Search Results (114)

Background: Glioblastoma (GBM) is a highly aggressive, invasive, and growth factor-independent grade IV glioma. Survival following the diagnosis is generally poor, with a median survival of approximately 15 months, and it is considered the most aggressive and lethal central nervous system tumor. Conventional treatments based on surgery, chemotherapy, and radiation therapy only delay progression, and death is inevitable. Malignant glioma cells are resistant to traditional therapies, potentially due to a subpopulation of glioma stem cells that are invasive and capable of rapid regrowth. Methods: This is a literature review. The systematic retrieval of information was performed on PubMed, Embase, and Google Scholar. Specified keywords were used in PubMed and the articles retrieved were published in peer-reviewed scientific journals and were associated with brain GBM cancer and the sodium iodide symporter (NIS). Additionally, the words ‘radionuclide therapy OR mesenchyma, OR radioiodine OR iodine-131 OR molecular imaging OR gene therapy OR translational imaging OR targeted OR theranostic OR symporter OR virus OR solid tumor OR combined therapy OR pituitary OR plasmid AND glioblastoma OR GBM OR GB OR glioma’ were also used in the appropriate literature databases of PubMed and Google Scholar. A total of 68,244 articles were found in this search on Mesenchymal Stem Cell Sodium Iodide Symporter and GBM. These articles were found till 2024. To study recent advances, a filter was added to include articles only from 2014 to 2024, duplicates were removed, and articles not related to the title were excluded. These came out to be 78 articles. From these, nine were not retrieved and only seven were selected after the removal of keyword mismatched articles. Appropriate studies were isolated, and important information from each of them was understood and entered into a database from which the information was used in this article. Results: As a result of their natural capacity to identify malignancies, MSCs are employed as tumor therapy vehicles. Because MSCs may be transplanted using several methods, they have been proposed as the ideal vehicles for NIS gene transfer. MSCs have been used as a delivery vector for anticancer drugs in many tumor models due to their capacity to move precisely to malignancies. Also, by directly injecting radiolabeled MSCs into malignant tumors, a therapeutic dosage of beta radiation may be deposited, with the added benefit that the tumor would only localize and not spread to the surrounding healthy tissues. Conclusion: The non-invasive imaging-based detection of glioma stem cells presents an alternate means to monitor the tumor and diagnose and evaluate recurrence. The sodium iodide symporter gene is a specific gene in a variety of human thyroid diseases that functions to move iodine into the cell. In recent years, an increasing number of studies related to the sodium iodide symporter gene have been reported in a variety of tumors and as therapeutic vectors for imaging and therapy. Gene therapy and nuclear medicine therapy for GBM provide a new direction. In all the preclinical studies reviewed, image-guided cell therapy led to greater survival benefits and, therefore, has the potential to be translated into techniques in glioblastoma treatment trials. Full article

Most existing multi-UAV collaborative search methods only consider scenarios of two-dimensional path planning or static target search. To be close to the practical scenario, this paper proposes a path planning method based on an action-mask-based multi-agent proximal policy optimization (AM-MAPPO) algorithm for multiple UAVs searching for moving targets in three-dimensional (3D) environments. In particular, a multi-UAV high–low altitude collaborative search architecture is introduced that not only takes into account the extensive detection range of high-altitude UAVs but also leverages the benefit of the superior detection quality of low-altitude UAVs. The optimization objective of the search task is to minimize the uncertainty of the search area while maximizing the number of captured moving targets. The path planning problem for moving target search in a 3D environment is formulated and addressed using the AM-MAPPO algorithm. The proposed method incorporates a state representation mechanism based on field-of-view encoding to handle dynamic changes in neural network input dimensions and develops a rule-based target capture mechanism and an action-mask-based collision avoidance mechanism to enhance the AM-MAPPO algorithm’s convergence speed. Experimental results demonstrate that the proposed algorithm significantly reduces regional uncertainty and increases the number of captured moving targets compared to other deep reinforcement learning methods. Ablation studies further indicate that the proposed action mask mechanism, target capture mechanism, and collision avoidance mechanism of the AM-MAPPO algorithm can improve the algorithm’s effectiveness, target capture capability, and UAVs’ safety, respectively. Full article

Deep neural networks (DNNs) are prominent in predictive analytics for accurately forecasting target variables. However, inherent uncertainties necessitate constructing prediction intervals for reliability. The existing literature often lacks practical methodologies for creating predictive intervals, especially for time series with trends and seasonal patterns. This paper explicitly details a practical approach integrating dual-output Monte Carlo Dropout (MCDO) with DNNs to approximate predictive means and variances within a Bayesian framework, enabling forecast interval construction. The dual-output architecture employs a custom loss function, combining mean squared error with Softplus-derived predictive variance, ensuring non-negative variance values. Hyperparameter optimization is performed through a grid search exploring activation functions, dropout rates, epochs, and batch sizes. Empirical distributions of predictive means and variances from the MCDO demonstrate the results of the dual-output MCDO DNNs. The proposed method achieves a significant improvement in forecast accuracy, with an RMSE reduction of about 10% compared to the seasonal autoregressive integrated moving average model. Additionally, the method provides more reliable forecast intervals, as evidenced by a higher coverage proportion and narrower interval widths. A case study on Thailand’s durian export data showcases the method’s utility and applicability to other datasets with trends and/or seasonal components. Full article

Infrared small target detection is an important and core problem in infrared search and track systems. Many infrared small target detection methods work well under the premise of a static background; however, the detection effect decreases seriously when the background changes dynamically. In addition, the spatiotemporal information of the target and background of the image sequence are not fully developed and utilized, lacking long-term temporal characteristics. To solve these problems, a novel long-term spatial–temporal tensor (LSTT) model is proposed in this paper. The image registration technique is employed to realize the matching between frames. By directly superimposing the aligned images, the spatiotemporal features of the resulting tensor are not damaged or reduced. From the perspective of the horizontal slice of this tensor, it is found that the background component has similarity in the time dimension and correlation in the space dimension, which is more consistent with the prerequisite of low rank, while the target component is sparse. Therefore, we transform the problem of infrared detection of a small moving target into a low-rank sparse decomposition problem of new tensors composed of several continuous horizontal slices of the aligned image tensor. The low rank of the background is constrained by the partial tubal nuclear norm (PTNN), and the tensor decomposition problem is quickly solved using the alternating-direction method of multipliers (ADMM). Our experimental results demonstrate that the proposed LSTT method can effectively detect small moving targets against a dynamic background. Compared with other benchmark methods, the new method has better performance in terms of detection efficiency and accuracy. In particular, the new LSTT method can extract the spatiotemporal information of more frames in a longer time domain and obtain a higher detection rate. Full article

As global urbanisation, industrialisation, and population growth escalate, the production of wastewater also increases, leading to significant water pollution on a global scale. This pollution poses severe threats to environmental health, wildlife, and human communities. In rural areas where centralised sewage systems are often absent, septic tanks play a crucial role in managing wastewater. They separate solids from liquids and facilitate the biological decomposition of organic matter. This paper utilises a Patent Landscape Review (PLR) to analyse the scope and direction of innovations in septic tank technology. Conducted on 23 September 2022, the patent search targeted filings from January 2001 through June 2022 to identify prevailing trends and advancements within this field. Through a detailed examination of 889 patents, categorised by keywords, processes, materials, and designs, this study offers a comprehensive overview of the patent landscape for septic tanks. Key findings indicate that fibreglass cylindrical tanks dominate the market due to their durability and efficiency. This review also highlights a growing trend towards modular septic systems, which offer scalable solutions adaptable to specific environmental conditions. Furthermore, some patents propose the repurposing of various objects as septic tanks, demonstrating a move towards sustainability by reducing waste and enhancing environmental conservation. This paper emphasises the importance of continued innovation in septic tank technology to address the challenges of effective wastewater management in underserved rural communities. Full article

Synthetic aperture radar (SAR) can detect moving targets on the ground/sea, and high-resolution imaging on the ground/sea has critical applications in both military and civilian fields. This paper attempts to use a spaceborne SAR system to detect and image moving targets in the air for the first time. Due to the high velocity of aerial targets, they usually appear as two-dimensional range and azimuth direction defocus in SAR images, and clutter will also have a profound impact on target detection. To solve the above problems, a method of detecting and focusing on a spaceborne SAR target based on a two-dimensional velocity search is proposed by combining the BP algorithm. According to the current environment of the aerial target and the number of system channels, the clutter suppression methods are set and combined with two-dimensional velocity search with different precision, the Shannon entropy under different search velocity groups is used to obtain the search velocity group closest to the actual velocity and realize the integrated processing of moving target detection–focused imaging parameter estimation. Combined with simulation data, the effectiveness of the proposed method is verified. Full article

This study investigates the problem of rapid search planning for moving targets in maritime emergencies using an improved adaptive immune genetic algorithm. Given the complexity and uncertainty inherent in searching for moving targets in maritime emergency situations, a task planning method based on the improved adaptive immunogenetic algorithm (IAIGA) is proposed to enhance search efficiency and accuracy. This method utilizes a priori information to construct the potential regions of the target and the distribution probability within each region. It establishes a “prediction-scheduling” search strategy model, planning a rapid search task for disconnected targets based on overlapping probability through the IAIGA. By incorporating an immune mechanism, the algorithm enhances its global search capability and robustness. Additionally, the adaptive strategy enables dynamic adjustment of the algorithm’s parameters to accommodate varying search scenarios. The experimental results demonstrate that the proposed IAIGA significantly outperforms traditional methods, providing higher search speeds and more accurate search results in the context of maritime emergency response. These findings offer effective technical support for maritime emergency operations. Full article

The unknown relative motions between synthetic aperture radar (SAR) and a ground moving target will lead to serious range cell migration (RCM) and Doppler frequency spread (DFS). The energy of the moving target will defocus, given the effect of the RCM and DFS. The moving target will easily produce Doppler ambiguity, due to the low pulse repetition frequency of radar, and the Doppler ambiguity complicates the corrections of the RCM and DFS. In order to address these issues, an efficient ground moving target focusing method for SAR based on scaled Fourier transform and scaled inverse Fourier transform is presented. Firstly, the operations based on the scaled Fourier transform and scaled inverse Fourier transforms are presented to focus the moving targets in consideration of Doppler ambiguity. Subsequently, in accordance with the detailed analysis of multiple target focusing, the spurious peak related to the cross term is removed. The proposed method can accurately eliminate the DFS and RCM, and the well-focused result of the moving target can be achieved under the complex Doppler ambiguity. Then, the blind speed sidelobe can be further avoided. The presented method has high computational efficiency without the step of parameter search. The simulated and measured SAR data are provided to demonstrate the effectiveness of the developed method. Full article

Background: Despite the prevalence and incidence of kidney stones progressively increasing worldwide, public awareness of this condition remains unclear. Understanding trends of awareness can assist healthcare professionals and policymakers in planning and implementing targeted health interventions. This study investigated online search interest in “kidney stone” by analyzing Google Trends, focusing on stationarity of the trends and predicting future trends. Methods: We performed time series analysis on worldwide Google monthly search data from January 2004 to November 2023. The Augmented Dickey–Fuller (ADF) test was used to assess the stationarity of the data, with a p-value below 0.05 indicating stationarity. Time series forecasting was performed using the autoregressive integrated moving average to predict future trends. Results: The highest search interest for “kidney stone” (score 100) was in August 2022, while the lowest was in December 2007 (score 36). As of November 2023, search interest remained high, at 92. The ADF test was significant (p = 0.023), confirming data stationarity. The time series forecasting projected continued high public interest, likely reflecting ongoing concern and awareness. Notably, diverse regions such as Iran, the Philippines, Ecuador, the United States, and Nepal showed significant interest, suggesting widespread awareness of nephrolithiasis. Conclusion: This study highlighted that “kidney stone” is a consistently relevant health issue globally. The increase and stationarity of search trends, the forecasted sustained interest, and diverse regional interest emphasize the need for collaborative research and educational initiatives. This study’s analysis serves as a valuable tool for shaping future healthcare policies and research directions in addressing nephrolithiasis related health challenges. Full article

Advances in electronics and medical diagnostics have made organic dyes extremely popular as key functional materials. From a practical viewpoint, it is necessary to assess the spectroscopic and physicochemical properties of newly designed dyes. In this context, the condensation of 1,3-dimethylbarbituric acid with electron-rich alkylaminobenzaldehyde derivatives has been described, resulting in a series of merocyanine-type dyes. These dyes exhibit intense blue-light absorption but weak fluorescence. An electron-donating alkylamino group at position C4 is responsible for the solvatochromic behavior of the dyes since the lone electron pair of the nitrogen atom is variably delocalized toward the barbituric ring, which exhibits electron-withdrawing properties. This was elucidated, taking into account the different geometry of the amino group. The intramolecular charge transfer in the molecules is responsible for the relatively high redshift in absorption and fluorescence spectra. Additionally, an increase in solvent polarity moves the absorption and fluorescence to lower energy regions. The observed solvatochromism is discussed in terms of the four-parameter Catalán solvent polarity scale. The differences in the behavior of the dyes were quantified with the aid of time-dependent density functional theory calculations. The obtained results made it possible to find regularities linking the basic spectroscopic properties of the compounds with their chemical structure. This is important in the targeted search for new, practically important dyes. Full article

Sustainability targets set by the United Nations, such as Zero Hunger by 2030, encourage the search for innovative solutions to enhance food production while preserving the environment. Alternative protein sources for feed, while conventional resources like soymeal and fishmeal become more expensive and scarcer, is one of the possibilities. Studies on substituting fishmeal with insect meal show promising results in terms of animal growth and feed efficiency. This paper aims to assess the socio-economic benefits and costs of insect meal substituting fishmeal in feed and to highlight the factors influencing performance most. The study evaluates the economic value of insect-based products, waste reduction, and reduced greenhouse gas emissions as socio-economic benefits. It combines empirical data derived from laboratory trials and two case studies covering black soldier fly (Hermetia illucens) and yellow mealworm (Tenebrio molitor). Current analyses reveal negative socio-economic balances, emphasizing that reduction of operating and investment costs through upscaling and technological advancements can give a positive move, as well as factors such as current market valuations for nutrients can change significantly. Thus, a negative balance at the moment does not mean that insect rearing, and larva processing are not desirable from a long-term socio-economic perspective. Full article

To make an Autonomous Underwater Vehicle (AUV) chase a moving target ship that generates wake, wake-responsive guidance can be used. This scenario is applicable to making an underwater torpedo pursue a moving target ship until hitting the target. The objective of our research is to make an AUV pursue a target ship assisted by passive sonar sensors as well as wake sensors. To track a maneuvering target without losing the target, the AUV applies both passive sonar sensors and two wake sensors. Two wake sensors are utilized to decide the turn direction of the AUV in zig-zag maneuvers. In practice, sharp maneuvers of the target can cause the AUV to exit the target’s wake abruptly. As the target ship’s wake is not detected by wake sensors and the AUV needs to search for the target ship, the AUV’s passive sonar measures the direction of sound generated from the target ship. Thereafter, the AUV chases the target ship until the target’s wake is detected again. As far as we know, our paper is novel in addressing wake-responsive AUV guidance assisted by passive sonar sensors. The effectiveness of the proposed guidance is verified using computer simulations. Full article

Effectively strategizing the trajectories of multiple Unmanned Aerial Vehicles (UAVs) within a dynamic environment to optimize the search for and tracking of mobile targets presents a formidable challenge. In this study, a cooperative coevolution motion-encoded particle swarm optimization algorithm called the CC-MPSO search algorithm is designed to tackle the moving target search issue effectively. Firstly, a Markov process-based target motion model considering the uncertainty of target motion is investigated. Secondly, Bayesian theory is used to formulate the moving target search as an optimization problem where the objective function is defined as maximizing the cumulative probability of detection of the target in finite time. Finally, the problem is solved based on the CC-MPSO algorithm to obtain the optimal search path nodes. The motion encoding mechanism converts the search path nodes into a set of motion paths, which enables more flexible handling of UAV trajectories and improves the efficiency of dynamic path planning. Meanwhile, the cooperative coevolution optimization framework enables collaboration between different UAVs to improve global search performance through multiple swarm information sharing, which helps avoid falling into local optimal solutions. The simulation results show that the CC-MPSO algorithm demonstrates efficacy, reliability, and superior overall performance when compared to the five commonly used swarm intelligence algorithms. Full article

The orthogonal basis functions (OBFs) method is a prevailing choice for the detection of undersurface magnetic anomaly targets. However, it requires the detecting platform or target to move uniformly along a straight path. To circumvent the restrictions, a new adaptive basis functions (ABFs) approach is proposed in this article. It permits the detection platform to search for a possible target at different speeds along any course. The ABFs are constructed using the real-time data of the onboard triaxial fluxgate, GPS module, and attitude gyro. Based on the pseudo-energy of an apparent target signal, the constant false alarm rate (CFAR) method is employed to judge whether a target is present. Moreover, by defining the pixel as a relative possibility for a target at a geographic location, a magnetic anomaly target imaging scheme is introduced by displaying the pixels onto the searching area. On-site experimental data are utilized to demonstrate the proposed approach. Compared with the traditional OBFs method, the present ABFs approach can substantially improve the detection possibility and reduce false alarms. Full article

Background: Preterm birth (PTB) is a leading cause of childhood disability, and it has become a key public health priority recognized by the World Health Organization and the United Nations. Objectives: This review will: (1) summarize current practice in the diagnosis and management of PTB, (2) outline developments in precision-based medicine for diagnostics to improve the care provided to pregnant women at risk of PTB, and (3) discuss the implications of current research in personalized medicine and the potential of future advances to influence the clinical care of women at risk of PTB. Methodology: This is a narrative literature review. Relevant journal articles were identified following searches of computerized databases. Key Results: Current and emerging technologies for the utility of personalized medicine in the context of PTB have the potential for applications in: (1) direct diagnostics to identify and target infection as one of the main known causes of PTB, (2) identifying novel maternal and fetal biomarkers, (3) the use of artificial intelligence and computational modeling, and (4) combining methods to enhance diagnosis and treatment. Conclusions: In this paper, we show how current research has moved in the direction of the targeted use of biomarkers in the context of PTB, with many novel approaches. Full article