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In this short review, corresponding to a talk given at the conference "Cosmology 2023 in Miramare", we combine an analysis of five regions observed by H.E.S.S. in the Galactic Center, intending to constrain the Dark Matter (DM) density profile in a WIMP annihilation scenario. For the analysis, we include the state-of-the-art Galactic diffuse emission Gamma-optimized model computed with DRAGON and a wide range of DM density profiles from cored to cuspy profiles, including different kinds of DM spikes. Our results are able to constrain generalized NFW profiles with an inner slope γ ≳ 1.3 . When considering DM spikes, the adiabatic spike is completely ruled out. However, smoother spikes given by the interactions with the bulge stars are compatible if γ ≲ 0.8 , with an internal slope of γ sp-stars = 1.5 . [ABSTRACT FROM AUTHOR]

VEGF-A is a key cytokine in tumor angiogenesis and a major therapeutic target for cancer. VEGF165 is the predominant isoform of VEGF-A, and it is the most potent angiogenesis stimulant. VEGFR2/KDR domains 2 and 3 (D2D3) bind to the N-terminal domain (NTD, residues 1-110) of VEGF165. Since removal of the heparin-binding domain (HBD, residues 111-165) markedly reduced the mitogenic activity of the growth factor, it has been proposed that the HBD plays a critical role in the mitogenicity of VEGF165. Here, we report that αvβ3 specifically bound to the isolated VEGF165 HBD but not to VEGF165 NTD. Based on docking simulation and mutagenesis, we identified several critical amino acid residues within the VEGF165 HBD required for αvβ3 binding, i.e., Arg123, Arg124, Lys125, Lys140, Arg145, and Arg149. We discovered that VEGF165 HBD binds to the KDR domain 1 (D1) and identified that Arg123 and Arg124 are critical for KDR D1 binding by mutagenesis, indicating that the KDR D1-binding and αvβ3-binding sites overlap in the HBD. Full-length VEGF165 mutant (R123A/R124A/K125A/K140A/R145A/R149A) defective in αvβ3 and KDR D1 binding failed to induce ERK1/2 phosphorylation, integrin β3 phosphorylation, and KDR phosphorylation and did not support proliferation of endothelial cells, although the mutation did not affect the KDR D2D3 interaction with VEGF165. Since β3-knockout mice are known to show enhanced VEGF165 signaling, we propose that the binding of KDR D1 to the VEGF165 HBD and KDR D2D3 binding to the VEGF165 NTD are critically involved in the potent mitogenicity of VEGF165. We propose that binding competition between KDR and αvβ3 to the VEGF165 HBD endows integrin αvβ3 with regulatory properties to act as a negative regulator of VEGF165 signaling. [ABSTRACT FROM AUTHOR]

Background: Mitochondrial health has gained attention in a number of diseases, both as an indicator of disease state and as a potential therapeutic target. The quality and amount of mitochondrial DNA (mtDNA) and RNA (mtRNA) can be important indicators of mitochondrial and cell health, but are difficult to measure in complex tissues. Methods: mtDNA and mtRNA in zebrafish retina samples were fluorescently labeled using RNAscope™ in situ hybridization, then mitochondria were stained using immunohistochemistry. Pretreatment with RNase was used for validation. Confocal images were collected and analyzed, and relative amounts of mtDNA and mtRNA were reported. Findings regarding mtDNA were confirmed using qPCR. Results: Signals from probes detecting mtDNA and mtRNA were localized to mitochondria, and were differentially sensitive to RNase. This labeling strategy allows for quantification of relative mtDNA and mtRNA levels in individual cells. As a demonstration of the method in a complex tissue, single photoreceptors in zebrafish retina were analyzed for mtDNA and mtRNA content. An increase in mtRNA but not mtDNA coincides with proliferation of mitochondria at night in cones. A similar trend was measured in rods. Discussion: Mitochondrial gene expression is an important component of cell adaptations to disease, stress, or aging. This method enables the study of mtDNA and mtRNA in single cells of an intact, complex tissue. The protocol presented here uses commercially-available tools, and is adaptable to a range of species and tissue types. [ABSTRACT FROM AUTHOR]

The Solow residual method, traditionally pivotal for calculating total factor productivity (TFP), is typically not applied to green TFP calculations due to its exclusion of undesired outputs. Diverging from traditional approaches and other frontier methodologies such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), this paper integrates undesired outputs and three types of spatial spillover effects into the conventional Solow framework, thereby creating a new spatiotemporal econometric Solow residual method (STE-SRM). Utilizing this novel method, the study computes the industrial green TFPs for 280 Chinese cities from 2003 to 2019, recalculates these TFPs using DEA-SBM and Bayesian SFA for the same cities and periods, and assesses the accuracy of the STE-SRM-derived TFPs through comparative analysis. Additionally, the paper explores the statistical properties of China's urban industrial green TFPs as derived from the STE-SRM, employing Dagum's Gini coefficient and spatial convergence analyses. The findings first indicate that by incorporating undesired outputs and spatial spillover into the Solow residual method, green TFPs are computable in alignment with the traditional Solow logic, although the allocation of per capita inputs and undesired outputs hinges on selecting the optimal empirical production function. Second, China's urban industrial green TFPs, calculated using the STE-SRM with the spatial Durbin model with mixed effects as the optimal model, show that cities like Huangshan, Fangchenggang, and Sanya have notably higher TFPs, whereas Jincheng, Datong, and Taiyuan display lower TFPs. Third, comparisons of China's urban industrial green TFP calculations reveal that those derived from the STE-SRM demonstrate broader but more concentrated results, while Bayesian SFA results are narrower and less concentrated, and DEA-SBM findings sit between these extremes. Fourth, the study highlights significant spatial heterogeneity in China's urban industrial green TFPs across different regions—eastern, central, western, and northeast China—with evident sigma convergence across the urban landscape, though absolute beta convergence is significant only in a limited subset of cities and time periods. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Journal of Sport & Exercise / Türk Spor ve Egzersiz Dergisi is the property of Turkish Journal of Sport & Exercise and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Autonomous navigation in agricultural fields presents a unique challenge due to the unpredictable outdoor environment. Various approaches have been explored to tackle this task, each with its own set of challenges. These include GPS guidance, which faces availability issues and struggles to avoid obstacles, and vision guidance techniques, which are sensitive to changes in light, weeds, and crop growth. This study proposes a novel idea that combining GPS and visual navigation offers an optimal solution for autonomous navigation in agricultural fields. Three solutions for autonomous navigation in cotton fields were developed and evaluated. The first solution utilized a path tracking algorithm, Pure Pursuit, to follow GPS coordinates and guide a mobile robot. It achieved an average lateral deviation of 8.3 cm from the pre-recorded path. The second solution employed a deep learning model, specifically a fully convolutional neural network for semantic segmentation, to detect paths between cotton rows. The mobile rover then navigated using the Dynamic Window Approach (DWA) path planning algorithm, achieving an average lateral deviation of 4.8 cm from the desired path. Finally, the two solutions were integrated for a more practical approach. GPS served as a global planner to map the field, while the deep learning model and DWA acted as a local planner for navigation and real-time decision-making. This integrated solution enabled the robot to navigate between cotton rows with an average lateral distance error of 9.5 cm, offering a more practical method for autonomous navigation in cotton fields. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related mortality. Traditional chemotherapy, while effective, often results in significant side effects, highlighting the need for more efficient cancer therapies. Recent advancements in nanotechnology have led to the development of strategies that aim to minimize toxicity to normal cells by more precise targeting of cancer cells. In this context, cobalt oxide nanoparticles (Co3O4 NPs) have shown promising anticancer potential. Our study focuses on evaluating the antioxidant, antibacterial, and anticancer properties of Co3O4 NPs synthesized using Vibrio sp. VLC, a bioluminescent bacterium. Results: XRD and FTIR analyses confirmed the successful synthesis of Co3O4 NPs, which displayed spherical morphology with an average diameter of 60 nm. The nanoparticles demonstrated significant antioxidant and antibacterial activities. The MTT assay indicated that the NPs caused dose- and time-dependent toxicity against CT26 cells, while exhibiting relatively lower toxicity towards normal cells. In vivo experiments further confirmed the significant tumor suppressive effects in BALB/c mice, with minimal side effects on the liver, spleen, and kidney tissues compared to the widespread toxicity of cisplatin. Conclusion: This study verifies the successful synthesis of Co3O4 NPs and their potent antioxidant, antibacterial, and anticancer activities. The biosynthesized Co3O4 NPs represent a promising targeted method for CRC therapy. However, further research is needed to elucidate their mechanism of action and also their application in the clinical phase. [ABSTRACT FROM AUTHOR]

The application of robotics has great implications for future food security, sustainable agricultural development, improving resource efficiency, reducing chemical pesticide use, reducing manual labor, and maximizing field output. Aiming at the problems of high labor intensity and labor shortage in the fields of pesticide application, weeding, and field information collection, a multifunctional and electric field management robot platform is designed, which has four switching steering modes (Ackermann steering, four-wheel steering, crab steering, and zero-radius steering), and its wheel-track can be automatically adjusted. Commonly used spraying booms, weeders, crop information collectors, and other devices can be easily installed on the robot platform. A multi-sensor integrated navigation system including a satellite positioning system, an RGB camera, and a multi-line lidar is designed to realize the unmanned driving of the robot platform in a complex field environment. Field tests have shown that the robot can follow the set route, and tests under simulated conditions have indicated that it can also dynamically correct paths based on crop rows by using a visual system. Results from multiple trials showed that the trajectory tracking accuracy meets the requirements of various field management operations. [ABSTRACT FROM AUTHOR]

Simple Summary: Creative or novel behaviors in bottlenose dolphins (Tursiops truncatus) can be indicators of flexible thinking and problem solving. Twelve bottlenose dolphins (five females, seven males) in managed care were reinforced for exhibiting different behaviors of their choosing in response to a hand gesture. Using a human-based theory of creativity, the dolphins' behaviors were assessed for four aspects: how many different behaviors they could produce in a session or in a row (fluency), how different the behaviors were from each other (flexibility), how simple or complex the behaviors were (elaboration), and how novel or new the behaviors were (originality). The results indicated that dolphins were variable in all aspects measured, with some animals producing more behaviors that were also more complex and variable in type and energy than the other dolphins. Behaviors were also invented by several dolphins. The dolphins were engaged and cognitively challenged by this task, which suggests this task facilitates cognitive welfare while providing a means to study innovative behavior across species. Creative or novel behaviors in bottlenose dolphins (Tursiops truncatus) can be indicators of flexible thinking and problem solving. Over 50 years ago, two rough-tooth dolphins demonstrated creative novel behaviors acquired through reinforcement training in human care. Since this novel training, a variety of species have been trained to respond to this conceptual cue. The current study assessed the creativity of 12 bottlenose dolphins (5 females, 7 males) housed at the Roatan Institute for Marine Sciences (RIMS) in Roatan, Honduras. Individual differences were found across four constructs measured for creativity: fluency, flexibility, elaboration, and originality. Variability in performance occurred across test sessions. Animals with less experience with this task performed fewer "innovative" behaviors as compared to more experienced animals. Despite errors, dolphins continued to attempt the task during test sessions, suggesting the concept of "innovate" was intrinsically rewarding and cognitively engaging. This task may be utilized across species to promote the comparative study of innovative or creative behavior as well as to promote cognitive welfare. [ABSTRACT FROM AUTHOR]

The precision of agro-technical operations is one of the main hallmarks of a modern approach to agriculture. However, ensuring the precise application of plant protection products or the performance of mechanical field operations entails significant costs for sophisticated positioning systems. This paper explores the integration of precision positioning based on the global navigation satellite system (GNSS) in agriculture, particularly in fieldwork operations, seeking solutions of moderate cost with sufficient precision. This study examines the impact of GNSSs on automation and robotisation in agriculture, with a focus on intelligent agricultural guidance. It also discusses commercial devices that enable the automatic guidance of self-propelled machinery and the benefits that they provide. This paper investigates GNSS-based precision localisation devices under real field conditions. A comparison of commercial and low-cost GNSS solutions, along with the integration of satellite navigation with advanced visual odometry for improved positioning accuracy, is presented. The research demonstrates that affordable solutions based on the common differential GNSS infrastructure can be applied for accurate localisation under real field conditions. It also underscores the potential of GNSS-based automation and robotisation in transforming agriculture into a more efficient and sustainable industry. [ABSTRACT FROM AUTHOR]

In recent decades, three major volcanic eruptions of different intensity have occurred (Mount Agung in 1963, El Chichón in 1982 and Mount Pinatubo in 1991), with reported climate impacts on seasonal to decadal timescales that could have been potentially predicted with accurate and timely estimates of the associated stratospheric aerosol loads. The Decadal Climate Prediction Project component C (DCPP-C) includes a protocol to investigate the impact of volcanic aerosols on the climate experienced during the years that followed those eruptions through the use of decadal predictions. The interest of conducting this exercise with climate predictions is that, thanks to the initialisation, they start from the observed climate conditions at the time of the eruptions, which helps to disentangle the climatic changes due to the initial conditions and internal variability from the volcanic forcing. The protocol consists of repeating the retrospective predictions that are initialised just before the last three major volcanic eruptions but without the inclusion of their volcanic forcing, which are then compared with the baseline predictions to disentangle the simulated volcanic effects upon climate. We present the results from six Coupled Model Intercomparison Project Phase 6 (CMIP6) decadal prediction systems. These systems show strong agreement in predicting the well-known post-volcanic radiative effects following the three eruptions, which induce a long-lasting cooling in the ocean. Furthermore, the multi-model multi-eruption composite is consistent with previous work reporting an acceleration of the Northern Hemisphere polar vortex and the development of El Niño conditions the first year after the eruption, followed by a strengthening of the Atlantic Meridional Overturning Circulation the subsequent years. Our analysis reveals that all these dynamical responses are both model- and eruption-dependent. A novel aspect of this study is that we also assess whether the volcanic forcing improves the realism of the predictions. Comparing the predicted surface temperature anomalies in the two sets of hindcasts (with and without volcanic forcing) with observations we show that, overall, including the volcanic forcing results in better predictions. The volcanic forcing is found to be particularly relevant for reproducing the observed sea surface temperature (SST) variability in the North Atlantic Ocean following the 1991 eruption of Pinatubo. [ABSTRACT FROM AUTHOR]

The AIFARMS Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability national AI institute brings together over 40 world‐class AI and agriculture researchers, with the common mission to develop foundational advances in AI and use them to ensure that future agriculture is environmentally friendly, sustainable, affordable, and accessible to diverse farming communities. Since its establishment in 2020, AIFARMS has advanced the state of the art in autonomous farming, cover crop planting, machine learning for improved outcomes from remote sensing, dynamic estimation of yield loss from weeds, and livestock management. The institute has prioritized the creation and utilization of high‐quality, openly available data sets for advancing foundational AI and tackling agricultural challenges. AIFARMS leverages a close partnership between UIUC and Tuskegee University to build programming for a skilled and diverse next‐generation workforce in digital agriculture. We are expanding the reach of AIFARMS outside of the current partners to collaborate with national AI institutions and international partners. [ABSTRACT FROM AUTHOR]

Reliable obstacle avoidance, which is essential for safe autonomous robot interaction with the real world, raises various challenges such as difficulties with obstacle perception and latent factor cognition impacting multi-modal obstacle avoidance. In this paper, we propose a Depth visUal Ego-motion Learning (DUEL) model, consisting of a cognitive generation network, a policy decision network and a potential partition network, to learn autonomous obstacle avoidance from expert policies. The DUEL model takes advantage of binocular vision to perceive scene depth. This serves as the input to the cognitive generation network which generates obstacle avoidance policies by maximizing its causal entropy. The policy decision network then optimizes the generation of the policies referring to expert policies. The generated obstacle avoidance policies are simultaneously transferred to the potential partition network to capture the latent factors contained within expert policies and perform multi-modal obstacle avoidance. These three core networks iteratively optimize the multi-modal policies relying on causal entropy and mutual information theorems, which are proven theoretically. Experimental comparisons with state-of-the-art models on 7 metrics demonstrate the effectiveness of the DUEL model. It achieves the best performance with an average ADE (Average Displacement Error) of 0.29 and average FDE (Final Displacement Error) of 0.55 across five different scenarios. Results show that the DUEL model can maintain an average obstacle avoidance success rate of 97% for both simulated and real world scenarios with multiple obstacles, demonstrating its success at capturing latent factors from expert policies. Our source codes are available at https://github.com/ACoTAI/DUEL. [ABSTRACT FROM AUTHOR]

We demonstrate label-free dynamic optical coherence tomography (D-OCT)-based visualization and quantitative assessment of patterns of tumor spheroid response to three anti-cancer drugs. The study involved treating human breast adenocarcinoma (MCF-7 cell-line) with paclitaxel (PTX), tamoxifen citrate (TAM), and doxorubicin (DOX) at concentrations of 0 (control), 0.1, 1, and 10 µM for 1, 3, and 6 days. In addition, fluorescence microscopy imaging was performed for reference. The D-OCT imaging was performed using a custom-built OCT device. Two algorithms, namely logarithmic intensity variance (LIV) and late OCT correlation decay speed (OCDS l ) were used to visualize the tissue dynamics. The spheroids treated with 0.1 and 1 µM TAM appeared similar to the control spheroid, whereas those treated with 10 µM TAM had significant structural corruption and decreasing LIV and OCDS l over treatment time. The spheroids treated with PTX had decreasing volumes and decrease of LIV and OCDS l signals over time at most PTX concentrations. The spheroids treated with DOX had decreasing and increasing volumes over time at DOX concentrations of 1 and 10 µM, respectively. Meanwhile, the LIV and OCDS l signals decreased over treatment time at all DOX concentrations. The D-OCT, particularly OCDS l , patterns were consistent with the fluorescence microscopic patterns. The diversity in the structural and D-OCT results among the drug types and among the concentrations are explained by the mechanisms of the drugs. The presented results suggest that D-OCT is useful for evaluating the difference in the tumor spheroid response to different drugs and it can be a useful tool for anti-cancer drug testing. [ABSTRACT FROM AUTHOR]

Many individuals undergo mating and/or other aspects of reproductive experience at some point in their lives, and pregnancy and childbirth in particular are associated with alterations in the prevalence of several psychiatric disorders. Research in rodents shows that maternal experience affects spatial learning and other aspects of hippocampal function. In contrast, there has been little work in animal models concerning how reproductive experience affects cost-benefit decision making, despite the relevance of this aspect of cognition for psychiatric disorders. To begin to address this issue, reproductively experienced (RE) and reproductively naïve (RN) female Long-Evans rats were tested across multiple tasks that assess different forms of cost-benefit decision making. In a risky decision-making task, in which rats chose between a small, safe food reward and a large food reward accompanied by variable probabilities of punishment, RE females chose the large risky reward significantly more frequently than RN females (greater risk taking). In an intertemporal choice task, in which rats chose between a small, immediate food reward and a large food reward delivered after a variable delay period, RE females chose the large reward less frequently than RN females. Together, these results show distinct effects of reproductive experience on different forms of cost-benefit decision making in female rats, and highlight reproductive status as a variable that could influence aspects of cognition relevant for psychiatric disorders. [ABSTRACT FROM AUTHOR]

X-ray fluorescence (XRF) spectroscopy is a non-destructive differential measurement technique widely utilized in elemental analysis. However, due to its inherent high non-linearity and noise issues, it is challenging for XRF spectral analysis to achieve high levels of accuracy. In response to these challenges, this paper proposes a method for XRF spectral analysis that integrates an adaptive genetic algorithm with a backpropagation neural network, enhanced by an attention mechanism, termed as the AGA-BP-Attention method. By leveraging the robust feature extraction capabilities of the neural network and the ability of the attention mechanism to focus on significant features, spectral features are extracted for elemental identification. The adaptive genetic algorithm is subsequently employed to optimize the parameters of the BP neural network, such as weights and thresholds, which enhances the model's accuracy and stability. The experimental results demonstrate that, compared to traditional BP neural networks, the AGA-BP-Attention network can more effectively address the non-linearity and noise issues of XRF spectral signals. In XRF spectral analysis of air pollutant samples, it achieved superior prediction accuracy, effectively suppressing the impact of background noise on spectral element recognition. [ABSTRACT FROM AUTHOR]

This paper presents a discrete element method (DEM) numerical model to elucidate mechanical behavior, particle crushing, and anisotropy evolution within a pressurized sand damper (PSD) subjected to cyclic loading. Computational simulations of the PSD under different initial pressures and stroke amplitudes were conducted and compared to experimental results. Good agreement was achieved between the DEM model and experimental results for the different cases. Force–displacement, particle crushing, shear, and normal stresses along with geometric and mechanical anisotropy degrees were closely monitored in different areas of the PSD. Dissipated energy was also monitored and used to calculate the specific damping capacity. Employing spherical particles, crushable clusters, and uncrushable clumps as sand particles revealed that the closest results to the experiments are obtained when using crushable clusters. The results show that the majority of crushing occurs in the vicinity of the center of the PSD and within the first loading cycle. Increasing stroke amplitude significantly influenced particle crushing, whereas increasing initial pressure was less considerable. In addition, a direct relationship between the PSD's direction of movement with shear and normal stresses and anisotropy degree was observed. Moreover, the contribution of mechanical anisotropy was more considerable than the geometric anisotropy to the overall anisotropy degree. Regarding dissipated energy, an increase in stroke amplitude resulted in higher dissipated energy whereas an increase in initial pressure had a minor influence on the dissipated energy. Based on the dissipated energy, it was found that the specific damping capacity was nearly equal to one for all cases studied. [ABSTRACT FROM AUTHOR]

Mobile robots have become increasingly important across various sectors and are now essential in agriculture due to their ability to navigate effectively and precisely in crop fields. Navigation involves the integration of several technologies, including robotics, control theory, computer vision, and artificial intelligence, among others. Challenges in robot navigation, particularly in agriculture, include mapping, localization, path planning, obstacle detection, and guiding control. Accurate mapping, localization, and obstacle detection are crucial for efficient navigation, while guiding the robotic system is essential to execute tasks accurately and for the safety of crops and the robot itself. Therefore, this study introduces a Guiding Manager for autonomous mobile robots specialized for laser-based weeding tools in agriculture. The focus is on the robot's tracking, which combines a lateral controller, a spiral controller, and a linear speed controller to adjust to the different types of trajectories that are commonly followed in agricultural environments, such as straight lines and curves. The controllers have demonstrated their usefulness in different real work environments at different nominal speeds, validated on a tracked mobile platform with a width of about 1.48 m, in complex and varying field conditions including loose soil, stones, and humidity. The lateral controller presented an average absolute lateral error of approximately 0.076 m and an angular error of about 0.0418 rad, while the spiral controller presented an average absolute lateral error of about 0.12 m and an angular error of about 0.0103 rad, with a horizontal accuracy of about ±0.015 m and an angular accuracy of about ±0.009 rad, demonstrating its effectiveness in real farm tests. [ABSTRACT FROM AUTHOR]

The advancement of Industry 4.0 has significantly propelled the widespread application of automated guided vehicle (AGV) systems within smart factories. As the structural diversity and complexity of smart factories escalate, the conventional two-dimensional plan-based navigation systems with fixed routes have become inadequate. Addressing this challenge, we devised a novel mobile robot navigation system encompassing foundational control, map construction positioning, and autonomous navigation functionalities. Initially, employing point cloud matching algorithms facilitated the construction of a three-dimensional point cloud map within indoor environments, subsequently converted into a navigational two-dimensional grid map. Simultaneously, the utilization of a multi-threaded normal distribution transform (NDT) algorithm enabled precise robot localization in three-dimensional settings. Leveraging grid maps and the robot's inherent localization data, the A* algorithm was utilized for global path planning. Moreover, building upon the global path, the timed elastic band (TEB) algorithm was employed to establish a kinematic model, crucial for local obstacle avoidance planning. This research substantiated its findings through simulated experiments and real vehicle deployments: Mobile robots scanned environmental data via laser radar and constructing point clouds and grid maps. This facilitated centimeter-level localization and successful circumvention of static obstacles, while simultaneously charting optimal paths to bypass dynamic hindrances. The devised navigation system demonstrated commendable autonomous navigation capabilities. Experimental evidence showcased satisfactory accuracy in practical applications, with positioning errors of 3.6 cm along the x-axis, 3.3 cm along the y-axis, and 4.3° in orientation. This innovation stands to substantially alleviate the low navigation precision and sluggishness encountered by AGV vehicles within intricate smart factory environments, promising a favorable prospect for practical applications. [ABSTRACT FROM AUTHOR]

This study focuses on real-time detection of maize crop rows using deep learning technology to meet the needs of autonomous navigation for weed removal during the maize seedling stage. Crop row recognition is affected by natural factors such as soil exposure, soil straw residue, mutual shading of plant leaves, and light conditions. To address this issue, the YOLOv5s network model is improved by replacing the backbone network with the improved MobileNetv3, establishing a combination network model YOLOv5-M3 and using the convolutional block attention module (CBAM) to enhance detection accuracy. Distance-IoU Non-Maximum Suppression (DIoU-NMS) is used to improve the identification degree of the occluded targets, and knowledge distillation is used to increase the recall rate and accuracy of the model. The improved YOLOv5s target detection model is applied to the recognition and positioning of maize seedlings, and the optimal target position for weeding is obtained by max-min optimization. Experimental results show that the YOLOv5-M3 network model achieves 92.2% mean average precision (mAP) for crop targets and the recognition speed is 39 frames per second (FPS). This method has the advantages of high detection accuracy, fast speed, and is light weight and has strong adaptability and anti-interference ability. It determines the relative position of maize seedlings and the weeding machine in real time, avoiding squeezing or damaging the seedlings. [ABSTRACT FROM AUTHOR]

Lipoprotein(a) [Lp(a)] is a risk factor for cardiovascular disease (CVD) and aortic stenosis. However, the data on the relationship between Lp(a) and mildly reduced estimated glomerular filtration rate (eGFR) has been disputed. This study was conducted to assess the relationship between Lp(a) concentrations and mildly reduced eGFR in healthy subjects.This community-based, cross-sectional study enrolled 1,064 volunteers aged ≥ 40 years who lived in Yonghong Community, Zhonglou District, Changzhou, China, between December 2016 and December 2017. A mildly reduced eGFR was defined as eGFR between 60 and 90 mL/min/1.73m2. A standardized questionnaire and biochemical measurements were used to gather information about participants. The serum concentration of Lp(a) was determined using the latex-enhanced immunoturbidimetric test. Of the total study population, 34.8% (n = 370) were men, and the mean age was 66.8 ± 8.5 years. A significant association existed between Lp(a) levels and the risk of mildly reduced eGFR. Individuals with the highest tertile of Lp(a) had higher odds of mildly reduced eGFR after adjusting for various confounders (adjusted odds ratio [OR]: 1.80, 95% confidence interval [CI]: 1.24–2.60, P = 0.0025) compared to those with the lowest tertile of Lp(a). Multivariable logistic regression of studies in which Lp(a) was presented as continuous variables showed consistent results (adjusted OR: 1.23 for 1-SD increment of Ln-Lp(a), 95% CI: 1.05–1.43). Subgroup analyses showed that study characteristics such as age, sex, obesity, diabetes, and hypertension status did not significantly affect the association (P for all interactions > 0.05). These results suggest that higher serum Lp(a) level was an independent risk factor for mildly reduced eGFR. [ABSTRACT FROM AUTHOR]

The clustered protocadherins (cPcdhs) play a critical role in the patterning of several CNS axon and dendritic arbors, through regulation of homophilic self and neighboring interactions. While not explored, primary peripheral sensory afferents that innervate the epidermis may require similar constraints to convey spatial signals with appropriate fidelity. Here, we show that members of the c-Pcdh (Pcdhc) family are expressed in both adult sensory neuron axons and in neighboring keratinocytes that have close interactions during skin reinnervation. Adult mice of both sexes were studied. Pcdhc knock-down either through small interfering RNA (siRNA) transduction or AAV-Cre recombinase transfection of adult mouse primary sensory neurons from floxed Pcdhc mice was associated with a remarkable rise in neurite outgrowth and branching. Rises in outgrowth were abrogated by Rac1 inhibition. Moreover, AAV-Cre knock-down in Pcdhc floxed neurons generated a rise in neurite self-intersections, and a robust rise in neighbor intersections or tiling, suggesting a role in sensory axon repulsion. Interestingly, preconditioned (3-d axotomy) neurons with enhanced growth had temporary declines in Pcdhc and lessened outgrowth from Pcdhc siRNA. In vivo, mice with local hindpaw skin Pcdhc knock-down by siRNA had accelerated reinnervation by new epidermal axons with greater terminal branching and reduced intra-axonal spacing. Pcdhc knock-down also had reciprocal impacts on keratinocyte density and nuclear size. Taken together, this work provides evidence for a role of Pcdhc in attenuating outgrowth of sensory axons and their interactions, with implications in how new reinnervating axons following injury fare amid skin keratinocytes that also express Pcdhc. [ABSTRACT FROM AUTHOR]

This study introduces two methods for crop identification and growth stage determination, focused primarily on enabling mobile robot navigation. These methods include a two-phase approach involving separate models for crop and growth stage identification and a one-phase method employing a single model capable of handling all crops and growth stages. The methods were validated with maize and sugar beet field images, demonstrating the effectiveness of both approaches. The one-phase approach proved to be advantageous for scenarios with a limited variety of crops, allowing, with a single model, to recognize both the type and growth state of the crop and showed an overall Mean Average Precision (mAP) of about 67.50%. Moreover, the two-phase method recognized the crop type first, achieving an overall mAP of about 74.2%, with maize detection performing exceptionally well at 77.6%. However, when it came to identifying the specific maize growth state, the mAP was only able to reach 61.3% due to some difficulties arising when accurately categorizing maize growth stages with six and eight leaves. On the other hand, the two-phase approach has been proven to be more flexible and scalable, making it a better choice for systems accommodating a wide range of crops. [ABSTRACT FROM AUTHOR]

Due to the accelerated growth of the world's population, food security and sustainable agricultural practices have become essential. The incorporation of Artificial Intelligence (AI)-enabled robotic systems in cultivation, especially in greenhouse environments, represents a promising solution, where the utilization of the confined infrastructure improves the efficacy and accuracy of numerous agricultural duties. In this paper, we present a comprehensive autonomous navigation architecture for holonomic mobile robots in greenhouses. Our approach utilizes the heating system rails to navigate through the crop rows using a single stereo camera for perception and a LiDAR sensor for accurate distance measurements. A finite state machine orchestrates the sequence of required actions, enabling fully automated task execution, while semantic segmentation provides essential cognition to the robot. Our approach has been evaluated in a real-world greenhouse using a custom-made robotic platform, showing its overall efficacy for automated inspection tasks in greenhouses. [ABSTRACT FROM AUTHOR]

Aims: Understanding the suite of environmental changes impacting migration and disentangling these from the dependencies between migratory stages is fundamental to understanding the drivers and mechanisms causing spatial shifts to migratory bird distributions. In this review, our objectives are to: (i) provide evidence of spatial change at all migratory stages, (ii) identify the key mechanisms driving change, (iii) discuss how anthropogenic change influences migratory patterns, (iv) highlight interdependencies between migratory stages and (v) offer a framework for understanding migratory pattern changes. Location: Migratory land birds globally. Methods: We defined a literature search to find examples of spatial changes in avian migrations and their underlying mechanisms. From the top 1000 search results, sorted by relevance, we retained 81 papers published between 1991 and 2020. Results: Spatial shifts in migratory bird distributions occur throughout the annual cycle, but patterns vary across taxa and migratory stages. Since avian migrants occupy multiple sites across the annual cycle, environmental change has particularly complex repercussions for their distributions. Multiple environmental changes can affect distributions at each migratory stage due to fitness asymmetries between migratory strategies and individuals responding flexibly to environmental changes within and across generations. Spatial changes at one stage (e.g. breeding) can also trigger further indirect spatial changes at other stages (e.g. stopovers) due to dependencies between stages. Conclusions: To understand the drivers and mechanisms underpinning spatial changes, comprehensive year‐round distributional data are crucial for observing change across multiple migratory stages and disentangling competing drivers. Biologging technology could elucidate the mechanisms causing changes to individual‐level strategies and uncover fitness asymmetries between strategies. Ultimately, gaining a clearer understanding of spatial changes will aid in predicting distributional responses to anthropogenic change in migratory birds. This is paramount for mitigating the effects of anthropogenic change and assessing future population viability within this vulnerable and extensive taxonomic group. [ABSTRACT FROM AUTHOR]

Introduction Nowadays, machine vision systems are extensively used in agriculture. The application of this technology in the field can help preserve agricultural resources while reducing manual labor and production costs. In the field of agricultural automation, accurately detecting crop rows is recognized as a crucial and challenging issue for weed identification and the automatic guidance of machines. Therefore, it is necessary to explore practical solutions to optimize this process. Hence, the purpose of this study is the precise identification of basil cultivation rows to enable the automatic navigation of robots in the cultivation field. Materials and Methods In the first stage of this research, six images from each growth period of basil plants (third, fourth, and fifth week) were taken and weeds were removed from the area between the crop rows using three different methods of area opening, dimensional removal, and masking. In the next stage, six images of crop rows without weeds were examined by performing image processing operations and implementing several routing algorithms, namely, Hough transform, wavelet transform, Gabor filter, linear regression, and an additional algorithm proposed in this study. The output of each of these algorithms was compared with the ideal path identified by the user. For this purpose, after capturing an image, green areas were extracted from it by performing the segmentation process. By applying each of the routing algorithms to the image, plant cultivation lines were identified and their equations were determined. Finally, the performance of the designed robot was evaluated using the most appropriate routing algorithm. Results and Discussion Examining the performance of three different methods of weed removal in three periods of plant growth (third, fourth, and fifth week) showed that during this interval, the masking method had the lowest error rate compared to the ideal path and the shortest average operation time of 1.64 seconds, followed by the dimensional removal and the area opening methods. Comparing the routes detected by different routing algorithms with the ideal routes and according to the results of the t-test at 5% probability level, the order of the studied routing methods from the most superior is as follows: the proposed algorithm, Gabor filter, linear regression, Hough transform and wavelet transform algorithm. Overall, the proposed algorithm had the highest rate of adaptation to the ideal path (with an average error of 3.65 pixels) and the shortest operation time (4.79 seconds) and was selected as the most appropriate routing algorithm and the performance of the designed robot was evaluated using it. Conclusion A reliable crop row detection algorithm can reduce production costs and preserve the environment. In this study, the masking method was used for removing weeds from the images. The new proposed routing algorithm has superior performance when compared with common routing algorithms such as the Gabor filter, linear regression, Hough transform, and wavelet transform. Additionally, it was shown that the designed robot using the proposed algorithm (with an average error of 3.65 pixels) has the desired performance. [ABSTRACT FROM AUTHOR]

Repeated and missed spraying are common problems during the working of boom sprayers, especially in the breakpoint continuous process. Therefore, the present study investigated a breakpoint continuous spraying system for automatic-guidance boom sprayers based on a hysteresis compensation algorithm for spraying. An operational breakpoint identification algorithm, which combines a real-time kinematic global navigation satellite system (RTK-GNSS) and wheel odometer, was proposed; a pre-adjusted proportional-integral-derivative (PID) control algorithm for the opening degree of the proportional control valve was designed in thus study. Tests were conducted to establish equations correlating the opening degree of the proportional control valve, pump output flow rate, and main pipeline flow rate, with an R2 ≥ 0.9525. The time to adjust to the target flow rate was experimentally tested. The breakpoint identification accuracy of the RTK-GNSS and RTK-GNSS + wheel odometer was experimentally assessed. A field spraying deposition variation experiment was conducted. According to the results, the system effectively eliminated missed spraying, with a maximum repeated spraying distance of ≤3.3 m, and it achieved a flow control error within 3%. This system also reduced the repeated spraying area and enhanced the pesticide spraying quality of breakpoint continuous spraying for automatic-guidance boom sprayers. [ABSTRACT FROM AUTHOR]

This study presents the development of a route planner, called Mission Planner, for an agricultural weeding robot that generates efficient and safe routes both in the field and on the farm using a graph-based approach. This planner optimizes the robot's motion throughout the farm and performs weed management tasks tailored for high-power laser devices in narrow-row crops (wheat, barley, etc.) and wide-row crops (sugar beet, maize, etc.). Three main algorithms were integrated: Dijkstra's algorithm to find the most optimal route on the farm, the VRMP (Visibility Road-Map Planner) method to select the route within cultivated fields when roads are not visible, and an improved version of the Hamiltonian path to find the best route between the crop lines. The results support the effectiveness of the strategies implemented, demonstrating that a robot can safely and efficiently navigate through the entire farm and perform an agricultural treatment, in this case study, in laser-based weed management. In addition, it was found that the route planner reduced the robot's operation time, thus improving the overall efficiency of precision agriculture. [ABSTRACT FROM AUTHOR]

The optimization of the number of transistors of combinational logic circuits can lead to faster and cheaper electronic devices, but it is an NP-complete problem. There are deterministic algorithms for this task, but their effectiveness is limited to small problems. Thus, the use of metaheuristics is suitable and Cartesian Genetic Programming (CGP) is widely adopted in the literature. In CGP, mutation is commonly the only operator used for generating new candidate solutions. As a result, the performance of this metaheuristic is dependent on the proper choice of mutation and its parameters. CGP mutation is usually based on uniform mutation and, thus, any modification has the same chance to occur. In order to improve the performance of CGP, a study of the mutation operator is carried out and an adaptive approach using an ϵ -greedy strategy for bias the selection of the node mutation type is proposed here. The proposal is evaluated using a benchmark from the literature. The results obtained indicate that the proposed adaptive mutation is promising, achieving the best mean results in 19 out of the 23 benchmark problems considered here. We also verified a better ability in generating feasible solutions than the standard CGP. [ABSTRACT FROM AUTHOR]

The diurnal cycle is often poorly reproduced in global climate model (GCM) simulations, particularly in terms of rainfall frequency and amplitude. While improvements in the regional climate model (RCM) with bias‐corrected boundaries have been reported in previous studies, they assumed that diurnal patterns are simulated correctly by the GCM, potentially leading to inaccuracies in the maximum rainfall timing and magnitude within the RCM domain. Here we provide the first examination of improvements to the diurnal cycle, within a RCM domain, achieved through the use of sophisticated bias‐corrected lateral and lower boundary conditions. Results show that the RCMs with bias‐corrected boundaries generally present improvement in capturing both rainfall timing and magnitude, particularly in northern Australia, where a strong diurnal pattern in rainfall is prevalent. We show that correcting systematic sub‐daily multivariate bias in RCM boundaries improves the diurnal rainfall cycle, which is particularly important in regions where short‐term intense precipitation occurs. Plain Language Summary: Global climate models have limitations in simulating precipitation characteristics at sub‐daily time scales, leading to significant bias in capturing rainfall timing and magnitude in regional climate model (RCM) simulations that use global climate model data as input. Previous studies have applied various bias correction approaches to the boundary conditions of RCMs, but they often assume that GCM sub‐daily rainfall patterns are simulated correctly, leading to a significant bias in maximum rainfall timing and magnitude within the domain. This study investigates the consequences of multivariate sub‐daily bias correction on the boundary conditions in order to evaluate the model performance for short‐term precipitation events. The results show that RCMs with bias‐corrected boundary conditions exhibit improvement across Australia, especially in northern Australia, which regularly experiences intense sub‐daily rainfall. Key Points: Regional climate model (RCM) with uncorrected boundaries poorly represented 3‐hourly precipitation with only 20%∼30% agreements compared to observationsSub‐daily bias correction of RCM lateral boundaries improves the sub‐daily representation of fields entering the RCM domainSub‐daily multivariate bias correction on the boundaries resulted in additional improvements, especially in northern Australia [ABSTRACT FROM AUTHOR]

Marine reserves are considered essential for sustainable fisheries, although their effectiveness compared to traditional fisheries management is debated. The effect of marine reserves is mostly studied on short ecological time scales, whereas fisheriesinduced evolution is a well-established consequence of harvesting. Using a sizestructured population model for an exploited fish population of which individuals spend their early life stages in a nursery habitat, we show that marine reserves will shift the mode of population regulation from low size-selective survival late in life to low, early-life survival due to strong resource competition. This shift promotes the occurrence of rapid ecological cycles driven by density-dependent recruitment as well as much slower evolutionary cycles driven by selection for the optimal body to leave the nursery grounds, especially with larger marine reserves. The evolutionary changes increase harvesting yields in terms of total biomass but cause disproportionately large decreases in yields of larger, adult fish. Our findings highlight the importance of carefully considering the size of marine reserves and the individual life history of fish when managing eco-evolutionary marine systems to ensure both population persistence as well as stable fisheries yields. [ABSTRACT FROM AUTHOR]

Obstacle avoidance control and navigation in unstructured agricultural environments are key to the safe operation of autonomous robots, especially for agricultural machinery, where cost and stability should be taken into account. In this paper, we designed a navigation and obstacle avoidance system for agricultural robots based on LiDAR and a vision camera. The improved clustering algorithm is used to quickly and accurately analyze the obstacle information collected by LiDAR in real time. Also, the convex hull algorithm is combined with the rotating calipers algorithm to obtain the maximum diameter of the convex polygon of the clustered data. Obstacle avoidance paths and course control methods are developed based on the danger zones of obstacles. Moreover, by performing color space analysis and feature analysis on the complex orchard environment images, the optimal H-component of HSV color space is selected to obtain the ideal vision-guided trajectory images based on mean filtering and corrosion treatment. Finally, the proposed algorithm is integrated into the Three-Wheeled Mobile Differential Robot (TWMDR) platform to carry out obstacle avoidance experiments, and the results show the effectiveness and robustness of the proposed algorithm. The research conclusion can achieve satisfactory results in precise obstacle avoidance and intelligent navigation control of agricultural robots. [ABSTRACT FROM AUTHOR]

Since 2014, economic sanctions between Russia and Western nations have significantly altered the global seafood trade. The consequent decline in bilateral trade also had spillover effects on the rest of the world (ROW). According to earlier studies, economic sanctions appear to negatively impact bilateral trade and income. However, few studies examine how Russian sanctions affect the world as a whole and estimate their effects on the fisheries industry. This study seeks to close this gap by quantifying the extent to which Russian sanctions have impacted trade in terms of trade deflection, trade destruction, trade depression, and trade creation. To this end, panel data from 185 countries were created for the years from 2005 to 2020. With trade policy variables that account for changes in trade channels, a structural gravity trade model was specified. Based on calculations using the Poisson pseudo-maximum likelihood (PPML) fixed effect model, economic sanctions led to a 119.28% surge in Russia's seafood imports from the rest of the world (ROW), alongside a 39% decline in imports from Western countries. The extent of trade deflection, which includes the exports of Western nations diverted from Russia to the ROW markets, increased by 5.49%. The results demonstrate that trade between sanctioned states, as well as global trade, is significantly impacted by economic sanctions. [ABSTRACT FROM AUTHOR]

Global navigation satellite systems (GNSSs) became an integral part of all aspects of our lives, whether for positioning, navigation, or timing services. These systems are central to a range of applications including road, aviation, maritime, and location-based services, agriculture, and surveying. The Global Positioning System (GPS) Standard Position Service (SPS) provides position accuracy up to 10 m. However, some modern-day applications, such as precision agriculture (PA), smart farms, and Agriculture 4.0, have demanded navigation technologies able to provide more accurate positioning at a low cost, especially for vehicle guidance and variable rate technology purposes. The Society of Automotive Engineers (SAE), for instance, through its standard J2945 defines a maximum of 1.5 m of horizontal positioning error at 68% probability ( 1 σ ), aiming at terrestrial vehicle-to-vehicle (V2V) applications. GPS position accuracy may be improved by addressing the common-mode errors contained in its observables, and relative GNSS (RGNSS) is a well-known technique for overcoming this issue. This paper builds upon previous research conducted by the authors and investigates the sensitivity of the position estimation accuracy of low-cost receiver-equipped agricultural rovers as a function of two degradation factors that RGNSS is susceptible to: communication failures and baseline distances between GPS receivers. The extended Kalman filter (EKF) approach is used for position estimation, based on which we show that it is possible to achieve 1.5 m horizontal accuracy at 68% probability ( 1 σ ) for communication failures up to 3000 s and baseline separation of around 1500 km. Experimental data from the Brazilian Network for Continuous Monitoring of GNSS (RBMC) and a moving agricultural rover equipped with a low-cost GPS receiver are used to validate the analysis. [ABSTRACT FROM AUTHOR]

Over 500 K (per year) cervical cancer cases are reported with a high mortality rate (6–9%). Automatically detecting cervical cancer using the Computer-Aided Diagnosis (CAD) tool at an early stage is important since it leads to successful treatment as pathologists. In this paper, we propose a tool that classifies cervical cancer cases from Pap smear cytology images using deep features. The proposed tool constitutes a Convolutional Neural Network (CNN) and a metaheuristic evolutionary algorithm called Opposition-based Harmony Search Algorithm (O-bHSA) for deep feature section. These features are classified using standard classifiers: SVM, MLP, and KNN. On two different publicly available datasets: Pap smear and liquid-based cytology, the proposed tool outperforms not only seven well-known optimization algorithms but also state-of-the-art methods. Codes are publicly available on GitHub. [ABSTRACT FROM AUTHOR]

A row-following system based on machine vision for a picking robot was designed in our previous study. However, the visual perception could not provide reliable information during headland turning according to the test results. A complete navigation system for a picking robot working in an orchard needs to support accurate row following and headland turning. To fill this gap, a headland turning method for an autonomous picking robot was developed in this paper. Three steps were executed during headland turning. First, row end was detected based on machine vision. Second, the deviation was further reduced before turning using the designed fast posture adjustment algorithm based on satellite information. Third, a curve path tracking controller was developed for turning control. During the MATLAB simulation and experimental test, different controllers were developed and compared with the designed method. The results show that the designed turning method enabled the robot to converge to the path more quickly and remain on the path with lower radial errors, which eventually led to reductions in time, space, and deviation during headland turning.

A camera equipped with a transparent shield can be modeled using the pinhole camera model and residual error vectors defined by the difference between the estimated ray from the pinhole camera model and the actual three-dimensional (3D) point. To calculate the residual error vectors, we employ sparse calibration data consisting of 3D points and their corresponding 2D points on the image. However, the observation noise and sparsity of the 3D calibration points pose challenges in determining the residual error vectors. To address this, we first fit Gaussian Process Regression (GPR) operating robustly against data noise to the observed residual error vectors from the sparse calibration data to obtain dense residual error vectors. Subsequently, to improve performance in unobserved areas due to data sparsity, we use an additional constraint; the 3D points on the estimated ray should be projected to one 2D image point, called the ray constraint. Finally, we optimize the radial basis function (RBF)-based regression model to reduce the residual error vector differences with GPR at the predetermined dense set of 3D points while reflecting the ray constraint. The proposed RBF-based camera model reduces the error of the estimated rays by 6% on average and the reprojection error by 26% on average. [ABSTRACT FROM AUTHOR]

The improvement of grain yield, quality, and resistance can be achieved through the utilization of heterosis. The combination of cytoplasmic male sterility (CMS) and fertility restoration (Rf) gene(s) greatly facilitates the commercial development of three-line hybrid rice based on heterosis. The basis for investigating the relationship between CMS and Rf genes lies in the rapid localization of wild rice fertility restoration genes. A set of the BC4F5 population derived from interspecific crosses between Xieqingzao B (XB) and the BC1F9 XB//Dongxiang wild rice (DWR)/XB line L5339 was used to detect quantitative trait loci (QTL) for fertility restoration. The population was then crossed with two male sterile lines, Zhong9A (Z9A) and DongB11A (DB11A), in order to generate a testcrossing population for investigating spikelet fertility. Based on the linkage mapping, seven QTLs were detected on chromosomes 1, 3, 5, 6, 8, and 10, explaining 2.76 to 12.46% of the phenotypic variation. Of them, two novel fertility restoration QTLs, qRf3 and qRf6, can restore fertility of the CMS-DWR line DB11A by 16.56% and 15.12%, respectively. By employing joint QTL-seq and GradedPool-Seq methods, two novel Rf QTLs for DB11A, qRf3 and qRf6, were identified at the physical locations of 10,900,001–11,700,000 bp and 28,016,785–31,247,556 bp, respectively. These findings are useful for exploring the natural variations of Rf genes in rice. Therefore, rice's new genetic resources for the selection and breeding of rice restorer lines provide promising candidates for QTL fine localization and clarification. [ABSTRACT FROM AUTHOR]

The prevalence of multidrug-resistant (MDR) bacteria has recently increased dramatically, seriously endangering human health. Herein, amoxicillin (Amoxi)-conjugated gold nanoparticles (AuNPs) were created as a novel drug delivery system to overcome MDR bacteria. MDR bacteria were isolated from a variety of infection sources. Phenotype, biotype, and 16S rRNA gene analyses were used for isolate identification. Additionally, Juniperus excelsa was used for the production of AuNPs. The conjugation of AuNPs with Amoxi using sodium tri-polyphosphate (TPP) as a linker to produce Amoxi-TPP-AuNPs was studied. The AuNP and Amoxi-TPP-AuNP diameters ranged from 15.99 to 24.71 nm, with spherical and hexagonal shapes. A total of 83% of amoxicillin was released from Amoxi-TPP-AuNPs after 12 h, and after 3 days, 90% of the medication was released. The Amoxi-TPP-AuNPs exhibited superior antibacterial effectiveness against MRSA and MDR E. coli strains. Amoxi-TPP-AuNPs had MICs of 3.6–8 µg mL−1 against the tested bacteria. This is 37.5–83 fold higher compared to values reported in the literature. Amoxi-TPP-AuNPs exhibit a remarkable ability against MRSA and E. coli strains. These results demonstrate the applicability of Amoxi-TPP-AuNPs as a drug delivery system to improve therapeutic action. [ABSTRACT FROM AUTHOR]

Most bacteria use the tubulin homolog FtsZ to organize their cell division. FtsZ polymers initially assemble into mobile complexes that circle around a ring‐like structure at the cell midpoint, followed by the recruitment of other proteins that will constrict the cytoplasmic membrane and synthesize septal peptidoglycan to divide the cell. Despite the need for FtsZ polymers to associate with the membrane, FtsZ lacks intrinsic membrane binding ability. Consequently, FtsZ polymers have evolved to interact with the membrane through adaptor proteins that both bind FtsZ and the membrane. Here, we discuss recent progress in understanding the functions of these FtsZ membrane tethers. Some, such as FtsA and SepF, are widely conserved and assemble into varied oligomeric structures bound to the membrane through an amphipathic helix. Other less‐conserved proteins, such as EzrA and ZipA, have transmembrane domains, make extended structures, and seem to bind to FtsZ through two separate interactions. This review emphasizes that most FtsZs use multiple membrane tethers with overlapping functions, which not only attach FtsZ polymers to the membrane but also organize them in specific higher‐order structures that can optimize cell division activity. We discuss gaps in our knowledge of these concepts and how future studies can address them. [ABSTRACT FROM AUTHOR]

Successive years of straw mulching and returning straw to the fields in Northeast China have made strip-tillage necessary, and reasonable strip-tillage operations can create conditions for crop growth. However, there are limited research studies on the related equipment applicable to this area. In this paper, an automatic control strip-tillage machine is designed. According to the conventional planting pattern of maize in this region, the operative processes of the machine were determined, and a suitable strip seedbed structure could then be constructed under straw mulching conditions. The type of coulters and the structural parameters of the V-type soil-crushing wheel were determined through theoretical analysis. Based on the air spring and electric linear actuator, the plowing depth control system and the straw width control system were developed, respectively, so as to improve the stability of the machine operation. Field tests showed that when the forward speed, tillage depth, and theoretical width were 6–12 km/h, 6–12 cm, and 18–24 cm, respectively, the straw clearing rate, soil crushing rate, and tillage depth and breadth stability were higher than 90%, and the soil flatness was less than 2 cm. All the indexes satisfied the agronomic and technological requirements of corn cultivation. The results of this study can provide equipment and technical support for the further popularization of conservation tillage technology. [ABSTRACT FROM AUTHOR]