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Plant diseases significantly affect the quality and yield of agricultural production. Conventionally, detection has relied on plant pathologists, but recent advances in deep learning, particularly the Vision Transformer (ViT) and Convolutional Neural Network (CNN), have made it feasible for automated plant disease identification. Despite their prominence, there are still significant gaps in our understanding of how these models differ in feature extraction and representation, particularly in complex multi-crop disease identification tasks. This challenge arises from the simultaneous need to learn crop-specific and disease-specific features for accurate identification of crop species and its associated diseases. To address this, we introduce Plant Disease Global-Local Features Fusion Attention Model (PlantAIM), a new hybrid framework that fuses global attention mechanisms of ViT with local feature extraction capabilities of CNN. PlantAIM aims to improve the model's ability to simultaneously learn and focus on crop-specific and disease-specific features. We conduct extensive evaluations to assess the robustness and generalizability of PlantAIM compared to state-of-the-art (SOTA) models, including scenarios with limited training samples and real-world environmental data. Our results show that PlantAIM achieves superior performance. This research not only deepens our understanding of feature learning for ViT and CNN models, but also sets a new benchmark in the dynamic field of plant disease identification. The code is available at github: PlantAIM

Abstract Membrane budding, which underlies fundamental processes like endocytosis, intracellular trafficking, and viral infection, is thought to involve membrane coat-forming proteins, including the most observed clathrin, to form Ω-shape profiles and helix-forming proteins like dynamin to constrict Ω-profiles’ pores and thus mediate fission. Challenging this fundamental concept, we report that polymerized clathrin is required for Ω-profiles’ pore closure and that clathrin around Ω-profiles’ base/pore region mediates pore constriction/closure in neuroendocrine chromaffin cells. Mathematical modeling suggests that clathrin polymerization at Ω-profiles’ base/pore region generates forces from its intrinsically curved shape to constrict/close the pore. This new fission function may exert broader impacts than clathrin’s well-known coat-forming function during clathrin (coat)-dependent endocytosis, because it underlies not only clathrin (coat)-dependent endocytosis, but also diverse endocytic modes, including ultrafast, fast, slow, bulk, and overshoot endocytosis previously considered clathrin (coat)-independent in chromaffin cells. It mediates kiss-and-run fusion (fusion pore closure) previously considered bona fide clathrin-independent, and limits the vesicular content release rate. Furthermore, analogous to results in chromaffin cells, we found that clathrin is essential for fast and slow endocytosis at hippocampal synapses where clathrin was previously considered dispensable, suggesting clathrin in mediating synaptic vesicle endocytosis and fission. These results suggest that clathrin and likely other intrinsically curved coat proteins are a new class of fission proteins underlying vesicle budding and fusion. The half-a-century concept and studies that attribute vesicle-coat contents’ function to Ω-profile formation and classify budding as coat-protein (e.g., clathrin)-dependent or -independent may need to be re-defined and re-examined by considering clathrin’s pivotal role in pore constriction/closure.

Pipe jacking is a trenchless technology used to install buried pipelines, such as sewer lines in wastewater management systems. Existing mechanistic approaches based on geomaterial strength parameters (i.e., friction angle and apparent cohesion) can provide an estimation of the potential jacking forces during construction. However, extracting intact rock cores for strength characterisation is challenging when dealing with highly weathered ‘soft rocks’ which exhibit RQD values of zero. Such was the case for a pipe jacking drive traversing the highly weathered lithology underlying Kuching City, Malaysia. Furthermore, mechanistic approaches face limitations during construction when jacking forces are dependent on operation parameters, such as jacking speed and lubrication. To address these knowledge gaps, the primary objectives of this study are the development of rock strength parameters based on in situ pressuremeter testing for the purpose of estimating jacking forces. Furthermore, this study investigates the influence of various pipe jacking operation parameters, with a particular focus on their impact on jacking forces in weathered ‘soft rocks’. To achieve this, a novel deep learning model with an attention mechanism is introduced. The proposed methods of rock strength parameters derived from pressuremeter testing and the utilisation of deep learning will help to provide insights into the key factors affecting the development of jacking forces. This paper successfully shows the use of in situ pressuremeter testing in developing Mohr–Coulomb (MC) parameters directly from the site. In addition, the developed deep learning model with an attention mechanism successfully highlights the significance of pipe jacking operation parameters with an accuracy of 88% in predicting the jacking forces.

Abstract Conversion of astrocytes into neurons in vivo offers an alternative therapeutic approach for neuronal loss after injury or disease. However, not only the efficiency of the conversion of astrocytes into functional neurons by single Neurog2, but also the conundrum that whether Neurog2-induced neuronal cells (Neurog2-iNs) are further functionally integrated into existing matured neural circuits remains unknown. Here, we adopted the AAV(2/8) delivery system to overexpress single factor Neurog2 into astrocytes and found that the majority of astrocytes were successfully converted into neuronal cells in multiple brain regions, including the midbrain and spinal cord. In the midbrain, Neurog2-induced neuronal cells (Neurog2-iNs) exhibit neuronal morphology, mature electrophysiological properties, glutamatergic identity (about 60%), and synapse-like configuration local circuits. In the spinal cord, astrocytes from both the intact and lesioned sources could be converted into functional neurons with ectopic expression of Neurog2 alone. Notably, further evidence from our study also proves that Neurog2-iNs in the intact spinal cord are capable of responding to diverse afferent inputs from dorsal root ganglion (DRG). Together, this study does not merely demonstrate the feasibility of Neurog2 for efficient in vivo reprogramming, it gives an indication for the Neurog2-iNs as a functional and potential factor in cell-replacement therapy.

Summary: Real-time confocal and super-resolution imaging reveals membrane dynamics of exo- and endocytosis, including hemi-fusion, fusion pore opening, expansion, constriction, closure (kiss-and-run), fused-vesicle shrinking (shrink fusion), and flat membrane transition to vesicles via intermediate Λ- and Ω-shape structures. Here, we describe a protocol for imaging these membrane dynamics, including primary culture of bovine adrenal chromaffin cells, fluorescent probe application, patch-clamp to deliver depolarization and evoke exo- and endocytosis, electron microscopy (EM), and real-time confocal and stimulated emission depletion (STED) microscopy.For complete details on the use and execution of this protocol, please refer to Zhao et al. (2016), Shin et al. (2018), and Shin et al. (2021). : Publisher's note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Background: The influence of amyloid protein-binding protein 2 (APPBP2) on lung cancer is unknown. Methods: The function and mechanisms of APPBP2 were investigated in the NSCLC cell lines A549 and H1299. The ectopic expression of APPBP2, PPM1D and SPOP in NSCLS were examined in samples collected from ten pairs of human lung adenocarcinoma cancer tissues and adjacent normal lung tissues. shRNA vector was used for APPBP2 knockdown. Quantitative PCR and western blot assays quantified the mRNA and protein level of APPBP2, PPM1D, and SPOP. Cell proliferation was measured with BrdU, MTT, colony formation assays, and xenograft tumour growth experiments. Cell migration and invasion were analysed with transwell and wound healing assays. Co-Immunoprecipitation assay detected protein–protein interactions. Findings: APPBP2 was upregulated in NSCLC tissues. Silencing APPBP2 in A549 and H1299 cells resulted in the inhibition of cell proliferation, migration, and invasion, enhancement of apoptosis, and a significant decrease in the expression of PPM1D and SPOP. Overexpression of PPM1D and SPOP attenuated the APPBP2-knockdown inhibition of NSCLC cells. Co-IP assay showed that PPM1D interacted with APPBP2. Interpretation: The expression level of APPBP2 positively correlates with NSCLC cell proliferation, migration, and invasiveness. APPBP2 contributes to NSCLC progression through regulating the PPM1D and SPOP signalling pathway. This novel molecular mechanism, underlying NSCLC oncogenesis, suggests APPBP2 is a potential target for diagnosis and therapeutic intervention in NSCLC. Fund: Key Program of Natural Science Research of Higher Education of Anhui Province (No. KJ2017A241), the National Natural Science Foundation of China (No. 81772493). Keywords: APPBP2, Lung cancer, Non-small cell lung cancer, PPM1D, SPOP

Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our society. Several recent studies have reported promising results in the classification of plant diseases from RGB images on the basis of Convolutional Neural Networks (CNN). These studies have been successfully experimented on a large number of crops and symptoms, and they have shown significant advantages in the support of human expertise. However, the CNN models still have limitations. In particular, CNN models do not necessarily focus on the visible parts affected by a plant disease to allow their classification, and they can sometimes take into account irrelevant backgrounds or healthy plant parts. In this paper, we therefore develop a new technique based on a Recurrent Neural Network (RNN) to automatically locate infected regions and extract relevant features for disease classification. We show experimentally that our RNN-based approach is more robust and has a greater ability to generalize to unseen infected crop species as well as to different plant disease domain images compared to classical CNN approaches. We also analyze the focus of attention as learned by our RNN and show that our approach is capable of accurately locating infectious diseases in plants. Our approach, which has been tested on a large number of plant species, should thus contribute to the development of more effective means of detecting and classifying crop pathogens in the near future.

Summary: Recent studies have revealed an essential role for embryonic cortical development in the pathophysiology of neurodevelopmental disorders, including autism spectrum disorder (ASD). However, the genetic basis and underlying mechanisms remain unclear. Here, we generate mutant human embryonic stem cell lines (Mut hESCs) carrying an NR2F1-R112K mutation that has been identified in a patient with ASD features and investigate their neurodevelopmental alterations. Mut hESCs overproduce ventral telencephalic neuron progenitors (ventral NPCs) and underproduce dorsal NPCs, causing the imbalance of excitatory/inhibitory neurons. These alterations can be mainly attributed to the aberrantly activated Hedgehog signaling pathway. Moreover, the corresponding Nr2f1 point-mutant mice display a similar excitatory/inhibitory neuron imbalance and abnormal behaviors. Antagonizing the increased inhibitory synaptic transmission partially alleviates their behavioral deficits. Together, our results suggest that the NR2F1-dependent imbalance of excitatory/inhibitory neuron differentiation caused by the activated Hedgehog pathway is one precursor of neurodevelopmental disorders and may enlighten the therapeutic approaches. : Zhang et al. find that a NR2F1-R112K mutation promotes inhibitory neuron differentiation by activating the Hedgehog signaling pathway, and corresponding mutant mice display behavioral deficits related to neurodevelopmental disorders, including autistic spectrum disorder (ASD), which could be partially alleviated by antagonizing the inhibitory synaptic transmission. Keywords: neurodevelopmental disorders, autism spectrum disorders, excitatory neuron, inhibitory neuron, dorsal neuron progenitor cell, ventral neuron progenitor cell, E/I imbalance, Hedgehog signaling pathway, NR2F1

Abstract Background Natural products from herbal medicines have long been investigated for their potentials as cancer therapeutics. Besides the development of several herbal medicine-derived anti-cancer agents, such as paclitaxel, vincristine and podophyllotoxin, many recent laboratory findings demonstrated that brusatol, a quassinoid from the seeds of Brucea sumatrana, exhibits potent tumor suppressing effect with improved disease outcome. Our recent finding further demonstrated that brusatol synergizes with the intrinsic metabolic burden in cancer cells. Main body Here, we summarized the recent investigations of brusatol as an experimental therapeutic for human malignancies, such as leukemia, lung cancer, pancreatic cancer and brain tumor. We also discussed the molecular target brusatol, with a focus on the Nuclear factor erythroid 2-related factor 2 (NRF2)-guided gene transcription, as well as glutathione de novo synthesis. Further, we discussed the challenges and future applications of brusatol for cancer therapy. Conclusion In conclusion, we believe increasing evidences have shown the value of brusatol as a novel strategy for cancer treatment, which may indicate future drug development and clinical translation.

The preservation of plant specimens in herbaria has been carried out for centuries in efforts to study and confirm plant taxa. With the increasing collection of herbaria made available digitally, it is practical to use herbarium specimens for the automation of plant identification. They are also substantially more accessible and less expensive to obtain compared to field images. In fact, in remote and inaccessible habitats, field images of rare plant species are still immensely lacking. As a result, rare plant species identification is challenging due to the deficiency of training data. To address this problem, we investigate a cross-domain adaptation approach that allows knowledge transfer from a model learned from herbarium specimens to field images. We propose a model called Herbarium–Field Triplet Loss Network (HFTL network) to learn the mapping between herbarium and field domains. Specifically, the model is trained to maximize the embedding distance of different plant species and minimize the embedding distance of the same plant species given herbarium–field pairs. This paper presents the implementation and performance of the HFTL network to assess the herbarium–field similarity of plants. It corresponds to the cross-domain plant identification challenge in PlantCLEF 2020 and PlantCLEF 2021. Despite the lack of field images, our results show that the network can generalize and identify rare species. Our proposed HFTL network achieved a mean reciprocal rank score of 0.108 and 0.158 on the test set related to the species with few training field photographs in PlantCLEF 2020 and PlantCLEF 2021, respectively. [ABSTRACT FROM AUTHOR]