Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising.

Calcium imaging has transformed neuroscience research by providing a methodology for monitoring the activity of neural circuits with single-cell resolution. However, calcium imaging is inherently susceptible to detection noise, especially when imaging with high frame rate or under low excitation dosage. Here we developed DeepCAD, a self-supervised deep-learning method for spatiotemporal enhancement of calcium imaging data that does not require any high signal-to-noise ratio (SNR) observations. DeepCAD suppresses detection noise and improves the SNR more than tenfold, which reinforces the accuracy of neuron extraction and spike inference and facilitates the functional analysis of neural circuits.

GaitSG: Gait Recognition with SMPLs in Graph Structure.

Gait recognition aims to identify a person based on his unique walking pattern. Compared with silhouettes and skeletons, skinned multi-person linear (SMPL) models can simultaneously provide human pose and shape information and are robust to viewpoint and clothing variances. However, previous approaches have only considered SMPL parameters as a whole and are yet to explore their potential for gait recognition thoroughly. To address this problem, we concentrate on SMPL representations and propose a novel SMPL-based method named GaitSG for gait recognition, which takes SMPL parameters in the graph structure as input. Specifically, we represent the SMPL model as graph nodes and employ graph convolution techniques to effectively model the human model topology and generate discriminative gait features. Further, we utilize prior knowledge of the human body and elaborately design a novel part graph pooling block, PGPB, to encode viewpoint information explicitly. The PGPB also alleviates the physical distance-unaware limitation of the graph structure. Comprehensive experiments on public gait recognition datasets, Gait3D and CASIA-B, demonstrate that GaitSG can achieve better performance and faster convergence than existing model-based approaches. Specifically, compared with the baseline SMPLGait (3D only), our model achieves approximately twice the Rank-1 accuracy and requires three times fewer training iterations on Gait3D.

CsCIPK11-Regulated Metalloprotease CsFtsH5 Mediates the Cold Response of Tea Plants.

Photosystem II repair in chloroplasts is a critical process involved in maintaining a plant's photosynthetic activity under cold stress. FtsH (filamentation temperature-sensitive H) is an essential metalloprotease that is required for chloroplast photosystem II repair. However, the role of FtsH in tea plants and its regulatory mechanism under cold stress remains elusive. In this study, we cloned a FtsH homolog gene in tea plants, named CsFtsH5, and found that CsFtsH5 was located in the chloroplast and cytomembrane. RT-qPCR showed that the expression of CsFtsH5 was increased with leaf maturity and was significantly induced by light and cold stress. Transient knockdown CsFtsH5 expression in tea leaves using antisense oligonucleotides resulted in hypersensitivity to cold stress, along with higher relative electrolyte leakage and lower Fv/Fm values. To investigate the molecular mechanism underlying CsFtsH5 involvement in the cold stress, we focused on the calcineurin B-like-interacting protein kinase 11 (CsCIPK11), which had a tissue expression pattern similar to that of CsFtsH5 and was also upregulated by light and cold stress. Yeast two-hybrid and dual luciferase (Luc) complementation assays revealed that CsFtsH5 interacted with CsCIPK11. Furthermore, the Dual-Luc assay showed that CsCIPK11-CsFtsH5 interaction might enhance CsFtsH5 stability. Altogether, our study demonstrates that CsFtsH5 is associated with CsCIPK11 and plays a positive role in maintaining the photosynthetic activity of tea plants in response to low temperatures.

Protective effects of whey protein hydrolysate on Bifidobacterium animalis ssp. lactis Probio-M8 during freeze-drying and storage.

We evaluated the potential of whey protein hydrolysate as a lyoprotectant for maintaining the cell viability of Bifidobacterium animalis ssp. lactis Probio-M8 during freeze-drying and subsequent storage. The moisture content and water activity of the lyophilized samples treated by different concentrations of whey protein hydrolysate were ≤5.23 ± 0.33 g/100 g and ≤0.102 ± 0.003, respectively. During storage at 25°C and 30°C, whey protein hydrolysate had a stronger protective effect on B. lactis Probio-M8 than the same concentration of whey protein. Using the Excel tool GinaFit, we estimated the microbial inactivation kinetics during storage. Whey protein hydrolysate reduced cell damage caused by an increase in temperature. Whey protein hydrolysate could protect cells by increasing the osmotic pressure as a compatible solute. Whey protein hydrolysate improved cell membrane integrity and reduced the amounts of reactive oxygen species and malondialdehyde produced. The findings indicated that whey protein hydrolysate was a novel antioxidant lyoprotectant that could protect probiotics during freeze-drying and storage.

High-axial-resolution single-molecule localization under dense excitation with a multi-channel deep U-Net.

Single-molecule localization microscopy (SMLM) can bypass the diffraction limit of optical microscopes and greatly improve the resolution in fluorescence microscopy. By introducing the point spread function (PSF) engineering technique, we can customize depth varying PSF to achieve higher axial resolution. However, most existing 3D single-molecule localization algorithms require excited fluorescent molecules to be sparse and captured at high signal-to-noise ratios, which results in a long acquisition time and precludes SMLM's further applications in many potential fields. To address this problem, we propose a novel 3D single-molecular localization method based on a multi-channel neural network based on U-Net. By leveraging the deep network's great advantages in feature extraction, the proposed network can reliably discriminate dense fluorescent molecules with overlapped PSFs and corrupted by sensor noise. Both simulated and real experiments demonstrate its superior performance in PSF engineered microscopes with short exposure and dense excitations, which holds great potential in fast 3D super-resolution microscopy.

Attention-based multi-scale features fusion for unobtrusive atrial fibrillation detection using ballistocardiogram signal.

BACKGROUND: Atrial fibrillation (AF) represents the most common arrhythmia worldwide, related to increased risk of ischemic stroke or systemic embolism. It is critical to screen and diagnose AF for the benefits of better cardiovascular health in lifetime. The ECG-based AF detection, the gold standard in clinical care, has been restricted by the need to attach electrodes on the body surface. Recently, ballistocardiogram (BCG) has been investigated for AF diagnosis, which is an unobstructive and convenient technique to monitor heart activity in daily life. However, here is a lack of high-dimension representation and deep learning analysis of BCG. METHOD: Therefore, this paper proposes an attention-based multi-scale features fusion method by using BCG signal. The 1-D morphology feature extracted from Bi-LSTM network and 2-D rhythm feature extracted from reconstructed phase space are integrated by means of CNN network to improve the robustness of AF detection. To the best of our knowledge, this is the first study where the phase space trajectory of BCG is conducted. RESULTS: 2000 segments (AF and NAF) of BCG signals were collected from 59 volunteers suffering from paroxysmal AF in this survey. Compared to the classical time and frequency features and the state-of-the-art energy features with the popular machine learning classifiers, AF detection performance of the proposed method is superior, which has 0.947 accuracy, 0.935 specificity, 0.959 sensitivity, and 0.937 precision, for the same BCG dataset. The experimental results show that combined feature could excavate more potential characteristics, and the attention mechanism could enhance the pertinence for AF recognition. CONCLUSIONS: The proposed method can provide an innovative solution to capture the diverse scale descriptions of BCG and explore ways to involve the deep learning method to accurately screen AF in routine life.

Adaptive optimization for axial multi-foci generation in multiphoton microscopy.

To improve imaging speed, multifocal excitation is widely adopted as a parallel strategy in laser-scanning microscopy. Specifically, axial multifocal microscopy is popular in neuroscience as it enables functional imaging of neurons in multiple depths simultaneously. However, previous phase searching algorithms for axial multi-foci generation generally generate foci of uniform intensities, which cannot compensate the scattering-induced power loss in deep tissue and causes inhomogeneous excitation. Here, we propose a novel adaptive optimization-based phase-searching method (AdaPS) to generate axial multi-foci with arbitrary intensity modulations for scattering-induced loss compensation. By adopting Adaptive Moment Estimation (Adam) as the searching algorithm, our method could escape from unsatisfactory local minima and stably converge to the optimal phase pattern with errors at least an order of magnitude lower. We validate AdaPS through both numerical simulations and experiments and demonstrate that AdaPS could provide uniform multi-depth imaging in scattering phantom and enable high-fidelity multi-depth recordings of neural network dynamics in mouse brain in vivo.

PeBL1, a novel protein elicitor from Brevibacillus laterosporus strain A60, activates defense responses and systemic resistance in Nicotiana benthamiana.

We report the identification, characterization, and gene cloning of a novel protein elicitor (PeBL1) secreted from Brevibacillus laterosporus strain A60. Through a purification process consisting of ion-exchange chromatography and high-performance liquid chromatography (HPLC), we isolated a protein that was identified by electrospray ionization quadrupole time of flight tandem mass spectrometry (ESI-Q-TOF-MS-MS). The 351-bp PeBL1 gene produces a 12,833-Da protein with 116 amino acids that contains a 30-residue signal peptide. The PeBL1 protein was expressed in Escherichia coli. The recombinant protein can induce a typical hypersensitive response (HR) and systemic resistance in Nicotiana benthamiana, like the endogenous protein. PeBL1-treated N. benthamiana exhibited strong resistance to the infection of tobacco mosaic virus-green fluorescent protein (TMV-GFP) and Pseudomonas syringae pv. tabaci compared to control N. benthamiana. In addition, PeBL1 triggered a cascade of events that resulted in defense responses in plants, including reactive oxygen species (ROS) production, extracellular-medium alkalization, phenolic-compound deposition, and expression of several defense-related genes. Real-time quantitative-PCR analysis indicated that the known defense-related genes PR-1, PR-5, PDF1.2, NPR1, and PAL were upregulated to varying degrees by PeBL1. This research not only provides insights into the mechanism by which beneficial bacteria activate plant systemic resistance, but also sheds new light on a novel strategy for biocontrol using strain A60.

Illumination estimation from specular highlight in a multi-spectral image.

The reflection spectrum of an object characterizes its surface material, but for non-Lambertian scenes, the recorded spectrum often deviates owing to specular contamination. To compensate for this deviation, the illumination spectrum is required, and it can be estimated from specularity. However, existing illumination-estimation methods often degenerate in challenging cases, especially when only weak specularity exists. By adopting the dichromatic reflection model, which formulates a specular-influenced image as a linear combination of diffuse and specular components, this paper explores two individual priors and one mutual prior upon these two components: (i) The chromaticity of a specular component is identical over all the pixels. (ii) The diffuse component of a specular-contaminated pixel can be reconstructed using its specular-free counterpart describing the same material. (iii) The spectrum of illumination usually has low correlation with that of diffuse reflection. A general optimization framework is proposed to estimate the illumination spectrum from the specular component robustly and accurately. The results of both simulation and real experiments demonstrate the robustness and accuracy of our method.

OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation.

Light field disparity estimation is an essential task in computer vision. Currently, supervised learning-based methods have achieved better performance than both unsupervised and optimization-based methods. However, the generalization capacity of supervised methods on real-world data, where no ground truth is available for training, remains limited. In this paper, we argue that unsupervised methods can achieve not only much stronger generalization capacity on real-world data but also more accurate disparity estimation results on synthetic datasets. To fulfill this goal, we present the Occlusion Pattern Aware Loss, named OPAL, which successfully extracts and encodes general occlusion patterns inherent in the light field for calculating the disparity loss. OPAL enables: i) accurate and robust disparity estimation by teaching the network how to handle occlusions effectively and ii) significantly reduced network parameters required for accurate and efficient estimation. We further propose an EPI transformer and a gradient-based refinement module for achieving more accurate and pixel-aligned disparity estimation results. Extensive experiments demonstrate our method not only significantly improves the accuracy compared with SOTA unsupervised methods, but also possesses stronger generalization capacity on real-world data compared with SOTA supervised methods. Last but not least, the network training and inference efficiency are much higher than existing learning-based methods. Our code will be made publicly available.

PCR Independent Strategy-Based Biosensors for RNA Detection.

RNA is an important information and functional molecule. It can respond to the regulation of life processes and is also a key molecule in gene expression and regulation. Therefore, RNA detection technology has been widely used in many fields, especially in disease diagnosis, medical research, genetic engineering and other fields. However, the current RT-qPCR for RNA detection is complex, costly and requires the support of professional technicians, resulting in it not having great potential for rapid application in the field. PCR-free techniques are the most attractive alternative. They are a low-cost, simple operation method and do not require the support of large instruments, providing a new concept for the development of new RNA detection methods. This article reviews current PCR-free methods, overviews reported RNA biosensors based on electrochemistry, SPR, microfluidics, nanomaterials and CRISPR, and discusses their challenges and future research prospects in RNA detection.

A critical review on the use of flue gas desulfurization gypsum to ameliorate saline-alkali soils and its prospect for reducing carbon emissions.

Flue gas desulfurization gypsum (FGDG), a solid waste produced during sulfur removal in coal-fired power plants, has applications in saline-alkali soil amelioration due to its function of calcium­sodium ion exchange. Existing research has focused on the use of gypsum to improve saline-alkali soils in non-coastal areas. However, coastal areas are not only extensively salinized, but an important source of methane, and surprisingly, FGDG may assist to decrease methane formation mainly by the action of sulfate radical. This is the first critical review to systematically discuss the effects of FGDG on both saline-alkali soil improvement and carbon emission control in tidal flats, including application status, amendment principles, environmental risks and methane emission control. After adding FGDG, soil salinization degree was weakened via adjusting soil structure, pH, exchangeable sodium percentage and electric conductivity, introduction of nutrients also promotes crop growth. The optimal FGDG dosage in tidal flats seems to be higher (>2 %) than that in non-coastal areas (<1 %). Its environmental risks regarding heavy metals and eutrophication are evaluated safe. In tidal areas, more methane is produced in hot seasons and ebb tides. Plants and invertebrates also promote methane release. FGDG controls methane production by promoting the activity of sulfate-reducing bacteria and inhibiting methanogens. Considering methane flux levels and seawater erosion, FGDG use in low tidal beach needs more research, while that in high and middle tidal beach is recommended. This review will expand applications and appropriate use of FGDG for reducing carbon emission and improving ecological services in coastal areas.

Proficiency testing for event-specific quantification of genetically modified maize MON87427: Performance assessment based on the metrologically traceable reference values as assigned values.

The Asia Pacific Metrology Program and the Accreditation Cooperation joint Proficiency Testing (PT) program for the quantification of genetically modified maize MON87427 was organized by the National Institute of Metrology, China, to enhance the measurement accuracy and metrological traceability in the region. Certified reference materials were employed as test samples; metrologically traceable certified reference values served as PT reference values (PTRVs) for evaluating the participants results. The consensus values obtained from the participants were higher than the assigned values, potentially due to the systematic effects of DNA extraction process. The participants' relatively poor overall performance by the ζ-score compared with z-score demonstrates their need to thoroughly investigate quantification bias to elevate the measurement capability of genetically modified (GM) content and deepen their understanding of uncertainty estimation. This program confirmed the importance of using metrologically traceable reference values instead of consensus values as PTRV for reliable performance assessment.

Review of CRISPR/Cas Systems on Detection of Nucleotide Sequences.

Nowadays, with the rapid development of biotechnology, the CRISPR/Cas technology in particular has produced many new traits and products. Therefore, rapid and high-resolution detection methods for biotechnology products are urgently needed, which is extremely important for safety regulation. Recently, in addition to being gene editing tools, CRISPR/Cas systems have also been used in detection of various targets. CRISPR/Cas systems can be successfully used to detect nucleic acids, proteins, metal ions and others in combination with a variety of technologies, with great application prospects in the future. However, there are still some challenges need to be addressed. In this review, we will list some detection methods of genetically modified (GM) crops, gene-edited crops and single-nucleotide polymorphisms (SNPs) based on CRISPR/Cas systems, hoping to bring some inspiration or ideas to readers.

Multi-triangles cylindrical origami and inspired metamaterials with tunable stiffness and stretchable robotic arm.

Kresling pattern origami-inspired structural design has been widely investigated using its bistable property and the single coupling degree of freedom (DOF). In order to obtain new properties or new origami-inspired structures, it needs to innovate the crease lines in the flat sheet of Kresling pattern origami. Here, we present a derivative of Kresling pattern origami-multi-triangles cylindrical origami (MTCO) with tristable property. The truss model is modified based on the switchable active crease lines during the folding motion of the MTCO. Using the energy landscape obtained from the modified truss model, the tristable property is validated and extended to Kresling pattern origami. Simultaneously, the high stiffness property of the third stable state and some special stable states are discussed. In addition, MTCO-inspired metamaterials with deployable property and tunable stiffness, and MTCO-inspired robotic arms with wide movement ranges and rich motion forms are created. These works promote research on Kresling pattern origami, and the design ideas of the metamaterials and robotic arms play a positive role in improving the stiffness of deployable structures and conceiving motion robots.

High-Speed and Accurate Diagnosis of Gastrointestinal Disease: Learning on Endoscopy Images Using Lightweight Transformer with Local Feature Attention.

In response to the pressing need for robust disease diagnosis from gastrointestinal tract (GIT) endoscopic images, we proposed FLATer, a fast, lightweight, and highly accurate transformer-based model. FLATer consists of a residual block, a vision transformer module, and a spatial attention block, which concurrently focuses on local features and global attention. It can leverage the capabilities of both convolutional neural networks (CNNs) and vision transformers (ViT). We decomposed the classification of endoscopic images into two subtasks: a binary classification to discern between normal and pathological images and a further multi-class classification to categorize images into specific diseases, namely ulcerative colitis, polyps, and esophagitis. FLATer has exhibited exceptional prowess in these tasks, achieving 96.4% accuracy in binary classification and 99.7% accuracy in ternary classification, surpassing most existing models. Notably, FLATer could maintain impressive performance when trained from scratch, underscoring its robustness. In addition to the high precision, FLATer boasted remarkable efficiency, reaching a notable throughput of 16.4k images per second, which positions FLATer as a compelling candidate for rapid disease identification in clinical practice.

Spatial redundancy transformer for self-supervised fluorescence image denoising.

Fluorescence imaging with high signal-to-noise ratios has become the foundation of accurate visualization and analysis of biological phenomena. However, the inevitable noise poses a formidable challenge to imaging sensitivity. Here we provide the spatial redundancy denoising transformer (SRDTrans) to remove noise from fluorescence images in a self-supervised manner. First, a sampling strategy based on spatial redundancy is proposed to extract adjacent orthogonal training pairs, which eliminates the dependence on high imaging speed. Second, we designed a lightweight spatiotemporal transformer architecture to capture long-range dependencies and high-resolution features at low computational cost. SRDTrans can restore high-frequency information without producing oversmoothed structures and distorted fluorescence traces. Finally, we demonstrate the state-of-the-art denoising performance of SRDTrans on single-molecule localization microscopy and two-photon volumetric calcium imaging. SRDTrans does not contain any assumptions about the imaging process and the sample, thus can be easily extended to various imaging modalities and biological applications.

dMIL-Transformer: Multiple Instance Learning Via Integrating Morphological and Spatial Information for Lymph Node Metastasis Classification.

Automated classification of lymph node metastasis (LNM) plays an important role in the diagnosis and prognosis. However, it is very challenging to achieve satisfactory performance in LNM classification, because both the morphology and spatial distribution of tumor regions should be taken into account. To address this problem, this article proposes a two-stage dMIL-Transformer framework, which integrates both the morphological and spatial information of the tumor regions based on the theory of multiple instance learning (MIL). In the first stage, a double Max-Min MIL (dMIL) strategy is devised to select the suspected top-K positive instances from each input histopathology image, which contains tens of thousands of patches (primarily negative). The dMIL strategy enables a better decision boundary for selecting the critical instances compared with other methods. In the second stage, a Transformer-based MIL aggregator is designed to integrate all the morphological and spatial information of the selected instances from the first stage. The self-attention mechanism is further employed to characterize the correlation between different instances and learn the bag-level representation for predicting the LNM category. The proposed dMIL-Transformer can effectively deal with the thorny classification in LNM with great visualization and interpretability. We conduct various experiments over three LNM datasets, and achieve 1.79%-7.50% performance improvement compared with other state-of-the-art methods.

Uncovering the complex regulatory network of spring bud sprouting in tea plants: insights from metabolic, hormonal, and oxidative stress pathways.

The sprouting process of tea buds is an essential determinant of tea quality and taste, thus profoundly impacting the tea industry. Buds spring sprouting is also a crucial biological process adapting to external environment for tea plants and regulated by complex transcriptional and metabolic networks. This study aimed to investigate the molecular basis of bud sprouting in tea plants firstly based on the comparisons of metabolic and transcriptional profiles of buds at different developmental stages. Results notably highlighted several essential processes involved in bud sprouting regulation, including the interaction of plant hormones, glucose metabolism, and reactive oxygen species scavenging. Particularly prior to bud sprouting, the accumulation of soluble sugar reserves and moderate oxidative stress may have served as crucial components facilitating the transition from dormancy to active growth in buds. Following the onset of sprouting, zeatin served as the central component in a multifaceted regulatory mechanism of plant hormones that activates a range of growth-related factors, ultimately leading to the promotion of bud growth. This process was accompanied by significant carbohydrate consumption. Moreover, related key genes and metabolites were further verified during the entire overwintering bud development or sprouting processes. A schematic diagram involving the regulatory mechanism of bud sprouting was ultimately proposed, which provides fundamental insights into the complex interactions involved in tea buds.

Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit.

A fundamental challenge in fluorescence microscopy is the photon shot noise arising from the inevitable stochasticity of photon detection. Noise increases measurement uncertainty and limits imaging resolution, speed and sensitivity. To achieve high-sensitivity fluorescence imaging beyond the shot-noise limit, we present DeepCAD-RT, a self-supervised deep learning method for real-time noise suppression. Based on our previous framework DeepCAD, we reduced the number of network parameters by 94%, memory consumption by 27-fold and processing time by a factor of 20, allowing real-time processing on a two-photon microscope. A high imaging signal-to-noise ratio can be acquired with tenfold fewer photons than in standard imaging approaches. We demonstrate the utility of DeepCAD-RT in a series of photon-limited experiments, including in vivo calcium imaging of mice, zebrafish larva and fruit flies, recording of three-dimensional (3D) migration of neutrophils after acute brain injury and imaging of 3D dynamics of cortical ATP release. DeepCAD-RT will facilitate the morphological and functional interrogation of biological dynamics with a minimal photon budget.