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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Dissection of the Active Ingredients and Potential Mechanism of Han-Shi-Yu-Fei-Decoction in Treating COVID-19 Based on In Vivo Substances Profiling and Clinical Symptom-Guided Network Pharmacology.

This work was aimed to elucidate the mechanism of action of Han-Shi-Yu-Fei-decoction (HSYFD) for treating patients with mild coronavirus disease 2019 (COVID-19) based on clinical symptom-guided network pharmacology. Experimentally, an ultra-high performance liquid chromatography technique coupled with quadrupole time-of-flight mass spectrometry method was used to profile the chemical components and the absorbed prototype constituents in rat serum after its oral administration, and 11 out of 108 compounds were identified. Calculatingly, the disease targets of Han-Shi-Yu-Fei symptoms of COVID-19 were constructed through the TCMIP V2.0 database. The subsequent network pharmacology and molecular docking analysis explored the molecular mechanism of the absorbed prototype constituents in the treatment of COVID-19. A total of 42 HSYFD targets oriented by COVID-19 clinical symptom were obtained, with EGFR, TP53, TNF, JAK2, NR3C1, TH, COMT, and DRD2 as the core targets. Enriched pathway analysis yielded multiple COVID-19-related signaling pathways, such as the PI3K/AKT signaling pathway and JAK-STAT pathway. Molecular docking showed that the key compounds, such as 6-gingerol, 10-gingerol, and scopoletin, had high binding activity to the core targets like COMT, JAK2, and NR3C1. Our work also verified the feasibility of clinical symptom-guided network pharmacology analysis of chemical compounds, and provided a possible agreement between the points of views of traditional Chinese medicine and western medicine on the disease.

CRISPR/Cas12a-based biosensing platform for the on-site detection of single-base mutants in gene-edited rice.

At present, with the accelerated development of the global biotechnology industry, novel transgenic technologies represented by gene editing are developing rapidly. A large number of gene-edited products featuring one or a few base indels have been commercialized. These have led to great challenges in the use of traditional nucleic acid detection technology and in safety regulation for genetically modified organisms (GMOs). In this study, we developed a portable clustered regularly interspaced short palindromic repeats/CRISPR-associated proteins 12a-based (CRISPR/Cas12a-based) biosensing platform named Cas12aFVD (fast visual detection) that can be coupled with recombinase polymerase amplification (RPA) for on-site detection of mutants in gene-edited rice in one tube. The detection procedure can be accomplished in 40 min with a visible result, which can be observed by the naked eye under blue light (470-490 nm). By accurate recognition of targets based on Cas12a/CRISPR RNA (crRNA), Cas12aFVD exhibits excellent performance for the detection of two- and three-base deletions, one-base substitution, and one-base insertion mutants with a limit of detection (LOD) of 12 copies/µl showing great potential for mutant detection, especially single-base mutants. The Cas12aFVD biosensing platform is independent of laboratory conditions, making it a promising and pioneering platform for the detection of gene-edited products.

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.

Transient Motion Classification Through Turbid Volumes via Parallelized Single-Photon Detection and Deep Contrastive Embedding.

Fast noninvasive probing of spatially varying decorrelating events, such as cerebral blood flow beneath the human skull, is an essential task in various scientific and clinical settings. One of the primary optical techniques used is diffuse correlation spectroscopy (DCS), whose classical implementation uses a single or few single-photon detectors, resulting in poor spatial localization accuracy and relatively low temporal resolution. Here, we propose a technique termed C lassifying R apid decorrelation E vents via P arallelized single photon d E tection (CREPE), a new form of DCS that can probe and classify different decorrelating movements hidden underneath turbid volume with high sensitivity using parallelized speckle detection from a 32 × 32 pixel SPAD array. We evaluate our setup by classifying different spatiotemporal-decorrelating patterns hidden beneath a 5 mm tissue-like phantom made with rapidly decorrelating dynamic scattering media. Twelve multi-mode fibers are used to collect scattered light from different positions on the surface of the tissue phantom. To validate our setup, we generate perturbed decorrelation patterns by both a digital micromirror device (DMD) modulated at multi-kilo-hertz rates, as well as a vessel phantom containing flowing fluid. Along with a deep contrastive learning algorithm that outperforms classic unsupervised learning methods, we demonstrate our approach can accurately detect and classify different transient decorrelation events (happening in 0.1-0.4 s) underneath turbid scattering media, without any data labeling. This has the potential to be applied to non-invasively monitor deep tissue motion patterns, for example identifying normal or abnormal cerebral blood flow events, at multi-Hertz rates within a compact and static detection probe.

Imaging Dynamics Beneath Turbid Media via Parallelized Single-Photon Detection.

Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard diffuse imaging methods measure optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such temporal correlation data to demonstrate deep-tissue video reconstruction of decorrelation dynamics. In this work, a single-photon avalanche diode array camera is utilized to simultaneously monitor the temporal dynamics of speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. Then a deep neural network is applied to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating tissue phantoms. The ability to reconstruct images of transient (0.1-0.4 s) dynamic events occurring up to 8 mm beneath a decorrelating tissue phantom with millimeter-scale resolution is demonstrated, and it is highlighted how the model can flexibly extend to monitor flow speed within buried phantom vessels.

A boundary migration model for imaging within volumetric scattering media.

Effectively imaging within volumetric scattering media is of great importance and challenging especially in macroscopic applications. Recent works have demonstrated the ability to image through scattering media or within the weak volumetric scattering media using spatial distribution or temporal characteristics of the scattered field. Here, we focus on imaging Lambertian objects embedded in highly scattering media, where signal photons are dramatically attenuated during propagation and highly coupled with background photons. We address these challenges by providing a time-to-space boundary migration model (BMM) of the scattered field to convert the scattered measurements in spectral form to the scene information in the temporal domain using all of the optical signals. The experiments are conducted under two typical scattering scenarios: 2D and 3D Lambertian objects embedded in the polyethylene foam and the fog, which demonstrate the effectiveness of the proposed algorithm. It outperforms related works including time gating in terms of reconstruction precision and scattering strength. Even though the proportion of signal photons is only 0.75%, Lambertian objects located at more than 25 transport mean free paths (TMFPs), corresponding to the round-trip scattering length of more than 50 TMFPs, can be reconstructed. Also, the proposed method provides low reconstruction complexity and millisecond-scale runtime, which significantly benefits its application.

Effects of biological activated carbon filter running time on disinfection by-product precursor removal.

Biological activated carbon (BAC) filtration is usually considered to be able to decrease formation potentials (FPs) of disinfection by-products (DBPs) in drinking water treatment plant (DWTP). However, BAC filters with long running time may release microbial metabolites to effluents and therefore increase FPs of nitrogenous DBPs with high toxicity. To verify this hypothesis, this study continuously tracked BAC filters in a DWTP for one year, and assessed effects of old (running time 8-9 years) and new (running time 0-13 months) BAC filters on FPs of 15 regulated and unregulated DBPs. Results revealed that dissolved organic carbon (DOC) removal was slightly higher in the new BAC than the old one. All fluorescent components of dissolved organic matter evidently declined after new BAC filtration, but fulvic acid-like and soluble microbial product-like substances increased after old BAC filtration, which could be caused by microbial leakage. Correspondingly, new BAC filter generally removed more DBP FPs than the old one. 46.5% HAA7 FPs from chlorination and 44.3% THM4 FPs from chloramination were removed by new BAC filter. However, some DBP FPs, especially HAN FPs, were poorly removed or even increased by the old BAC filter. Proteobacteria could be a main contributor for DBP precursor removal in BAC filters. Herminiimonas, most abundant genera in new BAC filter, may explain its better DOC and UV254 removal performance and lower DBP FPs, while Bradyrhizobium, most abundant genera in old BAC filter, might produce more extracellular polymeric substances and therefore increased N-DBP FPs in old BAC effluent. This study provided insight into variations of DBP FPs and microbial communities in the new and old BAC filters, and will be helpful for the optimization of DWTP design and operation for public health.

Memory Recall: A Simple Neural Network Training Framework Against Catastrophic Forgetting.

It is widely acknowledged that biological intelligence is capable of learning continually without forgetting previously learned skills. Unfortunately, it has been widely observed that many artificial intelligence techniques, especially (deep) neural network (NN)-based ones, suffer from catastrophic forgetting problem, which severely forgets previous tasks when learning a new one. How to train NNs without catastrophic forgetting, which is termed continual learning, is emerging as a frontier topic and attracting considerable research interest. Inspired by memory replay and synaptic consolidation mechanism in brain, in this article, we propose a novel and simple framework termed memory recall (MeRec) for continual learning with deep NNs. In particular, we first analyze the feature stability across tasks in NN and show that NN can yield task stable features in certain layers. Then, based on this observation, we use a memory module to keep the feature statistics (mean and std) for each learned task. Based on the memory and statistics, we show that a simple replay strategy with Gaussian distribution-based feature regeneration can recall and recover the knowledge from previous tasks. Together with the weight regularization, MeRec preserves weights learned from previous tasks. Based on this simple framework, MeRec achieved leading performance with extremely small memory budget (only two feature vectors for each class) for continual learning on CIFAR-10 and CIFAR-100 datasets, with at least 50% accuracy drop reduction after several tasks compared to previous state-of-the-art approaches.

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.