The advent of regenerative medicine has brought us the opportunity to regenerate, modify and restore human organs function. Stem cells, a key resource in regenerative medicine, are defined as ...clonogenic, self-renewing, progenitor cells that can generate into one or more specialized cell types. Stem cells have been classified into three main groups: embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs) and adult/postnatal stem cells (ASCs). The present review focused the attention on ASCs, which have been identified in many perioral tissues such as dental pulp, periodontal ligament, follicle, gingival, alveolar bone and papilla. Human dental pulp stem cells (hDPSCs) are ectodermal-derived stem cells, originating from migrating neural crest cells and possess mesenchymal stem cell properties. During last decade, hDPSCs have received extensive attention in the field of tissue engineering and regenerative medicine due to their accessibility and ability to differentiate in several cell phenotypes. In this review, we have carefully described the potential of hDPSCs to differentiate into odontoblasts, osteocytes/osteoblasts, adipocytes, chondrocytes and neural cells.
A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database ...images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning
A dynamic texture is a spatio-temporal generative model for video, which represents video sequences as observations from a linear dynamical system. This work studies the mixture of dynamic textures, ...a statistical model for an ensemble of video sequences that is sampled from a finite collection of visual processes, each of which is a dynamic texture. An expectation-maximization (EM) algorithm is derived for learning the parameters of the model, and the model is related to previous works in linear systems, machine learning, time- series clustering, control theory, and computer vision. Through experimentation, it is shown that the mixture of dynamic textures is a suitable representation for both the appearance and dynamics of a variety of visual processes that have traditionally been challenging for computer vision (for example, fire, steam, water, vehicle and pedestrian traffic, and so forth). When compared with state-of-the-art methods in motion segmentation, including both temporal texture methods and traditional representations (for example, optical flow or other localized motion representations), the mixture of dynamic textures achieves superior performance in the problems of clustering and segmenting video of such processes.
Real‐time monitoring of the evolution of bacterial infection‐associated multiple radical species is critical to accurately profile the pathogenesis and host‐defense mechanisms. Here, we present a ...unique dual wavelength near‐infrared (NIR) cyanine‐dyad molecular probe (HCy5‐Cy7) for simultaneous monitoring of reactive oxygen and nitrogen species (RONS) variations both in vitro and in vivo. HCy5‐Cy7 specifically turns on its fluorescence at 660 nm via superoxide or hydroxyl radical (O2.−, .OH)‐mediated oxidation of reduced HCy5 moiety to Cy5, while peroxynitrite or hypochlorous species (ONOO−, ClO−)‐induced Cy7 structural degradation causes the emission turn‐off at 800 nm. Such multispectral but reverse signal responses allow multiplex manifestation of in situ oxidative and nitrosative stress events during the pathogenic and defensive processes in both bacteria‐infected macrophage cells and living mice. Most importantly, this study may also provide new perspectives for understanding the bacterial pathogenesis and advancing the precision medicine against infectious diseases.
The molecular probe HCy5‐Cy7 turns on its emission at 660 nm via superoxide or hydroxyl radical‐mediated oxidation of the reduced HCy5 moiety to Cy5, while peroxynitrite or hypochlorous species‐induced Cy7 structural degradation leads to the emission turn‐off at 800 nm. This allows the imaging of in situ oxidative and nitrosative stress events during the pathogenic and defensive processes of bacterial infection in both macrophage cells and living mice.
State-of-the-art multi-object tracking (MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object detectors. In crowded ...scenes, however, detectors often fail to obtain accurate detections due to heavy occlusions and high crowd density. In this paper, we propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes. Using crowd density maps, we jointly model detection, counting, and tracking of multiple targets as a network flow program, which simultaneously finds the global optimal detections and trajectories of multiple targets over the whole video. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to errors in crowded scenes, or rely on a suboptimal two-step process using heuristic density-aware point-tracks for matching targets. Our approach yields promising results on public benchmarks of various domains including people tracking, cell tracking, and fish tracking.
The rate of glycolytic metabolism changes during differentiation of human embryonic stem cells (hESCs) and reprogramming of somatic cells to pluripotency. However, the functional contribution of ...glycolytic metabolism to the pluripotent state is unclear. Here we show that naive hESCs exhibit increased glycolytic flux, MYC transcriptional activity, and nuclear N-MYC localization relative to primed hESCs. This status is consistent with the inner cell mass of human blastocysts, where MYC transcriptional activity is higher than in primed hESCs and nuclear N-MYC levels are elevated. Reduction of glycolysis decreases self-renewal of naive hESCs and feeder-free primed hESCs, but not primed hESCs grown in feeder-supported conditions. Reduction of glycolysis in feeder-free primed hESCs also enhances neural specification. These findings reveal associations between glycolytic metabolism and human naive pluripotency and differences in the metabolism of feeder-/feeder-free cultured hESCs. They may also suggest methods for regulating self-renewal and initial cell fate specification of hESCs.
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•Naive hESCs show increased glycolysis compared to primed counterparts•High nuclear N-MYC is associated with human naive pluripotency•MEF-secreted factors make primed hESCs less reliant on glucose for proliferation•Reduction of glycolysis in feeder-free primed hESCs enhances neural specification
Gu et al. examine the associations between glycolytic metabolism and the pluripotency state of hESCs under different naive and primed growth conditions. They identify differences in the metabolic state and highlight potential metabolic approaches for regulating self-renewal and initial cell fate specification of hESCs.
On Distinctive Image Captioning via Comparing and Reweighting Wang, Jiuniu; Xu, Wenjia; Wang, Qingzhong ...
IEEE transactions on pattern analysis and machine intelligence,
2023-Feb.-1, 2023-Feb, 2023-2-1, 20230201, Volume:
45, Issue:
2
Journal Article
Peer reviewed
Recent image captioning models are achieving impressive results based on popular metrics, i.e., BLEU, CIDEr, and SPICE. However, focusing on the most popular metrics that only consider the overlap ...between the generated captions and human annotation could result in using common words and phrases, which lacks distinctiveness, i.e., many similar images have the same caption. In this paper, we aim to improve the distinctiveness of image captions via comparing and reweighting with a set of similar images. First, we propose a distinctiveness metric-between-set CIDEr (CIDErBtw) to evaluate the distinctiveness of a caption with respect to those of similar images. Our metric reveals that the human annotations of each image in the MSCOCO dataset are not equivalent based on distinctiveness; however, previous works normally treat the human annotations equally during training, which could be a reason for generating less distinctive captions. In contrast, we reweight each ground-truth caption according to its distinctiveness during training. We further integrate a long-tailed weight strategy to highlight the rare words that contain more information, and captions from the similar image set are sampled as negative examples to encourage the generated sentence to be unique. Finally, extensive experiments are conducted, showing that our proposed approach significantly improves both distinctiveness (as measured by CIDErBtw and retrieval metrics) and accuracy (e.g., as measured by CIDEr) for a wide variety of image captioning baselines. These results are further confirmed through a user study.
Nanoparticles have been widely used in detection and killing of bacteria; however, targeting bacteria is still challenging. Delicate design of nanoparticles is required for simultaneous targeting, ...detection, and therapeutic functions. Here the use of Au/MnFe2O4 (Au/MFO) Janus nanoparticles to target Gram‐positive bacteria via metabolic labeling is reported and realize integrated self‐reporting and thermal killing of bacteria. In these nanoparticles, the Au component is functionalized with tetrazine to target trans‐cyclooctene group anchored on bacterial cell wall by metabolic incorporation of d‐amino acids, and the MFO part exhibits peroxidase activity, enabling self‐reporting of bacteria before treatment. The spatial separation of targeting and reporting functions avoids the deterioration of catalytic activity after surface modification. Also important is that MFO facilitates magnetic separation and magnetic heating, leading to easy enrichment and magnetic thermal therapy of labeled bacteria. This method demonstrates that metabolic labeling with d‐amino acids is a promising strategy to specifically target and kill Gram‐positive bacteria.
Tetrazine‐functionalized Janus magnetic nanoparticles are developed to target trans‐cyclooctene‐labeled peptidoglycans of Gram‐positive bacteria that are specifically tagged via metabolic labeling with d‐amino acid. The magnetic nanoparticles exhibit peroxidase activity, which produces colorimetric signals to offer a self‐reporting method. The magnetic separation and magnetic heating empower the nanoparticles to eradicate the nanoparticle‐labeled bacteria with log reductions as high as three.
Quantum fluctuations give rise to Casimir forces between two parallel conducting plates, the magnitude of which increases monotonically as the separation decreases. By introducing nanoscale gratings ...to the surfaces, recent advances have opened opportunities for controlling the Casimir force in complex geometries. Here, we measure the Casimir force between two rectangular silicon gratings. Using an on-chip detection platform, we achieve accurate alignment between the two gratings so that they interpenetrate as the separation is reduced. Just before interpenetration occurs, the measured Casimir force is found to have a geometry dependence that is much stronger than previous experiments, with deviations from the proximity force approximation reaching a factor of ~500. After the gratings interpenetrate each other, the Casimir force becomes non-zero and independent of displacement. This work shows that the presence of gratings can strongly modify the Casimir force to control the interaction between nanomechanical components.
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