Yttrium oxide (Y2O3) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics, without requiring a high voltage forming process. The effect of current compliance on the Y2O3 RRAM devices was investigated and revealed that as the set current compliance values increased, the resistance values gradually decreased. Consequently, intermediate resistance values were obtained, which are suitable for multi-level cell (MLC) switching. The fabricated Y2O3 RRAM devices were capable of functioning as an MLC with a capacity of 2 bits in one cell, utilizing three low resistance states and one common high resistance state. The potential of the Y2O3 RRAM for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y2O3 RRAM demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This suggests that the proposed RRAM has the potential to be utilized as a memory component in practical neuromorphic systems.

Accurate channel estimation is of utmost importance for massive MIMO systems that allow providing significant improvements in spectral and energy efficiency. In this work, we investigate the spectral efficiency performance and present a channel estimator for multi-cell massive MIMO systems subjected to pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without prior knowledge of the inter-cell large-scale channel coefficients and noise power. The estimator approximates the performance of a linear Minimum Mean Square Error (MMSE) as the number of antennas increases. Following, we derive a lower bound closed-form spectral efficiency of the Maximum Ratio Combining (MRC) detector in the proposed channel estimator. The simulation results highlight that the proposed estimator performance approaches the linear minimum mean square error (LMMSE) channel estimator asymptotically.

Accurate channel estimation is of utmost importance for massive MIMO systems to provide significant improvements in spectral and energy efficiency. In this work, we present a study on the distribution of a simple but yet effective and practical channel estimator for multi-cell massive MIMO systems suffering from pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without previous knowledge of the inter-cell large-scale channel coefficients and noise power, asymptotically approximating the performance of the linear MMSE estimator as the number of antennas increases. We prove that the distribution of the proposed channel estimator can be accurately approximated by the circularly-symmetric complex normal distribution, when the number of antennas, M, deployed at the base station is greater than 10.

Does cell clustering influence intrinsic and acquired multi-drug resistance (MDR) differently? To address this question, we studied cultured monolayers (representing individual cells) and cultured spheroids (representing clusters) formed by drug-naïve (intrinsic MDR) and drug-exposed (acquired MDR) lines of ovarian cancer A2780 cells by cytometry of reaction rate constant (CRRC). MDR efflux was characterized by accurate and robust “cell number vs. MDR efflux rate constant (kMDR)” histograms. Both drug-naïve and drug-exposed monolayer cells presented unimodal histograms; the histogram of drug-exposed cells was shifted towards higher kMDR value suggesting greater MDR activity. Spheroids of drug-naïve cells presented a bimodal histogram indicating the presence of two subpopulations with different MDR activity. In contrast, spheroids of drug-exposed cells presented a unimodal histogram qualitatively similar to that of the monolayers of drug-exposed cells but with a moderate shift towards greater MDR activity. The observed greater effect of cell clustering on intrinsic than on acquired MDR can help guide the development of new therapeutic strategies targeting clusters of circulating tumor cells.

An adaptive operation strategy for on-demand control of DMFC system is proposed as an alternative method to enhance the voltage stability. Based on a single-cell DMFC stack, a newly simplified semi-empirical model is developed from the uniform-designed experimental results to describe the I-V relationship. Integrated with this model, the multi-objective optimization method is utilized to develop an adaptive operation strategy. Although the voltage instability is frequently encountered in unoptimized operations, the voltage deviation is successfully decreased to a required level by adaptive operations with operational adjustments. Moreover, the adaptive operations are also found to be able to extend the range of operating current density or to decrease the voltage deviation according to ones requirements. Numerical simulations are implemented to investigate the underlying mechanisms of the proposed adaptive operation strategy, and experimental adaptive operations are also performed on another DMFC system to validate the adaptive operation strategy. Preliminary experimental study shows a rapid response of DMFC system to the operational adjustment, which further validates the effectiveness and feasibility of the adaptive operation strategy in practical applications. The proposed strategy contributes to a guideline for the better control of output voltage from operating DMFC systems.

The most widely spread and neglected zoonotic disease – Leptospirosis, with an estimated 1.03 million cases and 58,900 deaths occurring annually, is a concern and can be prevented only by effective immunization. Due to the urgent need for a novel vaccine, various research organizations have been evaluating the protective immune response elicited by recombinant vaccines. In this study amino-acid sequences of Outermembrane protein (OMPL) 1, LipL 32, 41 and 46 from ten pathogenic serovars of Leptospira were selected to obtain epitopes that induce CD4+ and CD8+ T cell responses by binding to the MHC molecules. These B cell and T cell epitopes were used to design multi-epitope vaccine to which an adjuvant sequence was added at the N-terminal end and appropriate linkers were used to join the different epitopes to increase the efficiency of the vaccine constructs. Using an immuno-informatics approach, constructs were analyzed for the physiochemical properties, secondary and tertiary structure and its validation, docking with different receptors and prediction of binding affinity for each docked complex, followed by molecular dynamics simulation. Taking in consideration all the results, multi-epitope constructs designed may prove to be promising vaccine candidates against Leptospirosis and warrants experimental validation.

This review provides a snapshot of chronic bacterial infections through the lens of Burkholderia pseudomallei; detailing its ability to establish multi-nucleated giant cells (MNGC) within the host, leading to the formation of pyogranulomatous lesions. We explore the role of MNGC in melioidosis disease progression and pathology by comparing the similarities and differences of melioidosis to tuberculosis, outlining the concerted events in pathogenesis that lead to MNGC formation, discussing the factors that influence MNGC formation and how they fit into clinical findings reported in chronic cases. Finally, we speculate about future models and techniques that can be used to delineate the mechanisms of MNGC formation and function.

The highest efficiency solar cell won in the efficiency race does not always give the most excellent annual energy yield in the real world solar condition that the spectrum is ever-changing. The study of the radiative coupling of the concentrator solar cells implied that the efficiency could increase by the recycle of the radiative recombination generated by the surplus current in upper junction. Such configuration is called by a super-multi-junction cell. We expanded the model in the concentrator solar cell to non-concentrating installation. It was shown that this super-multi-junction cell configuration was found robust and can keep the maximum potential efficiency (50 % in realistic spectrum fluctuation) up to 10 junctions. The super-multi-junction cell is also robust in the bandgap engineering of each junction. Therefore, the future multi-junction may not be needed to tune the bandgap for matching the standard solar spectrum, as well as relying upon artificial technologies like ELO (Epitaxial lift-off), wafer-bonding, mechanical-stacking, and reverse-growth, but merely uses up-right and lattice-matching growth technologies. We have two challenging techniques; one is the optical cap layer that may be the directional photon coupling layer in the application of the photonics technologies, and another is the high-quality epitaxial growth with almost 100 % of the radiative efficiency.

Multi-rotor drones, a kind of unmanned equipment which is widely used in military, commercial consumption and other fields, has been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell mul-ti-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural lightweight, hydrogen storage methods, energy management strategies. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented.

Electrospinning is a simple versatile method to generate nanofibres. Remarkable progress has been made in the development of electrospinning process. The production of nanofibers is affected by many parameters, which influence the final material properties. Electrospun fibers have a wide range of applications such as energy storage and biomedical scaffolds. Polyacrylonitrile (PAN) has been widely used in the production of carbon nanofibers. PAN has received increasing interest in recent years, due to its excellent characteristics, such as spinnability, biocompatibility, and commercial viability, opening new applications in the biotechnological field. This paper provides an overview of the electrospinning process, its main parameters, and operation modes that affect nanofibers fabrication. A review of their biological applications is proposed. A focus is made on PAN fibers formation, functionalization, and application as scaffolds to allow cell growth. Overall, nanofibre scaffolds, made of stabilized PAN, appear as a powerful tool in medical applications needing controlled cell culture.

The hybrid system is analyzed and optimized to produce electric energy in Non-Interconnected Zones in the Colombian Caribbean region, contributing both to the improvement in the reduction of greenhouse gas emissions and to the rational use of energy. A comparative analysis of the performance of these systems was carried using a dynamic model in real wind and solar data. The model is integrated by a Southwest Wind Power Inc. wind turbine. AIR 403, a proton exchange fuel cell (PEM), an electrolyze, a solar panel and a charge regulator based on PID controllers to manipulate oxygen and hydrogen flows in the cell. The transient responses of the cell voltage, current, and power were obtained for the demand of 200 W for changes in solar radiation and wind speed for all days of the year 2013 in the Ernesto Cortissoz airport, Puerto Bolívar, Alfonso Lopez airport and Simon Bolívar airport, by regulating the flow of hydrogen and oxygen into the fuel cell. The maximum contribution of power generation from the fuel cell was presented for the Simon Bolívar airport in November with a value of 158,358W (9.45%). A multi-objective design optimization under a Pareto front is presented for each place studied to minimize the Levelized Cost of Energy and CO₂ emission, where the objective variables are the number of panel and stack in the PV system and PEM.

In daily travel and activities, pathfinding is a significant process. They are often used in transportation routes calculation. They have now evolved to be able to solve most situations of the pathfinding and its related problems. This review describes previous and recent studies on the pathfinding algorithms. It reviews the development of pathfinding algorithms in a classification base on their usage. The aim is to summarize the application of the pathfinding algorithms for the readers interested in the subject that can be used as a supplement.

Multi-Faith Spaces (MFS) are a relatively recent invention that quickly gained in significance. On the one hand, they offer a convenient solution for satisfying needs of people with diverse beliefs in the institutional context of hospitals, schools, airports, etc. On the other hand, as Andrew Crompton pointed out, they are politically significant because the multi-faith paradigm “is replacing Christianity as the face of public religion in Europe” (2012, p. 493). Due to their ideological entanglement, MFS are often used as the means to promote either a more privatised version of religion, or a certain denominational preference. Two distinct designs are used to achieve these means: negative in the case of the former, and positive in the latter. Neither is without problems, and neither adequately fulfils its primary purpose of serving diverse groups of believers. Both, however, seem to follow the biases and main problems of secularism. In this paper, I analyse recent developments of MFS to detail their main problems and answer the question, whether the MFS, and the underlying Multi-Faith Paradigm, can be classified as a continuation of secularism.

Multi-channel and multi-parameter near-infrared spectrometers (NIRS) have gradually become a new research direction and hot spot due to their ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks on the evaluation of reliability, sensitivity, stability and response consistency of the NIRS instrument. In this study, a set of test methods (the ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analysis crosstalk and verifying the reliability of NIRS were conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency in the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for the NIRS instrument reliability testing.

Although there are several access control systems in the literature for flexible policy management in multi-authority and multi-domain environments, achieving interoperability & scalability, without relying on strong trust assumptions, is still an open challenge. We present HMBAC, a distributed fine-grained access control model for shared and dynamic multi-authority and multi-domain environments, along with Janus, a practical system for HMBAC policy enforcement. The proposed HMBAC model supports: (a) dynamic trust management between different authorities; (b) flexible access control policy enforcement, defined at domain and cross-domain level; (c) a global source of truth for all entities, supported by an immutable, audit-friendly mechanism. Janus implements the HMBAC model and relies on the effective fusion of two core components. First, a Hierarchical Multi-Blockchain architecture that acts as a single access point that cannot be bypassed by users or authorities. Second, a Multi-Authority Attribute Based Encryption protocol that supports flexible shared multi-owner encryption, where attribute keys from different authorities are combined to decrypt data distributedly stored in different authorities. Our approach was implemented using Hyperledger Fabric as the underlying blockchain, with the system components placed in Kubernetes Docker container pods. We experimentally validated the effectiveness and efficiency of Janus, while fully reproducible artifacts of both our implementation and our measurements are provided.

The EU’s energy targets are to achieve at least 32% renewables in the energy mix by 2030. Part of the solution is strengthening consumer rights by empowering individuals to generate their own electricity. The study aimed to identify the most suitable energy system for electricity generation of up to 50 kW in the EU residential sector. The Multi-Criteria Decision Analysis was used to compare four systems: proton exchange membrane fuel cell with photovoltaic panels, photovoltaic panels, biomass powered Stirling engine, and solar powered Stirling engine. Based on the results, the most beneficial system for household electricity generation is the Biomass-fueled Stirling engine system due to its affordability, reliability, and low environmental impact. Governments and businesses can use these findings to improve information for the residential sector and enable the transition to renewable energy.

This research aimed to improve the interpretation of electrical resistivity (ER) results in concrete bridge decks by utilizing results from multiple nondestructive evaluations (NDE) techniques in machine learning algorithms. To achieve this, a parametric study was first conducted using numerical simulations to investigate the effect of various parameters on ER measurements, such as the degree of saturation, corrosion length, delamination depth, concrete cover, and the moisture condition of delamination. A data set from this study was used to build a machine-learning algorithm based on the Random Forest methodology. This algorithm was then implemented on data collected from a bridge deck in the BEAST® facility. The presented results demonstrate an improvement in the interpretation of ER measurements using data from other NDE technologies.

Regeneration is a fundamental process much attributed to functions of adult stem cells. In last decades delivery of suspended adult stem cells is widely adopted in regenerative medicine as a leading mean of cell therapy. However, adult stem cells can not complete the task of human body regeneration effectively by themselves as far as they need a receptive microenvironment (the niche) to engraft and perform properly. Understanding of mechanisms underlying mammalian regeneration lead us to an assumption that improved outcomes of cell therapy requires a specific microenvironment generated in damaged area prior to stem cell delivery. To certain extent it may be achieved by delivery of mesenchymal stromal cells (MSC), not in dispersed form, but rather self-organized in cell sheets (CS) – tissue-like structures comprising of viable cells and microenvironment components: extracellular matrix and soluble factors deposited in the matrix. In this communication we highlight a potential role of mesenchymal stromal cells (MSC) as regeneration organizers and speculate that this function emerges in CS. This concept shifts our understanding of therapeutic mechanism underlying a widely known CS-based delivery method for regenerative medicine.

Regeneration is a fundamental perpetual tissue process much attributed to functions of adult stem cell. In last decades delivery of adult stem cells to restore tissue structure is widely adopted in regenerative medicine. However, delivery of stem cells can not complete the task of human body regeneration effectively as far as cells need a basis to form a tissue. Certain improvement can be achieved when cells are organized to cells sheets (CS) – primitive tissue-engineered structures comprising of viable cells and ECM proteins. This led us to an assumption that after damage an intermediate feeder is structure formed as a ground for future tissue resulting in gradients of stimuli required to attract other cells (vascular, neural, epithelial, parenchyma etc.) in an organized manner. This feeder acquires increasing heterogeneity and becomes a guiding feeder that provides basic level of organization to increase complexity of structure up to normal tissue. We also highlight potential role of adult mesenchymal stromal cells (MSC) as the main source of guiding feeder and support it by our recent knowledge of their biological function. Finally, we assume that CS known to be a feasible delivery tool in therapeutics resemble guiding feeder and show our own data indicating properties of MSC-based CS. Furthermore, this concept shifts our understanding of therapeutic mechanism underlying a widely known delivery method for regenerative medicine.

The paper proposes TOPSIS method for solving multi-objective multi-level programming problem (MO-MLPP) with fuzzy parameters via fuzzy goal programming (FGP). At first, - cut method is used to transform the fuzzily described MO-MLPP into deterministic MO-MLPP. Then, for specific , we construct the membership functions of distance functions from positive ideal solution (PIS) and negative ideal solution (NIS) of all level decision makers (DMs). Thereafter, FGP based multi-objective decision model is established for each level DM for obtaining individual optimal solution. A possible relaxation on decisions for all DMs is taken into account for satisfactory solution. Subsequently, two FGP models are developed and compromise optimal solutions are found by minimizing the sum of negative deviational variables. To recognize the better compromise optimal solution, the concept of distance functions is utilized. Finally, a novel algorithm for MO-MLPP involving fuzzy parameters is provided and an illustrative example is solved to verify the proposed procedure.

The molecular basis of Down syndrome (DS) predisposition to leukemia is not fully understood but involves various factors such as chromosomal abnormalities, oncogenic mutations, epigenetic alterations, and changes in selection dynamics. Myeloid leukemia associated with DS (ML-DS) is preceded by a preleukemic phase called transient abnormal myelopoiesis driven by GATA1 gene mutations and progresses to ML-DS through additional mutations in cohesin genes, CTCF, RAS, or JAK/STAT pathway genes. DS-related ALL (ALL-DS) differs from non-DS ALL in terms of cytogenetic subgroups and genetic driver events and aberrant expression of CRLF2, JAK2 mutations, and RAS pathway activating mutations are frequent in ALL-DS. Recent advancements in single-cell multi-omics technologies have provided unprecedented insights into the cellular and molecular heterogeneity of DS-associated hematologic neoplasms. Single-cell RNA sequencing and digital spatial profiling enable the identification of rare cell subpopulations, characterization of clonal evolution dynamics, and exploration of the tumor microenvironment's role. These approaches may help identify new druggable targets and tailor therapeutic interventions based on distinct molecular profiles, ultimately improving patient outcomes with the potential to guide personalized medicine approaches and the development of targeted therapies.

The option of decarbonizing urban freight transport using Battery Electric Vehicle (BEV) seems promising.However, there is currently a strong debate whether Fuel Cell Electric Vehicle (FCEV) might be the bettersolution. The question arises as to how a fleet of FCEV influences the operating cost, the Greenhouse Gas(GHG) emissions and primary energy demand in comparison to BEVs and to Internal Combustion EngineVehicle (ICEV). To investigate this, we simulate the urban food retailing as a representative share of urbanfreight transport using a multi-agent transport simulation software. Synthetic routes as well as fleet size andcomposition are determined by solving a Vehicle Routing Problem (VRP). We compute the operating costsusing a total cost of ownership (Total Cost of Ownership (TCO)) analysis and the use phase emissions as wellas primary energy demand using the Well To Wheel (WTW) approach. While a change to BEV results in 17 -23% higher costs compared to ICEV, using FCEVs leads to 22 - 57% higher costs. Assuming today’s electricitymix, we show a GHG emission reduction of 25% compared to the ICEV base case when using BEV. Currenthydrogen production leads to a GHG reduction of 33% when using FCEV which however cannot be scaled tolarger fleets. Using current electricity in electrolysis will increase GHG emission by 60% compared to the basecase. Assuming 100% renewable electricity for charging and hydrogen production, the reduction from FCEVsrises to 73% and from BEV to 92%. The primary energy requirement for BEV is in all cases lower and forhigher compared to the base case. We conclude that while FCEV have a slightly higher GHG savings potentialwith current hydrogen, BEV are the favored technology for urban freight transport from an economic andecological point of view, considering the increasing shares of renewable energies in the grid mix.

We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

Cellular locomotion is required for survival, fertility, proper embryonic development, regeneration, and wound healing. Cell migration is a major component of metastasis, which accounts for two-thirds of all solid tumor deaths. While many studies have demonstrated increased energy requirements, metabolic rates, and migration of cancer cells compared to normal cells, few have systematically compared normal and cancer cell migration as well as energy requirements side by side. Thus, we investigated how non-malignant and malignant cells migrate utilizing several cell lines from the breast and lung. Initial screening was done in an unbiased high-throughput manner for the ability to migrate/invade on collagen and/or Matrigel. We unexpectedly observed that all the non-malignant lung cells moved significantly faster than cells derived from lung tumors regardless of growth media used. Given the paradigm-shifting nature of our discovery, we pursued possible mechanisms responsible. Neither mass, cell doubling, nor volume, accounted for the individual speed and track length of the normal cells. Non-malignant cells had higher levels of ATP at premigratory-wound induction stages. Meanwhile, cancer cells also increased ATP at premigratory-wound induction – but not to the levels of the normal cells, indicating the possibility for further therapeutic investigation.

The Peripheral giant cell granuloma (PGCG) is an asymptomatic, non-neoplastic and proliferative lesion of unknown etiology. Some possible pre-disposing factors such as hormonal changes dur-ing pregnancy, have been suggested. However, the association between PGCG and pregnancy is controversial. There are few reported clinical cases of pregnancy-associated PGCG in the litera-ture, and they occurred only in the lower jaw. The present report is on a 35-year-old female pa-tient at 36 weeks of gestation who presented with a PGCG in the central and lateral incisors of the upper jaw. The management consisted of surgical excision, along with curettage of the affected bony walls. The diagnosis was based on clinical results and imaging examinations, and it was histopathologically confirmed. The patient underwent postoperative follow-up evaluations at 3, 6 and 12 months, and there were no signs of recurrence.

Plant stem cell cultures have so far been established in only a few plant species using cambial meristematic cells. The presence of stem cells or stem cell-like cells in other organs and tissues of the plant body, as well as the possibility of de novo generation of meristematic cells from differentiated cells, allows to consider the establishment of stem cell cultures in a broader range of species. Therefore, this study aimed to establish a stem cell culture of the medicinal plant Calendula officinalis L. Callus tissues were induced from explants taken from leaves and roots. Appropriate combinations of plant growth regulators and light parameters for in vitro cultivation were selected. Already at this stage, stem and dedifferentiated cells could be identified, and cell suspension cultures were established from specific parts of the formed callus. Cell suspension cultures with a high proportion of stem cells originating from roots (92–93%) or leaves (72–73%) were developed. The amount of stem cells derived from the roots in the cell suspension reached a plateau at a level of 5.60–5.72105.

Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based architecture to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data is extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimizes the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimizing computational performance compared to other models.

One of the most important applications in the wireless sensor networks (WSN) is to classify mobile targets in the monitoring area. In this paper, a multi-DBN weighted voting classification algorithm is proposed on the basis of the Deep Belief Network (DBN) classifier and combined with the idea of voting method, which is implemented on the nodes of the WSN monitoring system by means of "upper training, lower transplantation" appraoch. The performance of the algorithm is verified by using real-world experimental data, and the results show that the proposed method has a higher accuracy in classifying the target signal features, achieving an average classification accuracy of 84.63% across four different types of moving targets. The experiment reveals that the multi-DBN weighted voting algorithm enhances the target classification accuracy by approximately 5% in comparison to the single DBN classifier, but the memory and computation time required for the algorithm to run are also increased at the same time. Compared to the FFNN classifier, which exhibited the highest classification accuracy among the four selected methods, the algorithm achieves an improvement of approximately 8.8% in classification accuracy. However, it incurs greater time overhead to run.

Unmanned Aerial Vehicles (UAVs) image object detection has great application value in military and civilian fields. However, the objects in the captured images from UAVs have problems of large scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Multi-Feature Fusion Pyramid Network (MF-FPN) is proposed, which can better fuse features of different scales and obtain features containing more context information through repeated use of feature maps, to better detect multi-scale targets. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, the proposed CGMDet was tested on VisDrone and UAVDT datasets and mAP0.5 of 50.9% and 48% was obtained, respectively. At the same time, our detector achieved the best results compared to other detectors.

Rapid assessment of maize yields in smallholder farming system is important to understand its spatial and temporal variability and for timely agronomic decision-support. Imageries acquired with unmanned air vehicles (UAV) offer opportunity to assess agronomic variables at field scale, however, it is not clear if this can be translated into reliable yield assessment on smallholder farms where field conditions, maize genotypes, and management practices vary within short distances. In this study, we assessed the predictability of maize grain yield using UAV-derived vegetation indices (VI), with(out) biophysical variables, in smallholder farms. High-resolution images were acquired with UAV-borne multispectral sensor at 4 and 8 weeks after sowing (WAS) on 31 farmers’ managed fields (FMFs) and 12 nearby Nutrient Omission Trials (NOT), all distributed across 5 locations within the core maize region of Nigeria. The NOTs included non-fertilized and fertilized plots (with and without micronutrients), sown with open-pollinated or hybrid maize genotypes. Acquired multispectral images were post-processed into several three (s) vegetation indices (VIs), normalized difference vegetation index (NDVI), normalized difference red-edge (NDRE), green-normalized difference vegetation index (GNDVI). Biophysical variables, plant height (Ht) and percent canopy cover (CC), were measured with the georeferenced plot locations recorded. In the NOTs, the nutrient status, not genotype, influenced the grain yield variability and outcome. The maximum grain yield observed in NOTs was 9.3 tha^-1, compared to 5.4 tha^-1 in FMF. Without accounting for between- and within-field variations, there was no relationship between UAV-derived VIs and grain yield at 4WAS (r<0.02, P>0.1), but significant correlations were observed at 8WAS (r≤0.3; p<0.001). Ht was positively correlated with grain yield at 4WAS (r=0.5, R²=0.25, p<0.001), and more strongly at 8WAS (r=0.7, R²=0.55, p<0.001), while relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMF (separately) through linear mixed-effects modeling, predictability of grain yield from UAV-derived VIs was generally (R²≤0.24), however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R² ≥0.62, RMSEP≤0.35) in NOTs but not in FMF. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), than in actual farmer-managed fields where various confounding agronomic factors can amplify noise-signal within the vegetation canopy.

In this work, we show that a late fusion approach to multi-modality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of Computer Vision (88.14%) and Leap Motion data classification (72.73%). With a large synchronous dataset of 18 BSL gestures collected from multiple subjects, two deep neural networks are benchmarked and compared to derive a best topology for each. The Vision model is implemented by a CNN and optimised MLP and the Leap Motion model is implemented by an evolutionary optimised deep MLP topology search. Next, the two best networks are fused for synchronised processing which results in a better overall result (94.44%) since complementary features are learnt in addition to the original task. The hypothesis is further supported by application of the three models to a set of completely unseen data where a multi-modality approach achieves the best results relative to the single sensor method. When transfer learning with the weights trained via BSL, all three models outperform standard random weight distribution when classifying ASL, and the best model overall for ASL classification was the transfer learning multi-modality approach which scored 82.55% accuracy.

The Onion Route Network (also called TOR) is by far the most efficient and widely used anonymity platform with millions of users daily and an expanding size and capacities. Since its public deployment in 2002, the Onion Routing network (also known as TOR) has maintained its leading position and dozens of propositions aiming to improve its performance and enhance the security (anonymity and privacy) have been made. Given the significance of this research area, this work seek to contribute into the improvement of TOR by investigating and testing revolutionary cryptographic and routing mechanism. This work is justified by the current TOR vulnerability and observed weaknesses, and set the challenging aim of covering these security flaws by proposing the relevant security and performances improvement such as the authenticated-encryption for onion construction, the lightened onion encapsulation approach and the secured circuit selection and cell routing mechanisms. The obtained results from implementing the proposed improvements and testing them into a TOR-like simulation platform permit to validate not only, the performances and security contributions brought by the improvement, but also the suitability of their potential implementation into the real TOR network.

Like living organisms, cancer cells require energy to survive and interact with their environment. Mitochondria are the main organelles for energy production and cellular metabolism. Recently, investigators demonstrated that cancer cells can hijack mitochondria from immune cells. This behavior sheds light on a pivotal piece in the cancer puzzle, the ‘dependence’ on the normal cells. This article illustrates the benefits of new, functional mitochondria for cancer cells that urge them to hijack mitochondria. It describes how functional mitochondria help cancer cells’ survival in the harsh tumor microenvironment, immune evasion, progression, and treatment resistance. Recent evidence has put forward the pivotal role of mitochondria in cancer stem cells’ metabolism. This theory highlights the mitochondria in cancer biology and explains how targeted anti-mitochondrial treatments can improve oncological outcomes.

Colony-stimulating factor 1 receptor (CFS-1R) is a myeloid receptor with a crucial role in monocyte survival and differentiation. Its overexpression is associated with aggressive tumor characterized by an immunosuppressive microenvironment and poor prognosis. CSF-1R ligands, IL-34 and M-CSF, are produced by many cells in the tumor microenvironment (TME), suggesting a key role for the receptor in the crosstalk between tumor, immune and stromal cells in the TME. Recently, CSF-1R expression was reported at the cell membrane of cancer cell from different solid tumors, capturing the interest of various research group interested in investigating the role of this receptor in non-myeloid cells. This review summarizes current data available on the expression and activity of CSF-1R in different tumor types. Notably, CSF-1R+ cancer cells have been shown to produce CSF-1R ligands, indicating that CSF-1R signaling is positively regulated in autocrine manner in cancer cells. Recent research demonstrated that CSF-1R signaling enhances cell transformation by supporting tumor cell proliferation, invasion, stemness and drug resistance. In addition, this review covers recent therapeutic strategies, including monoclonal antibodies and small molecule inhibitors, targeting the CSF-1R and designed to block the pro-oncogenic role of CSF-1R in cancer cell.

This comprehensive review delves into the multifaceted aspects of ERK signaling and the intricate mechanisms underlying distinct cellular fates. ERK1 and ERK2 (ERK) govern proliferation, transformation, epithelial-mesenchymal transition, differentiation, senescence, or cell death, con-tingent upon activation strength, duration, and context. The biochemical mechanisms underlying these outcomes are inadequately understood, shaped by signaling feedback and the spatial localization of ERK activation. Generally, ERK activation aligns with the Goldilocks principle in cell fate determination. Excessive or insufficient ERK activity inhibits growth, whereas moderated activation supports proliferation and survival. Unraveling the intricacies of how the degree of ERK activation dictates cell fate requires deciphering mechanisms encompassing protein stability, transcription factors downstream of ERK, and the chromatin landscape.

Urgent environmental challenges and emerging additive manufacturing (AM) technologies push research towards more performant and new materials. In the field of metallurgy, high entropy alloys (HEAs) have recently represented a topic of intense research because of their promising properties, such as high temperature strength and stability. Moreover, this class of multi-principal element alloys (MPEAs) have opened up researcher community to unexplored compositional spaces, making prosper literature of high-throughput methodologies and tools for rapidly screening large number of alloys. However, none of the methods has been aimed to design new MPEAs for AM process known as selective laser melting (SLM) so far. Here we conducted nanoindentation testing on single scan tracks of elemental powder blends and pre-alloyed powders after ball milling of AlTiCuNb and AlTiVNb. Results show that nanoindentation can represent an effective technique to gain information about phase evolution during laser scanning, contributing to accelerate the development of new MPEAs.

Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative ability to capture mobile targets, is becoming a hot research topic gradually. Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games. We define an Observation-constrained MVP (OMVP) problem in this paper and propose a Transformer-based Time and Team Reinforcement Learning scheme (T3OMVP) to address the problem. First, a new multi-vehicle pursuit model is constructed based on decentralized partially observed Markov decision processes (Dec-POMDP) to instantiate this problem. Second, by introducing and modifying the transformer-based observation sequence, QMIX is redefined to adapt to the complicated road structure, restricted moving spaces and constrained observations, so as to control vehicles to pursue the target combining the vehicle’s observations. Third, a multi-intersection urban environment is built to verify the proposed scheme. Extensive experimental results demonstrate that the proposed T3OMVP scheme achieves significant improvements relative to state-of-the-art QMIX approaches by 9.66%~106.25%. Code is available at https://github.com/pipihaiziguai/T3OMVP.

Abstract: Thermal desalination is yet a reliable technology in the treatment of brackish water and seawater; however, its demanding high energy requirements have lagged it compared to other non-thermal technologies such as reverse osmosis. This review provides an outline of the development and trends of the three most commercially used thermal or phase change technologies worldwide: Multi Effect Distillation (MED), Multi Stage Flash (MSF), and Vapor Compression Distillation (VCD). First, state of water stress suffered by regions with little fresh water availability and existing desalination technologies that could become an alternative solution are shown. The most recent studies published for each commercial thermal technology are presented, focusing on optimizing the desalination process, improving efficiencies, and reducing energy demands. Then, an overview of the use of renewable energy and its potential for integration into both commercial and non-commercial desalination systems is shown. Finally, research trends and their orientation towards hybridization of technologies and use of renewable energies as a relevant alternative to the current problems of brackish water desalination are discussed. This reflective and updated review will help researchers to have a detailed state of the art of the subject and to have a starting point for their research, since current advances and trends on thermal desalination are shown.

Carbon footprint mitigation actions have become a key issue for companies involved in urban freight transport. The electrification of the delivery fleets is one of the crucial sustainable actions carried out. To this end, it is necessary to explore several fleet renewal strategies over a finite planning horizon evaluating different types of electric powertrains for light commercial vehicles. The primary goal of this purpose-built analysis is to assist and boost the fleet managers' decisions when transitioning to alternative-fuelled vans, intending to maximize cost savings and reduce greenhouse gas emissions inventory. The model presented in this work is a Multi-Objective Linear Programming hybrid analysis taking into account the vehicle and energy supply infrastructure costs and the emissions reported in each inventory scope following the Greenhouse Gas Protocol sustainability guidelines. From this perspective, the conducted research offers a novel approximation to fleet management problems enabling a break-down cost analysis and an evaluation of the annual emissions company report. The comprehensive numerical simulations carried out over different study scenarios in Spain demonstrate that the optimization approach does not only show effective fleet renewal strategies but also identifies critical factors that impact the fleet's competitiveness, offering valuable insights for fleet managers and policymakers to meet carbon footprint reductions in a cost-effectively way.

A sustainable, scalable source of energy‐dense chemical fuel is urgently needed to ensure the security of our energy supply for future generations. Solar energy is the only renewable energy source of sufficient scale to replace fossil fuels and meet rising environmental demand. Hydrogen is expected to play a key role as an energy carrier in future energy systems of the world. As fossil fuel supplies become scarcer and environmental concerns increase, hydrogen is likely to become an increasingly important chemical energy carrier and eventually may become the principal chemical energy carrier. When most of the world’s energy sources become non-fossil based, hydrogen and electricity are expected to be the two dominant energy carriers for the provision of end-use services. Photocatalytic hydrogen evolution from water by solar energy over semiconductors offers a promising way for clean and renewable production of hydrogen. The TiO2-based materials have received considerable attention in recent years due to their extensive application in photocatalysis for hydrogen evolution. Though TiO2 is the most widely investigated photocatalyst for hydrogen evolution, it still provides low photocatalytic activity due to the wide bandgap that results in photocatalytic activity only under ultraviolet (UV) irradiation. Modification of TiO2 photocatalysts with several transition metals has been extensively studied to extend the absorbance capacity of TiO2 into the visible range. The effect of different photocatalytic deposition and reaction parameters also play major roles in enhancing the photostability of photoanode and increasing the hydrogen gas output, respectively.

Direct reprogramming of fibroblasts into induced cardiomyocytes (iCMs) holds a great promise for regenerative medicine and has been studied in several major directions. However, cell-cycle regulation, a fundamental biological process, has not been investigated during iCM-reprogramming. Here, our time-lapse imaging on iCMs, reprogrammed by Gata4, Mef2c, and Tbx5 (GMT) monocistronic retroviruses, revealed that iCM-reprogramming was majorly initiated at late-G1- or S-phase and nearly half of GMT-reprogrammed iCMs divided soon after reprogramming. iCMs exited cell cycle along the process of reprogramming with decreased percentage of EdU⁺/αMHC-GFP⁺ cells. S-phase synchronization post-GMT-infection could enhance cell-cycle exit of reprogrammed iCMs and yield more GFP^high iCMs, which achieved an advanced reprogramming with more expression of cardiac genes than GFP^low cells; however, S-phase synchronization didn’t enhance the polycistronic-MGT reprogramming, in which cell-cycle exit had been accelerated. In conclusion, post-infection synchronization of S-phase facilitated the early progression of GMT-reprogramming through a mechanism of enhanced cell-cycle exit.

Water area segmentation in remote sensing is of great importance for flood monitoring. To overcome some challenges in this task, we construct the Water Index and Polarization Information (WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net (MFGF-UNet) convolutional neural network. The WIPI dataset enhances the water information and reduces the data dimension. In particular, the Cloud-Free label provided in the database effectively mitigates the issue of scarcity of labeled samples Since a single form or uniform kernel size cannot handle the variety in size and shape of water bodies, we proposed the gated multi-filter inception (GMF-Inception) module in our MFGF-UNet. Moreover, we utilize the attention mechanism by introducing the Gated Channel Transform (GCT) skip connection and integrating GCT into GMF- Inception to further improve model performance. Extensive experiments on three benchmarks, including WIPI, Chengdu and GF2020 datasets demonstrate that our method achieves favorable performance with lower complexity and better robustness against six competing approaches. For example, on WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 of 0.9191, 0.7410, and 0.8421, respectively, which average F1 on three datasets is 0.0045 higher than the U-Net model and reduces 62% average GFLOPs. New WIPI dataset, code and trained models have been released on GitHub

Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

The hybrid bond graph has been studied in depth for scalar bond graphs, but how does this translate to the multi-bond graph? Here, the controlled junction – used to model structural switching such as contact – is extended to the multi-bond case. This is a simple process, assuming that all bonds switch simultaneously (which makes physical sense). A controlled 0-junction is applied to multi-bond graph of a car, which can lose contact with the ground in cornering. Dynamic causality features, but this can be accommodated using an equational submodel in 20-Sim (in a manner similar to that used with scalar bond graphs). The junction is proposed for subsequent work to develop a validated multi-body dynamics car model in cornering.

The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.

The framework of product life cycle (PLC) cost analysis is one of the most important evaluation tools for a contemporary high-tech company in an increasingly competitive market environment. The PLC-purchasing strategy provides the framework for a procurement plan and examines the sourcing strategy of a firm. The marketing literature emphasizes that ongoing technological change and shortened life cycles are important elements in commercial organizations. From a strategic viewpoint, the vendor has an important position between supplier, buyer and manufacturer. The buyer seeks to procure the products from a set of vendors to take advantage of economies of scale and to exploit opportunities for strategic relationships. However, previous studies have seldom considered vendor selection (VS) based on PLC cost (VSPLCC) analysis. The purpose of this paper is to solve the VSPLCC problems considering the situation of a single-buyer-multiple-supplier. For this issue, a new VSPLCC procurement model and solution procedure are derived by this paper to minimize net cost, rejection rate, late delivery and PLC cost subject to vendor capacities and budget constraints. Moreover, a real case in Taiwan is provided to show how to solve the VSPLCC procurement problem.

In this paper, a new approach to the sliding-mode control of single-phase inverters under linear and non-linear loads is introduced. The main idea behind this approach is to utilize a non-linear, flexible and multi-slope function in controller structure. This non-linear function makes the controller possible to control the inverter by a non-linear multi-slope sliding surface. In general, this sliding surface has two parts with different slopes in each part and the flexibility of the sliding surface makes the multi-slope sliding-mode controller (MSSMC) possible to reduce the total harmonic distortion, to improve the tracking accuracy, and to prevent overshoots leading to undesirable transient-states in output voltage which are occurred when the load current sharply rises. In order to improve the tracking accuracy and to reduce the steady-state error, an integral term of the multi-slope function is also added to the sliding surface. The improved performance of the proposed controller is confirmed by simulations and finally, the results of the proposed approach are compared with a conventional SMC and a SRFPI controller.

Background. Currently, most of the research in breast cancer has been carried out in conventional two-dimensional (2D) cell culture due to its practical benefits, however, the three-dimensional (3D) cell culture is becoming the model of choice in cancer research because it allows cell-cell and cell-extracellular matrix (ECM) interactions, mimicking the native microenvironment of tumors in vivo. Methods. in this work, we evaluated the effect of 3D cell organization on the expression pattern of miRNAs (by Small-RNAseq) and mRNAs (by microarrays) in the breast cancer SKBR3 cell line and analyzed the biological processes and signaling pathways regulated by the differ-entially expressed protein-coding genes (DE-mRNAs) and miRNAs (DE-microRNAs) found in the organoids. Results. We obtained well-defined cell-aggregated organoids with a grape cluster-like morphology with a size up to 9.2×105 μm3. The transcriptomic assays showed that cell growth in organoids significantly affected (all p < 0.01) the gene expression patterns of both, miRNAs and mRNAs, finding 20 upregulated and 19 downregulated DE-microRNAs, as well as 49 upregulated and 123 downregulated DE-mRNAs. In silico analysis showed that a subset of 11 upregulated DE-microRNAs target 70 downregulated DE-mRNAs. These genes are involved in 150 gene on-tology (GO) biological processes such as regulation of cell morphogenesis, regulation of cell shape, regulation of canonical Wnt signaling pathway, morphogenesis of epithelium, regulation of cy-toskeleton organization, as well as in the MAPK and AGE-RAGE signaling KEGG-pathways. In-terestingly, hsa-mir-122-5p (Fold Change (FC)=15.4), hsa-mir-369-3p (FC=11.4), and hsa-mir-10b-5p (FC=20.1) regulated up to the 81 % of the 70 downregulated DE-mRNAs. Conclu-sion, the organotypic 3D cell-organization architecture of breast cancer SKBR3 cells impacts the expression pattern of miRNAs-mRNAs network mainly through overexpression of hsa-mir-122-5p, hsa-mir-369-3p, and hsa-mir-10b-5p. All these findings suggest that the interaction between cell-cell and cell-ECM as well as the change in the culture architecture impacts gene ex-pression, and therefore, support the pertinence of migrating breast cancer research from conven-tional cultures to 3D models.

Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues at early stages. Each NDE method, meanwhile, has limitations that reduce the accuracy of the assessment. In this study, multiple NDE technologies were combined with machine learning algorithms to improve the interpretation of half-cell potential (HCP) and electrical resistivity (ER) measurements. A parametric study was performed to analyze the influence of five parameters on HCP and ER measurements, such as degree of saturation, corrosion length, delamination depth, concrete cover, and moisture condition of delamination. The results were obtained through finite element simulations and used to build two machine learning algorithms, a classification algorithm and a regression algorithm, based on the Random Forest methodology. The algorithms were tested using data collected from a bridge deck in the BEAST® facility. Both machine learning algorithms were effective in improving the interpretation of ER and HCP measurements using data from multiple NDE technologies.

Transfer factor is the name given to material derived from activated lymphocytes that is probably composed of a complex of a peptide and a short segment of RNA and which has the reported ability to transfer specific T cell immunity to uncommitted lymphocytes. Many independent groups around the world reported isolating transfer factors between 1955 and 1990 and demonstrating their ability to transfer passive immunity from one animal or individual to another, often within 24 hours of inoculation. Such activity is potentially revolutionary both in making T cell vaccines readily manufacturable and also because the existence of transfer factors would undermine the basic assumptions of the clonal selection theory, which currently dominates immunological theory. Unfortunately, lack of the microanalytical and synthetic techniques required to properly identify transfer factors, combined with safety factors associated with it derivation from blood sources susceptible to HIV and prion infections, put an end to transfer factor research after 1990. This paper reviews the evidence supporting transfer factor activity and suggests that this potentially revolutionary concept be resurrected and subjected to renewed scrutiny in light of CRISPR-Cas mechanisms and because of its potential to make possible T cell vaccination and provide a novel basis for understanding immunological function.

Background Glioblastoma (GBM) is a poor prognosis malignant grade 4 glioma. After surgical resection, standard therapy consists of concurrent radiotherapy (RT) and temozolomide (TMZ) followed by TMZ alone. The DC vaccination increased the amount of intratumoral-activated cytotoxic T lymphocytes and decreased the number of FoxP3-positive regulatory T cells. Based on these da-ta, we have developed a phase II protocol with the DC vaccine concomitant to standard RT-CT in patients undergoing surgery for GBM. Methods This is a single-arm, monocentric, phase II trial evaluating progression-free survival (PFS) and the safety of a DC vaccination integrated with standard therapy in resected GBM patients. The vaccine administration begins at the end of the RT-CT (Induction Phase) and is subsequently al-ternated with TMZ cycles (Maintenance Phase). The primary endpoints are PFS and safety. Among the secondary endpoints, are the in vitro and in vivo Immune response evaluations. Results By December 2022 , nine patients had been enrolled in the first step. Two patients progressed within three months after leukapheresis and none had experienced DC vaccine-related G3-4 tox-icity. Five patients had a positive DTH test against KLH. By June 2023. The median OS from leu-kapheresis was 23.1 months and 24.7 from surgery. Conclusions This combination therapy is well-tolerated, and we have achieved the two endpoints required for the first step. Therefore, the study will proceed to enroll the remaining 19 patients.

Biological parameters extracted from electrical signals from various body parts have been used for many years to analyze the human body and its behavior. In addition, electrical signals from cancer cell lines, normal cells, and viruses, among others, have been widely used for the detection of various diseases. Single-cell parameters such as cell and cytoplasmic conductivity, relaxation frequency, and membrane capacitance are important. There are many techniques available to characterize biomaterials, such as scanning tunneling microscopy (STM), nanotechnology, microstrip cavity resonance measurement, dielectric impedance measurement (DEPIM), etc. This article reviews the single cell isolation and sorting techniques, summarizes the common single cell operations such as impedance flow cytometry, cell electrical impedance, electrical impedance sensing, and summarizes the current single cell analysis techniques and their applications.

A new longitudinal flow heat dissipation theory for cylindrical batteries is proposed in order to increase the energy density and uniform temperature performance of cylindrical lithium-ion battery packs while also shrinking their size by roughly 10%. First, a genetic algorithm is used to identify the single cell's thermal properties. Based on this, modeling and simulation are used to examine the thermal properties of the longitudinal flow-cooled battery pack. It is found that the best coolant flow scheme has one inlet and one outlet from the end face, taking into account the cooling effect of the battery pack and engineering viability. Lastly, Thermal Dummy Cells (TDC) is used to conduct a validation test of the liquid cooling strategy. Additionally, the simulation and test results demonstrated that the liquid cooling solution can keep the battery pack's maximum temperature rise under the static conditions of a continuous, high-current discharge at a rate of 3C to 20 °C and under the dynamic conditions of the New European Driving Cycle (NEDC) to 2 °C. In applications where the space requirements for the battery pack are quite severe, the longitudinal flow cooling method has some advantages.

CD8 T cells and Natural Killer (NK) cells are cytotoxic lymphocytes important in the response to intracellular pathogens and cancer. Their activity depends on the integration of a large set of intracellular and environmental cues, including antigenic signal, cytokine stimulation and nutrients availability. This integration is achieved by signaling hubs such as the mechanistic target of Rapamycin (mTOR). mTOR is a conserved protein kinase, controlling cellular growth and metabolism in eukaryotic cells and therefore is essential for lymphocyte development and maturation. However, our current understanding of mTOR signaling comes mostly from studies performed in transformed cell lines, which constitute a poor model to comprehend metabolic pathway regulation. Therefore, it is only quite recently that the regulation of mTOR in primary cells has been assessed. Here we review the signaling pathways leading to mTOR activation in CD8 T and NK cells, focusing on activation by cytokines. We also discussed how this knowledge can contribute to immunotherapy development for intracellular pathogen and cancer treatment.

Energy is needed by cancer cells to stay alive and communicate with their surroundings. The primary organelles for cellular metabolism and energy synthesis are mitochondria. Researchers recently proved that cancer cells can steal immune cells' mitochondria using nanoscale tubes. This finding demonstrates the dependence of cancer cells on normal cells for their living and function. It also denotes the importance of mitochondria in cancer cells’ biology. Emerging evidence has demonstrated how mitochondria are essential for cancer cells to survive in the harsh tumor microenvironment, evade the immune system, obtain more aggressive features, and resist treatments. For instance, functional mitochondria can improve cancer resistance against radiotherapy by scavenging the released reactive oxygen species. Therefore, targeting mitochondria can potentially enhance oncological outcomes, according to this notion. The patients' reactions to radiation are varied, ranging from a complete response to even cancer progression during treatment. This concept illustrates how different levels of mitochondrial metabolism might contribute to this heterogeneity. Considering this notion can help to improve personalized oncological treatments. This article outlines the importance of mitochondrial metabolism in cancer biology and personalized treatments.

Metastasis is the leading cause of cancer death and can be realized through the phenomenon of tumor cell fusion. The fusion of tumor cells with other tumor or normal cells leads to the appearance of tumor hybrid cells (THCs) exhibiting novel properties such as increased proliferation and migration, drug resistance, decreased apoptosis rate and avoiding immune surveillance. Experimental studies showed the association of THCs with a high frequency of cancer metastasis; however, the underlying mechanisms remain unclear. Many other questions also remain to be answered: the role of genetic alterations in tumor cell fusion, the molecular landscape of cells after fusion, the lifetime and fate of different THCs, and the specific markers of THCs, and their correlation with various cancers and clinicopathological parameters. In this review, we discuss the factors and potential mechanisms involved in the occurrence of THCs, the types of THCs, and their role in cancer drug resistance and metastasis, as well as potential therapeutic approaches for the prevention and targeting of tumor cell fusion. In conclusion, we emphasize the current knowledge gaps in the biology of THCs that should be addressed to develop highly effective therapeutics and strategies for metastasis suppression.

Microbial symbioses exhibit astounding adaptations, yet all symbionts face the problem of how to reliably associate with host offspring every generation. A common strategy is vertical transmission, in which symbionts are directly transmitted from the female to her offspring. The diversity of symbionts and vertical transmission mechanisms is as expansive as the diversity of eukaryotic host taxa that house them. However, there are several common themes among these mechanisms based on the degree to which symbionts associate with the host germline during transmission. In this review, we detail three distinct vertical transmission strategies, starting with associations that are transmitted from host somatic cells to offspring somatic cells, either due to lacking a germline or avoiding it. A second strategy involves somatically-localized symbionts that migrate into the germline during host development. The third strategy we discuss is one in which the symbiont maintains continuous association with the germline throughout development. Unexpectedly, the vast majority of documented vertically inherited symbionts rely on the second strategy: soma-to-germline migration. Given that not all eukaryotes contain a sequestered germline and instead produce offspring from somatic stem cell lineages, this soma-to-germline migration is discussed in the context of multicellular evolution. Lastly, as recent genomics data have revealed an abundance of horizontal gene transfer events from symbiotic and non-symbiotic bacteria to host genomes, we discuss their impact on eukaryotic host evolution.

Here, we present a staged approach for an innovative repurposing of a portable infant humidicrib into a live cell growth, observation, and imaging system. Furthmore, humidicrib can support different variations of “umbilical” bioreactors, and can be used to conduct electrophysiology experiments and in situ immunohistochemistry. Modifications incorporate a closed loop carbon dioxide (CO₂) concentration control system with umbilical CO₂ and heating support for tailored bioreactors. The repurposing cost is inexpensive and allows for the continued observation and imaging of cells. This prototype unit has been used to continuously observe and image live primary neurons for up to 21 days. This demonstrates the repurposed units’ suitability for use in tissue culture based research, particularly where modifications to microscopes are required or where sensitive manipulation outside of a standard incubator is needed.

Fragmentation processes of multiply charged ions of C10H8 isomers produced in fast proton collision (velocity between 1.41 and 2.4 a.u.) is discussed in view of their fundamental molecular dynamics. Especially so in the mechanisms to produce a variety of carbon clusters from such ions. This aspect is assessed with the help of a multihit analysis of daughter ions in coincidence with elimination of H+ and CHn+ (n = 0 to 3). The elimination of H+/C+ was found to be very different from CH3+ loss. The loss of CH3+ was found to be accompanied with the formation of heavy ions like C9H5+, C9H5++ and C7H3+ by a cascade of momentum correlated dissociations. The most probable structures of large fragment ions in the CH3+ loss cascade are predicted with the help of computed electronic energy and the multihit ToF correlations of second and third hit. In addition, we report observation of super-dehydrogenation of naphthalene and azulene targets, with evidence of complete dehydrogenation in a single collision.

This paper proposes a multi-terminal adaptive collaborative operation method to solve the problem of unstable internal force tracking in clamping and handling unknown objects by multi-terminal robots. In the proposed method, the internal command force changes the complex internal force control problem into an internal force tracking problem from multi-slave to master. Moreover, we develop an algorithm for multi-slave setups to estimate object stiffness and motion uncertainty in the direction of the internal command force according to Lyapunov theory. Finally, the impedance control generates a reference trajectory for the multi-slave to maintain the desired internal force and track the master’s motion. Several experiments are conducted on a self-made robot equipped. The experimental results show that the oscillation amplitude of each slave end is less than 1 mm, the directional oscillation amplitude is less than 1 degree during the tracking of the desired commanded internal force. For objects with low stiffness, the error of the commanded internal force is less than 1 N (6%) per slave. The error in tracking the commanded internal force for objects with high stiffness is less than 2 N (8%). The results prove the feasibility and effectiveness of the proposed method.

In gravitational theory and astrophysical dynamics, singular initial value problems (IVPs) are frequently encountered. Finding the solutions to this class of IVPs can be challenging due to their complex nature. This study strives to circumvent the complexity by proposing a numerical method for solving such problems. The approach proposed in the current research seeks solutions to the IVP by partitioning the domain [0,L] of the problem into two intervals and solving the problem on each domain. The study seeks a closed-form solution to the IVP in the interval containing the singular point. A linearization technique and piecewise partitioning of the domain not containing the singularity are applied to the nonlinear IVP. The resulting linearized differential equation is solved using the Chebyshev spectral collocation method. Some examples are presented to illustrate the efficiency of the proposed method. Numerical analysis of the solution and residual errors are shown to ascertain convergence and accuracy. The results suggest that the technique gives accurate convergent solutions using a few collocation points.

Dracunculiasis (also known as Guinea worm disease) is caused by Dracunculus medinensis parasite and it spreads by drinking water containing Larvae of Guinea worm. The lack of safe water facilities, preventions and treatments resulted in highly dangerous consequences in its endemic regions. The economy of the affected regions totally falls down due to less production which is the result of agricultural field worker’s bad health. In this study, a multi epitope vaccine was designed against Dracunculus medinensis by using immune-informatics. The vaccine was designed by using T-Cell and B-Cell epitopes derived from Dracunculus medinensis proteins (Lactamase-B domain-containing protein, G-Domain containing protein and Ferrochelatase) in addition to Adjuvants and Linkers. The tertiary structure, physiochemical properties and immunogenic elements of vaccine were achieved. The validation of tertiary structure was accessed, and quality was achieved. In addition, the world coverage of parasite’s CTL and HTL epitopes is 95.61%. The stability of the chimeric vaccine was achieved through disulfide engineering. The molecular docking with Toll Like Receptor 4 (TLR-4) of vaccine showed its binding efficiency followed by Molecular Dynamic Simulation. The immune simulation suggested the mediated cell immunity and repeated antigen clearance. At the end, the optimized codon was used in in silico cloning to ensure vaccine’s higher exposure in bacterium E. coli strain K12. With further assessments, it is believed that the proposed multi epitope vaccine has strong immunogen to control Dracunculus medinensis which may result in better social and economic conditions of endemic regions.

Introduction Tuberculosis is common in Pakistan. Due to various factors including socioeconomic factors, compliance is poor to anti-tuberculosis drugs, leading to resistance. We aim to determine the prevalence of Multidrug resistance (MDR) tuberculosis in Pakistani population.Methods A prospective observational study was conducted from April 1, 2019, to December 31, 2019, in the Pulmonology department of a tertiary care hospital in Pakistan. Culture and sensitivity were assessed using a sputum sample or, in cases of an absent sputum sample, from Broncho alveolar lavage.ResultsApproximately 71.3% percent patients who had tuberculosis were found to be resistant to Isoniazid and around 48.6% did not respond to Rifampin. Multi-drug resistant was found in 29.4% participants.ConclusionMulti-drug resistance tuberculosis is very prevalent in Pakistan, which may increase burden on health care system and may lead to various complications of tuberculosis.

Phenotypic studies require large datasets for accurate inference and prediction. Collecting plant data in a farm can be very labor intensive and costly. This paper presents the design, architecture (hardware and software) and deployment of a distributed modular agricultural multi-robot system for row crop field data collection. The proposed system has been deployed in a soybean research farm at Iowa State University.

Studies performed at single-cell resolution have demonstrated the physiological significance of cell-to-cell variability. Various types of mathematical models and systems analyses of biological networks have further been used to gain a better understanding of the sources and regulatory mechanisms of such variability. In this work, we present a novel sensitivity analysis method, called molecular density function perturbation (MDFP), for the dynamical analysis of cellular heterogeneity. The proposed analysis is based on introducing perturbations to the density or distribution function of the cellular state variables at specific time points, and quantifying how such perturbations affect the state distribution at later time points. We applied the MDFP analysis to a model of signal transduction pathway involved in TRAIL (tumor necrosis factor-related apoptosis-inducing ligand)-induced apoptosis in HeLa cells. The MDFP analysis showed that caspase-8 activation regulates the timing of the switch-like increase of cPARP (cleaved poly(ADP-ribose) polymerase), an indicator of apoptosis. Meanwhile, the cell-to-cell variability in the commitment to apoptosis depended on mitochondrial outer membrane permeabilization (MOMP) and events following MOMP, including the release of Smac (second mitochondria-derived activator of caspases) and cytochrome-C from mitochondria, the inhibition of XIAP (X-linked inhibitor of apoptosis) by Smac and the formation of apoptosome.

In the framework of maritime surveillance, vessel detection techniques based on spaceborne Synthetic Aperture Radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. The paper deal with this topic presenting a novel approach exploiting a cascade application of a pre-screening, and discrimination phase. The latter leverage the SAR spectrum by means of sub-looks analysis. For the first time, the method has been validated with experiments on multi-frequency (C-, X-, and L-band) SAR images. For a future syngergic exploitation of more SAR missions, the developed dataset, composed of Sentinel-1, SAOCOM and COSMO-SkyMed images, is comprehensive of multiple images gathered over the same area with short time lag (below 15 minutes). Finally, the diversified processing chains and the results for each mission product and scenarios are discussed. Being the first dataset of SLC (Single Look Complex) SAR multi-frequency data, the present work intends to encourage additional research in this promising field of research.

With worsening environmental degradation and increasing international demand for human health, plant cell based technology will be more important. The aim of this review is to give an overview regarding key points of current progress and development trend of nature medical and edible plants from the framework of whole plant cell engineering including cell cultures, cell fusion and cell splitting derived biotechnology. Here we sum up three main application trends of medical plants cell cultures covering industrial output, technology upgrading and potential resource development. We also provide classification and typification insights to summarize the development of somatic hybridization in cereal, vegetables crops and other medicinal and edible plants as well as application of artificial polyploidy induction in major medicinal herbs. Development medicinal and edible homologous plants based on modern plant cell engineering will bring us a promising future.

Viruses can infect a cell via one or both routes viz. cell-to-cell (c-c) or cell-free (c-f) . Pathogenesis studies of various viruses, including HIV, have shown that c-c transmission yields a significantly higher infection magnitude than the c-f route. Expectedly, potent antibodies inhibited c-f infection more efficiently than with c-cell transmission. To achieve a one-step, synchronous infection cycle that provides amplified infection, we have studied a consistent and efficient c-c HIV infection model since 1992. H9 cells persistently infected with HTLV-IIIB (H3B cells) and uninfected target CD4+ lymphocyte line (HuT78) were mixed in a ratio of 1:4 respectively. We have recently used this model to produce HIV designer antigens that have been shown to elicit monoclonal as well as polyclonal specific antibodies against novel epitopes that are formed post virus-cell engagement, but prior to fusion. The model can be extended for HIV neutralizing antibody assays or drug inhibitors against high multiplicity of infection.

CD4+ T cells orchestrate and regulate immunity within jawed vertebrates, yet our understanding of their evolution, development, and cellular physiology has only begun to be unearthed in the past few decades. Discoveries of genetic diseases that ablate this cellular population have provided insight into their critical functions while transcriptomics, proteomics, and highresolution microscopy have recently revealed new insights into CD4+ T cell anatomy and physiology. This article compiles historical, microscopic, and multi omics data which can be used as a reference atlas to dissect cellular physiology within these influential cells and further understand pathologies like HIV infection that inflict human CD4+ T cells.

Novel Cellular Immunotherapy with engineered T cells has improved cancer treatment and established therapeutic promises to prevent tumor formation in clinical studies. Due to certain restrictions and difficulties, CAR and TCR T cells therapies were inadequate at points. CRISPR Cas9 genome-editing tool has a significant potential for these two cell-based therapies. As a specialized gene-editing technique, CRISPR Cas9 is used to repair genetic alternation with minimum damage. It is used as an adjunct to Immunotherapy to stimulate a more robust immune response. CRISPR has long outpaced other target-specific genome editing methods such as ZFNs and TALEN due to its high efficiency, competence in targeting, and stable operating condition. CRISPR can overcome the two major drawbacks of universal CAR T cells: allorejection and graft-vs-host disease. TCR-based T cell treatment can reduce inappropriate binding between endogenous and transgenic TCR, resulting in a reduction of severe toxicity. The CAR and TCR T based cell therapies uphold an excellent future for tumor malignancies This article has elucidated the administration of CRISPR Cas9 in Novel Cellular Immunotherapy, CAR, and TCR T cell therapy. However, this article did not fail to observe this technology's ethical concerns, limitations, and challenges. Furthermore, the article compares CRISPR-mediated allogeneic CAR T cell to TCR-T cell therapy.

The outbreak of the 2019 Novel Coronavirus (2019-nCoV) has rapidly spread from Wuhan, China to multiple countries, causing staggering number of infections and deaths. A systematic profiling of the immune vulnerability landscape of 2019-nCoV is lacking, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development. In this study, we predicted the potential of all the 2019-nCoV viral proteins to induce class I and II MHC presentation and form linear antibody epitopes. We showed that the enrichment for T cell and B cell epitopes is not uniform on the viral genome, with several focused regions that generate abundant epitopes and may be more targetable. We showed that genetic variations in 2019-nCoV, though fewer for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus, which should be considered in the development of therapies. We create an online database to broadly share our research outcome. Overall, we present an immunological resource for 2019-nCoV that could significantly promote both therapeutic development and mechanistic research.

Corneal ulcer (CU) is an ophthalmopathy characterized by depression of the corneal surface with at least one stromal loss. CU is common in canine and feline species and is usually caused, among others, by trauma, infections, toxic contamination and endocrine disorders. They usually result from an increased inflammatory response and are associated with some clinical signs such as blepharospasm, photophobia, epiphora, pain and loss of corneal transparency. Despite advances in conventional and pharmacological therapy, in many cases indolent and recurrent ulcer treatments still lead to loss of visual acuity of the animal. This paper aims to report the effect of topical application of canine adipose tissue-derived mesenchymal stem cell (cATMSCs) as treatment of recurrent CU in a Poodle dog breed that showed clear difficulty in the healing process associated with diabetes. The animal was submitted to two applications of cATMSCs and showed improvement in the blepharospasm, conjunctival hyperemia, mucopurulent ocular secretion, photophobia, corneal opacity, chemosis, pigmentation, neovascularization, and pain parameters. Besides, Fluorescein test, Schirmer test and ocular fundus exam also showed improvement in their values concomitantly with lesion resolution. Due this, we showed that cATMSC therapy contribute to the regeneration of corneal tissue in CU and may contribute to the treatment to others ophthalmopathies.

As part of our ongoing silybin project, this study aims to introduce a basic nitrogen-containing group to 7-OH of 3,5,20-O-trimethyl-2,3-dehydrosilybin or 3-OH of 5,7,20-O-trimethyl-2,3-dehydrosilybin via an appropriate linker for in vitro evaluation as potential anti-prostate cancer agents. The synthetic approaches to 7-O-substituted-3,5,20-O-trimethyl- 2,3-dehydrosilybins through a five-step procedure and to 3-O-substituted-5,7,20-O-trimethyl-2,3- dehydrosilybins via a four-step transformation have been developed. Thirty-two nitrogen-containing derivatives of silybin have been achieved through these synthetic methods for the evaluation of their antiproliferative activities towards both androgen-sensitive (LNCaP) and androgen-insensitive prostate cancer cell lines (PC-3 and DU145) using WST-1 cell proliferation assay. These derivatives exhibited greater in vitro antiproliferative potency than silybin. Among them, 11, 29, 31, 37, and 40 were identified as five optimal derivatives with IC50 values in the range of 1.40–3.06 µM, a 17- to 52-fold improvement in potency as compared with silybin. All these five optimal derivatives can arrest the PC-3 cell cycle in the G0/G1 phase and promote PC-3 cell apoptosis. Derivatives 11, 37, and 40 are more effective than 29 and 31 in activating PC-3 cell apoptosis.

A flavonoid Astragalin (kaempferol-3-O-β-D-glucopyranoside, Ast) has several biological activities including anti-oxidant, anti-HIV, and anti-allergic effects. Nonetheless, its insolubility in hydrophilic solvents imposes restrictions on its therapeutic applications. In this study, we investigated the effects of water-soluble astragalin-galactoside (kaempferol-3-O- β-D-isomaltotrioside, Ast-Gal) on dendritic cell (DC) maturation and T helper (Th) cell-mediated immune responses. Ast-Gal significantly increased maturation and activation of DCs through up-regulation of surface markers, such as CD80, CD86, and MHC II in a dose-dependent manner, while Ast had little effects. Also, Ast-Gal-treated DCs markedly secreted immune-stimulating cytokines such as IL-1β, IL-6, and IL-12. Importantly, Ast-Gal strongly increased expression of IL-12, a polarizing cytokine of Th1 cells. In a co-culture system of DCs and CD4+ T cells, Ast-Gal-treated DCs preferentially differentiates naïve CD4+ T cells into Th1 cells. The addition of neutralizing IL-12 mAb to cultures of Ast-Gal-treated DCs and CD4+ T cells significantly increased IFN- γ production, thereby indicating that Ast-Gal-stimulated DCs enhance the Th1 response through IL-12 production by DCs. Injection with Ast-Gal-treated DCs in mice increased IFN-γ-secreting Th1 cell population. Collectively, these findings indicate that hydrophilically modified astragalin can enhance Th1-mediated immune responses via DCs, and point to a possible application of water-soluble astragalin-galactoside as an immune adjuvant.

Tumor microenvironment including cancer-associated fibroblasts (CAF) has developed as an important target for understanding tumor progression, clinical prognosis and treatment responses of cancer. Cancer cells appear to transform normal fibroblasts (NF) into CAFs involving direct cell-cell communication and epigenetic regulations. This review summarizes the current understanding on miR involvement in cancer cell – tumor environment/stroma communication, transformation of NFs into CAFs, their involved targets and signaling pathways in these interactions; and clinical relevance of CAF-related miR expression profiles. There is evidence that miRs have very similar roles in activating hepatic (HSC) and pancreatic stellate cells (PSC) as part of precancerous fibrotic diseases. In summary, deregulated miRs affect various intracellular functional complexes, such as transcriptional factors, extracellular matrix, cytoskeleton, EMT/MET regulation, soluble factors, tyrosine kinase and G-protein signaling, apoptosis and cell cycle & differentiation, but also formation and composition of the extracellular microenvironment. These processes result in the clinical appearance of desmoplasia involving CAFs and fibrosis characterized by deregulated stellate cells. In addition, modulated release of soluble factors can act as (auto)activating feedback loop for transition of NFs into their pathological counterparts. Furthermore, epigenetic communication between CAFs and cancer cells may confer to cancer specific functional readouts and transition of NF into their pathological counterparts. MiR related epigenetic regulation with many similarities should be considered as key factor in development of cancer and fibrosis specific environment.

Deep convolutional neural network (CNN) has shown their great advantages in the single image deraining task. However, most existing CNN-based single image deraining methods still suffer from residual rain streaks and details lost. In this paper, we propose a deep neural network including the Multi-scale feature extraction module and the channel attention module, which are embed in the feature extraction sub-network and the rain removal sub-network respectively. In the feature extraction sub-network, the Multi-scale feature extraction module is constructed by a Multi-layer Laplacian pyramid, and is then integrated multi-scale feature maps by a feature fusion module. In the rain removal sub-network, the channel attention module, which assigns different weights to the different channels, is introduced for preserving image details. Experimental results on visually and quantitatively comparison demonstrate that the proposed method performs favorably against other state-of-the-art approaches

Defect detection in ferromagnetic substrates is often hampered by non-magnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of non-magnetic coatings on ferromagnetic substrates when using the eddy current testing technique. Based on the eddy current thin-skin effect and the lift-off insensitive inductance (LII), a simplified iterative algorithm is proposed for reducing the lift-off variation effect using a multi-frequency sensor. Compared to the previous techniques on compensating the lift-off error (e.g., the lift-off point of intersection) while retrieving the thickness, the simplified inductance algorithms avoid the computation burden of integration, which are used as embedded algorithms for the online retrieval of lift-offs via each frequency channel. The LII is determined by the dimension and geometry of the sensor, thus eliminating the need for empirical calibration. The method is validated by means of experimental measurements of the inductance of coatings with different materials and thicknesses on ferrous substrates (dual-phase alloy). The error of the calculated coating thickness has been controlled to within 3 % for an extended lift-off range of up to 10 mm.

Porous ceramics can be realized by different methods and are used for manifold applications, like cross-flow-membranes or wall-flow-filters, porous burners, solar receivers, structural design elements or catalytic supports. Within this paper three different alternative process routes are presented, which can be used to manufacture porous ceramic components with different properties or even graded porosity. The first process route bases on additive manufacturing (AM) of macro porous ceramic components, the second on AM of a polymeric template, which is used to manufacture porous ceramic components via replica technique. Finally, the third process route bases on an AM technology, which allows the manufacturing of multi-material or multi-property ceramic components, like components with dense and porous volumes in one complex shaped component.

Wild-type p53 cancer therapy-induced senescent cells frequently engulf and degrade neighboring ones inside a massive vacuole in their cytoplasm. After clearance of the internalized cell, the vacuole persists, seemingly empty, for several hours. Despite large vacuoles being associated with cell death, this process is known to confer a survival advantage to cancer engulfing cells, leading to therapy resistance and tumor relapse. Previous attempts to resolve the vacuolar structure and visualize their content using dyes were unsatisfying for lack of known targets and ineffective dye penetration and/or retain. Here, we overcame this problem by applying optical diffraction tomography and Raman spectroscopy to MCF7 doxorubicin-induced engulfing cells. We demonstrated a real ability of cell tomography and Raman to phenotype complex microstructures, such as cell-in-cells and vacuoles, and detect chemical species in extremely low concentrations within live cells in a completely label-free fashion. We show that vacuoles had a density indistinguishable to the medium, but were not empty, instead contained diluted cell-derived macromolecules, and we could discern vacuoles from medium and cells using their Raman fingerprint. Our approach is useful for the non-invasive investigation of senescent engulfing (and other peculiar) cells in unperturbed conditions, crucial for a better understanding of complex biological processes.

Natural killer (NK) cell therapy is one of the most promising treatments for Glioblastoma Multiforme (GBM). However, this emerging technology is limited by the availability of sufficient numbers of fully functional cells. Here, we investigated the efficacy of NK cells that were expanded and treated by interleukin-2 (IL-2) and heat shock protein70 (HSP70), both in vitro and in vivo. Proliferation and cytotoxicity assays were used to assess the functionality of NK cells in vitro, after which treated and naïve NK cells were administrated intra-cranially and systemically to compare the potential antitumor activities in our in vivo rat GBM models. In vitro assays provided strong evidence of NK cell efficacy against C6 tumor cells. In vivo tracking of NK cells showed efficient homing around and within the tumor site. Furthermore, significant amelioration of the tumor in rats treated with HSP70/Il-2 treated NK cells as compared to those subjected to non-treated NK cells, as confirmed by MRI, proved the efficacy of adoptive NK cell therapy. Moreover, results obtained with systemic injection confirmed migration of activated NK cells over the blood brain barrier and subsequent targeting of GBM tumor cells. Our data suggest that administration of HSP70/Il-2 treated NK cells may be a promising therapeutic approach to be considered in the treatment of GBM.

Metal-supported fuel cells (MSCs) offer potential material cost and robustness advantage over anode supported cells (ASCs). Because of the very good thermal shock stability of such MSCs, these can be heated up very quickly. However the challenge co-firing of metal substrate and electrolyte stays still unsolved. The production of the core component, the tape casted metal substrate, with defined shrinkage resulting from modification of metal powders is presented in the paper. This approach leads to an adjustment of the shrinkage mismatch during sintering, between metal and ceramic components, and adjustment of target specifications like porosity, green density and layer thickness.

In this article, we introduce some multi-messenger studies of blazars. Main contents contain the following parts, 1. the subclasses of blazars and the observational characteristics, 2. emission variabilities from the multi-messenger observations, 3. the physical origin for the multi-messenger studies. Based on mid-IR observations collected from the $WISE$ database, we collect two sources (BZQ J1700+6830 and BZQ J1842+6809) to analyze the mid-IR variabilities. \\ (1) For BZQ J1700+6830, the period is $P\approx$ 42 hr at the four bands ($W1$, $W2$, $W3$, $W4$); for BZQ J1842+6809, the period is $P\approx$ 123 hr at the two bands ($W1$, $W2$). \\ (2) Using the periodicities, we calculate that the central black hole mass ($BH$) should be $M = (5.44\sim33.0)\times10^8M_{\odot}$ for BZQ J1700+6830 and $M = (1.13\sim6.88)\times10^9M_{\odot}$ for BZQ J1842+6809.\\ (3) For the two sources, during the flaring stage, we find anti-clockwise variation trends based on the time-based relations built by the spectral indices ($\alpha$) and flux densities ($F$).\\ (4) We calculate the Doppler factor ($\delta$) when the sources are in the dimming or brightening stages, and find that with the frequency increasing, the Doppler factor become stronger.

The paper considers new effects of the nanoscale state of matter, which open up prospects for the creation of electronic devices using new physical principles. The contact of chemically homogeneous different sizes hydrated nanoparticles of yttrium-stabilized zirconium oxide (ZrO2 – x %mol Y2O3, x=0, 3, 4, 8; YSZ) with particle sizes of 7.5 nm and 7,5 nm; 7.5 nm and 9 nm; 7.5 nm and 11 nm; 7.5 nm and 14 nm in the form of compacts obtained using high hydrostatic pressure (HP-compacts of 300MPa) was studied at direct and alternating current. A unique size effect of the nonlinear (semiconductor) dependence of the electrical properties (in the region U <2.5 V, I ≤ 2.7 mA) of the contact of different-sized YSZ nanoparticles of the same chemical composition is revealed, which indicates the possibility of creating semiconductor structures of a new type based on chemically homogeneous nanostructured systems. The electronic structure of the near-surface regions of nanoparticles of a special type of oxide materials and the possibility, on this basis, to obtain specifically rectifying properties of the contacts were studied theoretically. Models of surface states of the Tamm type are constructed, but considering the Coulomb long-range action. The discovered variance and its dependence on the curvature of the surface of nanoparticles made it possible to study the conditions for the formation of a contact potential difference in cases of nanoparticles of the same radius (synergistic effect), different radii (doped and undoped variants), as well as to discover the possibility of describing a group of powder particles from material within the Anderson model. The established effect makes it possible to solve the problem of diffusion instability of semiconductor heterojunctions and opens up prospects for creating electronics devices with a fundamentally new level of properties for use in various fields of the national economy and breakthrough critical technologies.

Fluorescence microscopy provides an unparalleled tool for imaging biological samples. However, producing high-quality volumetric images quickly and without excessive complexity remains a challenge. Here, we demonstrate a simple multi-camera structured illumination microscope (SIM) capable of simultaneously imaging multiple focal planes, allowing for the capture of 3D fluorescent images without any axial movement of the sample. This simple setup allows for the acquisition of many different 3D imaging modes, including 3D time lapses, high-axial-resolution 3D images, and large 3D mosaics.

Our work uses Iterative Boltzmann Inversion (IBI) to study the coarse-grained interaction between 20 amino acids and the representative carbon nanotube CNT55L3. IBI is a multi-scale simulation method that has attracted the attention of many researchers in recent years. It can effectively modify the coarse-grained model derived from the Potential of Mean Force (PMF). IBI is based on the distribution result obtained by All-Atom molecular dynamics simulation, that is, the target distribution function, the PMF potential energy is extracted, and then the initial potential energy extracted by the PMF is used to perform simulation iterations using IBI. Our research results have gone through more than 100 iterations, and finally, the distribution obtained by coarse-grained molecular simulation (CGMD) can effectively overlap with the results of all-atom molecular dynamics simulation (AAMD). In addition, our work lays the foundation for the study of force fields for the simulation of the coarse-graining of super-large proteins and other important nanoparticles.

Multi-morbidity and polypharmacy are common in older people and pose a challenge for health and social care systems especially in context of global population ageing. They are complex and interrelated concepts in the care of older people that require early detection and patient centred decision making that are underpinned by the principles of multidisciplinary led comprehensive geriatric assessment (CGA). Personalised care plans need to remain responsive and adaptable to the needs of a patient, enabling an individual to maintain their independence.

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a 5-year period between 2003-2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS operations) over two regions: I) inside radar effective coverage and II) immediately outside radar coverage. The outcomes of the evaluation indicate a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.

Bioelectrocatalysis provides the intrinsic catalytic-functions of redox enzymes to non-specific electrode reactions and is the most important and basic concept for biosensors. This review starts by describing fundamental characteristics of bioelectrocatalytic reactions in mediated and direct electron transfer types from a theoretical viewpoint and summarizes amperometric biosensors based on multi-enzymatic cascades and for multi-analyte detection. The review also introduces prospective aspects of two new concepts of biosensors: mass-transfer-controlled (pseudo)steady-state amperometry at microelectrodes with enhanced enzymatic activity without calibration curves and potentiometric coulometry at enzyme/mediator-immobilized biosensors for absolute determination.

This article proposes the use of a multi-scale and multi-sensor approach to collect and modelling 3D data concerning wide and complex areas in order to obtain a variety of metric information in the same 3D archive, based on a single coordinate system. The employment of these 3D georeferenced products is multifaceted and the fusion or integration among different sensors data, scales and resolutions is promising and could be useful for the generation of a model that could be defined as hybrid. The correct geometry, accuracy, radiometry and weight of the data models are hereby evaluated comparing integrated processes and results from Terrestrial Laser Scanner (TLS), Mobile Mapping System (MMS), Unmanned Aerial Vehicle (UAV), terrestrial photogrammetry, using Total Station (TS) and Global Navigation Satellite System (GNSS) as topographic survey. The entire analysis underlines the potentiality of the integration and fusion of different solutions and is a crucial part of the “Torino 1911” project whose main purpose is mapping and virtually reconstructing the 1911 Great Exhibition settled in the Valentino Park in Turin (Italy).

The main focus of the in vitro toxicity assessment methods is on the viability of the cells, which is usually based on metabolism changes. Yet, when exposed to toxic substances, the cell triggers multiple signals in response. With this in mind, we have developed a promising cell-based toxicity method that observes various cell responses when exposed to toxic substances (either death, division or remaining viable). Based on the collective cell response, we observed and predicted the dynamics of the cell population to determine the toxicity of the toxicant. The method was tested with two different conformations: In the first conformation, we exposed a monoculture model of blood macrophages to UV light, hydrogen peroxide, nutrient deprivation, tetrabromobisphenol A, fatty acids and 5-fluorouracil. In the second, we exposed a coculture liver model consisting of hepatocytes, hepatic stellate cells, Kupffer cells and liver sinusoidal endothelial cells to rifampicin, ibuprofen and 5-fluorouracil. The method showed good accuracy compared to established toxicity assessment methods. In addition, this approach provided more representative information on the toxic effects of the compounds as it considers the different cellular responses induced by toxic agents.

Macro-autophagy (autophagy hereafter) is an evolutionarily conserved cellular process that has long been recognized as an intracellular mechanism for maintaining cellular homeostasis. It involves the formation of a membraned structure called the autophagosome, which carries cargo that includes toxic protein aggregates and dysfunctional organelles to the lysosome for degradation and recycling. Autophagy is primarily considered and studied as a cell-autonomous mechanism. However, recent studies have illuminated an underappreciated facet of autophagy, i.e., non-autonomously regulated autophagy. Non- autonomously regulated autophagy involves the degradation of autophagic components, including organelles, cargo, and signaling molecules, and is induced in neighboring cells by signals from primary adjacent or distant cells/tissues/organs. This review provides insight into the complex molecular mechanisms governing non-autonomously regulated autophagy, highlighting the dynamic interplay between cells within tissue/organ or distinct cell types in different tissues/organs. Emphasis is placed on modes of intercellular communication that include secreted molecules, including microRNAs, and their regulatory roles in orchestrating this phenomenon. Furthermore, we explore the multidimensional roles of non-autonomously regulated autophagy in various physiological contexts, spanning tissue development and aging, as well as its importance in diverse pathological conditions, including cancer and neurodegeneration. By studying the complexities of non-autonomously regulated autophagy, we hope to gain insights into the sophisticated intercellular dynamics within multicellular organisms, including mammals. These studies will uncover novel avenues for therapeutic intervention to modulate intercellular autophagic pathways in altered human physiology.

Combined radiation with hemorrhage (combined injury, CI), exacerbates hematopoietic acute radiation syndrome and mortality compared to radiation alone (RI). We evaluated the effects of RI or CI on blood cell depletion as a biomarker to differentiate the two. Male CD2F1 mice were exposed to 8.75 Gy γ- radiation (60Co). Within 2 h of RI, animals were bled under anesthesia 0% (RI) or 20% (CI) of total blood volume. Blood samples were collected at 4-5 h and days 1, 2, 3, 7, and 15 after RI. CI decreased WBC at 4-5 h and continued to decrease them until day 3; counts then stayed at the nadir up to day 15. CI decreased neutrophils, lymphocytes, monocytes, eosinophils and basophils more than RI on day 1 or day 2. CI decreased RBCs, hemoglobin and hematocrit on days 7 and 15 compared to RI, whereas Hemorrhage alone returned to the baseline on days 7 and 15. RBCs depleted after CI faster than post RI. Hemorrhage alone increased platelet counts on days 2, 3, and 7, which returned to the baseline on day 15. Our data suggest that WBC depletion may be a biomarker within 2 days post-CI and RBC depletion after 3 days post-CI.

Cell junction that is usually associated with dynamic cytoskeletons is essential for a wide range of cellular activities such as cell migration, cell communication, barrier function and signal transduction. Real-time observation of cell junction facilitates understanding of the mechanisms by which cell junction regulate relevant cellular activities. In this study, we examined the binding capacity of a modified tridecapeptide from Cx43 to cell junction protein ZO-1 and sought to create a fluorescent peptide that could be used to label cell junction. To introduce the fluorescent peptide into the cells, a cell-penetrating peptide was linked to the modified tridecapeptide and synthesized. The binding of the modified tridecapeptide was tested using pull down and immunoprecipitation assay. The ability of the peptide to label cell junction was assessed by adding it to fixed or cultured Caco-2 cells. Testing assay revealed that the Cx43-derived peptide can bind to ZO-1. Besides, the peptide was able to label cell junction of fixed cell, although no obvious cell junction labeling was observed clearly in living cells, probably due to the inadequate affinity. These results indicate that a peptide-based strategy for labeling cell junction may be feasible, and efforts to improve its affinity are warranted in the future.

The multidrug-resistant fungal pathogens belonging to the Candida haemulonii complex and the phylogenetically related species Candida auris are well-known for causing infections that are difficult to treat due to their multidrug-resistance profile. C. auris is even more worrisome due to its ability to cause outbreaks in healthcare settings. These emerging yeasts produce a wide range of virulence factors that facilitate the development of the infectious process. In recent years, the aggregative phenotype has been receiving attention, as it is mainly associated with defects in cellular division and its possible involvement in helping the fungus to escape from host immune responses. In this study, we initially investigated the aggregation ability of 18 clinical isolates belonging to the C. haemulonii species complex and C. auris. Subsequently, we evaluated the effects of physicochemical factors on the fungal aggregation competence. The results demonstrated that cell aggregation was a typically time-dependent event, in which almost all studied fungal isolates exhibited high aggregation after 2 h of incubation at 37°C. The aggregation was not impacted by pH, temperature, -mercaptoethanol (a protein-denaturing agent) and EDTA (a chelator agent). Conversely, proteinase K, trypsin and sodium dodecyl sulfate significantly diminished the aggregation. Collectively, our results demonstrated that the aggregation ability in these opportunistic yeast pathogens is time-dependent, and surface proteins and hydrophobic interactions seem to mediate cell aggregation, since the presence of proteases and anionic detergent affected the aggregation ability. However, further studies are necessary to better elucidate this phenomenon.

Different studies corroborate a role for ceramide synthases and their downstream products, ceramides, in modulation of apoptosis and autophagy in the context of cancer. These mechanisms of regulation, however, appear to be context dependent in terms of ceramides’ fatty acid chain length, subcellular localization, and the presence or absence of their downstream targets. Our current understanding of the role of ceramide synthases and ceramides in regulation of apoptosis and autophagy could be harnessed to pioneer the development of new treatments to activate or inhibit a single type of ceramide synthase, thereby regulating the apoptosis induction or cross talk of apoptosis and autophagy in cancer cells. Moreover, the apoptotic function of ceramide suggests that ceramide analogues can pave the way for the development of novel cancer treatments. Therefore, in the current review paper we discuss the impact of ceramide synthases and ceramides in regulation of apoptosis and autophagy in context of different types of cancers. We also briefly introduce the latest methods to analyze the lipids in biological samples. Finally, we discuss the drug development strategies focusing on the ceramide synthases and ceramides as future therapeutic approaches in cancer therapy.

Apple (Malus x domestica) fruit size is dependent on cell division and cell expansion, processes which are subsequently regulated by plant hormones such as auxins, gibberellins, and cytokinins. In this study, we investigated the role of cell division and cell expansion in apple growth and identified which of the two was more deterministic to final fruit size. Three cultivars of different sizes were selected, namely, ‘Twenty Ounce’ (large-sized), ‘Royal Gala’ (medium-sized), and ‘Crabapple’ (small-sized). Gene expression and cell size analyses were conducted over the course of two consecutive seasons. The expression patterns of three classes of genes were markedly similar across all cultivars. Two cell division markers, namely, MdCDKB2;2 and MdANT2, were discovered to be correlatively expressed as both displayed initially high expression levels, which gradually declined from the early to late stages of growth time course. For cell expansion markers, MdEXP3 was upregulated as the cells expanded, while MdARF106 was expressed in both the cell division and expansion stages. Meanwhile, the ripening related gene MdACO1 was expectedly expressed only during the ending stages associated with ripening. Interestingly, the cell measurements taken regularly from each cultivar throughout the same eperimental timespan showed that cell sizes were unaltered and remained constant from initial pollination at the zeroth Days After Pollination (DAP), to ripening at 120 Days After Full Bloom (DAFB).

The fate of a viral infection in the host begins with various types of cellular responses, such as abortive, productive, latent, and destructive infections. Apoptosis, necroptosis, and pyroptosis are the three major types of regulated cell death mechanisms that play critical roles in viral infection response. Cell shrinkage, nuclear condensation, bleb formation, and retained membrane integrity are all signs of osmotic imbalance-driven cytoplasmic swelling and early membrane damage in necroptosis and pyroptosis. Caspase-driven apoptotic cell demise is considered in many circum-stances as an anti-inflammatory, and some pathogens hijack the cell death signaling routes to initiate a targeted attack against the host. In this review, we selected mechanisms by which viruses interfere with cell death are discussed in-depth and are used to illustrate the general principles and cellular signaling mechanisms of virus-host specific molecule interactions.

Neural stem cell (NSC) based therapies are at the forefront of regenerative medicine strategies to combat illness and injury of the central nervous system (CNS). In addition to their ability to produce new cells, NSCs secrete a variety of products, known as the NSC secretome, that have been shown to ameliorate CNS disease pathology and promote recovery. As pre-clinical and clinical research to harness the NSC secretome for therapeutic purposes advances, a more thorough understanding of the endogenous NSC secretome can provide useful insight into the functional capabilities of NSCs. In this review, we focus on research investigating the autocrine and paracrine functions of the endogenous NSC secretome across life. We also compare the NSC secretome across species, finding signs of conserved parallels between rodent, human and zebrafish NSC secretomes. Throughout development and adulthood, we find evidence that the NSC secretome is a critical component of how endogenous NSCs regulate themselves and their niche. We also find gaps in current literature, most notably in the clinically relevant domain of endogenous NSC paracrine function in the injured CNS. Future investigations to further define the endogenous NSC secretome and its role in CNS tissue regulation are necessary to bolster our understanding of NSC-niche interactions and to aid in the generation of safe and effective NSC-based therapies.

Superantigens are unconventional antigens which recognise immune receptors outside the usual binding sites e.g. complementary determining regions (CDRs), to elicit a response within the target cell. T-cell superantigens crosslink T-cell receptors and MHC Class II molecules on antigen-presenting cells, leading to lymphocyte recruitment, induction of cytokine storms and T-cell anergy or apoptosis among many other effects. B-cell superantigens, on the other hand, bind immunoglobulin receptors on B-cells affecting opsonisation, IgG-mediated phagocytosis, and drive B-cells into apoptosis. Here, through a review of the structural basis for recognition of immune receptors by superantigens, we show that their binding interfaces share specific physicochemical characteristics when compared with other protein-protein interaction complexes. Given that antibody-binding superantigens have been exploited extensively in industrial antibody purification, these observations could facilitate further protein engineering to optimize the use of superantigens in this and other areas of biotechnology.

Syncytium formation, i.e., cell-cell fusion resulting in the formation of multinucleated cells, is a hallmark of infection by paramyxoviruses and other important viruses. This natural mechanism has historically been a diagnostic marker for paramyxovirus infection in vivo and is now widely studied for virus-induced membrane fusion in vitro. However, the role of syncytium formation in within-host dissemination and pathogenicity of viruses remains poorly understood. The diversity of henipaviruses and their wide host range and tissue tropism make them particularly appropriate models to characterize the drivers of syncytium formation and its implications for virus fitness and pathogenicity. Based on the henipavirus literature, we summarized current knowledge on the mechanisms driving syncytium formation, mostly acquired from in vitro studies, and on the in vivo distribution of syncytia. While these data suggest that syncytium formation widely occurs across henipaviruses, hosts and tissues, we identified important data gaps that undermined our understanding of the role of syncytium formation in virus pathogenesis. Based on these observations, we propose solutions of varying complexity to fill these data gaps, from better practices in data archiving and publication for in vivo studies, to experimental approaches in vitro.