UNASSIGNED: Field wheat ear counting is an important step in wheat yield estimation, and how to solve the problem of rapid and effective wheat ear counting in a field environment to ensure the stability of food supply and provide more reliable data support for agricultural management and policy making is a key concern in the current agricultural field.
UNASSIGNED: There are still some bottlenecks and challenges in solving the dense wheat counting problem with the currently available methods. To address these issues, we propose a new method based on the YOLACT framework that aims to improve the accuracy and efficiency of dense wheat counting. Replacing the pooling layer in the CBAM module with a GeM pooling layer, and then introducing the density map into the FPN, these improvements together make our method better able to cope with the challenges in dense scenarios.
UNASSIGNED: Experiments show our model improves wheat ear counting performance in complex backgrounds. The improved attention mechanism reduces the RMSE from 1.75 to 1.57. Based on the improved CBAM, the R2 increases from 0.9615 to 0.9798 through pixel-level density estimation, the density map mechanism accurately discerns overlapping count targets, which can provide more granular information.
UNASSIGNED: The findings demonstrate the practical potential of our framework for intelligent agriculture applications.

BACKGROUND: Automatic and precise jujube yield prediction is important for the management of orchards and the allocation of resources. Traditional yield prediction techniques are based on object detection, which predicts a box to achieve target statistics, but are often used in sparse target settings. Those techniques, however, are challenging to use in real-world situations with particularly dense jujubes. The box labeling is labor- and time-intensive, and the robustness of the system is adversely impacted by severe occlusions. Therefore, there is an urgent need to develop a robust method for predicting jujube yield based on images. But in addition to the extreme occlusions, it is also challenging due to varying scales, complex backgrounds, and illumination variations.
RESULTS: In this work, we developed a simple and effective feature enhancement guided network for yield estimation of high-density jujube. It has two key designs: Firstly, we proposed a novel label representation method based on uniform distribution, which provides a better characterization of object appearance compared to the Gaussian-kernel-based method. This new method is simpler to implement and has shown greater success. Secondly, we introduced a feature enhancement guided network for jujube counting, comprising three main components: backbone, density regression module, and feature enhancement module. The feature enhancement module plays a crucial role in perceiving the target of interest effectively and guiding the density regression module to make accurate predictions. Notably, our method takes advantage of this module to improve the overall performance of our network. To validate the effectiveness of our method, we conducted experiments on a collected dataset consisting of 692 images containing a total of 40,344 jujubes. The results demonstrate the high accuracy of our method in estimating the number of jujubes, with a mean absolute error (MAE) of 9.62 and a mean squared error (MSE) of 22.47. Importantly, our method outperforms other state-of-the-art methods by a significant margin, highlighting its superiority in jujube yield estimation.
CONCLUSIONS: The proposed method provides an efficient image-based technique for predicting the yield of jujubes. The study will advance the application of artificial intelligence for high-density target recognition in agriculture and forestry. By leveraging this technique, we aim to enhance the level of planting automation and optimize resource allocation.

Cryo-electron microscopy (Cryo-EM) and cryo-electron tomography (cryo-ET) produce 3-D density maps of biological molecules at a range of resolution levels. Pattern recognition tools are important in distinguishing biological components from volumetric maps with the available resolutions. One of the most distinct characters in density maps at medium (5-10 Å) resolution is the visibility of protein secondary structures. Although computational methods have been developed, the accurate detection of helices and β-strands from cryo-EM density maps is still an active research area. We have developed a tool for protein secondary structure detection and evaluation of medium resolution 3-D cryo-EM density maps which combines three computational methods (SSETracer, StrandTwister, and AxisComparison). The program was integrated in UCSF Chimera, a popular visualization software in the cryo-EM community. In related work, we have developed BundleTrac, a computational method to trace filaments in a bundle from lower resolution cryo-ET density maps. It has been applied to actin filament tracing in stereocilia with good accuracy and can be potentially added as a tool in Chimera.

Membrane proteins reside at interfaces between aqueous and lipid media and solving their molecular structure relies most of the time on removing them from the membrane using detergent. Luckily, this solubilization process does not strip them from all the associated lipids and single-particle cryo-transmission electron microscopy (SP-TEM) has proved a very good tool to visualise both protein high-resolution structure and, often, many of its associated lipids. In this review, we observe membrane protein structures from the Protein DataBank and their associated maps in the Electron Microscopy DataBase and determine how the SP-TEM maps allow lipid visualization, the type of binding sites, the influence of symmetry, resolution and other factors. We illustrate lipid visualization around and inside the protein core, show that some lipid bilayers in the core can be shifted with respect to the membrane and how some proteins can actively bend the lipid bilayer that binds to them. We conclude that resolution improvement in SP-TEM will likely enable many more discoveries regarding the role of lipids bound to proteins.

Single-particle electron cryomicroscopy (cryoEM) has become an indispensable tool for studying structure and function in macromolecular assemblies. As an integral part of the cryoEM structure determination process, computational tools have been developed to build atomic models directly from a density map without structural templates. Nearly a decade ago, we created Pathwalking, a tool for de novo modeling of protein structure in near-atomic resolution cryoEM density maps. Here, we present the latest developments in Pathwalking, including the addition of probabilistic models, as well as a companion tool for modeling waters and ligands. This software was evaluated on the 2021 CryoEM Ligand Challenge density maps, in addition to identifying ligands in three IP3R1 density maps at ~3 Å to 4.1 Å resolution. The results clearly demonstrate that the Pathwalking de novo modeling pipeline can construct accurate protein structures and reliably localize and identify ligand density directly from a near-atomic resolution map.

Key questions in paleoneurology concern the timing and emergence of derived cerebral features within the human lineage. Endocasts are replicas of the internal table of the bony braincase that are widely used in paleoneurology as a proxy for reconstructing a timeline for hominin brain evolution in the fossil record. The accurate identification of cerebral sulci imprints in endocasts is critical for assessing the topographic extension and structural organisation of cortical regions in fossil hominins. High-resolution imaging techniques combined with established methods based on population-specific brain atlases offer new opportunities for tracking detailed endocranial characteristics. This study provides the first documentation of sulcal pattern imprints from the superolateral surface of the cerebrum using a population-based atlas technique on extant human endocasts. Human crania from the Pretoria Bone Collection (South Africa) were scanned using micro-CT. Endocasts were virtually extracted, and sulci were automatically detected and manually labelled. A density map method was applied to project all the labels onto an averaged endocast to visualise the mean distribution of each identified sulcal imprint. This method allowed for the visualisation of inter-individual variation of sulcal imprints, for example, frontal lobe sulci, correlating with previous brain-MRI studies and for the first time the extensive overlapping of imprints in historically debated areas of the endocast (e.g. occipital lobe). In providing an innovative, non-invasive, observer-independent method to investigate human endocranial structural organisation, our analytical protocol introduces a promising perspective for future research in paleoneurology and for discussing critical hypotheses on the evolution of cognitive abilities among hominins.

Crowd counting has been widely studied by deep learning in recent years. However, due to scale variation caused by perspective distortion, crowd counting is still a challenging task. In this paper, we propose a Densely Connected Multi-scale Pyramid Network (DMPNet) for count estimation and the generation of high-quality density maps. The key component of our network is the Multi-scale Pyramid Network (MPN), which can extract multi-scale features of the crowd effectively while keeping the resolution of the input feature map and the number of channels unchanged. To increase the information transfer between the network layer, we used dense connections to connect multiple MPNs. In addition, we also designed a novel loss function, which can help our model achieve better convergence. To evaluate our method, we conducted extensive experiments on three challenging benchmark crowd counting datasets. Experimental results show that compared with the state-of-the-art algorithms, DMPNet performs well in both parameters and results. The code is available at: https://github.com/lpfworld/DMPNet.

Despite the abundance of available software tools, optimal particle selection is still a vital issue in single-particle cryoelectron microscopy (cryo-EM). Regardless of the method used, most pickers struggle when ice thickness varies on a micrograph. IceBreaker allows users to estimate the relative ice gradient and flatten it by equalizing the local contrast. It allows the differentiation of particles from the background and improves overall particle picking performance. Furthermore, we introduce an additional parameter corresponding to local ice thickness for each particle. Particles with a defined ice thickness can be grouped and filtered based on this parameter during processing. These functionalities are especially valuable for on-the-fly processing to automatically pick as many particles as possible from each micrograph and to select optimal regions for data collection. Finally, estimated ice gradient distributions can be stored separately and used to inspect the quality of prepared samples.

The thousand grain weight is an index of size, fullness and quality in crop seed detection and is an important basis for field yield prediction. To detect the thousand grain weight of rice requires the accurate counting of rice. We collected a total of 5670 images of three different types of rice seeds with different qualities to construct a model. Considering the different shapes of different types of rice, this study used an adaptive Gaussian kernel to convolve with the rice coordinate function to obtain a more accurate density map, which was used as an important basis for determining the results of subsequent experiments. A Multi-Column Convolutional Neural Network was used to extract the features of different sizes of rice, and the features were fused by the fusion network to learn the mapping relationship from the original map features to the density map features. An advanced prior step was added to the original algorithm to estimate the density level of the image, which weakened the effect of the rice adhesion condition on the counting results. Extensive comparison experiments show that the proposed method is more accurate than the original MCNN algorithm.

Cryo-electron microscopy (cryo-EM) has recently emerged as a prominent biophysical method for macromolecular structure determination. Many research efforts have been devoted to produce cryo-EM images, density maps, at near-atomic resolution. Despite many advances in technology, the resolution of the generated density maps may not be sufficiently adequate and informative to directly construct the atomic structure of proteins. At medium-resolution (∼4-10 Å), secondary structure elements (α-helices and β-sheets) are discernible, whereas finding the correspondence of secondary structure elements detected in the density map with those on the sequence remains a challenging problem. In this paper, an automatic framework is proposed to solve α-helix correspondence problem in three-dimensional space. Through modeling of the sequence with the aid of a novel strategy, the α-helix correspondence problem is initially transformed into a complete weighted bipartite graph matching problem. An innovative correlation-based scoring function based on a well-known and robust statistical method is proposed for weighting the graph. Moreover, two local optimization algorithms, which are Greedy and Improved Greedy algorithms, have been presented to find α-helix correspondence. A widely used data set including 16 reconstructed and 4 experimental cryo-EM maps were chosen to verify the accuracy and reliability of the proposed automatic method. The experimental results demonstrate that the automatic method is highly efficient (86.25% accuracy), robust (11.3% error rate), fast (∼1.4 s), and works independently from cryo-EM skeleton.