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Abstract Unmanned aerial vehicles (UAVs) play a crucial role in various domains such as military, civil, industrial, and so on. However, the coordination and task allocation of multiple UAVs in engineering practices face numerous challenges. As the scale of battlefields expands, and the diversity of UAV missions and constraints increases, the existing task allocation methods suffer from issues such as a mismatch between theoretical models and real‐world applications, low task execution efficiency, and poor responsiveness in dynamic environments. To address these challenges, this paper proposes an improved genetic algorithm (GA)‐based approach for multi‐UAV cooperative task allocation. By collecting battlefield information, decomposing tasks, and considering UAV resource types, an optimization model for multi‐UAV cooperative task allocation is constructed. The proposed method, using an improved GA, generates a set of Pareto‐optimal task solutions for decision‐makers. Case studies demonstrate that this approach effectively enhances task execution efficiency and reduces the total flight distance cost of UAVs.

Abstract Intravitreal (IVT) injection of anti‐vascular endothelial growth factor (anti‐VEGF) has greatly improved the treatment of many retinal disorders, including wet age‐related macular degeneration (wAMD), which is the third leading cause of blindness. However, frequent injections can be difficult for patients and may lead to various risks such as elevated intraocular pressure, infection, and retinal detachment. To address this issue, researchers have found that IVT injection of anti‐VEGF proteins at their maximally viable concentration and dose can be an effective strategy. However, the intrinsic protein structure can limit the maximum concentration due to stability and solution viscosity. To overcome this challenge, we developed a novel anti‐VEGF protein called nanoFc by fusing anti‐VEGF nanobodies with a crystallizable fragment (Fc). NanoFc has demonstrated high binding affinity to VEGF165 through multivalency and potent bioactivity in various bioassays. Furthermore, nanoFc maintains satisfactory chemical and physical stability at 4°C over 1 month and is easily injectable at concentrations up to 200 mg/mL due to its unique architecture that yields a smaller shape factor. The design of nanoFc offers a bioengineering strategy to ensure both strong anti‐VEGF binding affinity and high protein concentration, with the goal of reducing the frequency of IV injections.

Abstract Wet age‐related macular degeneration (wet AMD) is the most common cause of blindness, and chronic intravitreal injection of anti‐vascular endothelial growth factor (VEGF) proteins has been the dominant therapeutic approach. Less intravitreal injection and a prolonged inter‐injection interval are the main drivers behind new wet AMD drug innovations. By rationally engineering the surface residues of a model anti‐VEGF nanobody, we obtained a series of anti‐VEGF nanobodies with identical protein structures and VEGF binding affinities, while drastically different crystallization propensities and crystal lattice structures. Among these nanobody crystals, the P212121 lattice appeared to be denser and released protein slower than the P1 lattice, while nanobody crystals embedding zinc coordination further slowed the protein release rate. The polymorphic protein crystals could be a potentially breakthrough strategy for chronic intravitreal administration of anti‐VEGF proteins.

Abstract Hazy image obtained in the low‐light environment has the characteristics of low contrast, non‐uniform illumination, color cast and much noise. In this paper, a method is put forward which can be properly applied to recover low‐light hazy images. The original image is first decomposed into glow layer and haze layer with a modified color channel transformation for glow artifacts and color balanced. A new light segmentation function is proposed next by using gamma correction of channel difference and setting threshold levels to determine if the pixel belongs to light source regions. Then the ambient illuminance map is estimated using maximum reflectance prior to computing the atmosphere light in the light and non‐light regions. Finally, a novel nonlinear image depth estimation model is established to build the relationship between the image depth map and three image features including luminance, saturation and gradient map for the light areas. The experimental results prove that the dehazing algorithm is reliable for removing haze and glow artifacts of active light sources, reducing much noise and improving the visibility.

Glucocorticoid‐induced TNF receptor‐related (GITR) can act as a co‐stimulatory receptor, representing a potential target for safely enhancing immunotherapy efficacy. GITR is triggered by a GITR ligand or an agonist antibody and activates CD8+ and CD4+ effector T cells, reducing tumor‐infiltrating Treg numbers and resulting in activation of immune responses and tumor cell destruction by effector T cells. GITR is an attractive target for immunotherapy, especially in combination therapy with immune checkpoint inhibitors, as is being explored in clinical trials. Using H2L2 transgenic mice encoding the human immunoglobulin variable region and hybridoma technology, we generated a panel of fully human antibodies that showed excellent specific affinity and strong activation of human T cells. After conversion to fully human antibodies and engineering modification, we obtained an anti‐GITR antibody hab019e2 with enhanced antitumor activity in a B‐hGITR MC38 mouse model compared to Tab9H6V3, an anti‐GITR antibody that activates T cells and inhibits Treg suppression from XenoMouse. As a fully human antibody with its posttranslational modification hot spot removed, the hab019e2 antibody exerted more potent therapeutic effects, and may have potential as a novel and developable antibody targeting GITR for follow‐up drug studies.

Background Existing studies on cardiovascular diseases (CVDs) often focus on individual‐level behavioral risk factors, but research examining social determinants is limited. This study applies a novel machine learning approach to identify the key predictors of county‐level care costs and prevalence of CVDs (including atrial fibrillation, acute myocardial infarction, congestive heart failure, and ischemic heart disease). Methods and Results We applied the extreme gradient boosting machine learning approach to a total of 3137 counties. Data are from the Interactive Atlas of Heart Disease and Stroke and a variety of national data sets. We found that although demographic composition (eg, percentages of Black people and older adults) and risk factors (eg, smoking and physical inactivity) are among the most important predictors for inpatient care costs and CVD prevalence, contextual factors such as social vulnerability and racial and ethnic segregation are particularly important for the total and outpatient care costs. Poverty and income inequality are the major contributors to the total care costs for counties that are in nonmetro areas or have high segregation or social vulnerability levels. Racial and ethnic segregation is particularly important in shaping the total care costs for counties with low poverty rates or social vulnerability level. Demographic composition, education, and social vulnerability are consistently important across different scenarios. Conclusions The findings highlight the differences in predictors for different types of CVD cost outcomes and the importance of social determinants. Interventions directed toward areas that have been economically and socially marginalized may aid in reducing the impact of CVDs.

Abstract Background GPRIN1 may be a novel tumor regulator, but its role and mechanism in tumors are still unclear. Methods First, a pan‐cancer correlation analysis was conducted on the expression and prognosis of GPRIN1 based on the data downloaded from The Cancer Genome Atlas (TCGA) database. Second, the Starbase database was used to predict the upstream miRNAs and lncRNAs of GPRIN1, and the expression analysis, survival analysis, and correlation analysis were performed to screen the microRNA (miRNAs)/long non‐coding RNAs (lncRNAs) that had a correlation with kidney renal papillary cell carcinoma (KIRP) or lung adenocarcinoma (LUAD). Third, the CIBERSORT algorithm was employed to calculate the proportion of various types of immune cells, and then the R packages were used for evaluating the relation between GPRIN1 expression and tumor immune cell infiltration as well as between GPRIN1 and the immune cell biomarker. Finally, the correlation analysis was made on GPRIN1 and immune checkpoints (CD274, CTLA4, and PDCD1). Results The pan‐cancer analysis suggested that GPRIN1 was up‐expressed in KIRP and LUAD, and it correlated with poor prognosis. LINC00894/MMP25‐AS1/SNHG1/LINC02298/MIR193BHG‐miR‐140‐3p was likely to be the most promising upstream regulation pathway of GPRIN1. Upexpression of LINC00894/MMP25‐AS1/SNHG1/LINC02298/MIR193BHG and downexpression of miR‐140‐3p were found relevant with poor outcomes of KIRP and LUAD. GPRIN1 expression was significantly correlated with tumor immune cell infiltration, immune cell biomarkers, and immune checkpoints. Conclusions The competitive endogenous (ceRNA) of miR‐140‐3p‐GPRIN1 axis and its upstream lncRNAs are closely related to KIRP and LUAD, and might affect the prognosis and therapeutic effect of KIRP and LUAD.

Nanomaterials are gradually shining in the petroleum industry and have made great achievements in EOR, pipeline coating, fracturing, and other aspects. In practical application, they have the advantages of long validity period and more environmental friendliness. At the same time, due to the short effective period of conventional plugging removal measures such as acidizing and fracturing, which are not friendly to the environment, nanomaterials are gradually applied to reduce pressure and increase injection of injection wells, and good application results are obtained. In this study, a new biological nanomaterial with long-term injection enhancement characteristics was evaluated. To have a better understanding of the reservoir performance of water injection wells after pressure reduction and injection increase measures, it is necessary to establish the well test model after the step-down and injection increase measures of biological nanomaterials that are taken for water injection wells. In this paper, the influence of the injection amount of bionanomaterials on the permeability is studied, and the rule of the permeability around the injection well with the distance from the bottom of the well is analyzed. On this basis, the flow mathematical model of three zones (inner zone, transition zone, and outer zone) of bionanosolution is established. Then, using the basic principles of fluid mechanics and the Laplace transform principle, a mathematical model of the bottom hole pressure response of water injection wells in the third zone of the bionanosolution reservoir is obtained, and the influence of the key process parameters of the bionanosolution water injection on the bottom hole pressure is analyzed. The established well test interpretation model can be used to calculate the near wellbore permeability, reservoir skin factor, reservoir sweep radius, and reservoir resistance coefficient. Based on application examples, it was determined that the fitting results of the interpretation chart are in good agreement with the field test data, and the reservoir parameters obtained from the interpretation are reasonable and reliable. The findings of this study can help for better understanding of the dynamic change law of reservoir after injection of biological nanomaterials or similar plugging removal measures (the improvement effect varies with the distance from the bottom hole). To the best of our knowledge, it is the first paper on the application of biological nanomaterials in reducing pressure and increasing injection in injection wells.

Abstract Seedling emergence and establishment are fragile processes that determine the direction and structure of forest succession and regeneration. However, seedling emergence and establishment are easily affected by biotic and abiotic (environmental) factors. A dense and expanding understory of dwarf bamboo is one such important factor that can seriously hinder the seedling regeneration. We conducted a field experiment to investigate the emergence and establishment of canopy tree seedlings under artificially controlled densities of dwarf bamboo. We found that understory dwarf bamboo obstructed seedling emergence but reduced the death of seedlings. Although understory dwarf bamboo reduced the median retention time of seedlings, dense bamboo increased the mean survival time of seedlings. Our results suggest that understory dwarf bamboo has multiple selectivities for tree seedling emergence and establishment: high‐density dwarf bamboo was beneficial to evergreen species but lower‐density of bamboo was conducive to the survival of deciduous species, it means the dwarf bamboo potentially alters successional trajectories of forest communities. Path analysis revealed that the most important factors affecting tree seedling emergence and death were the abundance of seeds in the seed bank and the density of emerged seedlings, and that the soil temperature promoted seedling emergence but increased seedling death, the thickness of litter limited seedling emergence, and the leaf area index of the bamboo canopy limited seedling death. The present study suggests that dwarf bamboo can directly alter the microenvironment, significantly reducing light levels and soil temperature but increasing the thickness of litter and soil humus, thereby indirectly impacting the regeneration of tree seedlings. Our results indicate that various factors affected seedling emergence, and there were complex indirect relationships among these factors. In general, biological factors had a stronger influence on tree seedling regeneration than environmental factors.

The construction method of tunnel has a critical impact on the stress and strain field during excavation. This study simulated the construction of Qichongcun tunnel by applying a different excavation method, including double-sided method, single-sided method, and three-step dynamic balance excavation method. The distribution and variation of the stress, vertical displacement, horizontal displacement of surrounding rocks, and stress of initial and secondary lining were studied. The results showed that large displacement of surrounding rocks would be induced during the construction in three-step method, but the stress of the second lining was small after completion of the construction. The displacement and stress of the three-step dynamic balance excavation method were larger than those of the other two methods, but it still met the requirements of safety.

Abstract RNA helicase DDX21 plays vital roles in ribosomal RNA biogenesis, transcription, and the regulation of host innate immunity during virus infection. How DDX21 recognizes and unwinds RNA and how DDX21 interacts with virus remain poorly understood. Here, crystal structures of human DDX21 determined in three distinct states are reported, including the apo‐state, the AMPPNP plus single‐stranded RNA (ssRNA) bound pre‐hydrolysis state, and the ADP‐bound post‐hydrolysis state, revealing an open to closed conformational change upon RNA binding and unwinding. The core of the RNA unwinding machinery of DDX21 includes one wedge helix, one sensor motif V and the DEVD box, which links the binding pockets of ATP and ssRNA. The mutant D339H/E340G dramatically increases RNA binding activity. Moreover, Hill coefficient analysis reveals that DDX21 unwinds double‐stranded RNA (dsRNA) in a cooperative manner. Besides, the nonstructural (NS1) protein of influenza A inhibits the ATPase and unwinding activity of DDX21 via small RNAs, which cooperatively assemble with DDX21 and NS1. The structures illustrate the dynamic process of ATP hydrolysis and RNA unwinding for RNA helicases, and the RNA modulated interaction between NS1 and DDX21 generates a fresh perspective toward the virus–host interface. It would benefit in developing therapeutics to combat the influenza virus infection.

Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle’s category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds’ sensitivity to its flying space is also utilized to increase the particles’ speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles’ velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds’ instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems.

Cell division is closely related to telomerase activity (hTERT mRNA). Lower expression of lymphocitic hTERT mRNA may easily cause cell aging, which is not beneficial to maintaining a durable lymphocyte division. To date, there is no study to investigate IFNα therapy on hTERT mRNA expression in PBMCs of patients with chronic hepatitis B (CHB). We quantitatively detected hTERT mRNA from study subjects and made each hTERT mRNA normalized (NhTERT mRNA). Mean NhTERT mRNA level was lower in either CHB group, but it significantly increased in IFNα-treated group compared with CHB control group, and a longer duration of IFNα therapy could increase the level. Moreover, the mean NhTERT mRNA in subgroup with HBeAg loss was significantly higher than that in subgroup without. NhTERT mRNA was markedly correlated with CD3+ T lymphocyte count and CD4+/CD8+ ratio. The results showed that IFNα therapy could upregulate the expression of hTERT mRNA in PBMCs.