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The hyperspectral image (HSI) distinguishes itself in material identification through its exceptional spectral resolution. However, its spatial resolution is constrained by hardware limitations, prompting the evolution of HSI super-resolution (SR) techniques. Single HSI SR endeavors to reconstruct high-spatial-resolution HSI from low-spatial-resolution inputs, and recent progress in deep learning-based algorithms has significantly advanced the quality of reconstructed images. However, convolutional methods struggle to extract comprehensive spatial and spectral features. Transformer-based models have yet to harness long-range dependencies across both dimensions fully, thus inadequately integrating spatial and spectral data. To solve the above problem, in this paper, we propose a new HSI SR method, SSAformer, which merges the strengths of CNNs and Transformers. It introduces specially designed attention mechanisms for HSI, including spatial and spectral attention modules, and overcomes the previous challenges in extracting and amalgamating spatial and spectral information. Evaluations on benchmark datasets show that SSAformer surpasses contemporary methods in enhancing spatial details and preserving spectral accuracy, underscoring its potential to expand HSI’s utility in various domains, such as environmental monitoring and remote sensing.

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.

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.

ABSTRACT: 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.

Imaging in scattering media is challenging due to signal attenuation and strong coupling of scattered and signal photons. The authors present a boundary migration model of the scattered field, converting scattered measurements in spectral form to scene information in temporal domain, and image Lambertian objects in highly scattering media.

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.

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.

Tea plants (Camellia sinensis (L.) O. Kuntze) frequently suffer severe damage as a result of freezing temperatures in early spring, which severely affect tea quality and tea production in China. Emerging evidence has demonstrated that the ethylene signaling pathway plays an important role in tea plants’ freezing responses. However, how ethylene modulates the response to freezing in sprouting tea shoots is not clear. This study verified that the measurement of relative electrolyte leakage in young shoots after 1 h at −5 °C is a rapid way to evaluate their freezing tolerance in the laboratory. Further exploration of the mechanism involved in increasing tea-shoot freezing tolerance by monitoring changes in the transcription of ethylene-related genes and cold signaling-related genes, and the physiological and biochemical changes after the application of ethephon (2-chloroethylphosphonic acid, an ethylene release reagent), revealed that exogenous ethephon significantly increased the freezing tolerance of tea shoots within 3 days of treatment, while concomitantly altering the expression of the ethylene signaling pathway-related genes (i.e., CsETR1, CsETR2, and CsEBF1). Moreover, antioxidant enzyme activities, including superoxide dismutase, catalase, and peroxidase, were uniformly upregulated, which might constitute a major physiological change induced by ethylene signaling and may be responsible for the observed increase in freezing resistance. Nevertheless, soluble sugars and starch, trehalose metabolism, and cold signaling-related genes did not appear relevant to the freezing tolerance increase following ethephon application. This study demonstrated that the freezing tolerance of sprouting tea shoots can be rapidly increased by the exogenous activation of the ethylene signaling pathway and upregulation of the plant’s antioxidant system.

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.

Abstract 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.

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 ClassifyingRapid decorrelationEvents viaParallelized single photon dEtection (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.

Abstract The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

Abstract 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.

Lipid membranes are heterogeneous and dynamically regulated in cells. Here the authors report a Spectrum and Polarisation Optical Tomography (SPOT) method where they use Nile Red dye to resolve membrane morphology, polarity and phase in cells.

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.

3D Fourier ptychographic microscopy can recover objects with high resolution across large volumes, which is very challenging in 3D imaging particularly because multiple scattering may occur within the sample. In this paper, we assume the thick sample is composed of a number of thin slices, and employ the beam propagation method (BPM) to model the propagation process of the light wave among successive slices capturing multiple scattering effects. 3D imaging is then accomplished by utilizing the gradient descent method to minimize the difference between the estimated intensity images by BPM and the captured measurements with angle-varied illuminations. We adopt a time-reversal scheme to obtain the gradient of the transmitted light intensity with respect to the complex refractive index of the volumetric sample and use the error backpropagation method to update the 3D sample iteratively. To further preserve the sharpness of the edges, we introduce a sparsity regularization term into the optimization process. Our method can achieve higher performance of 3D reconstruction compared to the original multi-slice approach, demonstrated in both simulation and experiment.

Diffuse correlation spectroscopy (DCS) is a well-established method that measures rapid changes in scattered coherent light to identify blood flow and functional dynamics within a tissue. While its sensitivity to minute scatterer displacements leads to a number of unique advantages, conventional DCS systems become photon-limited when attempting to probe deep into the tissue, which leads to long measurement windows (∽1 sec). Here, we present a high-sensitivity DCS system with 1024 parallel detection channels integrated within a single-photon avalanche diode array and demonstrate the ability to detect mm-scale perturbations up to 1 cm deep within a tissue-like phantom at up to a 33 Hz sampling rate. We also show that this highly parallelized strategy can measure the human pulse at high fidelity and detect behaviorally induced physiological variations from above the human prefrontal cortex. By greatly improving the detection sensitivity and speed, highly parallelized DCS opens up new experiments for high-speed biological signal measurement.

Our previous research showed that koumiss intervention could alleviate symptoms of chronic atrophic gastritis (CAG) via the modulation of gut microbiota. The present work aimed to further decipher the mechanism of the observed functional effect by analyzing changes in the fecal metabolomes of these patients. Significant modulation was observed in the levels of metabolites relating to several pathways, namely biosynthesis of primary and secondary bile acid, as well as metabolism of unsaturated fatty acid, linoleic acid and arachidonic acid, phenylalanine, and retinol. Koumiss treatment also decreased the level of some fecal proinflammatory markers, as evidenced by significant decrease in the fecal interleukin 38 (P

Stereo matching is essential and fundamental in computer vision tasks. In this paper, a novel stereo matching algorithm based on disparity propagation using edge-aware filtering is proposed. By extracting disparity subsets for reliable points and customizing the cost volume, the initial disparity map is refined through filtering-based disparity propagation. Then, an edge-aware filter with low computational complexity is adopted to formulate the cost column, which makes the proposed method independent on the local window size. Experimental results demonstrate the effectiveness of the proposed scheme. Bad pixels in our output disparity map are considerably decreased. The proposed method greatly outperforms the adaptive support-weight approach and other conditional window-based local stereo matching algorithms.

The basic principle of nonlocal means is to denoise a pixel using the weighted average of the neighbourhood pixels, while the weight is decided by the similarity of these pixels. The key issue of the nonlocal means method is how to select similar patches and design the weight of them. There are two main contributions of this paper: The first contribution is that we use two images to denoise the pixel. These two noised images are with the same noise deviation. Instead of using only one image, we calculate the weight from two noised images. After the first denoising process, we get a pre-denoised image and a residual image. The second contribution is combining the nonlocal property between residual image and pre-denoised image. The improved nonlocal means method pays more attention on the similarity than the original one, which turns out to be very effective in eliminating gaussian noise. Experimental results with simulated data are provided.

Specular reflection removal is indispensable to many computer vision tasks. However, most existing methods fail or degrade in complex real scenarios for their individual drawbacks. Benefiting from the light field imaging technology, this paper proposes a novel and accurate approach to remove specularity and improve image quality. We first capture images with specularity by the light field camera (Lytro ILLUM). After accurately estimating the image depth, a simple and concise threshold strategy is adopted to cluster the specular pixels into "unsaturated" and "saturated" category. Finally, a color variance analysis of multiple views and a local color refinement are individually conducted on the two categories to recover diffuse color information. Experimental evaluation by comparison with existed methods based on our light field dataset together with Stanford light field archive verifies the effectiveness of our proposed algorithm.