of the composition of proteins that interact with plasma DNA will provide a better understanding of the homeostasis of circulating nucleic acids and the various modes of interaction with target cells, which may be useful in the development of gene targeted therapy approaches. The goal of the present study is to shed light on the composition and architecture of histone-containing nucleoprotein complexes (NPCs) from the blood plasma of healthy females (HFs) and breast cancer patients (BCPs) and to explore the relationship of proteins with crucial steps of tumor progression: epithelial-mesenchymal transition (EMT), cell proliferation, invasion, cell migration, stimulation of angiogenesis, and immune response. MALDI-TOF mass spectrometric analysis of NPCs isolated from blood samples using affine chromatography was performed. Bioinformatics analysis showed that the shares of DNA-binding proteins in the compositions of NPCs in normal and cancer patients are comparable and amount to 40% and 33%, respectively; in total, we identified 38 types of DNA-binding motifs. Functional enrichment analysis using FunRich 3.13 showed that, in BCP blood, the share of DNA-binding proteins involved in nucleic acid metabolism increased, while the proportion of proteins involved in intercellular communication and signal transduction decreased. The representation of NPC passenger proteins in breast cancer also changes: the proportion of proteins involved in transport increases and the share of proteins involved in energy biological pathways decreases. Moreover, in the HF blood, proteins involved in the processes of apoptosis were more represented in the composition of NPCs and in the BCP blood-in the processes of active secretion. For the first time, bioinformatics approaches were used to visualize the architecture of circulating NPCs in the blood and to show that breast cancer has an increased representation of passenger proteins involved in EMT, cell proliferation, invasion, cell migration, and immune response. Using breast cancer protein data from the Human Protein Atlas (HPA) and DEPC, we found that 86% of NPC proteins in the blood of BCPs were not previously annotated in these databases. The obtained data may indirectly indicate directed protein sorting in NPCs, which, along with extracellular vesicles, can not only be diagnostically significant molecules for liquid biopsy, but can also carry out the directed transfer of genetic material from donor cells to recipient cells.

β-1,3-glucans are a kind of natural polysaccharide with immunomodulatory, antitumor, and anti-inflammatory properties. Curdlan, as the simplest linear β-1,3-glucan, possesses a variety of biological activities and thermogelation properties. However, due to the complexity and variability of the conformations of curdlan, the exact structure-activity relationship remains unclear. We prepare a chemically crosslinked curdlan hydrogel with the unique single-helical skeleton (named S gel) in 0.4 wt% NaOH at 40 °C, confirmed by diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). X-ray diffractometry (XRD) data show that S gel maintains the single-helical crystal structure, and the degree of crystallinity of the S gel is ~24%, which is slightly lower than that of the raw powder (~31%). Scanning electron microscopy (SEM) reveals that S gel has a continuous network structure, with large pores measuring 50-200 μm, which is consistent with its high swelling property. Using the 13 C high-resolution magic angle spinning nuclear magnetic resonance (HRMAS NMR) method, we determine that most of the single-helical skeleton carbon signals in the swollen S gel are visible, suggesting that the single-helical skeleton of S gel exhibits fascinating mobility at room temperature. Finally, we reveal that the binding of S gel to coagulation Factor G from tachypleus amebocyte lysate increases and saturates at 20 μL tachypleus amebocyte lysate per mg of S gel. Our prepared S gel can avoid the transformation of curdlan conformations and retain the bioactivity of binding to coagulation Factor G, making it a valuable material for use in the food industry and the pharmaceutical field. This work deepens the understanding of the relationship between the single-helical structure and the activity of curdlan, promoting the development and application of β-1,3-glucans.

Spatiotemporal information on individual trajectories in urban rail transit is important for operational strategy adjustment, personalized recommendation, and emergency command decision-making. However, due to the lack of journey observations, it is difficult to accurately infer unknown information from trajectories based only on AFC and AVL data. To address the problem, this paper proposes a spatiotemporal probabilistic graphical model based on adaptive expectation maximization attention (STPGM-AEMA) to achieve the reconstruction of individual trajectories. The approach consists of three steps: first, the potential train alternative set and the egress time alternative set of individuals are obtained through data mining and combinatorial enumeration. Then, global and local potential variables are introduced to construct a spatiotemporal probabilistic graphical model, provide the inference process for unknown events, and state information about individual trajectories. Further, considering the effect of missing data, an attention mechanism-enhanced expectation-maximization algorithm is proposed to achieve maximum likelihood estimation of individual trajectories. Finally, typical datasets of origin-destination pairs and actual individual trajectory tracking data are used to validate the effectiveness of the proposed method. The results show that the STPGM-AEMA method is more than 95% accurate in recovering missing information in the observed data, which is at least 15% more accurate than the traditional methods (i.e., PTAM-MLE and MPTAM-EM).

In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as k -means and hierarchical clustering. However, the optimization algorithm used with this method can be slowed down significantly by the complexity of the fusion penalty, which increases the computational burden. This paper introduces a random projection ADMM algorithm based on the Bernoulli distribution and develops a double random projection ADMM method for high-dimensional fusion clustering. These new approaches significantly outperform the classical ADMM algorithm due to their ability to significantly increase computational speed by reducing complexity and improving clustering accuracy by using multiple random projections under a new evaluation criterion. We also demonstrate the convergence of our new algorithm and test its performance on both simulated and real data examples.

Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the suggested framework is an intelligent Internet of Things (IoT) system designed to reduce interference. By using adaptive communication protocols and sophisticated interference management algorithms, the framework minimizes interference caused by overlapping transmissions and guarantees effective data sharing. This can be accomplished by creating an optimization model that takes into account the dynamic nature of the smart home environment and intelligently allocates resources. By maximizing the signal quality at the destination and optimizing the distribution of frequency channels and transmission power levels, the model seeks to minimize interference. A deep learning technique is used to augment the optimization model by adaptively learning and predicting interference patterns from real-time observations and historical data. The experimental results show how effective the suggested hybrid strategy is. While the deep learning model adjusts to shifting interference dynamics, the optimization model efficiently controls resource allocation, leading to better data reception performance at the destination. The system's robustness is assessed in various kinds of situations to demonstrate its flexibility in responding to changing smart home settings. This work not only offers a thorough framework for interference reduction but also clarifies how deep learning and mathematical optimization can work together to improve the dependability of data reception in smart homes.

Polyethylene terephthalate glycol (PETG) and silicon nitride (Si 3 N 4 ) were combined to create five composite materials with Si 3 N 4 loadings ranging from 2.0 wt.% to 10.0 wt.%. The goal was to improve the mechanical properties of PETG in material extrusion (MEX) additive manufacturing (AM) and assess the effectiveness of Si 3 N 4 as a reinforcing agent for this particular polymer. The process began with the production of filaments, which were subsequently fed into a 3D printer to create various specimens. The specimens were manufactured according to international standards to ensure their suitability for various tests. The thermal, rheological, mechanical, electrical, and morphological properties of the prepared samples were evaluated. The mechanical performance investigations performed included tensile, flexural, Charpy impact, and microhardness tests. Scanning electron microscopy and energy-dispersive X-ray spectroscopy mapping were performed to investigate the structures and morphologies of the samples, respectively. Among all the composites tested, the PETG/6.0 wt.% Si 3 N 4 showed the greatest improvement in mechanical properties (with a 24.5% increase in tensile strength compared to unfilled PETG polymer), indicating its potential for use in MEX 3D printing when enhanced mechanical performance is required from the PETG polymer.

In the classification task, label noise has a significant impact on models' performance, primarily manifested in the disruption of prediction consistency, thereby reducing the classification accuracy. This work introduces a novel prediction consistency regularization that mitigates the impact of label noise on neural networks by imposing constraints on the prediction consistency of similar samples. However, determining which samples should be similar is a primary challenge. We formalize the similar sample identification as a clustering problem and employ twin contrastive clustering (TCC) to address this issue. To ensure similarity between samples within each cluster, we enhance TCC by adjusting clustering prior to distribution using label information. Based on the adjusted TCC's clustering results, we first construct the prototype for each cluster and then formulate a prototype-based regularization term to enhance prediction consistency for the prototype within each cluster and counteract the adverse effects of label noise. We conducted comprehensive experiments using benchmark datasets to evaluate the effectiveness of our method under various scenarios with different noise rates. The results explicitly demonstrate the enhancement in classification accuracy. Subsequent analytical experiments confirm that the proposed regularization term effectively mitigates noise and that the adjusted TCC enhances the quality of similar sample recognition.

Proteomic and metabolomic research enables quantitation of the molecular profile of athletes. Multiomic profiling was conducted using plasma samples collected from 18 male athletes performing aerobic activity (running) at high altitude. Metabolomic profiling detected changes in the levels of 4-hydroxyproline, methionine, oxaloacetate, and tyrosine during the recovery period. Furthermore, proteomic profiling revealed changes in expression of proteins contributing to the function of the immune system, muscle damage, metabolic fitness and performance, as well as hemostasis. Further research should focus on developing metabolic models to monitor training intensity and athlete adaptation.

We deal with absolutely continuous probability distributions with finite all-positive integer-order moments. It is well known that any such distribution is either uniquely determined by its moments (M-determinate), or it is non-unique (M-indeterminate). In this paper, we follow the maximum entropy approach and establish a new criterion for the M-indeterminacy of distributions on the positive half-line (Stieltjes case). Useful corollaries are derived for M-indeterminate distributions on the whole real line (Hamburger case). We show how the maximum entropy is related to the symmetry property and the M-indeterminacy.

Magnesium, as one of the most abundant cations in the human body, plays an important role in both physiological and pathological processes. In this study, it was shown that a promising biomedical material, Mg-substituted hydroxyapatite (Mg-HA), can be synthesized via a fast mechanochemical method. For this method, the nature of magnesium-containing carriers was shown to be important. When using magnesium oxide as a source of magnesium, the partial insertion of magnesium cations into the apatite structure occurs. In contrast, when magnesium hydroxide or monomagnesium phosphate is used, single-phase Mg-HA is formed. Both experimental and theoretical investigations showed that an increase in the Mg content leads to a decrease in the lattice parameters and unit cell volume of Mg-HA. Density functional theory calculations showed the high sensitivity of the lattice parameters to the crystallographic position of the calcium site substituted by magnesium. It was shown experimentally that the insertion of magnesium cations decreases the thermal stability of hydroxyapatite. The thermal decomposition of Mg-HA leads to the formation of a mixture of stoichiometric HA, magnesium oxide, and Mg-substituted tricalcium phosphate phases.

The aim of this study is to solve a practical problem encountered in the automotive industry, especially the failure of a cracked lower control arm made of al 6062 T6 material during static and crash physical tests, and to characterize the behavior of cracked parts made of aluminum materials using the fracture mechanics parameters. As a first step, we carried out a numerical study and simulation using Abaqus/CAE 2020 software and the finite element method to determine the stress concentration and load limit capacity for different car weight cases. The von Mises stress variation shows crack initiation and propagation to be in the area of the lower control arm's attachment to the vehicle platform, where stress is concentrated. These numerical results are consistent with the experimental test results found by automotive manufacturers. Also, we find that the mechanical load that can support this part is below 4900 N for good performance. In the second step, we use the results of the first section to simulate the failure of a lower control arm with a crack defect. This paper investigates the stress intensity factor KI in mode I for different lengths ( L ) and depths ( a ) of the crack in the lower control arm using the extended finite element method (XFEM) under Abaqus/CAE. For crack failure initiation and progression, we relied on the traction separation law, specifically the maximum principal stress (MAXPS) criterion. The KI factor was evaluated for the materials steel and Al 6062 T6. The results obtained from the variation of the KI coefficient as a function of crack depth ( a ) and the thickness ( t ) show that the crack remains stable even when a depth ratio ( a / t = 0.8) is reached for the steel material. However, the crack in the Aluminum 6062 T6 material becomes unstable at depth ( a / t = 0.6), with a high risk of total failure of the lower control arm.

The inferred rate of default (IRD) was first introduced as an indicator of default risk computable from information publicly reported by the Bulgarian National Bank. We have provided a more detailed justification for the suggested methodology for forecasting the IRD on the bank-group- and bank-system-level based on macroeconomic factors. Furthermore, we supply additional empirical evidence in the time-series analysis. Additionally, we demonstrate that IRD provides a new perspective for comparing credit risk across bank groups. The estimation methods and model assumptions agree with current Bulgarian regulations and the IFRS 9 accounting standard. The suggested models could be used by practitioners in monthly forecasting the point-in-time probability of default in the context of accounting reporting and in monitoring and managing credit risk.

With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge-LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge-LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network's total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%.

Background: Tracking the migration pathways of living cells after their introduction into a patient's body is a topical issue in the field of cell therapy. Questions related to studying the possibility of long-term intravital biodistribution of mesenchymal stromal cells in the body currently remain open., Methods: Forty-nine laboratory animals were used in the study. Modeling of local radiation injuries was carried out, and the dynamics of the distribution of mesenchymal stromal cells labeled with [ 89 Zr]Zr-oxine in the rat body were studied., Results: the obtained results of the labelled cell distribution allow us to assume that this procedure could be useful for visualization of local radiation injury using positron emission tomography. However, further research is needed to confirm this assumption., Conclusions: intravenous injection leads to the initial accumulation of cells in the lungs and their subsequent redistribution to the liver, spleen, and kidneys. When locally injected into tissues, mesenchymal stromal cells are not distributed systemically in significant quantities.

Due to the absence of specific interactions, carbosilane dendrimers are ideal models to study the effect of a hyperbranched regular structure on the molecular response to external influences. In this work, we have studied the conformational behavior of single polybutylcarbosilane dendrimers under confinement between impermeable flat surfaces using atomistic molecular dynamics simulations. Dendrimers of different generations belonging to two homologous series with a tetra-functional core and three- and four-functional branches were simulated. The analysis of the dependence of the internal energy of the dendrimers on the wall distance allowed us to determine the critical degree of compression at which the dendrimers are able to change their shape without energy loss. The effects of generation number and branching functionality on the number of wall contacts, density distribution and shape changes were elucidated. It was found that for high generation dendrimers, the inner layers are not accessible for external interaction. It was shown that the excess stresses occurring at high compressions are concentrated in the structural center of the dendrimer. The nature of the elastic response, which is strongly nonlinear, was analyzed at different compressions depending on the dendrimer architecture and generation. We believe that our results are useful for further studies of dendrimer films under compression and can also serve as a basis for developing model concepts to describe the dynamics of dendrimer melts.

In this study, we successfully address a significant research and engineering gap by quantitatively assessing the impact of varying compressive loading rates on the mechanical behavior of four popular thermoplastic polymers in material-extrusion-based (MEX) 3D printing. Raw powders of polycarbonate (PC), polyethylene terephthalate glycol (PETG), polymethyl methacrylate (PMMA), and thermoplastic polyurethane (TPU) were processed through melt extrusion, and the filaments were used to 3D-print the test samples. For completeness, thermogravimetric analysis and a compressive test following the ASTM-D695 standard were conducted. Ultimately, the compressive strength and yield stress, the compressive modulus of elasticity and toughness, and the maximum compressive sensitivity index were thoroughly documented. Specimens were tested in strain rates from 1.3 mm/min to 200 mm/min. The compressive strength (40% for the PMMA) and stiffness (29% for the TPU) increased with the increase in the strain rate in all polymers tested. PC had the highest strain rate sensitivity. Significant variations in deformation and fracture modes were observed and thoroughly documented throughout this study. Our findings can be useful in industrial engineering as valued design optimization input parameters in various applications involving the above-mentioned polymeric materials.

Hydroxyapatite (HAP) is the main mineral component of bones and teeth. It is widely used in medicine as a bone filler and coating for implants to promote new bone growth. Ion substitutions into the HAP structure highly affect its properties. One of the most important substituents is magnesium. This paper presents new results obtained using high-precision hybrid density functional theory calculations for Mg/Ca substitutions in HAP in a wide magnesium concentration range within a 2 × 2 × 2 supercell model. Experimental data on the mechanochemical synthesis of HAP-Mg samples with different Mg concentrations are also presented. A comparison between the experiment and the theory showed good agreement: the HAP-Mg unit cell parameters and volume decreased with increasing degree of Mg/Ca substitution. The changes in the distances between the Ca and O, Ca and H, and Mg and O ions upon Mg/Ca substitution in different calcium positions was analyzed. The resulting asymmetry and distortion of the cell parameters were evaluated. It was shown that bulk modulus, energy levels, and band gap depend on the degree of Mg substitutions in the Ca1 and Ca2 positions. The formation energies of Mg/Ca substitutions showed non-monotonic behavior that was different for Ca1 and Ca2 positions. The Ca2 position had a slightly higher probability (~5 meV/f.u.) of substitution than Ca1 position at a Mg concentration x = 0.5. At x = 1, substitution in both positions can coexist. The simulated IR spectra for different Mg/Ca substitutions showed that Mg in the Ca2 position changes the IR spectrum more significantly than Mg in the Ca1 position. Similar changes were recorded in the IR spectra of the synthesized samples. The electronic structure is shown to be sensitive to the number and position of substitutions, which may be used to tweak the optical properties of the HAP-Mg material.

The structural and physical properties of the new titanium- and niobium-rich type-A high-entropy alloy (HEA) superconductor Nb0.34Ti0.33Zr0.14Ta0.11Hf0.08 (in at.%) were studied by X-ray powder diffraction, energy dispersive X-ray spectroscopy, magnetization, electrical resistivity, and specific heat measurements. In addition, electronic structure calculations were performed using two complementary methods: the Korringa-Kohn-Rostoker Coherent Potential Approximation (KKR-CPA) and the Projector Augmented Wave (PAW) within Density Functional Theory (DFT). The results obtained indicate that the alloy exhibits type II superconductivity with a critical temperature close to 7.5 K, an intermediate electron-phonon coupling, and an upper critical field of 12.2(1) T. This finding indicates that Nb0.34Ti0.33Zr0.14Ta0.11Hf0.08 has one of the highest upper critical fields among all known HEA superconductors.

Numerous observations have supported the idea that various types of noncoding RNAs, including tRNA fragments (tRFs), are involved in communications between the host and its microbial community. The possibility of using their signaling function has stimulated the study of secreted RNAs, potentially involved in the interspecies interaction of bacteria. This work aimed at identifying such RNAs and characterizing their maturation during transport. We applied an approach that allowed us to detect oligoribonucleotides secreted by Prevotella copri ( Segatella copri ) or Rhodospirillum rubrum inside Escherichia coli cells. Four tRFs imported by E. coli cells co-cultured with these bacteria were obtained via chemical synthesis, and all of them affected the growth of E. coli . Their successive modifications in the culture medium and recipient cells were studied by high-throughput cDNA sequencing. Instead of the expected accidental exonucleolysis, in the milieu, we observed nonrandom cleavage by endonucleases continued in recipient cells. We also found intramolecular rearrangements of synthetic oligonucleotides, which may be considered traces of intermediate RNA circular isomerization. Using custom software, we estimated the frequency of such events in transcriptomes and secretomes of E. coli and observed surprising reproducibility in positions of such rare events, assuming the functionality of ring isoforms or their permuted derivatives in bacteria.

We study the role of unstable points in the Bohmian flow of a 2d system composed of two non-interacting harmonic oscillators. In particular, we study the unstable points in the inertial frame of reference as well as in the frame of reference of the moving nodal points, in cases with 1, 2 and multiple nodal points. Then, we find the contributions of the ordered and chaotic trajectories in the Born distribution, and when the latter is accessible by an initial particle distribution which does not satisfy Born's rule.

In the present paper, we introduce a class of robust Z-estimators for moment condition models. These new estimators can be seen as robust alternatives for the minimum empirical divergence estimators. By using the multidimensional Huber function, we first define robust estimators of the element that realizes the supremum in the dual form of the divergence. A linear relationship between the influence function of a minimum empirical divergence estimator and the influence function of the estimator of the element that realizes the supremum in the dual form of the divergence led to the idea of defining new Z-estimators for the parameter of the model, by using robust estimators in the dual form of the divergence. The asymptotic properties of the proposed estimators were proven, including here the consistency and their asymptotic normality. Then, the influence functions of the estimators were derived, and their robustness is demonstrated.

We explore the metric structure of networks with higher-order interactions and introduce a novel definition of distance for hypergraphs that extends the classic methods reported in the literature. The new metric incorporates two critical factors: (1) the inter-node distance within each hyperedge, and (2) the distance between hyperedges in the network. As such, it involves the computation of distances in a weighted line graph of the hypergraph. The approach is illustrated with several ad hoc synthetic hypergraphs, where the structural information unveiled by the novel metric is highlighted. Moreover, the method's performance and effectiveness are shown through computations on large real-world hypergraphs, which indeed reveal new insights into the structural features of networks beyond pairwise interactions. Namely, using the new distance measure, we generalize the definitions of efficiency, closeness and betweenness centrality for the case of hypergraphs. Comparing the values of these generalized measures with their analogs calculated for the hypergraph clique projections, we show that our measures provide significantly different assessments on the characteristics (and roles) of the nodes from the information-transferability point of view. The difference is brighter for hypergraphs in which hyperedges of large sizes are frequent, and nodes relating to these hyperedges are rarely connected by other hyperedges of smaller sizes.

A comparative study of the electronic structure of the salen ligand in the H 2 (Salen) molecule and the [Ni(Salen)] complex was performed using the experimental methods of XPS, UV PES, and NEXAFS spectroscopy along with DFT calculations. Significant chemical shifts of +1.0 eV (carbon), +1.9 eV (nitrogen), and -0.4 eV (oxygen) were observed in the 1s PE spectra of the salen ligand atoms when passing from a molecule to a complex, unambiguously indicating a substantial redistribution of the valence electron density between these atoms. It is proposed that the electron density transfer to the O atoms in [Ni(Salen)] occurred not only from the Ni atom, but also from the N and C atoms. This process seemed to be realized through the delocalized conjugated π-system of the phenol C 2p electronic states of the ligand molecule. The DFT calculations (total and partial DOS) for the valence band H 2 (Salen) and [Ni(Salen)] described well the spectral shape of the UV PE spectra of both compounds and confirmed their experimental identification. An analysis of the N and O 1s NEXAFS spectra clearly indicated that the atomic structure of the ethylenediamine and phenol fragments was retained upon passing from the free salen ligand to the nickel complex.

Graph-structured data, operating as an abstraction of data containing nodes and interactions between nodes, is pervasive in the real world. There are numerous ways dedicated to extract graph structure information explicitly or implicitly, but whether it has been adequately exploited remains an unanswered question. This work goes deeper by heuristically incorporating a geometric descriptor, the discrete Ricci curvature (DRC), in order to uncover more graph structure information. We present a curvature-based topology-aware graph transformer, termed Curvphormer. This work expands the expressiveness by using a more illuminating geometric descriptor to quantify the connections within graphs in modern models and to extract the desired structure information, such as the inherent community structure in graphs with homogeneous information. We conduct extensive experiments on a variety of scaled datasets, including PCQM4M-LSC, ZINC, and MolHIV, and obtain a remarkable performance gain on various graph-level tasks and fine-tuned tasks.

Triple-negative breast cancer (TNBC) is a heterogeneous disease with varying responses to neoadjuvant chemotherapy (NAC). The identification of biomarkers to predict NAC response and inform personalized treatment strategies is essential. In this study, we conducted large-scale gene expression meta-analyses to identify genes associated with NAC response and survival outcomes. The results showed that immune, cell cycle/mitotic, and RNA splicing-related pathways were significantly associated with favorable clinical outcomes. Furthermore, we integrated and divided the gene association results from NAC response and survival outcomes into four quadrants, which provided more insights into potential NAC response mechanisms and biomarker discovery.

The influence of the components of plaster mortars on their properties is considered in a lot of studies at a qualitative level without searching for optimal compositions of these multicomponent composite materials. The purpose of this study was to obtain the experimental-statistical models based on the results of the designed experiment, allowing the influence of light fillers and polymer admixtures on the properties of the mortars to be evaluated and analysed; the compositions complying with specified requirements and compromised optimally by a number of properties should be found. The quantities of fine limestone and perlite as well as of the hydroxyethyl methyl cellulose and dispersible polymer were varied in the experiment. The effective viscosity and thixotropy of the mix, compression, tensile, adhesive strength, frost resistance, and density of hardened mortars were determined for 18 compositions according to the experiment design. The obtained models have allowed the individual and synergetic effects of mix components to be evaluated. The fine perlite has turned out to have the greatest positive effect on the properties. This porous filler increases the strength while decreasing the density of the mortars. It is shown how the composition complying with specified requirements-and the best based on several properties-has been found.

The synthesis and design of low-cost visible-light-active catalysts for the photodegradation of organic dyes have been regarded as an efficient way to use solar energy in addressing environmental issues. We report the fabrication of MoS 2 /CdS nanoparticles functionalized with Bi 2 S 3 nanoflakes. The ternary composites of "MoS 2 /CdS/Bi 2 S 3 " were synthesized in situ by a hydrothermal method at different temperatures. The changes in structural, optical, and morphological properties of the synthesized CdS/MoS 2 /Bi 2 S 3 were explored. The effects of Bi 2 S 3 on CdS/MoS 2 were thoroughly studied by performing an X-ray diffractometer (XRD), a scanning electron microscope (SEM), an ultra-violet-visible spectrometer (Uv-vis), and Fourier transform infrared spectroscopic (FT-IR) studies of the nanoparticles. XRD confirms the cubical crystal structure of the nanoparticles. SEM studies possess the modulation in the surface morphology with the tenability in volume ratios of "MoS 2 /CdS/Bi 2 S 3 " composites. It was observed that the bandgaps calculated using absorption measurements could be manipulated from 2.40 eV to 0.97 eV with varying Bi 2 S 3 in the MoS 2 /CdS nanostructures. FT-IR confirmed the synthesis of "MoS 2 /CdS/Bi 2 S 3 " nanoparticles. On allowing the visible light to fall for 120 min, it was observed that "MoS 2 /CdS/Bi 2 S 3 " degrades the methylene blue up to 90%. The calculated results of "MoS 2 /CdS/Bi 2 S 3 " suggest that the synthesized material could be a strong candidate for photodegradation applications. This research work explains the synthesis of MoS 2 /CdS/Bi 2 S 3 -based nanocomposites for the degradation of dye using a photocatalytic process. The final results show that this catalyst effectively degrades the dye.

Recently, Ledoux, Nair, and Wang proved that the Fisher information along the heat flow is log-convex in dimension one, that is d2dt2log(I(Xt))≥0 for n=1, where Xt is a random variable with density function satisfying the heat equation. In this paper, we consider the high dimensional case and prove that the Fisher information is square root convex in dimension two, that is d2dt2IX≥0 for n=2. The proof is based on the semidefinite programming approach.

Intelligent medical robots can effectively help doctors carry out a series of medical diagnoses and auxiliary treatments and alleviate the current shortage of social personnel. Therefore, this paper investigates how to use deep reinforcement learning to solve dynamic medical auscultation tasks. We propose a constant force-tracking control method for dynamic environments and a modeling method that satisfies physical characteristics to simulate the dynamic breathing process and design an optimal reward function for the task of achieving efficient learning of the control strategy. We have carried out a large number of simulation experiments, and the error between the tracking of normal force and expected force is basically within ±0.5 N. The control strategy is tested in a real environment. The preliminary results show that the control strategy performs well in the constant force-tracking of medical auscultation tasks. The contact force is always within a safe and stable range, and the average contact force is about 5.2 N.

In recent years, due to the aging of the population and the development of diagnostic medicine, the number of identified diseases associated with the accumulation of amyloid proteins has increased. Some of these proteins are known to cause a number of degenerative diseases in humans, such as amyloid-beta (Aβ) in Alzheimer's disease (AD), α-synuclein in Parkinson's disease (PD), and insulin and its analogues in insulin-derived amyloidosis. In this regard, it is important to develop strategies for the search and development of effective inhibitors of amyloid formation. Many studies have been carried out aimed at elucidating the mechanisms of amyloid aggregation of proteins and peptides. This review focuses on three amyloidogenic peptides and proteins-Aβ, α-synuclein, and insulin-for which we will consider amyloid fibril formation mechanisms and analyze existing and prospective strategies for the development of effective and non-toxic inhibitors of amyloid formation. The development of non-toxic inhibitors of amyloid will allow them to be used more effectively for the treatment of diseases associated with amyloid.

A successful vaccination implies the induction of effective specific immune responses. We intend to find biomarkers among various immune cell subpopulations, cytokines and antibodies that could be used to predict the levels of specific antibody- and cell-mediated responses after measles-mumps-rubella vaccination. We measured 59 baseline immune status parameters (frequencies of 42 immune cell subsets, levels of 13 cytokines, immunoglobulins) before vaccination and 13 response variables (specific IgA and IgG, antigen-induced IFN-γ production, CD107a expression on CD8+ T lymphocytes, and cellular proliferation levels by CFSE dilution) 6 weeks after vaccination for 19 individuals. Statistically significant Spearman correlations between some baseline parameters and response variables were found for each response variable ( p < 0.05). Because of the low number of observations relative to the number of baseline parameters and missing data for some observations, we used three feature selection strategies to select potential predictors of the post-vaccination responses among baseline variables: (a) screening of the variables based on correlation analysis; (b) supervised screening based on the information of changes of baseline variables at day 7; and (c) implicit feature selection using regularization-based sparse regression. We identified optimal multivariate linear regression models for predicting the effectiveness of vaccination against measles-mumps-rubella using the baseline immune status parameters. It turned out that the sufficient number of predictor variables ranges from one to five, depending on the response variable of interest.

Combining adaptive and innate immunity induction modes, the repertoire of immunoglobulin M (IgM) can reflect changes in the internal environment including malignancies. Previously, it was shown that a mimotope library reflecting the public IgM repertoire of healthy donors (IgM IgOme) can be mined for efficient probes of tumor biomarker antibody reactivities. To better explore the interpretability of this approach for IgM, solid tumor-related profiles of IgM reactivities to linear epitopes of actual tumor antigens and viral epitopes were studied. The probes were designed as oriented planar microarrays of 4526 peptide sequences (as overlapping 15-mers) derived from 24 tumor-associated antigens and 209 cancer-related B cell epitopes from 30 viral antigens. The IgM reactivity in sera from 21 patients with glioblastoma multiforme, brain metastases of other tumors, and non-tumor-bearing neurosurgery patients was thus probed in a proof-of-principle study. A graph representation of the binding data was developed, which mapped the cross-reactivity of the mixture of IgM (poly)specificities, delineating different antibody footprints in the features of the graph-neighborhoods and cliques. The reactivity graph mapped the major features of the IgM repertoire such as the magnitude of the reactivity (titer) and major cross-reactivities, which correlated with blood group reactivity, non-self recognition, and even idiotypic specificities. A correlation between an aspect of this image of the IgM IgOme, namely, small cliques reflecting rare self-reactivities and the capacity of subsets of the epitopes to separate the diagnostic groups studied was found. In this way, the graph representation helped the feature selection in its filtering step and provided reduced feature sets, which, after recursive feature elimination, produced a classifier containing 51 peptide reactivities separating the three diagnostic groups with an unexpected efficiency. Thus, IgM IgOme approaches to repertoire studies is greatly augmented when self/viral antigens are used and the data are represented as a reactivity graph. This approach is most general, and if it is applicable to tumors in immunologically privileged sites, it can be applied to any solid tumors, for instance, breast or lung cancer.

Acquiring adequate sensory information and using it to provide motor control are important issues in the process of creating walking robots. The objective of this article is to present control algorithms for the optimization of the walking cycle of an innovative walking robot named "Big Foot". The construction of the robot is based on minimalist design principles-only two motors are used, with which Big Foot can walk and even overcome obstacles. It is equipped with different types of sensors, with some of them providing information necessary for the realization of an optimized walk cycle. We examine two laws of motion-sinusoidal and polynomial-where we compare the results with constant angular velocity motion. Both proposed laws try to find balance between minimizing shock loads and maximizing walking speed for a given motor power. Experimental results are derived with the help of a 3D-printed working prototype of the robot, with the correct realization of the laws of motion being ensured by the use of a PD controller receiving data from motor encoders and tactile sensors. The experimental results validate the proposed laws of motion and the results can be applied to other walking robots with similar construction.

Noise exists inherently in realistic quantum systems and affects the evolution of quantum systems. We investigate the dynamics of quantum networks in noisy environments by using the fidelity of the quantum evolved states and the classical percolation theory. We propose an analytical framework that allows us to characterize the stability of quantum networks in terms of quantum noises and network topologies. The calculation results of the framework determine the maximal time that quantum networks with different network topologies can maintain the ability to communicate under noise. We demonstrate the results of the framework through examples of specific graphs under amplitude damping and phase damping noises. We further consider the capacity of the quantum network in a noisy environment according to the proposed framework. The analytical framework helps us better understand the evolution time of a quantum network and provides a reference for designing large quantum networks.

The locus-specific methylation of three genes (GSTP1, RNF219, and KIAA1539, also known as FAM214B) in the total pool of blood cell-free DNA, including cell-free DNA from plasma and cell-surface-bound DNA, of patients with prostate cancer and healthy donors was studied on the MiSeq platform. Our study found a higher methylation index of loci for total cell-free DNA compared with cell-free DNA. For total cell-free DNA, the methylation of GSTP1 in each of the 11 positions provided a complete separation of cancer patients from healthy donors, whereas for cell-free DNA, there were no positions in the three genes allowing for such separation. Among the prostate cancer patients, the minimum proportion of GSTP1 genes methylated in any of the 17 positions was 12.1% of the total circulated DNA fragments, and the minimum proportion of GSTP1 genes methylated in any of the 11 diagnostically specific positions was 8.4%. Total cell-free DNA was shown to be more convenient and informative as a source of methylated DNA molecules circulating in the blood than cell-free DNA.

Pyrite has been used in photo-Fenton reactions for the degradation of pollutants, but the application of photo-Fenton processes with extra H 2 O 2 in real water/wastewater treatment has still been limited by the economic cost of H 2 O 2 and artificial light sources. Herein, citric acid (CA) and simulated/natural sunlight are used to develop a pyrite-based photo-Fenton system (pyrite-CA-light) in situ generating H 2 O 2 through the enhanced activation of molecular oxygen. The degradation of pharmaceuticals and personal care products (PPCPs), especially acetaminophen (APAP) as the main target pollutant, in the pyrite-CA-light system was investigated. The effects of influencing factors such as various organic acids, APAP concentration, pH, pyrite dosage, CA concentration and co-existing anions (HCO 3 - , Cl - , NO 3 - , SO 4 ) were examined. At a pyrite dosage of 0.1 g L 2- , CA concentration of 0.6 mM and an initial pH of 6.0, the degradation efficiency of APAP (30 μM) was 99.1% within 30 min under the irradiation of xenon lamp (70 W, λ ≥ 350 nm). Almost the same high efficiency of APAP degradation (93.9%) in the system was achieved under natural sunlight irradiation (ca. 650 W m 2 PO 4 - ) were examined. At a pyrite dosage of 0.1 g L -1 , CA concentration of 0.6 mM and an initial pH of 6.0, the degradation efficiency of APAP (30 μM) was 99.1% within 30 min under the irradiation of xenon lamp (70 W, λ ≥ 350 nm). Almost the same high efficiency of APAP degradation (93.9%) in the system was achieved under natural sunlight irradiation (ca. 650 W m -2 ). The scavenging experiments revealed that the dominant active species for degrading APAP was hydroxyl radical (HO • ). Moreover, a quantitative structural-activity relationship (QSAR) model for pseudo-first-order rate constants ( k ) and highest occupied molecular orbital (HOMO). This work provides an innovative strategy of the photo-Fenton process for the degradation of PPCPs using natural minerals and ordinary carboxylic acid under sunlight.obs ) was established with a high significance (R 2 = 0.932, p = 0.001) by using three descriptors: octanol-water partition coefficient (log K ow), dissociation constant (pK a ) and highest occupied molecular orbital (HOMO). This work provides an innovative strategy of the photo-Fenton process for the degradation of PPCPs using natural minerals and ordinary carboxylic acid under sunlight.

The ever-growing number of methods for the generation of synthetic bulk and single cell RNA-seq data have multiple and diverse applications. They are often aimed at benchmarking bioinformatics algorithms for purposes such as sample classification, differential expression analysis, correlation and network studies and the optimization of data integration and normalization techniques. Here, we propose a general framework to compare synthetically generated RNA-seq data and select a data-generating tool that is suitable for a set of specific study goals. As there are multiple methods for synthetic RNA-seq data generation, researchers can use the proposed framework to make an informed choice of an RNA-seq data simulation algorithm and software that are best suited for their specific scientific questions of interest.

The effect of a functional gradient in the cross-section material (FGM) of a thin ribbon-like interfacial deformable inclusion on the stress-strain state of a piecewise homogeneous linear-elastic matrix under longitudinal shear conditions is considered. Based on the equations of elasticity theory, a mathematical model of such an FGM inclusion is constructed. An analytic-numerical analysis of the stress fields for some typical cases of the continuous functional gradient dependence of the mechanical properties of the inclusion material is performed. It is proposed to apply the constructed solutions to select the functional gradient properties of the inclusion material to optimize the stress-strain state in its vicinity under the given stresses. The derived equations are suitable with minor modifications for the description of micro-, meso- and nanoscale inclusions. Moreover, the conclusions and calculation results are easily transferable to similar problems of thermal conductivity and thermoelasticity with possible frictional heat dissipation.

Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth's surface for more than 4 years and has become an important data source for a large number of research and policy-making initiatives. Unfortunately, a scan line corrector (SLC) on Landsat-7 broke down in May 2003, which caused the loss of up to 22 percent of any given scene. We present a single-image approach based on leveraging the abilities of the deep image prior method to fill in gaps using only the corrupt image. We test the ability of deep image prior to reconstruct remote sensing scenes with different levels of corruption in them. Additionally, we compare the performance of our approach with the performance of classical single-image gap-filling methods. We demonstrate a quantitative advantage of the proposed approach compared with classical gap-filling methods. The lowest-performing restoration made by the deep image prior approach reaches 0.812 in r2, while the best value for the classical approaches is 0.685. We also present the robustness of deep image prior in comparing the influence of the number of corrupted pixels on the restoration results. The usage of this approach could expand the possibilities for a wide variety of agricultural studies and applications.

Accurate information about growing crops allows for regulating the internal stocks of agricultural products and drawing strategies for negotiating agricultural commodities on financial markets. Machine learning methods are widely implemented for crop type recognition and classification based on satellite images. However, field classification is complicated by class imbalance and aggregation of pixel-wise into field-wise forecasting. We propose here a Bayesian methodology for the aggregation of classification results. We report the comparison of class balancing techniques. We also report the comparison of classical machine learning methods and the U-Net convolutional neural network for classifying crops using a single satellite image. The best result for single-satellite-image crop classification was achieved with an overall accuracy of 77.4% and a Macro F1-score of 0.66. Bayesian aggregation for field-wise classification improved the result obtained using majority voting aggregation by 1.5%. We demonstrate here that the Bayesian aggregation approach outperforms the majority voting and averaging strategy in overall accuracy for the single-image crop classification task.

SARS-CoV-2 is a rapidly evolving pathogen that has caused a global pandemic characterized by several consecutive waves. Based on epidemiological and NGS data, many different variants of SARS-CoV-2 were described and characterized since the original variant emerged in Wuhan in 2019. Notably, SARS-CoV-2 variants differ in transmissibility and pathogenicity in the human population, although the molecular basis for this difference is still debatable. A significant role is attributed to amino acid changes in the binding surface of the Spike protein to the ACE2 receptor, which may facilitate virus entry into the cell or contribute to immune evasion. We modeled in silico the interaction between Spike RBDs of Wuhan-Hu-1, Delta, and Omicron BA.1 variants and ACE2 at different pHs (pH 5 and pH 7) and showed that the strength of this interaction was higher for the Omicron BA.1 RBD compared to Wuhan-Hu-1 or Delta RBDs and that the effect was more profound at pH 5. This finding is strikingly related to the increased ability of Omicron variants to spread in the population. We also noted that during its spread in the population, SARS-CoV-2 evolved to a more charged, basic composition. We hypothesize that the more basic surface of the Omicron variant may facilitate its spread in the upper respiratory tract but not in the lower respiratory tract, where pH estimates are different. We calculated the amyloidogenic properties of Spike RBDs in different SARS-CoV-2 variants and found eight amyloidogenic regions in the Spike RBDs for each of the variants predicted by the FoldAmyloid program. Although all eight regions were almost identical in the Wuhan to Gamma variants, two of them were significantly longer in both Omicron variants, making the Omicron RBD more amyloidogenic. We discuss how the increased predicted amyloidogenicity of the Omicron variants RBDs may be important for protein stability, influence its interaction with ACE2 and contribute to immune evasion.

The design and manufacturing technology of interference-absorbing short-wave filters based on a layered composition of Si-SiO on a sapphire substrate of various shapes was developed. A transition layer of SiO was applied to the surface of the substrate, alternating with layers of Si-SiO with an odd number of quarter-wave layers of materials with high (Si) and low refractive indices (SiO), and the application of an outer layer of SiO as an appropriate control of the materials' thickness. The optical properties of the infrared light filter were studied. It was established that the created design of the light filter provides the minimum light transmission in the visible region of the spectrum from 0.38 to 0.78 µm and the maximum in the near infrared region from 1.25 to 5 µm and has stable optical indicators. A method for studying the stress-strain state and strength of a multilayer coating of a light filter under the action of a local arbitrarily oriented load was developed. For simplicity in the analysis and for obtaining results in the analytical form, the one-dimensional model of the configuration "multilayer covering-firm substrate" constructed earlier by authors was used. From a mechanical point of view, the upper protective layer of the multilayer coating was modeled by a flexible plate, and the inner operational composite N-layer was subjected to Winkler's hypothesis about the proportionality of stresses and elastic displacements.

High-quality atomic models providing structural information are the results of their refinement versus diffraction data (reciprocal-space refinement), or versus experimental or experimentally based maps (real-space refinement). A proper real-space refinement can be achieved by comparing such a map with a map calculated from the atomic model. Similar to density distributions, the maps of a limited and even inhomogeneous resolution can also be calculated as sums of terms, known as atomic images, which are three-dimensional peaky functions surrounded by Fourier ripples. These atomic images and, consequently, the maps for the respective models, can be expressed analytically as functions of coordinates, atomic displacement parameters, and the local resolution. This work discusses the practical feasibility of such calculation for the real-space refinement of macromolecular atomic models.