Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF-target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application, utilizing the R programming language, which predicts TF-target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF-target gene networks. Despite its current limitation to human data, it provides a platform for exploring gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species.

Infection with the malaria parasite Plasmodium, remains a major cause of illness and death world-wide. The development of evasion strategies by the parasite from host immunity and its adaptability to antimalarial drugs has raised the urgency for developing new strategies to combat this disease. One promising avenue is targeting telomeres – sequences found at chromosome termini– and telomerase – the enzyme that catalysis synthesis of telomeres using an intrinsic RNA template. As telomeres shorten critically, cells undergo replicative senescence or apoptosis. Plasmodium, lacking telomerase activity in its human stages, exploits alternative mechanisms, accelerating telomere shortening, aging, and immune evasion. Chemical telomerase inhibitors show promise as antimalarials. In this review, we examine the potential of targeting this system in malaria therapy, with telomerase inhibitors offering a novel approach. A systematic search on PubMed using Boolean techniques identified 246 relevant articles from 1985 to March 31, 2024. After filtering, 43 articles met inclusion criteria, supplemented by snowball sampling. The telomerase inhibitor drug strategy is widely applicable against cancer. However, given the drugability of telomerase and the absolute requirement for active telomere maintenance in the vast majority of parasites including plasmodium, telomerase and telomere maintenance could well represent the Achilles heel of the parasite

Passive radar detection emerges as a pivotal method for environmental perception and target de-tection within radar applications. Leveraging its advantages, including minimal electromagnetic pollution and efficient spectrum utilization, passive radar methodologies have garnered increasing interest. In recent years, there has been an increasing selection of passive radar signal sources, and the emerging 5G has the characteristics of high frequency band, high bandwidth, and a large number of base stations, which make it have significant advantages in passive radar. Therefore, this paper introduces a passive radar target detection method based on 5G signals and designs a rotating target speed measurement experiment. In the experiment, this paper validated the method of de-tecting rotating targets using 5G signals and evaluated the measurement accuracy, providing a research foundation for passive radar target detection using 5G signals and detecting rotating targets such as drone rotors.

WHO reported an estimated 249 million malaria cases and 608,000 malaria deaths in 85 countries in 2022. A total of 94% of malaria deaths occurred in Africa, 80 % of which were children under 5. In other words, one child dies every minute from malaria. The RTS,S/AS01 malaria vaccine, that uses the Plasmodium falciparum circumsporozoite protein (CSP) to target sporozoite infection of the liver, achieved modest efficacy. The Malaria Vaccine Implementation Program (MVIP), coordinated by the WHO and completed at the end of 2023, found that immunization reduced mortality by only 13%. To further reduce malaria death, development of a more effective malaria vaccine is a high priority. Three major malaria vaccine targets being considered are the level of sporozoite liver infection, merozoite red blood cell (RBC) infection, and mosquito midgut infection. These targets involve specific ligand-receptor interactions. However, current malaria vaccine candidates that target two major parasite population bottlenecks, liver infection and mosquito midgut infection, do not focus on such parasite ligands. Here we evaluate the potential of newly identified parasite ligands as novel malaria vaccine antigens.

Tumor heterogeneity is a major obstacle in cancer therapy. We offer a novel perspective on tumor heterogeneity, informed by expanding upon the current view on tumor evolution. It is commonly known that in cancer, it is the advantageous genes and mutations that drive tumor capacities and the progression. We recognize an earlier, often overlooked, but necessary thus crucial step preceding the activation of advantageous genes functionality, namely, cellular response. We suggest that cancer is driven by an adaptive action taken by any organism when confronted with adversity - a cellular response to generate specific cancer capacities demanded to overcome survival threat - the responses that prompt the utilization of the functionality of advantageous genetic mutations and genes to attain these capacities and drive the disease. The ongoing development of cancer capabilities, as cells endeavor to obtain a competitive advantage, results in increased molecular chaos, observed as tumor heterogeneity. When the evolutionary drive for survival is impeded by therapeutic inhibition of critical genes, cell death occurs, a phenomenon thus termed oncogene addiction. We summarize the implications of this new framework for understanding tumor evolution and anti-cancer drug discovery, as well as understanding of basic cancer biology.

Visual Sensor Networks (VSNs) have been the focus of numerous studies, emphasizing the quality of the information they generate. The primary goal of these networks is to capture data from an area, extract the pertinent information from the visual scene, and facilitate communication. However, the presence of objects in the study area can impacted the network's efficiency, linking the quality of acquired data directly to the positioning of the cameras. Strategically positioning multiple cameras in a potentially complex area, while ensuring effective communication between nodes, presents a challenging issue in VSNs applications. This paper introduces a specific method to enhance the deployment of visual sensors in indoor environments. The research addresses the theme of multi-objective optimization of VSN deployment, integrating various criteria: maximizing overall visual coverage, maximizing the observation of zones of interest, maximizing observation redundancy of zones of interest and ensuring communication connectivity for data transfers. To tackle this problem, we simulate the observed environment incorporating cameras, obstacles, and considering zones of interest. Camera positioning is performed while considering visual constraints and the scene's characteristics. The quality of positioning is evaluated based on the defined objective functions. Initially, a comparative study is conducted with a state-of-the-art approach, integrating the first three objective functions. Subsequently, we incorporate the fourth objective function to ensure data transfers. The proposed method demonstrates efficient camera network deployments in indoor areas, considering global coverage and observation redundancy. In contrast to the state-of-the-art, these results are achieved in areas with visual obstacles while ensuring communication connectivity between the different cameras.

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

The proliferation of SARS-COV-2 through enhanced viral replication and its subsequent triggering of cytokine storm, are some of the hallmark phenotypes in severe COVID-19 patient cases. Cyclosporine, an immunosuppressant drug and Selinexor, an inhibitor of nuclear transporter protein, both have been successfully demonstrated to be effective against SARS-COV-2 infection by targeting those functions individually. However, the highly multifactorial pathology of SARS-COV-2 infection hinders any mono-therapeutic strategy to become an optimal option. In this study, we assess the potential efficacy of the Cyclosporine-Selinexor combination on an integrated interactome by adopting a network-medicine-based repositioning technique, where disease proximity, functional proximity and their topological separation are evaluated, followed by a robust statistical significance test. Results have shown that both drug target modules are highly proximal to the SARS-COV-2 disease modules in terms of network topology and functional associations, in a statistically significant manner, individually. Functional enrichment of both drug modules and SARS-COV-2 infected modules has shown that two drugs target the functions related to viral replication and cytokine storm during infection. Moreover, a high degree of network separation been those two drug target modules has been observed, revealing ``complementary exposure`` patterns, rendering this drug combination as an effective one against SARS-COV-2 infection. We hope that our results will encourage researchers to further investigate the potency of Cyclosporine and Selinexor combination in vivo or in vitro, and ultimately lead that up to clinical trials to treat SARS-COV-2 patients.

This investigation introduces an efficient method for assessing the reliability of semi-rigid steel frame connections in existing buildings, explicitly targeting the Top Angle and Seat Pad (TA-SP) semi-rigid connection commonly used in Australian cities' high-rise buildings. The moment capacity of these connections was examined through parameter power model testing, meticulously considering the bending, shear, and prying forces actions to determine the weakest elements in the connection system. A plastic hinge model was applied to ensure computational efficiency and accuracy in assessing the connections' ultimate strength. Comparatively, the finite element (F.E) method M−θ curves were introduced to scrutinise the nonlinear behaviour of the connection, providing validation for the predicted analytical approach. Further emphasis was placed on identifying the reliability of the weakest elements in the connection system, employing the First-Order and Second-Order Reliability Methods models to estimate their reliability index. The correlation between the model's failure probability and a percentile error was also explored, utilising Monte Carlo simulation reference for refined validation. Novel formulas and methodologies were introduced to compute the locked-chain-series system reliability, which accounts for the inherent material and geometric variability. The findings highlight the critical significance of the weakest element in determining the overall reliability of the existing TA-SP connection.

Abstract: Rationale. Object tracking has significance in many applications ranging from control of unmanned vehicles to autonomous monitoring of specific situations and events, especially when providing safety for patients with certain adverse conditions such as epileptic seizures. Conventional tracking methods face many challenges, such as the need for dedicated attached devices or tags, influence by high image noise, complex object movements, and intensive com-putational requirements. We have developed earlier computationally efficient algorithms for global optical flow reconstruction of group velocities that provide means for convulsive seizure detection and have potential applications in fall and apnea detection. Here, we address the chal-lenge of using the same calculated group velocities for object tracking in parallel. Methods. We propose a novel optical flow-based method for object tracking. It utilizes real-time image se-quences from the camera and directly reconstructs global motion-group parameters of the con-tent. These parameters can steer a rectangular region of interest surrounding the moving object to follow the target. The method successfully applies to multi-spectral data, further improving its effectiveness. Results. Experimental results on simulated tests and complex real-world data demonstrate the method's capabilities. The proposed optical flow reconstruction can provide accurate, robust, and faster results compared to current state-of-the-art approaches.

Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive integral sliding mode control method with a feedforward compensation disturbance observer to enhance the capability of a microsatellite attitude control system for gaze tracking of high-speed moving targets. Specifically, first, we present the attitude control system model for microsatellites and the calculation method for the desired attitude of target tracking based on image feedback. Then, an adaptive integral sliding mode attitude control algorithm with a feedforward compensation disturbance observer, which meets the requirements of high-precision tracking control, is designed. The developed algorithm utilizes the disturbance observer to observe the friction torque of the flywheel and compensates for it through feedforward control. It also employs the adaptive integral sliding mode control algorithm to reduce the impact of uncertain disturbances, decrease the steady-state error of the system, and enhance the attitude control precision. Simulation experiments demonstrated that the designed disturbance observer can successfully observe the frictional disturbance torque of the flywheel. The attitude Euler angle control precision for high-speed moving target tracking reached 0.009°, and the angular velocity control precision reached 0.005°/s, validating the effectiveness of the proposed approach.

The present minireview assessed the benefits of using artificial intelligence applications in healthcare at predicting the targets of bioactive small molecules in human. Method. The tool is a tuned algorithm, with novel data in web interface. Results. We used tables to order the information of Swiss Target Prediction that allow predictions on combination of chemotherapies and probable side effects. This tool is useful to understand the molecular mechanisms underlying a given phenotype or bioactivity, to rationalize possible favorable or unfavorable side effects and to predict off-targets of known molecules as well as to clear the way for drug repurposing. Predictions are done using a ligand-based approach to compare the similarity between a query molecule and the known ligands of a large collection of protein targets. Conclusion. This study allowed us to formulate recommendations for the use of some of the chemotherapeutic agent groups in cancer patients at a low cost. It is very important and beneficial for patients that take medicine for a longer period and on whose lives depend on such treatment.

This article utilizes the Canny edge extraction algorithm based on contour curvature and the cross-correlation template matching algorithm to extensively study the impact of high-repetition-rate CO2 pulsed Laser on the target extraction and tracking performance of the infrared imaging detector. It establishes a quantified dazzling pattern for lasers on infrared imaging systems. By conducting laser dazzling and damage experiments, a detailed analysis of the normalized correlation between target and dazzling images is performed to quantitatively describe laser dazzling effects. Simultaneously, an evaluation system, including target distance and laser power evaluation factors, is established to determine the dazzling level and whether the target is recognizable. The research results reveal that laser power and target position are crucial factors affecting the detection performance of infrared imaging-guided weapon systems under laser dazzling. Different laser powers are required to successfully interfere with the recognition algorithm of infrared imaging-guided weapons at different distances. And laser dazzling will produce a lot of false edge information, which seriously affects the performance of pattern recognition algorithm. In laser damage experiments, the detector experienced functional damage, with a quarter of the image displaying as completely black. The energy density threshold required for functional damage to the detector is approximately 3 J/cm2. The dazzling assessment conclusions also apply to evaluating damage results. Finally, the proposed evaluation formula aligns with experimental results, objectively reflecting the actual impact of laser dazzling on the target extraction and tracking performance of infrared imaging systems. This study provides an in-depth and accurate analysis for understanding the influence of lasers on the performance of infrared imaging-guided weapons.

Currently, the most promising work seems to be aimed not so much at the development of new drugs, but rather at creating ways of targeted delivery of existing drugs. In this paper, we show that polymeric micelles based on chitosan, heparin grafted with lipoic and oleic acids residues demonstrate the potential for doxorubicin delivery to tumors due to pH-, glutathione-sensitivity. Smart delivery systems use the Aikido principle, i.e. they target tumor cells according to their features. Natural polysaccharides (heparin and chitosan) provide the pH sensitivity of micelles: the release of doxorubicin (Dox) is enhanced in a slightly acidic environment (model tumor microenvironment). Fatty acid residues are necessary for the formation of micelles and solubilization of cytostatics in a hydrophobic core. Lipoic acid residues formed a labile S-S cross-linking between polymer chains (the first method), and also covalently attached doxorubicin molecules through glutathione-sensitive S-S bridges (the second option) – determine Redox sensitivity of micelles to tumors. The characterization of polymeric micelles and prodrugs (Dox-polymer) was carried out using FTIR spectroscopy. The release of doxorubicin from micelles occurs slowly (20% / 6h) in an environment with a pH of 7.4 and the absence of glutathione, while in a slightly acidic environment and in the presence of 10 mM glutathione, the rate increases up 6 times, with an increase in the effective concentration up to 5 times after 7 h. The permeability of free doxorubicin, micellar formulations (covalent S-S crosslinked and not) into Raji, K562, A875 cancer cells was studied using confocal laser scanning microscopy (CLSM), demonstrated an increase in the permeability of micellar formulations based on Dox. Thus, we have presented micellar formulations of doxorubicin for targeted delivery to tumor, what is important in the aspects of reducing non-target toxicity, increasing the selectivity of cytostatics against tumors and overcome multidrug resistance.

Infrared small target detection (ISTD) plays a crucial role in both civilian and military applications. The detection of small targets against dense cluttered backgrounds remains a challenging task, requiring the collaboration of false alarm source elimination and target detection. Existing approaches primarily focus on modeling targets while often neglecting false alarm sources. To address this limitation, we propose a Target and False Alarm Collaborative Detection Network to leverage the information provided by false alarm sources and the background. Firstly, we introduce a False Alarm Source Estimation Block (FEB) that estimates potential interferences present in the background by extracting features on multiple scales and employing stepwise upsampling for feature fusion. Subsequently, we propose a framework that employs multiple FEBs to eliminate false alarm sources across multiple scales. Finally, a Target Segmentation Block (TSB) is introduced to accurately segment the targets to produce the final detection result. Experiments on public datasets demonstrate that, compared to other methods, our model achieves high accuracy in segmenting targets while being able to extract false alarm sources which can be used for further studies.

Tumor immunotherapy represented by immune target blocking and chimeric antigen receptor cell therapy has developed rapidly. Among them, Natural Killer Cell (NK) is gradually becoming another preferred immunotherapy method after T cell immunotherapy because of its unique killing effect in innate immunity and adaptive immunity. Although NK cell therapy has achieved promising results in clinical trials, there are still some problems, such as low efficacy in solid tumors, insufficient penetration of NK cells and high cost of treatment. Nanomaterials are expected to be an excellent tool to improve the anti-tumor immune response of NK cells due to their advantages such as chemical specificity, biocompatibility and simple synthesis. Nanomaterials can enhance the cytotoxicity of NK cells, participate in the modification and noninvasive cell-tracking patterns of NK cells，and mediate their homing and infiltration, and thus significantly improve the efficiency of NK cell immunotherapy. In this review, we will briefly introduce the methods and mechanisms of tumor immunotherapy, emphasize the research progress of NK cell-based immunotherapy and imaging, and finally summarize the direction and research progress of nanomaterials in NK cell-based immunotherapy.

Space instable target simulator (SITS) is a vital actuator for ground verification of on-orbital capture technology, the motion performance of which directly affects simulation credibility. Different delays reduce the stability of SITS and ultimately lead to its divergence. In order to achieve high fidelity simulation, the impact of force measurement delay, discrete control cycle, and simulator response delay on stability is analyzed firstly. Then, the dynamic equation and transfer function identification model of hybrid simulator is constructed, and the necessary and sufficient conditions of its stability and convergence are obtained using the Routh criterion. After that, a switching compensator with variable gain is proposed, the compensation principle diagram of which is build, and its mathematical model including the energy observer and tracking differentiator is also established. Finally, three sets of numerical simulations are conducted to validate the correctness of the stability analysis and effectiveness of proposed compensation method. Simulation results show that all three types of delay can cause SITS to lose stability under critical stable motion state, and the delay in force measurement has the greatest impact, followed by the influence of control cycle. Compared with the force applied to simulated target, velocity and its recovery coefficient of space instable target using fixed gain and linear gain compensation, the proposed compensator has significantly better performance.

Duchenne muscular dystrophy (DMD) is a progressive neuromuscular disease caused by mutations to the dystrophin gene - resulting in deficiency of dystrophin protein, loss of myofiber integrity in skeletal and cardiac muscle, and eventual cell death and replacement with fibrotic tissue. Pathologic cardiac manifestations occur in nearly every DMD patient, with development of cardiomyopathy - the leading cause of death - inevitable by adulthood. As early cardiac abnormalities are difficult to detect, timely diagnosis and appropriate treatment modalities remain a challenge. There is no cure for DMD – treatment is aimed at delaying disorder progression and alleviating symptoms. A comprehensive understanding of the pathophysiological mechanisms is crucial to development of targeted treatments. While established hypotheses of underlying mechanisms include sarcolemmal weakening, upregulation of pro-inflammatory cytokines, and perturbed ion homeostasis, mitochondrial stress has recently come into focus as a potential key contributor. Several experimental compounds targeting the skeletal muscle pathology of DMD are in development, but effects of such agents on cardiac function remain unclear. Synergistic integration of small molecule- and gene-target-based drugs with metabolic, immune, or ion balance-enhancing compounds into a combinatorial therapy offers potential for treating dystrophin deficiency-induced cardiomyopathy, making it crucial to understand the underlying mechanisms driving the disorder.

Viruses continue to provide vital insights into the field of control of gene expression. Viral transactivators, in concert with other viral and cellular proteins, regulate expression of self, other viruses, and host genes with profound effects on infected cells, underlying inflammation, control of immune responses, and pathogenesis. The multifunctional Tat proteins of lentiviruses (HIV-1, HIV-2, and SIV) transactivate gene expression by recruiting host proteins and binding to trans-acting responsive regions (TARs) in viral and host RNAs. SARS-CoV-2 nucleocapsid participates in early viral transcription, recruits similar cellular proteins, and shares intracellular, surface, and extracellular distribution with Tat. SARS-CoV-2 nucleocapsid interacting with the Replication-Transcription complex might, therefore, transactivate viral and cellular RNAs in transcription and reactivation of self and other viruses, acute and chronic pathogenesis, immune evasion, and viral evolution, constituting a potential pan-coronaviral therapeutic target. Here, we show by using primary and secondary structural comparisons that the leaders of SARS-CoV-2 and other coronaviruses contain TAR-like sequences in stem-loops 2 and 3, the coronaviral nucleocapsid C-terminal domains (N-CTDs) harbor a region of similarity to TAR-binding regions of lentiviral Tat proteins, and the coronaviral nonstructural protein 12 has a cysteine-rich metal binding, dimerization domain similar to that in lentiviral Tat proteins.

Existing studies often lack a systematic solution for Unmanned Aerial Vehicles (UAV) inspection system, which hinders their widespread application in crack detection. To enhance its practicality, this study proposes a formal and systematic framework for UAV inspection systems, specifically designed for automatic crack detection and pavement distress evaluation. The framework integrates UAV data acquisition, deep learning based crack identification, and road damage assessment in a comprehensive and orderly manner. Firstly, the flight control strategy is presented, and road crack data is collected using the DJI Mini 2 UAV imagery, establishing a high-quality UAV crack image datasets with ground truth information. Secondly, a validation and comparison study is conducted to enhance the automatic crack detection capability and provide an appropriate deployment scheme for UAV inspection systems. This study develops automatic crack detection models based on mainstream deep learning algorithms(namely, Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) in urban road scenarios. The results demonstrate that the Faster-RCNN algorithm achieves the highest accuracy and is suitable for online data collection of UAV and offline inspection at work stations. Meanwhile, the YOLO models, while slightly lower in accuracy, are the fastest algorithm and are suitable for lightweight deployment of UAV with online collection and real-time inspection. Quantitative measurement methods for road cracks are presented to assess road damage, which will enhance the application of UAV inspection systems and provide factual evidence for maintenance decisions made by road authorities.

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by swelling in at least one joint. Owing to an overactive immune response, extra-articular manifestations are observed in certain cases, with interstitial lung disease (ILD) being the most common. Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is characterized by chronic inflammation of the interstitial space, which causes fibrosis and the scarring of lung tissue. Controlling inflammation and pulmonary fibrosis in RA-ILD is important because they are associated with high morbidity and mortality. Pirfenidone and nintedanib are specific drugs against idiopathic pulmonary fibrosis and have shown efficacy against RA-ILD in several clinical trials. Immunosuppressants and disease-modifying antirheumatic drugs (DMARDs) with antifibrotic effects have also been used to treat RA-ILD. Immunosuppressants moderate the overexpression of cytokines and immune cells to reduce pulmonary damage and slow the progression of fibrosis. DMARDs with mild antifibrotic effects target specific fibrotic pathways to regulate fibrogenic cellular activity, extracellular matrix homeostasis, and oxidative stress levels. Therefore, specific medications are required to effectively treat RA-ILD. In this review, the commonly used RA-ILD treatments are discussed based on their molecular mechanisms and clinical trial results. In addition, a computational approach is proposed to develop specific drugs for RA-ILD.

Past research suggests a continuity between perception and memory, as reflected in influences of orienting of spatial attention by cues presented after a visual target offset (post-target cues) on target perception. Conducting two experiments, we tested and confirmed this claim. Our study revealed an elevated reliance on post-target cues for target detection with diminishing target visibility, leading to better performance in validly versus invalidly cued trials, indicative for contrast gain. We demonstrated this post-target cueing impact on target perception without a postcue response prompt, meaning that our results truly reflected a continuity between perception and memory rather than a task-specific impact of having to memorize the target due to a response prompt. We further showed an influence of attention by the post-target cues with cues presented away from a clearly visible target, meaning that visual interactions at the target location provided no better explanation of our results. Our results generalize prior research with liminal targets and confirm the view of a perception-memory continuum, so that visual target processing is not shielded against visuospatial orienting of attention elicited by events following offset of the visual target.

Leishmaniases are zoonotic vector-borne diseases caused by a wide variety of Leishmania species with complex transmission cycles involving different reservoirs, potential new hosts and vectors. Similarly, to other eukaryotes, Leishmania release extracellular vesicles (LEVs) to play important initial interactions that are crucial to modulate the subsequent systemic immune response on the establishment of infection in humans and others hosts like dogs. Recent studies in endemic areas of Brazil concluded that canine infections were predominantly due to L. amazonensis and not restricted to L. infantum (syn. Leishmania chagasi). Under these premises, the diagnosis of leishmaniasis needs to be improved with the identification of current etiological agent and the histopathologic features, highlighting the differential diagnosis and the molecular components of LEVs. In this way, the dual aim of that study is to register collected observations of natural canine infections, enhancing the relevance of differential diagnosis in companion animals and inserting in vitro results in the field of LEVs that still research gaps to be filled to understand the mechanisms and biological aspects involving the parasite-host interactions. Therefore, improve these studies of Parasitology research is important for diagnostic, prognostic, treatment advances and continuing need for global prevention, control, elimination/eradication of these parasitic infections.

Despite UAV multi-target detection exhibits considerable developmental potential worldwide, it suffers distinct challenges compared with traditional tasks in this field. These challenges include insufficient feature extraction capabilities for small targets, limited capabilities for multi-dimensional feature fusion, as well as constraints on hardware computation parameters. Especially in mission scenarios such as disaster detection, these challenges will be further amplified. Consequently, this paper introduces SSE-YOLO, an innovative YOLO framework algorithm specially designed to address these challenges. To enhance the model's feature extraction capability, we employ the SPDConv module to replace the original Conv in the backbone section, utilizing depth-separable convolution instead of traditional convolution pooling. Concurrently, we eliminate the SPPF module at the bottom and address a new Separate Kernel Attention Pyramid Pooling (SKAPP) module, substantially enhancing the feature fusion capability at the model's core. Moreover, to address the challenge of multi-dimensional feature fusion, we replace the Concat module of the neck and head with E-BiFPN, transmitting feature information from the backbone to the lower network through four CBS blocks, which effectively resolves the issue of lost contextual information in the model. Meanwhile, SSE-YOLO undergoes ablation experiments on the VisDrone2019 dataset to evaluate its effectiveness against alternative methods, and experimental results illustrate the model's exceptional precision in detecting UAV targets. In comparison to models with comparable experimental accuracy, SSE-YOLO requires remarkably fewer parameters. On the VisDrone2019-DET-test-dev dataset, SSE-YOLO enhances mAP by 17.3%, with a 42.5% reduction in the parameter amounts. Therefore, the proposed method effectively tackles the challenge of reconciling low parameters and high accuracy, presenting a novel pathway for deep learning-based UAV multi-target detection.

In the industrial field, the 3D target detection algorithm PointPillars has gained popularity. Improving target detection accuracy while maintaining high efficiency has been a significant challenge. To address the issue of low target detection accuracy in the PointPillars 3D target detection algorithm, this paper proposes an algorithm based on feature enhancement to improve the backbone network. The algorithm enhances preliminary feature information of the Backbone network by modifying it based on PointPillars with the aid of channel attention and spatial attention mechanisms. To address the inefficiency caused by the excessive number of subsampled parameters in PointPillars, FasterNet (a lightweight and efficient feature extraction network) is utilized for downsampling and forming different scale feature maps. To prevent the loss and blurring of extracted features resulting from the use of inverse convolution, we utilize the lightweight and efficient up-sampling modules Carafe and Dysample for adjusting resolution. Experimental results indicate improved accuracy under all difficulties of the KITTI dataset, demonstrating the superiority of the algorithm over PointPillars.

Several traps are recommended for monitoring corn earworm, Helicoverpa zea (Boddie), an important pest of field, vegetable and fruit crops in the U.S. These traps, which are meant to capture H. zea in many cases end up capturing other insects. Helicoverpa zea has recently been found feeding on different seeds and dual-type (seed and fiber) hemp, Cannabis sativa L. (Rosales: Cannabaceae) causing serious damage. Limited work has been done on developing integrated pest management (IPM) for H. zea industrial hemp in Missouri. We evaluated the attractiveness of different traps with the aim of developing a monitoring system for the adult male H. zea in industrial hemp fields in two Missouri locations In addition, we recorded other non-target insects in the traps to determine trap selectivity. Commercially available green, clear, tricolor bucket traps Heliothis Scentry and Scentry Delta 1X traps baited with H. zea sex pheromones were evaluated in 2021 and 2022. Tricolor traps captured significantly more adult male H. zea than the other traps in both years. Non-target insects, mainly Xylocopa virginica (L.) (Hymenoptera: Apidae), Bombus spp (Hymenoptera: Apidae), and Apis mellifera L. (Hymenoptera: Apidae) were also captured. The tricolor trap captured the most bees. The presence of non-target species highlights the risk of using tricolor traps as a monitoring tool. This data provides information for planning the monitoring of corn earworm in industrial hemp farms in Missouri.

The seismic damage in reinforced concrete bridges is identified in this study using the "M and P" hybrid technique initially developed for planar frames. The proposed methodology involves a series of pushover and instantaneous modal analyses with a progressively increasing target deck displacement along the longitudinal direction of the bridge. From the results of these analyses, the diagram of the instantaneous eigenfrequency of the bridge, ranging from the health state to near collapse, is plotted against the inelastic seismic deck displacement. By pre-determining the eigen-frequency of an existing bridge along its longitudinal direction through "monitoring and frequency identification", the target deck displacement corresponding to the damage state can directly be found from this diagram. Subsequently, the damage can be identified by examining the results of the pushover analysis at the step where the target deck displacement is indicated. The effectiveness of this proposed technique is evaluated in the context of multiple span bridges with unequal pier heights, illustrated through an example of a four-span bridge. The findings demonstrate that the damage potential in bridge piers can be successfully identified by combining the results of a monitoring process and pushover analysis.

Background: Chronic Myeloid Leukemia (CML) is initiated in bone marrow due to chromosomal translocation t(22;9) leading to fusion oncogene BCR-ABL. Targeting BCR-ABL by tyrosine kinase inhibitors (TKI) have changed fatal CML into an almost curable disease. Despite that, TKIs lose their effectiveness due to disease progression. Unfortunately, mechanism of CML progression is poorly understood and common biomarkers for CML progression are unavailable. This study was conducted to find out novel biomarkers of CML progression by employing whole exome sequencing (WES).Materials and Methods: WES of accelerated phase (AP-) and blast crisis (BC-) CML patients was carried out, with chronic phase CML (CP-CML) patients as control. After DNA library preparation and exome enrichment, clustering and sequencing was carried out using Illumina platforms. Statistical analysis was carried out using [SAS/STAT] software version 9.4 and R package employed to find mutations shared exclusively by all AP-/BC-CML. Confirmation of mutations was carried out using Sanger sequencing and protein structure modelling using I-Tasser followed by mutant generation and visualization using PyMOL. Results: Three novel genes (ANKRD36, ANKRD36B and PRSS3) were mutated exclusively in all AP-/BC-CML patients. Only ANKRD36 gene mutations (c.1183_1184 delGC and c.1187_1185 dupTT) were confirmed by Sanger sequencing. Protein modelling studies showed that mutations induce structural changes in ANKRD36 protein. Conclusions: Our studies show that ANKRD36 is a potential common biomarker and drug target of early CML progression. ANKRD36 is yet uncharacterized in human. It has the highest expression in bone marrow, specifically myeloid cells. We recommend carrying out further studies to explore the role of ANKRD36 in biology and progression of CML.

The delineation of the clinical target volumes (CTVs) for radiation therapy is time-consuming, requires intensive training and shows high inter-observer variability. Supervised deep learning methods depend heavily on consistent training data, thus state-of-the-art research focuses on making CTV labels more homogeneous and strictly bounding them to current standards. International consensus expert guidelines standardize CTV delineation by conditioning the extension of the clinical target volume on surrounding anatomical structures. Training strategies that directly following the construction rules given in the expert guidelines or the possibility to quantify the conformance of manually drawn contours with the guidelines are still missing. 71 anatomical structures that are relevant for CTV delineation in head and neck cancer patients according to the expert guidelines were segmented on 104 CT scans to assess the possibility to automate their segmentation by state-of-the-art deep learning methods. nnU-Net models were trained to automatically segment those 71 structures on planning CT scans. We report the DICE, HD and sDICE for 71 + 5 anatomical structures for most of which no previous segmentation accuracies have been reported. For those structures, for that prediction values have been reported, our segmentation accuracy matched or exceeded the reported values. The predictions from our models were always better than those predicted by the TotalSegmentator. The sDICE with 2mm margin was larger than 80% for almost all structures. Individual structures with decreased segmentation accuracy are analyzed and discussed with respect to their impact on the CTV delineation following the expert guidelines. No deviation is expected to affect the rule-based automation of the CTV delineation.

Abiotic stress is an adverse environmental factor that severely affects plant growth and development, and plants have developed complex regulatory mechanisms to adapt to these unfavourable conditions through long-term evolution. In recent years, many transcription factor families of genes have been identified to regulate the ability of plants to respond to abiotic stresses. Among them, AP2/ERF (APETALA2/ethylene responsive factor) family is a large class of plant-specific that regulate plant response to abiotic stresses and can also play a role in regulating plant growth and development. This paper reviews the structural features and classification of AP2/ERF transcription factors, that transcriptional regulation, reciprocal proteins, downstream genes and hormone-dependent signalling and hormone-independent signalling pathways are involved in the response to abiotic stress. The study of AP2/ERF transcription factors and interacting proteins, as well as the identification of their downstream target genes, can provide us with a more comprehensive understanding of the mechanism of plant action in response to abiotic stress, which can improve the tolerance ability of plants in abiotic stress, and provide more theoretical basis for rice to obtain more yields under abiotic stress.

This study addresses the escalating threat of data theft ransomware, a form of cyberattack that exfiltrates sensitive information from victim networks and demands ransom for its non-disclosure. Unlike traditional crypto-ransomware, data theft ransomware leverages various infiltration techniques to gain unauthorized access and control over critical data assets. The study introduces a novel Moving Target Defense (MTD) framework, specifically designed for the Windows operating system environment, to counter these sophisticated attacks. MTD increases the unpredictability of the system by dynamically altering its configurations, thereby disrupting the adversary's ability to execute successful attacks. Our research includes the development and empirical evaluation of the MTD framework, demonstrating its effectiveness in reducing ransomware operational capabilities through quantitative analysis, qualitative observations, and statistical significance testing. We also explore the challenges and complexities involved in implementing MTD in real-world scenarios and propose strategies to overcome these barriers. The study concludes with recommendations for future research directions and the potential of MTD in enhancing cybersecurity defense mechanisms.

To address the challenges associated with the difficult electromagnetic (EM) scattering computations of anisotropic dielectric-coated metallic composite targets, we proposed an efficient hybrid algorithm for simulating the EM scattering of complex targets with anisotropic dielectric coatings. This method, based on Impedance Boundary Condition (IBC), utilizes surface impedance vectors to describe the EM properties of the dielectric. It fully leverages the respective advantages of the low-frequency Method of Moments (MoM) and the high-frequency Physical Optics (PO) to achieve high-precision and rapid EM simulation of anisotropic dielectric-coated metallic composite targets. Employing boundary conditions and equivalent principles, we equivalently transform the EM scattering problem of targets with thin dielectric coatings into the radiation problem of equivalent EM current on impedance surfaces. This enables the high-precision and rapid calculation of Radar Cross Sections (RCS) for complex targets with anisotropic dielectric coatings. Using examples such as a square plate structure, a simplified aircraft, and a complex satellite model, the simulation results exhibit a high degree of agreement with full-wave numerical solutions and demonstrate a significant advantage in computational resources. This research convincingly demonstrates the accuracy and effectiveness of the proposed method.

The retrieval of tree height percentiles with satellite lidar waveforms over the mountainous ar-eas is greatly challenged due to slanted and rugged topographic relief of underlying ground. A novel algorithm is proposed to extract the height percentiles based on the target response waveform (TRW) that is resolved by using a Richardson–Lucy deconvolution algorithm with an adaptive iterative time. The height metrics of the energy percentiles of 25%, 50%, 75% and 95% for the cumulative TRW distribution are determined by their vertical distances relative to the ground elevation in this study. To validate the proposed algorithm, we select the received waveforms of the Global Ecosystem Dynamics Investigation (GEDI) lidar over the Pahvant Mountains of central Utah, USA. The results reveal that the resolved TRWs resemble closely to the actual target response waveforms from the coincident airborne lidar data, with the mean values of the coefficient of correlation, total bias and root mean square error (RMSE) taking val-ue of 0.92, 0.0813 and 0.0016. In addition, the accuracies of the derived height percentiles from the proposed algorithm have been greatly improved compared with the conventional Gaussian de-composition method and the slope-adaptive waveform metrics method. The RMSE values de-crease by the mean values of 1.96 m and 2.72 m, respectively. It demonstrates that the proposed algorithm has good potential in the extraction of forest structure parameter over rugged moun-tainous areas.

In this paper, an intelligent blind guide system based on 2D LiDAR and RGB-D camera sensing is proposed, and the system is mounted on the smart cane. The intelligent guide system relies on 2D LiDAR, RGB-D camera, IMU, GPS, Jetson nano B01, STM32 and other hardware. The main advantage of the intelligent guide system proposed by us is that the distance between the smart cane and obstacles can be measured by 2D LiDAR based on Cartographer algorithm, thus achieving Simultaneous localization and mapping (SLAM). At the same time, through the improved yolov5 algorithm, pedestrians, vehicles, pedestrian crosswalks, traffic lights, warning posts, stone piers, tactile paving and other objects in front of the visually impaired can be quickly and effectively identified. Laser SLAM and improved yolov5 obstacle identification tests were carried out inside a teaching building on the campus of Hainan Normal University and on a pedestrian crossing on Longkun South Road in Haikou City, Hainan Province. The results show that the intelligent guide system developed by us can drive the wheels and omnidirectional wheels at the bottom of the smart cane, and give the smart cane a self-leading blind guide function like a "guide dog", which can effectively guide the visually impaired to avoid obstacles and reach the predetermined destination, and can quickly and effectively identify the obstacles on the way out. The laser SLAM speed of this system is 25~31FPS, which can realize the short-distance obstacle avoidance and navigation function both in indoor and outdoor envionments. The improved yolov5 helps to identify 86 types of objects, the recognition rate for pedestrian crosswalks and for vehicles are 84.6% and 71.8%, respectively; the overall recognition rate for 86 types of objects is 61.2%, and the obstacle recognition rate of the intelligent guide system is 25-26FPS.

Autonomous target recognition (ATR) plays a crucial role in maintaining situational awareness during environmental monitoring. Unmanned aerial vehicles (UAVs) equipped with autonomous target recognition technology can gather and analyze real-time information about targets, including their locations, sizes, and types. However, UAV-captured images in complex real-world environments often display significant variations in perspective and scale due to changes in UAV altitude and distance. Existing methods for autonomous target recognition on UAVs struggle to capture targets from large field-of-view and multi-scale images, resulting in low recognition accuracy and high false-positive rates. This paper introduces two novel Slimmable neural network models, namely SE-YOLOv5s and ST-YOLOv5s, which are based on the YOLOv5s architecture. These models incorporate the Squeeze and Excitation and Swin-Transformer mechanisms to enhance the ability to extract features from large field-of-view and multi-scale images. To evaluate their performance, experiments were conducted on the Visdrone19 aerial dataset. Compared to the state-of-the-art YOLOv5s algorithm, the utilization of SE-YOLOv5s and ST-YOLOv5s for autonomous target recognition on low-altitude drones resulted in improvements in both accuracy and false-positive rates. These proposed methods combine Slimmable neural network design with feature enhancement mechanisms, addressing the challenges posed by complex real-world environments in UAV missions. The advancements in autonomous target recognition on low-altitude drones will significantly contribute to enhancing situational awareness in future environmental monitoring.

In the dynamic landscape of drug discovery, Computer-rAided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADD's historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADD's predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. The paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Despite recent public health and hygiene advancements, enteric fever, commonly referred to as typhoid fever, remains a common disease in developing nations. The common mode of infection is contaminated water or food. Additionally, person-to-person transmission through poor hygiene and sewage contamination of water supplies has been blamed for most outbreaks. The exact burden of typhoid has yet to be discovered because of the lack of surveillance systems in many developing nations. This makes it difficult to estimate the number of cases. Recent studies have shown that the actual number of cases of typhoid has been estimated to be around 21.6 million annually. The mortality studies suggest that the incidence of typhoid is highest in children under five years old. Typhoid fever and paratyphoid fever have been demonstrated to be life-threatening illnesses. Salmonella serotype Typhi (S. Typhi) is the causative organism of typhoid fever whereas Salmonella serotype Paratyphi is the causative organism for paratyphoid fever. The S. Typhi bacterium is known to be resistant to various drugs. There is a dire need to develop new drugs to treat and overcome the current drug-resistant S. Typhi. The identification of new targets marks the beginning of the process of developing new anti-S. Typhi drugs. The S. Typhic genome sequencing and recent cutting-edge molecular biology tools have led to the discovery of numerous new inhibitors and targets. The goal of this review is to identify the most critical targets in the S. Typhi that are targeted by drugs. It also highlights the various promising vaccines on the market or are still in preclinical studies. A detailed understanding of the targets could help researchers develop safer and more efficient antibacterial agents against S. Typhi.Additionally, the advancement of molecular techniques and the knowledge of the Salmonella pathogenesis pathways have opened better avenues to develop effective antibiotics and vaccines against this pathogen. This review also sought to identify and summarize critical structures of this pathogen that play significant roles in the maturation, development, and pathogenesis of S. Typhi. The endpoint of this work is to provide valuable information on potential therapeutic targets of S. Typhi for drug and vaccine developers.

In this paper, based on the symmetrical reliability theory, a research method for the safety factor of overall overturning stability of continuous beam bridge under carbon fiber reinforced polymers (CFRP) during cantilever and horizontal rotation construction is proposed. Under the premise that the given target reliability index is known, the method solves the overall overturning stability safety factor of continuous beam bridge under carbon fiber reinforced polymers during cantilever and horizontal rotation construction by using the symmetrical reliability analysis theory and considering the randomness of structural parameters. This method is used to calculate the safety factor and the design reinforcement aera of CRFP of overturning stability and the design reinforcement aera of CRFP of a continuous beam bridge, and the parameters are analyzed. The calculation results show that the randomness of parameters will have a great impact on the overall safety factor and the CRFP aera of overturning stability of cantilever and horizontal rotation construction of continuous beam bridge, and ignoring the influence of randomness of parameters will lead to overestimation of the overall safety factor and underestimation of the CRFP aera of overturning stability of cantilever and horizontal rotation construction of continuous beam bridge.

Side-scan sonar (SSS) detection is a key method in applications such as underwater environmental security and subsea resource development. The use of acoustic images for seabed target detection has gradually become a mainstream underwater detection method. However, many existing detection approaches primarily concentrate on tracking the evolution path of optical image object detection tasks, resulting in complex structures and limited versatility. To tackle this issue, we introduce a pioneering Dual-Domain Multi-Frequency Network (D2MFNet) meticulously crafted to harness the distinct characteristics of SSS image detection. In D2MFNet, aiming at the underwater detection requirements of small scenes, we introduce a novel method for optimize and improve the detection sensitivity of different frequency ranges and propose a Multi-Frequency Combined Attention Mechanism (MFCAM). This mechanism amplifies the relevance of dual-domain features across different channels and space. Moreover, recognizing that SSS images can provide richer insights after frequency domain conversion, we introduce a Dual-Domain Feature Pyramid Network (D2FPN). By incorporating frequency domain information representation, D2FPN significantly augments the depth and breadth of feature information in underwater small datasets. Our methods are seamlessly designed for integration into existing networks, offering plug-and-play functionality with substantial performance enhancements. We have conducted extensive experiments to validate the efficacy of our proposed techniques, and the results showcase their state-of-the-art performance. MFCAM improves the mAP by 16.9% in the KLSG dataset and 15.5% in the SCTD dataset. The mAP of D2FPN was improved by 8.4% in the KLSG dataset and by 9.8% in the SCTD dataset. We will make our code and models publicly available at https://dagshub.com/estrellaww00/D2MFNet.

Sterol regulatory element-binding proteins (SREBPs) are transcription factors that regulate genes involved in the biogenesis of cholesterol, fatty acids, and triglycerides. SREBPs are involved in the pathogenesis of non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), fibrosis, and hepatocellular carcinoma (HCC). We review the mechanisms by which SREBPs regulate lipogenesis, the alterations in these processes associated with liver diseases, and therapeutic strategies to target these pathways.

Drug discovery utilizes high-throughput screening (HTS) methods applying target and cell-based assays. This review discusses the challenges and benefits associated with these assays in HTS. Discussed the strategies that can be applied for the development of screening assays through primary and secondary screens for target identification. Further discussion on identify-ing the most efficacious drugs following these approaches in cancer. Even though various drugs have been identified to treat cancer, there is high demand of more relevant phenotypic assays to produce desired diseased phenotype to only highlighting the specific targets instead of off-targets. Hopefully, the new developing strategies could provide innovative drugs to treat cancer patients to reduce mortality rate.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence and structural knowledge of drugs, targets, and pockets using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model’s performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, the supremacy of S2DTA became strikingly apparent. It showcased a noteworthy 25.2% reduction in Mean Absolute Error (MAE) and an impressive 20.1% decrease in Root Mean Square Error (RMSE). Furthermore, S2DTA exhibited substantial advancements in other pivotal metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2. These metrics experienced remarkable increments of at least 19.6%, 17.5%, 8.1%, and a remarkable 49.4%, respectively. Finally, we conducted interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism, fully proving that S2DTA is a valuable and accurate tool for predicting DTA. For further exploration, the source data and code repository can be accessed at https://github.com/dldxzx/S2DTA.

The integration of bioinformatics in drug discovery has revolutionized the field of pharmaceutical research. The use of computational tools and techniques has enabled researchers to analyze vast amounts of data, identify potential drug targets, and design new drugs with greater precision and efficiency. The integration of bioinformatics has also facilitated the development of personalized medicine, where drugs can be tailored to individual patients based on their genetic makeup. However, there are still challenges that need to be addressed, such as the need for more accurate predictive models and the ethical considerations surrounding the use of patient data. Overall, the integration of bioinformatics in drug discovery holds great promise for improving human health and advancing our understanding of disease mechanisms. In this mini-review, we discuss how Bioinformatics plays a crucial role in each step of drug discovery by providing tools and techniques to analyze large amounts of data generated from various sources, as well as the challenges and the opportunities offered by bioinformatics.

Advanced cutaneous melanoma is considered the most aggressive type of skin cancer with variable rates of treatment response. Nowadays, there are available some classes of immunotherapy and target therapy for its treatment. Immunotherapy can inhibit tumor growth and its recurrence by triggering host's immune system, while targeted therapy acts inhibiting specific molecules or signaling pathways. However, melanoma response to these treatments are highly heterogenous, and frequently become resistant. Epigenome (DNA/histone modification) contribute to cancer initiation and progression. Epigenetic alterations are divided into four levels of gene expression regulation: DNA methylation, histone modification, chromatin remodeling and non-coding RNAs regulation. The deregulation of lysine methyltransferase enzymes is associated with tumor initiation, invasion, development of metastases, changes in immune microenvironment and drug resistance. The study of lysine histone methyltransferase (KMT) inhibitors is important to cancer epigenetic mechanisms understanding and to biological processes. In addition to immunotherapy and target therapy, the research and development of KMT inhibitors is ongoing. Many studies are exploring the therapeutic implications and possible side effects of these compounds, besides their adjuvant potential to the approved current therapies. Importantly, as with any drug development, safety, efficacy, and specificity are crucial considerations when developing KMT inhibitors for clinical applications.

Zinc, being a part of many proteins and enzymes, plays an important role in the vital processes of the body. Racemic α-lipoic acid (LA) has a wide range of biological activities: antioxidant, antidiabetic, anticancer, and it is also used in diseases such as Parkinson's and Alzheimer's. Nowadays, the synthesis and structural studies of new zinc complexes with biologically active compounds are of great importance for drug discovery. This work shows the studies carried out on the synthesis, structure, identification, also biological activity using in silico methods of a complex compound based on LA and zinc. A complex compound of zinc with LA (ZnLA) was synthesized in the presence of LA and zinc acetate in ethanol. The identification of ZnLA by (FT-IR, XRD and TGA-DTA), also DFT calculations at the B3LYP/def2-TZVPP level of theory was performed. The molecular docking investigation involved docking ZnLA with two different protein receptors such us Glycogen phosphorylase (2GPA) and Kv potassium channel beta subunit (1ZSX) using CB-Dock 2 server and Auto-Dock Vina program to determine the mechanism of action and biological activity. This study reveals potential ligand-protein interactions at the atomic level and suggests that the treatment of diabetes (non-insulin dependent) with ZnLA.

Biofilm-associated proteins (BAPs) are important in the development and resilience of biofilms, which are intricate populations of microbes protected by extracellular matrix. All members of the BAP family are known to possess common characteristics such as high molecular weight and a signal sequence for extracellular secretion. It has been shown that BAPs are directly linked to the pathogenicity of many bacteria. This mini review highlights several potential strategies to target BAPs for drug development.

Underwater robotics equipped with visual detection systems have the function of detecting un-derwater artifacts, which is of great significance to deep-sea archaeology. Underwater artifacts are located in complex environments with poor imaging conditions, and the targets have chal-lenging problems such as breakage, stacking, and sediment burial, which causes the marine cul-tural target detection failures. To solve these problems, this paper proposes an underwater cul-tural target detection algorithm based on the deformable deep aggregation network model for underwater robotics exploration. To fully extract the target feature information of underwater targets in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. Besides, the BAM attention module is designed for fea-ture optimization, which enhances the potential feature information of the target while weak-ening the background interference information. Finally, the target prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validat-ed and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, the present al-gorithm has been practically deployed on the underwater robotics. In the deep-sea tests, the ar-tifact detection frame rate reaches up to 19 fps, which satisfies the real-time target detection。

: Early and accurate detection of infectious diseases is a key step for surveillance, epidemiology, and control, notably timely disease diagnosis, patient management and follow-up. In this study, we aimed to develop hand-held ultra-fast duplex PCR assays coupled to amplicon detection by lateral flow (LF) immunoassay to deliver a rapid, and simple molecular diagnostic test for con-comitant detection and identification of the main Leishmania parasites encountered in Tunisia and the Old World. We selected two DNA targets to amplify L. major/L. tropica and L. infantum/L. tropica group of species DNAs, respectively. We optimized the experimental conditions of a du-plex ultra-fast PCR. The amplification is performed by a portable Palm convection PCR machine within 18 min and the products are detected by a LF cassette within 10 minutes. The test allows the identification of the infecting species according to the position and number of test lines re-vealed. Tested on a selection of DNAs of representative Leishmania strains of the three studied species (N=37), the ultra-fast duplex PCR-LF showed consistent, stable, and reproducible results. The analytical limit of detection of the test was 0.4pg for L. major, 4pg for L. infantum and 40pg for L. tropica.

Cotton leaf curl Kokhran virus (CLCuKoV) (genus, Begomovirus; family, Geminiviridae) is highly infectious, widespread and the most dangerous pathogen of cotton (Gossypium hirsutum L.) that is responsible to a serious disorder, cotton leaf curl disease (CLCuD). Begomoviruses are spread very efficiently by the whitefly Bemisia tabaci cryptic species, causing economic losses to cotton crop, all over the world. The ‘Lucknow’ strain of CLCuKoV has emerged as a divergent isolate that could cause CLCuD. The monopartite ssDNA genome of CLCuKoV-Lu (2.7 Kb) contains six open reading frames (ORFs) that was shown to encode four major proteins. RNA interference (RNAi)-based antiviral innate immunity is a sequence-specific biological phenomenon and a powerful tool to control plant viruses. The present study aims to determine cotton locus-derived microRNAs (ghr-miRNAs) that are identified for targeting the CLCuKoV-Lu ss-DNA-encoded mRNAs using a predictive approach that involves four computational algorithms, miRanda, RNA22, psRNATarget and RNAhybrid. Mature ghr-miRNA sequences (n=80) from allotetraploid upland cotton (2n = 4x = 52) were selected from the miRBase and were tested for alignment with the CLCuKoV-Lu genome. Among the 80 cotton locus-derived ghr-miRNAs evaluated, only one consensus cotton locus-derived ghr-miRNA (ghr-miR2950) was concluded to have effective ghr-miRNA target site at common nucleotide position 82 in the CLCuKoV-Lu genome respectively, using a stringent criterion, identified by all the algorithms used. The miRNA targeting is reliant on base pairing of miRNA-mRNA target pairings. Conservation of the hybridization binding site of the predicted ghr-miR2950 was validated using multiple sequence alignment within all the strains of CLCuKoV. We constructed a regulatory interaction network of miRNA–mRNA to identify novel targets. The efficacy of the predicted miRNAs against CLCuKoV-Lu was evaluated by RNAi-mediated targeted mRNA cleavage. The current investigated miRNA targets provide evidence for the development of CLCuD-resistant cotton plants.

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multi-scene and multi-size, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.

Background: The target volume for post-mastectomy radiation therapy (PMRT) in breast cancer patients with reconstruction has been a subject of debate. This study aimed to assess the radiation dose distribution and treatment outcomes associated with different chest wall (CW) delineation according to ESTRO ACROP guideline for patients underwent implant-based reconstruction. Methods: We conducted a retrospective review of breast cancer patients who underwent mastectomy followed by two-stage implant-based breast reconstruction and adjuvant radiation therapy (RT) between 2007 and 2022. The expanders/implants were positioned retropectorally. The chest wall target volumes were categorized into two groups: the prepectoral group, which excluded the deep lymphatic plexus, and the whole expander group. Results: The study included 26 patients, 15 in prepectoral group and 11 in whole expander group. No significant differences were observed in normal organs exposure between the two groups. There was a trend toward a lower ipsilateral lung mean dose in the prepectoral group (10.2 vs. 11.1 Gy, p=0.06). Both groups exhibited limited instances of reconstruction failure and local recurrence. Conclusions: For patients undergoing two-stage expander-implant retropectoral breast reconstruction and PMRT, our data have provided comparable outcomes and normal organs exposure for those omitting the deep lymphatic plexus.

U-Net, based on a deep convolutional neural network (CNN), has been clinically used to au-to-segment normal organs and potentially target volumes. However, CNNs with local geometric dependencies may limit the accuracy of segmentation. Additionally, the performance of CNNs can vary depending on the selection of network hyper-parameters, which was mitigated by the proposition of nnU-Net. We chose a vision transformer architecture called VT U-Net, which features a self-attention excluding the convolution layer, to overcome the limitations of CNNs by utilizing global geometric information of images. The VT U-Net v.2 became more powerful thanks to the adaptive hyper-parameter optimizer embedded in nnU-Net. However, despite leveraging the benefits of nnU-Net, VT U-Net v.2 still had additional network hyper-parameters that needed to be optimally chosen. Accordingly, among various hyper-parameters, this study attempted to find the optimal combination of the patch size and the embedded dimension regarding the transformer. From the 4-fold cross-validation, the modified VT U-Net v.2 showed the highest average performance for planning target volume (PTV) segmentation among the investigated networks. Though nnU-Net was based on convolution layers, the adaptive hyper-parameter optimizers turned out to enhance the performance. It was also confirmed that network hyper-parameters affected the segmentation accuracy of vision transformers.

Sea cucumber detection represents a significate step in underwater environmental perception, which is an indispensable part of the intelligent subsea fishing system. However, various complex factors such as water turbidity declines the clarity of underwater images, presenting a challenge to vision-based underwater target detection. Therefore, accurate, real-time and lightweight detection models are required. First of all, the development of subsea target detection is summarized in this presented work. Besides, since target detection methods based on deep learning such as YOLOv5 and DETR, which are respectively examples of one-stage and anchor-free deep leaning methods, have been increasingly applied in underwater detection scenarios. Based on the analysis of state-of-the-art underwater sea cucumber detection approaches and aiming to provide a reference for practical subsea identification, the sea cucumber detection based on the YOLOv5 and DETR are investigated and compared in detail. For each approach, the detection experiment is carried out on the derived dataset which contains a wide variety of sea cucumber sample images. The compared experiments demonstrate that the overall outperformance of YOLOv5 in terms of low computing consumption and high precision, particularly in detection of small and dense features. Nevertheless, the DETR exhibits rapid development and holds promising prospects in underwater object detection applications, owing to its relatively simple architecture and ingenious attention mechanism.

Bistatic scattering coefficient have important application of ocean active microwave remote sensing. In this paper, a general expression of the composite scattering coefficient for multiple dielectric targets of one-dimensional fractal sea surface is derived by MOM method. Secondly, the bistatic scattering coefficient is computed, there are two class missile targets and one class ship target of one-dimensional fractal sea surface, and the influence of the water dielectric constant, fractal dimension, wind speed, the target dielectric constant and spatial position is discussed, and the corresponding conclusions are given. The proposed algorithm is able to solve some composite scattering problems, such as “dielectric or conductive object + rough surface”, “dielectric or conductive floating object +rough ”and it has certain generalization and reference.

The microbiome is pivotal in maintaining health and influencing disease by modulating essential inflammatory and immune responses. Hepatocellular carcinoma (HCC), ranking as the third most common cause of cancer-related fatalities globally, is influenced by the gut microbiome through bidirectional interactions between the gut and liver, as evidenced in both mouse models and human studies. Consequently, biomarkers based on gut microbiota represent promising non-invasive tools for the early detection of HCC. There is a growing body of evidence suggesting that the composition of the gut microbiota may play a role in the efficacy of immunotherapy in different types of cancer, thus it could be used as a predictive biomarker. In this review, we will dissect the gut microbiome's role as a potential predictive and diagnostic marker in HCC and evaluate the latest progress in leveraging the gut microbiome as a novel therapeutic avenue for HCC patients, with a special emphasis on immunotherapy.

The ubiquitin proteasome system (UPS) utilizes an orchestrated enzymatic cascade of E1, E2, and E3 ligases to add single or multiple ubiquitin-like molecules as post-translational modification (PTM) to proteins. Ubiquitination can alter protein functions and/ or marks ubiquitinated proteins for proteasomal degradation but deubiquitinases (DUBs) can reverse protein ubiquitination. While the importance of DUBs as regulatory factors in the UPS is undisputed, many questions remain on DUB selectivity for protein targeting, their mechanism of action, and the impact of DUBs on the regulation of diverse biological processes. Furthermore, little is known about the expression and role of DUBs in tumors of the human central nervous system (CNS). In this comprehensive review, we have used publicly available transcriptional datasets to determine the gene expression profiles of 99 deubiquitinases (DUBs) from five major DUB families in seven primary pediatric and adult CNS tumor entities. Our analysis identified selected DUBs as potential new functional players and biomarkers with prognostic value in specific subtypes of primary CNS tumors. Collectively, our analysis highlights an emerging role for DUBs in regulating CNS tumor cell biology and offers a rationale for future therapeutic targeting of DUBs in CNS tumors.

Parasitic diseases are among the most serious public health problems in developing countries, causing high mortality and economic burdens. The Trypanosomatidae family includes Leishmania spp. and Trypanosoma cruzi, which cause leishmaniasis and Chagas disease, respectively. Both these parasites are among the major agents of neglected tropical diseases (NTDs). Leishmaniasis is currently the second most widespread vector-borne parasitic disease after malaria. The World Health Organization (WHO) records approximately 0.7-1 million newly diagnosed leishmaniasis cases each year, resulting in approximately 20,000-30,000 deaths. Also, 25 million people worldwide are at risk of Chagas disease and an estimated 6 million people are infected with Trypanosoma cruzi. Despite the similarity of the parasites, the evolution of the disease and the clinical indexes are explicitly different in African, Asian, and American forms of infection. Clinical therapies are associated with drug resistance and side effects. As a result, the discovery of novel therapeutic agents has emerged as a top priority and a promising alternative. Peptide-based drugs are an attractive class of therapeutic agents. They are recognized for being highly selective and effective, as well as safe and well tolerated. Moreover, they are now being explored in the development of novel therapeutics for a variety of health disorders, including oncology, endocrinology, metabolic, cardiovascular, and bone diseases. In addition, they are being explored in tropical diseases such as leishmaniasis and Chagas disease. We will discuss current knowledge regarding the parasite life cycle, signs and symptoms, diagnosis, and treatment options for leishmaniasis and Chagas disease.

Some traditional robots are based on offline programming reciprocal motion, and with the continuous upgrading of vision technology, more and more tasks are replaced by machine vision. For the current problem of insufficient accuracy of robot target localization based on binocular vision, and an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance For the current problem of insufficient accuracy of robot target localization based on binocular vision, an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance of multi Firstly, the RANSAC algorithm is improved based on the SURF algorithm; then the parallax gradient method is applied to iterate the matched point pairs several times to further optimize the data; then the 3D reconstruction is completed by the program technique; finally the obtained data is finally the obtained data is input into the robot arm and the camera internal and external parameters are obtained by the calibration method so that the robot can accurately locate and The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi- The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi-vision system.

Owing to the upsurge in the number of endangered species and understanding animal patterns in general as well as population demographics; the monitoring of wildlife species is an essential for the conservation and safety of animals. In order to organize and manage the reserves, the nature bequeaths to us, we need to have hands-on information of their population and food trends, conditions where they survive and other species in the ecosystem. The paper presents a vision-based approach to monitor wildlife using an aerial platform. A quad-rotor based aerial platform is used for the very first time for this purpose. Field imaging is done using a digital cellphone camera mounted on the platform to acquire video of horses in the field. Two techniques, Lucas-Kanade and Horn-Schunck methods are applied on the acquired set of images and the results are compared. Noise due to fluctuations and light conditions are minimized using Gaussian and HSV filters. Experiments show results with an absolute mean difference of 2.84 pixels and 8.50 pixels for changes in X and Y directions respectively for the two approaches.

Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and Marine resources survey. However, the visibility of the underwater environment and the accuracy of target detection can be affected by complex underwater light changes and unpredictable background noise. To address these issues, we propose an improved underwater target detection algorithm based on YOLOv8n. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region's features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIOU loss function with the Wise-IOU loss function. To conduct our experiments, we create our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on the underwater target detection were 88.6% and 51.8%, respectively, and the improved algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOV8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The improved YOLOV8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The floating-point computation volume of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method.

The effectiveness of a recently proposed methodology in the identification of damage in planar, multi-storey, Reinforced Concrete (RC) moment-frames, which develop a plastic-yield mechanism on their beams, is showcased here via the examining of a group of such existing multi-storey frames with three or more unequal spans. According to the methodology, the diagram of the instantaneous Eigen-Frequencies of the frame in the nonlinear regime is drawn as a function of the inelastic seismic roof displacement by performing a sequence of pushover and instantaneous modal analyses with gradually increasing target displacement. Using this key-diagram, the locations of severe seismic damage in an existing moment-frame can be evaluated if the instantaneous fundamental eigen-frequency of the damaged frame, at an analysis step within the nonlinear area, is known in advance by “the monitoring and the identification of frequencies” using a local network of uniaxial accelerometers. This is a hybrid technique because both procedures, the instrumental Monitoring of the structure and the Pushover analysis on the frame (M and P technique), are combined. Moreover, the damage image of the planar multi-storey moment-frame is illustrated, and the lateral stiffness matrix of the damaged frame is calculated with high accuracy.

Patients who have undergone surgery in early life may be at elevated risk for neuropathic pain in later life. The risk factors for this susceptibility are not fully understood. Here, we used a mouse chronic pain model to test the hypothesis that early exposure to general anesthetic (GA) causes cellular and molecular alterations in dorsal spinal cord (DSC) and dorsal root ganglion (DRG) that produces a predisposition to neuropathic pain via an upregulation of the mammalian target of the rapamycin (mTOR) signaling pathway. Mice were exposed to isoflurane at postnatal day 7 (P7) and underwent spared nerve injury at P28 which causes chronic pain. Selected groups were treated with rapamycin, an mTOR inhibitor, for eight weeks. Behavioral tests show early isoflurane exposure enhances susceptibility to chronic pain, and rapamycin treatment improves outcomes. Immunohistochemistry, Western blotting, and q-PCR indicates that isoflurane upregulates mTOR expression and neural activity in DSC and DRG. Accompanying upregulation of mTOR and rapamycin-reversible changes in chronic pain-associated markers, including N-cadherin, cAMP response element-binding protein (CREB), purinergic P2Y12 receptor, glial fibrillary acidic protein (GFAP) in DSC; and connexin 43, phospho-extracellular signal-regulated kinase (p-ERK), GFAP, Iba1 in DRG, were observed. We conclude that early GA exposure alters the development of pain circuits such that mice are subsequently more vulnerable to chronic neuropathic pain states.

With technological transformations such as big data, blockchain, artificial intelligence, and cloud computing, digital techniques are penetrating the field of finance. Digital finance is a resource-saving and environmentally friendly innovative financial service. It shows great green attributes and can drive the flow of financial resources towards environmentally friendly enterprises, thereby promoting green and low-carbon development. This paper investigates the effects of digital finance on green and low-carbon cyclic development and the mechanism. First, the level of green and low carbon cyclic development in China is estimated from multiple aspects based on the panel data of 31 provinces by using the spatio-temporal range entropy weight method. Then, the working mechanism of digital finance on green and low carbon cyclic development is revealed by performing empirical tests based on panel regression models, mediating effect models, and threshold models. Finally, the effects of digital finance and conventional variables on green and low-carbon cyclic development are investigated by using a random forest model and a CatBoost model in based on the machine learning. The results show that digital finance has significant positive effects on green and low-carbon cyclic development, and technological innovation plays a key role in the effects of digital finance on green and low-carbon cyclic development; meanwhile, the driving effect of digital finance on green and low carbon cyclic development shows nonlinear features with increasing “marginal effect”; besides, both digital finance and conventional factors have significant impacts on green and low carbon cyclic development. This study provides an empirical basis and path reference for digital finance to achieve “carbon peak, carbon neutralization” in China.

The development of biosensors for target detection plays a crucial role in advancing various fields of bioscience. This work presents the development of a genosensor that exploits the colorimetric phenol-sulfuric acid sugar reaction for the detection of DNA, and RNA as specific targets, and DNA intercalator molecules. The biosensor combines simplicity and reliability to create a novel bio-assay for accurate and rapid analysis. A 96-well microplate based on polystyrene polymer was used as the platform for an unmodified capture DNA immobilization via a silanization process and with (3-Aminopropyl) triethoxysilane (APTES). After that, a hybridization step was carried out to catch the target molecule, followed by adding phenol and sulfuric acid to quantify the amount of DNA or RNA sugar backbone. This reaction generates a yellow-orange color on the wells measured at 490 nm that was proportional to the target concentration. Under the optimum conditions, a calibration curve was obtained for each target. The developed biosensor demonstrated high sensitivity, good selectivity, and linear response over a wide concentration range for DNA and RNA targets. Additionally, the biosensor was successfully employed for the detection of DNA intercalator agents that inhibit the hybridization of DNA complementary to the immobilized capture DNA. The developed biosensor offers a potential tool for sensitive and selective detection in various applications, including virus diagnosis, genetic analysis, pathogenic bacteria monitoring, and drug discovery.

Titanium oxynitride thin films are deposited by DC reactive magnetron sputtering. A pure titanium target is sputtered in a reactive atmosphere composed of argon, oxygen and nitrogen gases. The oxygen mass flow rate as well as that of the nitrogen gas are both pulsed during the deposition time using an independent and rectangular signal for each reactive gas. A constant pulsing period T = 45 s is applied for both reactive gases and a delay time  of 34 s between N2 and O2 injection times is set for all depositions. Oxygen and nitrogen duty cycles are systematically and independently changed from 0 to 100% of their pulsing period. From real time measurements of the Ti target potential and total sputtering pressure, it is shown that the reactive process alternates between oxidized, nitrided and elemental sputtering modes as a function of the oxygen and nitrogen injection times. The full poisoning of the Ti target surface by oxygen and/or nitrogen can be avoided for some given ranges of O2 and N2 duty cycles. Deposition rates of titanium oxynitride films are substantially enhanced and can be adjusted between that of pure Ti and TiN films with a gradual transition of their optical transmittance in the visible range. These results support that titanium oxynitride compounds exhibiting absorbent to transparent behaviors can be precisely sputter-deposited by means of a two reactive gases pulsing process.

This paper proposes a multimodal fusion 3D target detection algorithm based on the attention mechanism to improve the performance of 3D target detection. The algorithm utilizes point cloud data and information from camera. For image feature extraction, the ResNet50+FPN architecture extracts features at four levels. Point cloud feature extraction employs the voxel method and FCN to extract point and voxel features. The fusion of image and point cloud features is achieved through regional point fusion and regional voxel fusion methods. After information fusion, the Coordinate attention mechanism and SimAM attention mechanism extract fusion features at a deep level. The algorithm's performance is evaluated using the DAIR-V2X dataset. The results show that compared to the Part-A2 algorithm, the proposed algorithm improves the mAP value by 7.9% in BEV view and 7.8% in 3D view at IOU=0.5 (cars) and IOU=0.25 (pedestrians and cyclist). At IOU=0.7 (cars) and IOU=0.5 (pedestrians and cyclist), the mAP value of the SECOND algorithm is improved by 5.4% in the BEV view and 4.3% in the 3D view, compared to other comparison algorithms.

Remote sensing imagery is of great significance for policy decisions, especially for disaster assessment and disaster relief. To ensure the privacy and inviolability of personal buildings, the information containing these buildings must be anonymized during the remote sensing mapping process. Traditional processing methods for these targets in remote sensing mapping are mainly based on manual retrieval and image editing tools, which are inefficient. Deep learning provides a new direction for target hiding. Although the image inpainting method based on deep learning is faster than the manual method, the cost of training calculation is a disadvantage. And the element-wise product operation used in the model increases the risk of vanished or exploded gradients. We propose a Residual Attention Target Hiding (RATH) model for remote sensing target hiding based on deep learning. RATH uses residual attention modules to replace gated convolutions, reducing parameters and mitigating gradient issues. The residual attention module preserves gated convolution performance but provides an adjustable kernel size. RATH retains gated convolutions for dynamic feature selection and balances model depth and width. Furthermore, this paper modifies the contextual attention layer by adjusting the fusion process to enlarge the fusion patch size. Finally, we extend the edge-guided function to preserve the original target information and confound viewers. Ablation studies on an open dataset prove RATH’s efficiency for image inpainting and target hiding. RATH achieves state-of-the-art results with lower complexity. And it has the highest similarity for edge-guided target hiding. RATH enables robust, efficient target hiding for privacy protection in remote sensing imagery while balancing performance and complexity. Experiments show RATH's superiority over existing methods in hiding arbitrary-shaped targets.

: The radar multi target tracking (MTT) technique requires prior knowledge of a number of parameters about the sensor, the target and backgrounds. The Integrated Track Splitting (ITS) is a fully automatic track-while-scan (TWS) target tracking algorithm capable of extracting and tracking a target in a dense clutter environment using quality false track discrimination (FTD) methodology. The computational complexity in ITS algorithm is limited, compared to other algorithms they use statistical methods to discriminate between false and true tracks, such as multiple hypothesis tracking (MHT), mainly due to the FTD performed. The paper provides an analysis of tracking parameters that allows determining the limit of the possibility of successful target tracking. Extensive experiments have confirmed that the recursive determination of the probability of the existence of a track during tracking can confirm a true track and reject a false track. The clutter density, number of random occurred targets, targets load during the maneuver and the target detection probability were varied. The results of experiments, carried out via Monte Carlo simulations, shown over representative confirmed true tracks (CTT) diagrams, root mean square error position and normalized tracking efficiency parametric diagrams allow the user to select optimal multi-target tracking parameters for different scenarios and clutter densities.

This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate target tracking in the presence of unknown background wind and target motion, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on Lyapunov guidance vector field is proposed. The convergence analysis show that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller met the requirement of airspeed constraint. In addition, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit which is defined by Lyapunov guidance vector field to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results of indicate that, the temporal-phase-based speed controller can achieve fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits fast response speed and high estimation accuracy.

This paper discusses the use of networks of Inertial Measurement Units (IMU) for the reconstruction of trajectories from sensor data. Logistics is a natural application domain, to verify the quality of the handling of goods. This is a mass application and the economics of logistics impose that IMUs to be used must be low cost and use basic computational devices. The approach in the paper converts a strategy from the literature, used in the multi-target following problem, to reaching a consensus in a network of IMUs. The paper presents results on how to achieve the consensus in trajectory reconstruction, along with covariance intersection data fusion of the information obtained by all nodes in the network.

The aim of this study is to analyse problems related to thermal mapping obtained from thermal data acquired from Unmanned Aerial Systems (UAS) equipped with thermal cameras. We focused on an accurate analysis of uncertainties introduced by the PIX4D Mapper software used to obtain the surface temperature maps of thermal images acquired by the UAS. To achieve this aim, we used artificial thermal reference during the surveys, as well as natural hot targets, i.e. thermal anomalies in the Pisciarelli hydrothermal system in Campi Flegrei caldera (CFc). Artificial thermal targets, expressly created and designed for this goal, are a prototype here called “developed thermal target” (DTT) made by the drone Laboratory at Istituto Nazionale di Geofisica e Vulcanologia - Osservatorio Vesuviano (INGV-OV). We show the results obtained by three surveys during which thermal targets were positioned on land at different flight heights of the UAS. Different heights were also necessary to test spatial resolution of the DTT with the used thermal camera as well as possible temperature differences between the raw images acquired by UAS with the thermal mapping obtained from the PIX4D Mapper software. In this work we have estimated the uncertainty that may be introduced by the mosaic procedure and furthermore we find an attenuation of the measured temperatures introduced by the different distances between the thermal anomaly and sensor. These results appear to be of great importance for the subsequent calibration phase of the thermal maps especially in cases where these methodologies are applied for monitoring purposes of volcanic/geothermal areas.

High salinity in soil affects the normal growth and development of crop. MicroRNA171 (miR171) plays a momentous role in plant resistance to adversity stress. The functions of Sly-miR171d and Sly-miR171e on growth of tomato and fruit nutritional quality under salt stress were studied. The results showed that the seed germination and seedling growth of tomato that silencing of Sly-miR171d/e by short tandem target mimic (STTM) were better than those of wild-type (WT). Silencing of Sly-miR171d/e seeds maintained a high germination rate, while WT tomato seeds barely germinated under 50 mM NaCl stress. Under 100 mM NaCl stress, silencing of Sly-miR171d/e increased the expression of their target gene SlGRAS24 with increasing duration of salt stress, while they enhanced the antioxidant activity, reduced reactive oxygen species (ROS) accumulation, and enhanced the salt tolerance of tomato plant by regulating gibberellin (GA) level. Moreover, the preharvest and postharvest fruit nutritional quality under salt stress was also studied, and the results showed that silencing of Sly-miR171d/e increased soluble solids, lycopene, total carotenoids, Vitamin C, organic acids and phenolic substances in tomato fruit. The present study demonstrated that silencing of Sly-miR171d/e enhanced plant salt tolerance through the GA pathway, and improved fruit nutritional quality under salt stress.

Despite being an abundant marine organism in Indonesia, black sea cucumber is still underutilised due to its slightly bitter taste. Previous studies have hinted at the potential of black sea cucumber as an anti-cancer agent. However, specific identification of bioactive compounds that can interact with cancer proteins is still lacking. In the same place, cancer ranks third as Indonesia's leading cause of death. Therefore, this study aims to identify potential anti-cancer compounds from black sea cucumbers using a comprehensive in silico drug discovery approach. This research uses machine learning, molecular docking, and ADMET analysis to identify bioactive compounds that specifically interact with cancer proteins. A combination of the Cascade Deep Forest algorithm and ECFP-AAIndex1 feature combination proved to be the most effective in predicting these interactions. Through molecular docking validation, four bioactive compounds with strong binding affinity were identified: Afimoxifene, Danazol, Taxifolin, and Terfenadine. ADMET analysis highlighted Taxifolin as the most promising candidate, as it passed most ADMET parameters. Further wet laboratory studies are required to confirm the effects and potential of these compounds as anti-cancer agents. This study builds a foundation for future investigations into alternative cancer treatments using abundant natural resources.

Trypanosomatids are mainly responsible for leishmaniasis, sleeping sickness, and Chagas disease, which are the most challenging among the neglected tropical diseases due to the problem of drug resistance. Although problems of target deconvolution and polypharmacology are encountered, a target-based approach is a rational method for screening drug candidates targeting a biomolecule that causes diseases. The present study aims to summarize the latest information re-garding potential inhibitors of squalene synthase and farnesyl phosphate synthase with anti-Trypanosomatidae activity. The information was obtained by referencing textbooks and major scientific databases from their inception until April 2023. Based on in vitro experiments, more than seventy compounds were reported to inhibit squalene synthase and farnesyl diphosphate synthase. Among these compounds, more than 30 were found to be active in vitro against Trypanosomatidae, inferring that these compounds can be prospected as scaffolds for the development of new drugs against trypanosomatid-related infections. Over-all, natural and synthetic products can inhibit enzymes that are crucial for the survival and virulence of trypanosomatids. Moreover, in vitro experiments have confirmed the activity of more than half of these inhibitors using cell-based as-says. Nevertheless, additional studies on the cytotoxicity, pharmacokinetics, and lead optimization of potent anti-Trypanosomatid compounds should be investi-gated.

LOXL2, a copper-dependent amine oxidase, has emerged as a promising therapeutic target in hepatocellular carcinoma (HCC). Increased LOXL2 expression in HCC has been linked with aggressive phenotype and represents a poor prognostic factor. Here, we focus on mechanisms by which LOXL2 orchestrates multiple oncogenic functions in HCC development. We are reviewing current knowledge about the roles of LOXL2 in the modulation of the HCC tumor microenvironment, formation of premetastatic niches, and epithelial-mesenchymal transition. Also, we are highlighting the complex interplay between LOXL2 and hypoxia, angiogenesis, and vasculogenic mimicry in HCC. At the end of the review, we summarize current LOXL2 inhibitors and discuss their potential in HCC precision treatment.

Online speech enhancement is of great interest, because of its diverse areas of application. The Demucs architecture has been recently shown to achieve a high level of performance, even with small audio segments, while requiring a relatively small amount of computational resources. However, an issue that it bears, as well as most of state-of-the-art speech enhancement techniques, is that of target speech selection: it is assumed that only one speech source is present. However, in real-life scenarios, more than one speech source may be present, in it is uncertain which speech source within the fed mixture should be enhanced. Thus, it is of interest to complement speech enhancement techniques (such as Demucs-based) with a target selection scheme, so as to ensure that only the source of interest is enhanced. In this work, two target selection strategies are explored: 1) an embedding-based strategy, using a codified sample of the target speech, and 2) a location-based strategy, using a beamforming-based pre-filter to select the target that is in front of a 2-microphone array. It is shown that while both strategies improve the performance of the Demucs-based technique when one or more speech interferences are present, they both have their pros and cons. Specifically, while the beamforming-based strategy achieves overall a better performance compared to the embedding-based strategy, it is sensitive against the location variation of the target speech source which the embedding-based strategy does not suffer from.

In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of the different reflection patterns. The proposed framework consists of four processes including signal processing, cloud points clustering, target tracking, and target recognition. Signal processing translates the raw signals into spare cloud points. These points are merged into several clusters, each representing a single target. Target tracking estimates the new locations of each detected target. A novel convolutional neural network model is developed for drone and/or non-drone targets feature extraction and recognition. For performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments is used for training and testing the convolutional neural network. The proposed recognition model achieves an accuracy of 98.4% for one target and 98.1% for two targets.

Buffalo are silent breeders, therefore detecting estrus is a serious challenge. There is a rising need for sensitive and precise biomarkers in this scenario. Recent research on miRNA has demon-strated the importance of these molecules as biomarkers. Though there have been miRNA studies in saliva during the estrous cycle, there have been few miRNA research in blood samples. The current study was designed to look at blood miRNAs during the oestrous cycle in heifers (n=5) to address the issue of silent estrus. On the day of estrus and diestrus, blood samples from 60 heifers were obtained and pooled into (n=5) separate samples. Ultrasonography and progesterone assay was performed to confirm estrus. Then, employing particular miRNA adapters, small RNA se-quencing of miRNA was performed using the Illumina Miseq 2500. The UEA sRNA bioinfor-matics workbench identified 94 substantially differently expressed miRNAs (p>0.05) from these data. In estrus, 63 miRNA were upregulated and 31 miRNA were downregulated. When fold change was increased to (log2foldchange >1; q value less than 0.05), 25 miRNAs were elevated during estrus. miR-497, miR-582, miR-10174, miR-23, miR-223, miR-1296 were upregulated, whereas miR-10167, 671, 1246,122 were downregulated. miR-497 is unusually elevated (log2 foldchange>5) when compared to another miRNA (log2 foldchange >5) miRNet 2.0, Cytoscape, and MIENTURNET network software found that miR-497 has more degree centrality, above 60; it is associated with more than 60 nodes, followed by miR-93

Mycobacterium tuberculosis (Mtb) is the etiological agent of tuberculosis (TB), a disease that alt-hough preventable and curable, remains a global epidemic due to the emergence of resistance and a latent form responsible for a long period of treatment. Drug discovery in TB is a challenging task due to the heterogeneity of the disease, the emergence of resistance and an uncomplete knowledge of the pathophysiology of the disease. The limited permeability of the cell wall and the presence of multiple efflux pumps remain a major barrier to achieve effective intracellular drug accumulation. While the complete genome sequence of Mtb has been determined and several potential protein targets have been validated, the lack of adequate models for in vitro and in vivo studies is a limit-ing factor in TB drug discovery programs. In current therapeutic regimens, less than 0.5% of bac-terial proteins are targeted being the biosynthesis of the cell wall and the energetic metabolism two of the most important processes exploited for TB chemotherapeutics. This review provides an overview on the current challenges in TB drug discovery and emerging Mtb druggable proteins, and how chemical probes for protein profiling enabled the identification of new targets and bi-omarkers, paving the way to disruptive therapeutic regimens and diagnostic tools.

The Na+/K+ ATPase is a protein involved in the active transport of ions across the cellular membrane. Ouabain is a cardiotonic glycoside that, by inhibiting the Na+/K+ pump, interferes with cell processes mediated directly by the pump, but also indirectly influences other cellular processes, such as cellular cycle and proliferation, growth, cell differentiation, angiogenesis, migration, adhesion and invasion. We used the SK-BR-3 breast cancer cell line, mesenchymal stem cells (MSCs), and tumor-associated fibroblasts (TAFs) in vitro for determining the changes induced by Ouabain in concentration of 10-5 M, 10-6 M, 10-8 M, 10-9 M on these cellular types, employing specific methods: SEM for morphological evaluation; immunocytochemistry, flowcytometry to assess immunophenotypical markers expression, cell cycle and Annexin V/PI assay for cellular proliferation and viability testing; flowchamber assay to evaluate functional adhesion behavior; RT-PCR for Na+/K+ ATPase subunits molecular expression. Being indicative of the pro-apoptotic and inhibitory effect of Ouabain on tumor invasion and metastasis, the results support the addition of Ouabain to the oncological therapeutic arsenal, trailing the “repurposing drugs” approach.

MicroRNAs (MiRNAs) are a class of non-coding single-stranded RNA molecules of approximately 20-24 nucleotides in plants that play an important regulatory role in a variety of biological processes such as plant growth and development and response to various abiotic stresses. For example Drought, Salt, Cold, High temperature, Heavy metals and Nutrition. MiRNAs affect gene expression by manipulating the cleavage, translational expression or DNA methylation of target mRNAs. This review describes the current progress made on the way miRNAs are produced and regulated and the way miRNA/target gene is used in plant responses to abiotic stresses. Studying the molecular mechanism of action of miRNAs downstream target genes can optimize the genetic manipulation of crop growth and development conditions that provide a more theoretical basis for improving crop production.

Compared to wide-field telescopes, small-field detection systems have higher spatial resolution, resulting in stronger detection capabilities and higher positioning accuracy. When detecting by small-field in synchronous orbit, both space debris and fixed stars are imaged as point targets, making it difficult to distinguish them. In addition, with the improvement of detection capabilities, the number of stars in the background rapidly increases, which puts higher requirements on recognition algorithms. Therefore, star detection is indispensable for identifying and locating space debris in complex backgrounds. To address these difficulties, this paper proposes a real-time star extraction method based on adaptive filtering and multi frame projection. We use bad point repair and background suppression algorithms to preprocess star image. Afterwards, we analyze and enhance the target signal-to-noise ratio(SNR). Then, we use multi frame projection to fuse information. Subsequently, adaptive filtering, adaptive morphology, and adaptive median filtering algorithms are proposed to detect trajectories. Finally, the projection is released to locate the target. Our recognition algorithm has been verified by real star images, and the images were captured using small-field telescope. The experimental results demonstrate the effectiveness of the algorithm proposed in this paper. We successfully extracted hip-27066 star, which has magnitudes about 12 and SNR about 1.5. Compared with existing methods, our algorithm has advantages in both recognition rate and false-alarm rate, and can be used as real time target recognition algorithm for space-based synchronous orbit detection payload.

When ground-penetrating radar is used to detect targets within concrete, the location of the targets, the identification of different shapes, properties and less obvious echoes all greatly increase the interpretation time of the staff and can easily cause misjudgment of the echo images. In this paper, the ground-penetrating radar echo images (B-scan) after processing are mean filtered to eliminate the direct waves that interfere greatly with the echoes. The RFB-s structure is added to the YOLOv3-SPP network structure, while the Anchor value is optimized and the EIOU loss function is introduced. For four types of data with different shapes and properties at random target locations, three models, YOLOv3, YOLOv3-SPP and the improved YOLOv3-SPP, are used for classification and identification, and the proposed algorithm models are comprehensively evaluated using model evaluation metrics. The experimental results show that the algorithm models proposed in this paper have good recognition effect in ground-penetrating radar echo image target detection.

Infrared dim and small target detection is a key technology of various detection tasks. However, due to the lack of shape, texture, and other information, it is a challenging task to detect dim and small targets. Recently, since many traditional algorithms ingore global information of infrared images, they generate some false alarms in complicated environments. To address this problem, in this paper, a coarse-to-fine deep learning-based method is proposed to detect dim and small targets. Firstly, a coarse-to-fine detection framework integrating deep learning and background prediction is applied for detecting targets. The framework contains of coarse detection module and fine detection module. In coarse detection stage, Region Proposal Network (RPN) is employed to generate masks in target candidate regions. Then, for further optimize the result, inpainting is utilized to predict the background by the global semnantic of images. In this paper, inpainting algorithm with a mask aware dynamic filtering module is incorporated into the fine detection stage to estimate background in candidate target areas. Finally, compared with existing algorithms, the experimental results indicate that the proposed framework have effective detection capability and robustness for complex surroundings.

Currently, ovarian cancer (OC) is a target of intense biomarkers research because of its frequent late diagnosis and poor prognosis. Serum determination of Human epididymis protein 4 (HE4) is a very important early detection test. Most interestingly, HE4 plays a unique role in OC as it has been implicated not only in OC diagnosis but also in the prognosis and recurrence of this lethal neoplasm, actually acting as a clinical biomarker. There are several evidence about the predictive power of HE4 clinically, conversely less has been described concerning its role in OC oncogenesis. Based on these considerations, the main goal of this review is to clarify the role of HE4 in OC proliferation, angiogenesis, metastatization, immune response and also in the development of targeted therapy. Through a deeper understanding of its functions as a key molecule in the oncogenetic processes underlying OC, HE4 could be possibly considered as an essential resource not only for diagnosis but also for prognosis and therapy choice.

Alfalfa primarily produces seeds through cross-pollination among different individuals. Self-pollination results in severe inbreeding depression, such as weak seedlings and termination of growth. Identifying the genetic loci associated with vigorous plant growth could enable the elimination of deleterious alleles and eventually develop inbred alfalfa lines for hybrid production. In this study, 215 alfalfa accessions were self-pollinated for three generations. Within accessions, pairs of weak and strong plants were sampled and bulked for exome sequencing. We extracted individual DNA from 534 plants that included parental clones, strong and weak pairs of plants, and plants selected based on the number of seeds produced. Among them, we formed 42 pools, including 16 with strong plants and 17 with weak plants, 3 top-seeded plants, 3 low-seeded plants, and 3 no-seeded plants. Along with 79 individuals, these pools were sequenced in the target regions covering 112,626 contigs across the entire alfalfa genome. From the 121 samples, (42+79) genotyped, 13.2 million SNPs including indels were generated. After filtering for MAF (>5%), depth (<20X), and no missing genotype and removing indels, a total of 588,136 SNPs were obtained which were used for final analysis. A genome-wide association study was performed and identified 11 genetic loci associated with alfalfa plant growth vigor. The GO analysis generated 12 significantly enriched GO terms. The associated SNPs were on, and near genes involved in stress response, defense responses against pathogens, and plant reproduction. These identified SNPs benefit the development of alfalfa inbred lines by purging the deleterious alleles and biomass improvement through marker-assisted selection.

The safe and reliable operation of autonomous vehicles in complex and dynamic environments is heavily dependent upon exteroceptive and proprioceptive sensors. A LiDAR system generates accurate 3D point clouds, which are crucial for the detection, classification, and tracking of multiple targets. LiDAR data, however, presents significant challenges due to its density, noise, and varying sampling rates. In this study, various clustering and MTT techniques for LiDAR point clouds will be identified and classified within the context of autonomous driving to assess the state-of-the-art methods’ key challenges and their performances. We have categorized clustering and MTT methods used in AV applications, identified research gaps and challenges and analyzed existing algorithms and their challenges in detail. Researchers and practitioners in the field of autonomous driving will find this review to be a valuable resource.

The integration of deep learning methods, especially Convolutional Neural Networks (CNN), and Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been widely deployed in the field of radar signal processing. Nevertheless, these methods are frequently regarded as black-box models due to the limited visual interpretation of their internal feature representation and parameter organization. In this paper, we propose an innovative approach named C-RISE, which builds upon the RISE algorithm to provide a post-hoc interpretation technique for black-box models used in SAR Images Target Recognition. C-RISE generates saliency maps that effectively visualize the significance of each pixel. Our algorithm outperforms RISE by clustering masks that capture similar fusion features into distinct groups, enabling more appropriate weight distribution and increased focus on the target area. Furthermore, we employ Gaussian blur to process the masked area, preserving the original image structure with optimal consistency and integrity. C-RISE has been extensively evaluated through experiments, and the results demonstrate superior performance over other interpretation methods based on perturbation when applied to neural networks for SAR image target recognition. Furthermore, our approach is highly robust and transferable compared to other interpretable algorithms, including white-box methods.

Serial femtosecond crystallography (SFX) using an X-ray free-electron laser (XFEL) enables determination of room-temperature structures without causing radiation damage. Using an optical pump-probe or mix-and-injection, SFX enables the intermediate state visualization of a molecular reaction. In SFX experiments, serial and stable microcrystal delivery to the X-ray interaction point is vital for reasonable data collection and efficient beam time. The Pohang Accelerator Laboratory X-ray Free Electron Laser (PAL-XFEL) facility established SFX instruments at a nanocrystallography and coherent imaging (NCI) experimental station. Various sample delivery methods, including injection, fixed-target scanning, and hybrid methods, have been developed and applied to collect XFEL diffraction data. Herein, we review the currently available sample delivery methods for SFX at the NCI experimental station at the PAL-XFEL. This article will help PAL-XFEL users access the SFX system for their experiments.

The study sought to investigate the effect of Multi-choice Goal programming in project Portfolio Analysis, using Lagos State Project Portfolio from between 2017-2021 as a study case. Specifically, the objectives examined the effectiveness of multi-choice goal programming in analyzing the project Portfolio of Lagos State Government and investigated the possible lapses in the use of Multi-choice Goal programming in analyzing the project Portfolio of Lagos State Government. Data were gathered from the State financial Statements and analyzed using Lingo-17 Software. Based on the analysis, the study discovered that the goals formulated can be optimally and minimally attained as the positive deviational and negative deviational variables depict zero. This indicates that the cost incurred on the project portfolio was absolutely minimized during the period and at the same time; Lagos State Government derived the most preferred benefits from it. The study therefore concluded that the State minimized the project costs and at the same time maximized benefits while meeting its overall objectives.

Traditional camera sensors rely on human eyes for observation. However, the human eye 1 is prone to fatigue when observing targets of different sizes for a long time in complex scenes, and 2 human cognition is limited, which often leads to judgment errors and greatly reduces the efficiency. 3 Target recognition technology is an important technology to judge the target category in camera 4 sensor. In order to solve this problem, a small size target detection algorithm for special scenarios was 5 proposed by this paper. Its advantage is that this algorithm not only has higher precision for small 6 size target detection, but also can ensure that the detection accuracy of each size is not lower than the 7 existing algorithm. In this paper, a new down-sampling method was proposed, which could better 8 preserve the context feature information. The feature fusion network was improved to effectively 9 combine shallow information and deep information. A new network structure was proposed to 10 effectively improve the detection accuracy of the model. In terms of accuracy, it is better than: YOLOX, 11 YOLOXR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny and YOLOv8.Three authoritative public data sets 12 were used in this experiment: a) On Visdron data sets (small size targets), DC-YOLOv8 is 2.5% more 13 accurate than YOLOv8. b) On Tinyperson data sets (minimal size targets), DC-YOLOv8 is 1% more 14 accurate than YOLOv8. c) On PASCAL VOC2007 data sets (Normal size target), DC-YOLOv8 is 0.5% 15 more accurate than YOLOv8.

In recent years, the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has become a highly sought-after topic for its wide applications in the field of target recognition, detection, and tracking. However, SAR automatic target recognition (ATR) models based on deep neural networks (DNN) are suffering from adversarial examples. Generally, non-cooperators rarely disclose any information about SAR-ATR models, making adversarial attacks challenging. In this situation, we propose Transferable Adversarial Network (TAN) to attack these models with highly transferable adversarial examples. The proposed method improves the transferability via a two-player game, in which we simultaneously train two encoder-decoder models: a generator that crafts malicious samples through a one-step forward mapping from original data, and an attenuator that weakens the effectiveness of malicious samples by capturing the most harmful deformations. In particular, compared to traditional iterative methods, our approach is able to one-step map original samples to adversarial examples, thus enabling real-time attacks. Experimental results indicate that the proposed approach achieves state-of-the-art transferability with acceptable adversarial perturbations and minimum time costs compared to existing attack methods, i.e., it excellently realizes real-time transferable adversarial attacks.

The network pharmacology (NP) approach is a valuable novel methodology for understanding the complex pharmacological mechanisms of medicinal herbs. In addition, various in silico analysis techniques combined with the NP can improve the understanding of various issues in natural product research. This study assessed the therapeutic effects of Arum ternata (AT), Poria cocos (PC), and Zingiber officinale (ZO) on hyperlipidemia after network pharmacologic analysis. A protein–protein interaction (PPI) network of forty-one key targets was analyzed to discover core functional clusters of the herbal compounds. The KEGG pathway and gene ontology (GO) term enrichment analysis identified significant categories of hypolipidemic mechanisms. The STITCH database indicated a high connection with several statin drugs deduced by the similarity in targets. AT, PC, and ZO regulated the genes related to the energy metabolism and lipogenesis in HepG2 cells loaded with free fatty acids (FFAs). Furthermore, a combinational effect of the mixture of three herbs was found. The herbal combination exerted superior efficacy compared to a single herb, particularly in regulating acetyl-CoA carboxylase (ACC) and carnitine palmitoyltransferase 1 (CPT-1). In conclusion, the network pharmacologic approach was used to assess potential targets of the herbal combination for treatment. Experimental data from FFAs-induced HepG2 cells suggested that the combination of AT, PC, and ZO might attenuate hyperlipidemia and its associated hepatic steatosis.

In the event of an abrupt sunlight reduction scenario there is a time window that occurs between when food stores would likely run out for many countries (~6 months or less) and ~1 year when resilient foods are scaled up. A promising temporary resilient food is leaf protein concentrate (LPC). Although it is possible to extract LPC from tree biomass (e.g. leaves and needles), neither the yields nor the toxicity of the protein concentrates for humans from the most common tree species has been widely investigated. To help fill this knowledge gap, this study uses high-resolution mass spectrometry and an open source toolchain for non-targeted screening of toxins on five common North American coniferous species: Western Cedar, Douglas Fir, Ponderosa Pine, Western Hemlock, and Lodgepole Pine. The yields for LPC extraction from the conifers ranged from 1% to 7.5%. The toxicity screenings confirm that these trees may contain toxins that can be consumed in small amounts and additional studies including measuring the quantity of each toxin are needed. The results indicate that LPC is a promising candidate to be used as resilient food, but future work is needed before LPCs from conifers can be used as a wide-scale human food.

MicroRNAs (miRNAs) are small non-coding RNAs that play a central role in the post-transcriptional regulation of biological processes. miRNAs regulate transcripts by direct binding involving the Argonaute protein family. The exact rules of binding are not known, and several in silico miRNA target prediction methods have been developed to date. Deep Learning has recently revolutionized miRNA target prediction. However, the higher predictive power comes with decreased ability to interpret increasingly complex models. Here, we present a novel interpretation technique, called attribution sequence alignment, for miRNA target site prediction models that can interpret such Deep Learning models on a two-dimensional representation of miRNA and putative target sequence. Our method produces a human readable visual representation of miRNA:target interactions and can be used as a proxy for further interpretation of biological concepts learned by the neural network. We demonstrate applications of this method in clustering of experimental data into binding classes, as well as using the method to narrow down predicted miRNA binding sites on long transcript sequences. Importantly, the presented method works with any neural network model trained on a two-dimensional representation of interactions and can be easily extended to further domains such as protein-protein interactions.

Automatic target detection of remote sensing images (RSI) plays an important role in military reconnaissance, disaster monitoring, and target rescue. The core task of remote sensing target detection is to judge the target categories and complete precise location. However, the existing target detection algorithms have limited accuracy and weak generalization capability for remote sensing images with complex backgrounds. To achieve accurate detection of different categories targets in remote sensing images, this study presents a novel feature enhancement single shot multibox detector (FESSD) algorithm for remote sensing target detection. The FESSD introduces feature enhancement module and attention mechanism into the convolution neural networks (CNN) model, which can effectively enhance the feature extraction ability and nonlinear relationship between different convolution features. Specifically, the feature enhancement module is used to extract the multi-scale feature information, and enhance the model nonlinear learning ability; the self-learning attention mechanism (SAM) is used to expand the convolution kernel local receptive field, which makes the model extract more valuable features. In addition, the nonlinear relationship between different convolution features is enhanced using the feature pyramid attention mechanism (PAM). The advantage of FESSD over other state-of-the-art target detection methods is validated by experiments on the presented seven-class target detection dataset (SD-RSI) and the public DIOR dataset.

The Xpert® Xpress SARS-CoV-2 and Xpert® Xpress SARS-CoV-2/Flu/RSV tests were rapidly developed and widely used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. In response to emerging genetic variability, a new SARS-CoV-2 target (RNA-dependent RNA-polymerase) has been added to both tests: Xpert® Xpress CoV-2 plus and Xpert® Xpress CoV-2/Flu/RSV plus test. A rapid evaluation of both tests was performed in South Africa, using residual respiratory specimens. Residual respiratory specimens (n=125) were used to evaluate the Xpert® Xpress CoV-2 plus test and included 50 genotyped specimens. The Xpert® Xpress CoV-2/Flu/RSV plus test was assessed using 45 genotyped SARS-CoV-2 specimens, ten influenza A, ten Influenza B and twenty respiratory syncytial virus specimens. Results were com-pared to in-country standard of care tests. Genotyped specimens tested the performance of the test under pressure from circulating SARS-CoV-2 variants of concern. Reference material was included to assess the test limits and linearity. The Xpert® Xpress CoV-2 plus test performance compared to reference results across residual respiratory specimens was good (positive per-centage agreement (PPA)=95.2%, negative percentage agreement (NPA)=95.0%) The Xpert® Xpress CoV-2/Flu/RSV plus test showed good performance across all residual respiratory specimens (PPA=100%, NPA=98.3%). All genotyped variants of concern were detected by both tests. The Xpert® Xpress CoV-2 plus and Xpert® Xpress CoV-2/Flu/RSV plus tests can be used to diagnose SARS-CoV-2, and to diagnose and differentiate SARS-CoV-2, influenza A, influenza B and respiratory syncytial virus respectively. The NPA was lower than the recommended 99%, but was influenced by the low number of negative specimens tested. The variants of concern assessed did not affect test performance. It is recommended that sites perform their own assessments compared to in-country standard of care tests.

Recent studies have proven that synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNN) are vulnerable to adversarial examples. However, existing attacks are easily failed in the case where adversarial perturbations cannot be fully fed to victim models. We call this situation perturbation offset. Moreover, since background clutter takes up most of the areas in SAR images and has low relevance to recognition results, fooling models with global perturbations is quite inefficient. This paper proposes a semi-whitebox attack network, called Universal Local Adversarial Network (ULAN), to generate universal adversarial perturbations (UAP) for the target regions of SAR images. In the proposed network, we calculate the model’s attention heatmaps through layer-wise relevance propagation (LRP), which is used to locate the target regions of SAR images that have high relevance to recognition results. In particular, we utilize a generator based on the U-Net to learn the mapping from noise to UAPs and craft adversarial examples by adding the generated local perturbations to target regions. Experiments indicate that the proposed method fundamentally prevents perturbation offset and achieves comparable attack performance to conventional global UAPs by perturbing only a quarter or less of SAR image areas.

Global crop yields are estimated to be reduced by 30–40% per year on account of plant pests and pathogens. Agricultural insect pests raise concerns about constraining global food security and climate changes contributing to the rise of infestation. The current management relies on plant breeding, associated or not with transgenes and chemical pesticides. Both approaches face serious technology obsolescence on the field due to resistance breakdown or development of insecticide resistance. The need for new Modes of Action (MoA) approaches in managing crop health grows each year, driven by market demands to reduce economic losses and phytosanitary requirements to meet the consumer perception. Disabling pest genes by sequence-specific expression silencing is considered a promising tool in the development of environment and health respectful biopesticides. The specificity conferred by long dsRNA-base solutions give support to minimizing effects on off-targeted genes in the insect pest genome and the target gene in non-target organisms (NTOs). In this review, we summarize the current status of gene silencing by RNA interference (RNAi) for agricultural control. More specifically, we focus on the engineering, development and application of gene silencing to control Lepidoptera by the employment of non-transforming dsRNA technologies. Despite some delivery and stability drawbacks of topical applications, we reviewed works showing convincing proof-of-concept results that point to imminent innovative solutions. Considerations about the regulamentation of the ongoing research on dsRNA-based pesticides to produce commercialized products for exogenous application are discussed. Academic and industry initiatives reveal a worthy effort to accomplish controlling Lepidoptera pests with this new mode of action to provide more sustainable and reliable technologies to field management. New data on genomics of this taxon encourage the increment of a customized target genes portfolio. As a case of study, we illustrate how dsRNA and associated methodologies could be applied to control an important Lepidopteran coffee pest.

Pesticides are widely used in agriculture as a pest control strategy. Despite the benefits of pesticides on crop yields, the persistence of chemical residues in soil have an unintended impact on non-targeted microorganisms. In this study, we evaluated the impact of the combined fungicide (difenoconazole, epoxiconazole, and kresoxim-methyl) on fungal and bacterial communities of Phaeozem. In the fungicide-treated soil, the Shannon index of both fungal and bacterial communities was decreased, while Chao1 index did not differ compared to the control soil. Among bacterial taxa, the relative abundance of Athrobacter, Sphingomicrobium, and Sphingomonas increased in fungicide-treated soil due to their ability to utilize fungicides and other toxic compounds. Rhizopus and plant-beneficial Chaetomium were the dominant fungal genera, which increased 2-4 times in the fungicide-treated soil, while the relative abundance of Mortierella and Talaromyces decreased. Fusarium acuminatum was the most abundant phytopathogenic fungus that causes root rot disease of wheat, but applied fungicide treatment decreased their diversity in the soil 2 times, which is consistent on the observed plants.