Search

Dynamically evolving adhesions between cells and extracellular matrix (ECM) transmit time‐varying signals that control cytoskeletal dynamics and cell fate. Dynamic cell adhesion and ECM stiffness regulate cellular mechanosensing cooperatively, but it has not previously been possible to characterize their individual effects because of challenges with controlling these factors independently. Therefore, a DNA‐driven molecular system is developed wherein the integrin‐binding ligand RGD can be reversibly presented and removed to achieve cyclic cell attachment/detachment on substrates of defined stiffness. Using this culture system, it is discovered that cyclic adhesion accelerates F‐actin kinetics and nuclear mechanosensing in human mesenchymal stem cells (hMSCs), with the result that hysteresis can completely change how hMSCs transduce ECM stiffness. Results are dramatically different from well‐known results for mechanotransduction on static substrates, but are consistent with a mathematical model of F‐actin fragments retaining structure following loss of integrin ligation and participating in subsequent repolymerization. These findings suggest that cyclic integrin‐mediated adhesion alters the mechanosensing of ECM stiffness by hMSCs through transient, hysteretic memory that is stored in F‐actin. [ABSTRACT FROM AUTHOR]

As folates are essential for embryonic development and growth, it is necessary to accurately determine the levels of folates in plasma and red blood cells (RBCs) for clinical intervention. The aims of this study were to develop and validate a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for quantitation of folates in plasma and RBCs and to examine the association between plasma and RBC folate concentrations and gestational diabetes mellitus (GDM), gestational hypertension (GH) and preeclampsia (PE). With the in-house developed LC-MS/MS, a retrospective cross-sectional study was conducted. The healthy pregnant women of first- (n = 147), second- (n = 84) and third-trimester (n = 141) or the women diagnosed with GDM (n = 84), GH (n = 58) or PE (n = 23), that were aged between 22 and 46 years old and registered at our institute, were subjected for measurement of folic acid (FA) and 5-methyltetrahydrofolate (5-MTHF), followed by appropriate statistical association analysis. The assay for simultaneous quantitation of FA and 5-MTHF in plasma and RBCs was linear, stable, with imprecision less than 15% and recoveries within ±10%. The lower limits of quantification for FA and 5-MTHF measurement in whole blood were 0.57 and 1.09 nmol/L, and in plasma were 0.5 and 1 nmol/L, respectively. In the association analysis, the patients with lower RBC folate level (<906 nmol/L) presented higher risks of PE development (OR 4.861 [95% CI 1.411–16.505]) by logistic regression and restricted cubic spline (RCS) regression in a nonlinear fashion. In addition, higher level of plasma folates in pregnancy was significantly associated with GH risk but may be protective for the development of GDM. The in-house developed LC-MS/MS method for folates and metabolites in plasma or RBC showed satisfactory analytical performance for clinical application. Further, the levels of folates and metabolites were diversely associated with GDM, GH and PE development. [ABSTRACT FROM AUTHOR]

In recent years, research on few-shot learning (FSL) has been fast-growing in the 2D image domain due to the less requirement for labeled training data and greater generalization for novel classes. However, its application in 3D point cloud data is relatively under-explored. Not only need to distinguish unseen classes as in the 2D domain, 3D FSL is more challenging in terms of irregular structures, subtle inter-class differences, and high intra-class variances when trained on a low number of data. Moreover, different architectures and learning algorithms make it difficult to study the effectiveness of existing 2D FSL algorithms when migrating to the 3D domain. In this work, for the first time, we perform systematic and extensive investigations of directly applying recent 2D FSL works to 3D point cloud related backbone networks and thus suggest a strong learning baseline for few-shot 3D point cloud classification. Furthermore, we propose a new network, Point-cloud Correlation Interaction (PCIA), with three novel plug-and-play components called Salient-Part Fusion (SPF) module, Self-Channel Interaction Plus (SCI+) module, and Cross-Instance Fusion Plus (CIF+) module to obtain more representative embeddings and improve the feature distinction. These modules can be inserted into most FSL algorithms with minor changes and significantly improve the performance. Experimental results on three benchmark datasets, ModelNet40-FS, ShapeNet70-FS, and ScanObjectNN-FS, demonstrate that our method achieves state-of-the-art performance for the 3D FSL task. [ABSTRACT FROM AUTHOR]

BACKGROUND: Pesticides are extensively applied globally. Pesticide residues induce calamitous effects on the environment and untargeted organisms. Public concerns for the safety of freshwater organisms and the challenges posed by aquatic contaminants remain high. In the present study, the acute toxicity of avermectins (AVMs) to the crayfish, Procambarus clarkii was evaluated. We also evaluated the potential effects of AVM on the biochemical and transcriptomic status of the hepatopancreas and gastrointestinal tract in P. clarkii. RESULTS: The 24, 48, 72, 96 h median lethal concentrations (LC50) of AVM on crayfish were 2.626, 1.162, 0.723, 0.566 mg L−1, respectively. The crayfish were then exposed to 0.65 mg L−1 of AVM for 96 h. AVM significantly altered biochemical parameters including AChE and CAT activities in the hepatopancreas, and AChE, SOD and Na + ‐K + ‐ATPase activities in the gastrointestinal tract at several time points. Furthermore, transcriptomic analysis identified 953 and 1851 differentially‐expressed genes (DEGs) in the hepatopancreas and gastrointestinal tract, respectively. KEGG enrichment showed that the gene expression profiles of the hepatopancreas and gastrointestinal tract were distinct from each other. The DEGs in the hepatopancreas were mostly enriched with stress‐response pathways, while the majority of the DEGs in the gastrointestinal tract belonged to metabolism‐related pathways. CONCLUSION: We demonstrated that the AVM induced acute toxicity, oxidative stress, osmoregulation disturbance, neurotoxicity and transcriptome imbalance in crayfish. These findings unraveled the detrimental effects of AVMs exposure on crayfish. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Point cloud instance segmentation has achieved huge progress with the emergence of deep learning. However, these methods are usually data-hungry with expensive and time-consuming dense point cloud annotations. To alleviate the annotation cost, unlabeled or weakly labeled data is still less explored in the task. In this paper, we introduce the first semi-supervised point cloud instance segmentation framework (SPIB) using both labeled and unlabelled bounding boxes as supervision. To be specific, our SPIB architecture involves a two-stage learning procedure. For stage one, a bounding box proposal generation network is trained under a semi-supervised setting with perturbation consistency regularization (SPCR). The regularization works by enforcing an invariance of the bounding box predictions over different perturbations applied to the input point clouds, to provide self-supervision for network learning. For stage two, the bounding box proposals with SPCR are grouped into some subsets, and the instance masks are mined inside each subset with a novel semantic propagation module and a property consistency graph module. Moreover, we introduce a novel occupancy ratio guided refinement module to refine the instance masks. Extensive experiments on the challenging ScanNet v2 dataset demonstrate our method can achieve competitive performance compared with the recent fully-supervised methods. [ABSTRACT FROM AUTHOR]

Point cloud instance segmentation has achieved huge progress with the emergence of deep learning. However, these methods are usually data-hungry with expensive and time-consuming dense point cloud annotations. To alleviate the annotation cost, unlabeled or weakly labeled data is still less explored in the task. In this paper, we introduce the first semi-supervised point cloud instance segmentation framework (SPIB) using both labeled and unlabelled bounding boxes as supervision. To be specific, our SPIB architecture involves a two-stage learning procedure. For stage one, a bounding box proposal generation network is trained under a semi-supervised setting with perturbation consistency regularization (SPCR). The regularization works by enforcing an invariance of the bounding box predictions over different perturbations applied to the input point clouds, to provide self-supervision for network learning. For stage two, the bounding box proposals with SPCR are grouped into some subsets, and the instance masks are mined inside each subset with a novel semantic propagation module and a property consistency graph module. Moreover, we introduce a novel occupancy ratio guided refinement module to refine the instance masks. Extensive experiments on the challenging ScanNet v2 dataset demonstrate our method can achieve competitive performance compared with the recent fully-supervised methods. [ABSTRACT FROM AUTHOR]

The Yes‐associated protein‐1 (YAP1) is an essential regulator of human Hippo signaling pathway and functions through interaction with TEA domain‐4 (TEAD4) transcription factor involved in the tumorigenesis of nasopharyngeal cancer. Previously, a parallel helix‐helix interaction (PHHI) was identified as the key hotspot at YAP1‐TEAD4 complex interface and has been exploited as an attractive druggable target to disrupt the complex. In this study, we investigated a roughly orthogonal cation‐π‐π stacking system across the crystal PHHI packing interface by integrating computational modeling and binding assay, which forms between one YAP1 helical residue Phe69 and two TEAD4 helical residues Phe373/Lys376. A synergistic effect between cation‐π and π‐π interactions was observed; they separately represent two wings of the stacking system. The π‐electron is primarily responsible for the synergistic effect. Combination between diverse aromatic/charged amino acids. as well as neutral alanine on the cation‐π‐π stacking, revealed that the presence of aromatic tryptophan and charged arginine at, respectively, the residues 373 and 376 of TEAD4 helix can considerably improve PHHI binding affinity by ~6‐fold, whereas neutral alanine substitution on each residue and on both would reduce the affinity significantly, confirming a strong synergistic effect involved in the roughly orthogonal cation‐π‐π stacking system at YAP1‐TEAD4 PHHI interface. [ABSTRACT FROM AUTHOR]

In this article, we study two challenging problems in semisupervised cross-view learning. On the one hand, most existing methods assume that the samples in all views have a pairwise relationship, that is, it is necessary to capture or establish the correspondence of different views at the sample level. Such an assumption is easily isolated even in the semisupervised setting wherein only a few samples have labels that could be used to establish the correspondence. On the other hand, almost all existing multiview methods, including semisupervised ones, usually train a model using a fixed dataset, which cannot handle the data of increasing views. In practice, the view number will increase when new sensors are deployed. To address the above two challenges, we propose a novel method that employs multiple independent semisupervised view-specific networks (ISVNs) to learn representation for multiple views in a view-decoupling fashion. The advantages of our method are two-fold. Thanks to our specifically designed autoencoder and pseudolabel learning paradigm, our method shows an effective way to utilize both the labeled and unlabeled data while relaxing the data assumption of the pairwise relationship, that is, correspondence. Furthermore, with our view decoupling strategy, the proposed ISVNs could be separately trained, thus efficiently handling the data of increasing views without retraining the entire model. To the best of our knowledge, our ISVN could be one of the first attempts to make handling increasing views in the semisupervised setting possible, as well as an effective solution to the noncorresponding problem. To verify the effectiveness and efficiency of our method, we conduct comprehensive experiments by comparing 13 state-of-the-art approaches on four multiview datasets in terms of retrieval and classification. [ABSTRACT FROM AUTHOR]

Imbalanced data distribution in crowd counting datasets leads to severe under-estimation and over-estimation problems, which has been less investigated in existing works. In this paper, we tackle this challenging problem by proposing a simple but effective locality-based learning paradigm to produce generalizable features by alleviating sample bias. Our proposed method is locality-aware in two aspects. First, we introduce a locality-aware data partition (LADP) approach to group the training data into different bins via locality-sensitive hashing. As a result, a more balanced data batch is then constructed by LADP. To further reduce the training bias and enhance the collaboration with LADP, a new data augmentation method called locality-aware data augmentation (LADA) is proposed where the image patches are adaptively augmented based on the loss. The proposed method is independent of the backbone network architectures, and thus could be smoothly integrated with most existing deep crowd counting approaches in an end-to-end paradigm to boost their performance. We also demonstrate the versatility of the proposed method by applying it for adversarial defense. Extensive experiments verify the superiority of the proposed method over the state of the arts. [ABSTRACT FROM AUTHOR]

Mesenchymal stem cells adopt differentiation pathways based upon cumulative effects of mechanosensing. A cell's mechanical microenvironment changes substantially over the course of development, beginning from the early stages in which cells are typically surrounded by other cells and continuing through later stages in which cells are typically surrounded by extracellular matrix. How cells erase the memory of some of these mechanical microenvironments while locking in memory of others is unknown. Here, we develop a material and culture system for modifying and measuring the degree to which cells retain cumulative effects of mechanosensing. Using this system, we discover that effects of the RGD adhesive motif of fibronectin (representative of extracellular matrix), known to impart what is often termed "mechanical memory" in mesenchymal stem cells via nuclear YAP localization, are erased by the HAVDI adhesive motif of the N-cadherin (representative of cell-cell contacts). These effects can be explained by a motor clutch model that relates cellular traction force, nuclear deformation, and resulting nuclear YAP re-localization. Results demonstrate that controlled storage and removal of proteins associated with mechanical memory in mesenchymal stem cells is possible through defined and programmable material systems. Mesenchymal stem cells adopt differentiation pathways based upon mechanical cues in their environment which change throughout development. Here the authors develop a material and culture system to modify and measure the degree to which cells retain cumulative effects of mechanosensing to explore how cells erase the memory of some cues while locking in memory of others. [ABSTRACT FROM AUTHOR]

Thanks to the low storage cost and high query speed, cross-view hashing (CVH) has been successfully used for similarity search in multimedia retrieval. However, most existing CVH methods use all views to learn a common Hamming space, thus making it difficult to handle the data with increasing views or a large number of views. To overcome these difficulties, we propose a decoupled CVH network (DCHN) approach which consists of a semantic hashing autoencoder module (SHAM) and multiple multiview hashing networks (MHNs). To be specific, SHAM adopts a hashing encoder and decoder to learn a discriminative Hamming space using either a few labels or the number of classes, that is, the so-called flexible inputs. After that, MHN independently projects all samples into the discriminative Hamming space that is treated as an alternative ground truth. In brief, the Hamming space is learned from the semantic space induced from the flexible inputs, which is further used to guide view-specific hashing in an independent fashion. Thanks to such an independent/decoupled paradigm, our method could enjoy high computational efficiency and the capacity of handling the increasing number of views by only using a few labels or the number of classes. For a newly coming view, we only need to add a view-specific network into our model and avoid retraining the entire model using the new and previous views. Extensive experiments are carried out on five widely used multiview databases compared with 15 state-of-the-art approaches. The results show that the proposed independent hashing paradigm is superior to the common joint ones while enjoying high efficiency and the capacity of handling newly coming views. [ABSTRACT FROM AUTHOR]

Purpose: The KEAP1-NFE2L2 (Kelch-like ECH-associated protein 1 (KEAP1)-Nuclear factor (erythroid-derived 2)-like 2 (NFE2L2)) mutations are associated with resistance to chemotherapy or immunotherapy in non-small cell lung cancer (NSCLC). Conversely, it has been reported that NFE2L2 mutations potentiate improved clinical outcome with immunotherapy. However, therapeutic benefits for patients with KEAP1/NFE2L2 mutations remain unclear. The purpose of this study was to investigate the association between KEAP1/NFE2L2 and NSCLC prognosis, and to explore whether immunotherapy can improve prognosis in populations with KEAP1/NFE2L2 mutations. Experimental Design: The impact of KEAP1/NFE2L2 mutations on survival outcomes in NSCLC patients received immunotherapy and chemotherapy was verified in the randomized phase II/III POPLAR/OAK trials (blood-based sequencing, bNGS cohort, POPLAR (n = 211) and OAK (n = 642)). The Cancer Genome Atlas (TCGA) NSCLC cohort (n=998) and an in-house Chinese NSCLC cohort (n=733) was used For the analysis of immune-related markers. Results: Compared with KEAP1/NFE2L2 wild-type, patients with KEAP1/NFE2L2 mutations were significantly associated with poorer overall survival (OS, HR = 1.97, 95% CI 1.48–2.63, P < 0.001) on atezolizumab and docetaxel (HR = 1.66, 95% CI 1.28–2.16, P < 0.001). In KEAP1/NFE2L2 mutant group, there was no significant difference in median OS between atezolizumab and docetaxel (HR 0.74, 95% CI 0.53–1.03, P = 0.07). NFE2L2/KEAP1 mutations were significantly associated with higher TMB values and PD-L1 expression in the OAK/POPLAR and in-house Chinese NSCLC cohorts. GSEA revealed that KEAP1/NFE2L2mutant subgroup was associated with deficient infiltration of CD4+ T cells, NK T cells and natural Treg cells, and lower expression of DNA damage response genes in TCGA NSCLC cohort. Conclusions: Our study revealed that patients with KEAP1/NFE2L2 mutations have a worse prognosis than wild-type patients, both on immunotherapy and chemotherapy. In addition, in patients with KEAP1/NFE2L2 mutations, immunotherapy did not significantly improve prognosis compared to chemotherapy. [ABSTRACT FROM AUTHOR]

Hydrogels are commonly doped with stiff nanoscale fillers to endow them with the strength and stiffness needed for engineering applications. Although structure–property relations for many polymer matrix nanocomposites are well established, modeling the new generation of hydrogel nanocomposites requires the study of processing–structure–property relationships because subtle differences in chemical kinetics during their synthesis can cause nearly identical hydrogels to have dramatically different mechanical properties. The authors therefore assembled a framework to relate synthesis conditions (including hydrogel and nanofiller mechanical properties and light absorbance) to gelation kinetics and mechanical properties. They validated the model against experiments on a graphene oxide (GO) doped oligo (ethylene glycol) diacrylate (OEGDA), a system in which, in apparent violation of laws from continuum mechanics, doping can reduce rather than increase the stiffness of the resulting hydrogel nanocomposites. Both model and experiment showed a key role light absorbance‐dominated gelation kinetics in determining nanocomposite mechanical properties in conjunction with nanofiller reinforcement, with the nanofiller's attenuation of chemical kinetics sometimes outweighing stiffening effects to explain the observed, anomalous loss of stiffness. By bridging the chemical kinetics and mechanics of nanocomposite hydrogels, the authors' modeling framework shows promise for broad applicability to design of hydrogel nanocomposites. [ABSTRACT FROM AUTHOR]

Single-image dehazing has been an important topic given the commonly occurred image degradation caused by adverse atmosphere aerosols. The key to haze removal relies on an accurate estimation of global air-light and the transmission map. Most existing methods estimate these two parameters using separate pipelines which reduces the efficiency and accumulates errors, thus leading to a suboptimal approximation, hurting the model interpretability, and degrading the performance. To address these issues, this article introduces a novel generative adversarial network (GAN) for single-image dehazing. The network consists of a novel compositional generator and a novel deeply supervised discriminator. The compositional generator is a densely connected network, which combines fine-scale and coarse-scale information. Benefiting from the new generator, our method can directly learn the physical parameters from data and recover clean images from hazy ones in an end-to-end manner. The proposed discriminator is deeply supervised, which enforces that the output of the generator to look similar to the clean images from low-level details to high-level structures. To the best of our knowledge, this is the first end-to-end generative adversarial model for image dehazing, which simultaneously outputs clean images, transmission maps, and air-lights. Extensive experiments show that our method remarkably outperforms the state-of-the-art methods. Furthermore, to facilitate future research, we create the HazeCOCO dataset which is currently the largest dataset for single-image dehazing. [ABSTRACT FROM AUTHOR]

Photoresponsive hydrogels (PRHs) are soft materials whose mechanical and chemical properties can be tuned spatially and temporally with relative ease. Both photo‐crosslinkable and photodegradable hydrogels find utility in a range of biomedical applications that require tissue‐like properties or programmable responses. Progress in engineering with PRHs is facilitated by the development of theoretical tools that enable optimization of their photochemistry, polymer matrices, nanofillers, and architecture. This review brings together models and design principles that enable key applications of PRHs in tissue engineering, drug delivery, and soft robotics, and highlights ongoing challenges in both modeling and application. [ABSTRACT FROM AUTHOR]

In this article, we study two less-touched challenging problems in single image dehazing neural networks, namely, how to remove haze from a given image in an unsupervised and zero-shot manner. To the ends, we propose a novel method based on the idea of layer disentanglement by viewing a hazy image as the entanglement of several “simpler” layers, i.e., a hazy-free image layer, transmission map layer, and atmospheric light layer. The major advantages of the proposed ZID are two-fold. First, it is an unsupervised method that does not use any clean images including hazy-clean pairs as the ground-truth. Second, ZID is a “zero-shot” method, which just uses the observed single hazy image to perform learning and inference. In other words, it does not follow the conventional paradigm of training deep model on a large scale dataset. These two advantages enable our method to avoid the labor-intensive data collection and the domain shift issue of using the synthetic hazy images to address the real-world images. Extensive comparisons show the promising performance of our method compared with 15 approaches in the qualitative and quantitive evaluations. The source code could be found at http://www.pengxi.me. [ABSTRACT FROM AUTHOR]

While analyzing the performance of state-of-the-art R-CNN based generic object detectors, we find that the detection performance for objects with low object-region-percentages (ORPs) of the bounding boxes are much lower than the overall average. Elongated objects are examples. To address the problem of low ORPs for elongated object detection, we propose a hybrid approach which employs a Faster R-CNN to achieve robust detections of object parts, and a novel model-driven clustering algorithm to group the related partial detections and suppress false detections. First, we train a Faster R-CNN with partial region proposals of suitable and stable ORPs. Next, we introduce a deep CNN (DCNN) for orientation classification on the partial detections. Then, on the outputs of the Faster R-CNN and DCNN, the algorithm of adaptive model-driven clustering first initializes a model of an elongated object with a data-driven process on local partial detections, and refines the model iteratively by model-driven clustering and data-driven model updating. By exploiting Faster R-CNN to produce robust partial detections and model-driven clustering to form a global representation, our method is able to generate a tight oriented bounding box for elongated object detection. We evaluate the effectiveness of our approach on two typical elongated objects in the COCO dataset, and other typical elongated objects, including rigid objects (pens, screwdrivers and wrenches) and non-rigid objects (cracks). Experimental results show that, compared with the state-of-the-art approaches, our method achieves a large margin of improvements for both detection and localization of elongated objects in images. [ABSTRACT FROM AUTHOR]

Answering questions using multi-modal context is a challenging problem, as it requires a deep integration of diverse data sources. Existing approaches only consider a subset of all possible interactions among data sources during one attention hop. In this paper, we present a holistic multi-modal memory network (HMMN) framework that fully considers interactions between different input sources (multi-modal context and question) at each hop. In addition, to hone in on relevant information, our framework takes answer choices into consideration during the context retrieval stage. Our HMMN framework effectively integrates information from the multi-modal context, question, and answer choices, enabling more informative context to be retrieved for question answering. Experimental results on the MovieQA and TVQA datasets validate the effectiveness of our HMMN framework. Extensive ablation studies show the importance of holistic reasoning and reveal the contributions of different attention strategies to model performance. [ABSTRACT FROM AUTHOR]

TRAF2 is a crucial adaptor protein involved in various signaling pathways. However, its biological functions in nasopharyngeal carcinoma (NPC) remain largely unknown. In the present study, we found that TRAF2 was overexpressed in nasopharyngeal carcinoma (NPC) cells. Knockdown of TRAF2 with shRNA significantly suppressed NPC cell proliferation and colony formation. The growth of xenograft tumor significantly reduced after TRAF2 was silenced. Conversely, the ectopic overexpression of TRAF2 significantly promoted cell proliferation and anchorage-independent growth. In TRAF2 knockdown cells, EGF-induced activation of transcriptional factors, including MSK1, CREB, and ATF2, markedly decreased. Accordingly, the transcriptional activity of AP-1 was substantially decreased in TRAF2-deficient cells. With the suppression of gene transcription, the expression of cyclin D1 was significantly impaired, which gave rise to the G0/G1 cell cycle arrest. Moreover, the overexpression of TRAF2 in NPC cells was associated with resistance to irradiation, and the potency of irradiation was substantially enhanced after TRAF2 was knocked down. Briefly, our studies demonstrated that TRAF2 had a crucial role in NPC development, and it might be of great potential to targeting TRAF2 for NPC prevention and treatment. [ABSTRACT FROM AUTHOR]

Isoliquiritigenin (ILQ) is a natural product isolated from licorice root which has served as traditional Chinese medicine for a long time. Recently, the antitumor effects of ILQ have been widely studied in various cancers, but the role and related mechanisms of ILQ in esophageal squamous carcinoma cells (ESCC) are still poorly understood. In our studies, ILQ showed profound antitumor activities in ESCC cells. In vitro, ILQ substantially inhibited cell proliferation and anchorage-independent growth in a panel of human ESCC cells. Mechanism studies showed that EGFR signaling pathway played an important role for ILQ to exert its antitumor activity in ESCC. Exposure to isoliquiritigenin substantially decreased EGF-induced EGFR activation and its downstream Akt and ERK1/2 signaling pathway. EGFR knockdown with shRNA in ESCC cell significantly reduced the sensitivity of cancer cells to ILQ. Moreover, it was found that ILQ had a significantly inhibitory effect on AP-1 family, the protein of Jun and Fos subfamilies was substantially downregulated, and the transcriptional activity of AP-1 family was dramatically suppressed by ILQ. By reducing the expression of cyclin D1, ESCC cells were induced G0/G1 arrest, and cell division was substantially blocked. Finally, the antitumor potency of ILQ was validated in xenograft models and the tumor growth was prominently restrained by ILQ. Briefly, our study showed that ILQ, or its analogue, appeared to be a promising new therapeutic agent for ESCC management. [ABSTRACT FROM AUTHOR]

Night-time pedestrian retrieval is a cross-modality retrieval task of retrieving person images between day-time visible images and night-time thermal images. It is a very challenging problem due to modality difference, camera variations, and person variations, but it plays an important role in night-time video surveillance. The existing cross-modality retrieval usually focuses on learning modality sharable feature representations to bridge the modality gap. In this article, we propose to utilize auxiliary information to improve the retrieval performance, which consistently improves the performance with different baseline loss functions. Our auxiliary information contains two major parts: cross-modality feature distribution and contextual information. The former aligns the cross-modality feature distributions between two modalities to improve the performance, and the latter optimizes the cross-modality distance measurement with the contextual information. We also demonstrate that abundant annotated visible pedestrian images, which are easily accessible, help to improve the cross-modality pedestrian retrieval as well. The proposed method is featured in two aspects: the auxiliary information does not need additional human intervention or annotation; it learns discriminative feature representations in an end-to-end deep learning manner. Extensive experiments on two cross-modality pedestrian retrieval datasets demonstrate the superiority of the proposed method, achieving much better performance than the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Dimension reduction is a fundamental task of machine learning and computer vision, which is widely used in a variety of industrial applications. Over past decades, a lot of unsupervised and supervised algorithms have been proposed. However, few of them can automatically determine the feature dimension that could be adaptive to different data distributions. To obtain a good performance, it is popular to seek the optimal dimension by exhaustively enumerating some possible values. Clearly, such a scheme is ad hoc and computationally extensive. Therefore, a method which can automatically estimate the feature dimension in an efficient and principled manner is of significant practical and theoretical value. In this paper, we propose a novel supervised subspace learning method called multiple marginal Fisher analysis (MMFA), which can automatically estimate the feature dimension. By maxing the interclass separability among marginal points while minimizing within-class scatter, MMFA obtains low-dimensional representations with outstanding discriminative properties. Extensive experiments show that MMFA not only outperforms other algorithms on clean data, but also show robustness on corrupted and disguised data. [ABSTRACT FROM AUTHOR]

Sparse coding-based anomaly detection has shown promising performance, of which the keys are feature learning, sparse representation, and dictionary learning. In this paper, we propose a new neural network for anomaly detection (termed AnomalyNet) by deeply achieving feature learning, sparse representation, and dictionary learning in three joint neural processing blocks. Specifically, to learn better features, we design a motion fusion block accompanied by a feature transfer block to enjoy the advantages of eliminating noisy background, capturing motion, and alleviating data deficiency. Furthermore, to address some disadvantages (e.g., nonadaptive updating) of the existing sparse coding optimizers and embrace the merits of neural network (e.g., parallel computing), we design a novel recurrent neural network to learn sparse representation and dictionary by proposing an adaptive iterative hard-thresholding algorithm (adaptive ISTA) and reformulating the adaptive ISTA as a new long short-term memory (LSTM). To the best of our knowledge, this could be one of the first works to bridge the $\ell _{1}$ -solver and LSTM and may provide novel insight into understanding LSTM and model-based optimization (or named differentiable programming), as well as sparse coding-based anomaly detection. Extensive experiments show the state-of-the-art performance of our method in the abnormal events detection task. [ABSTRACT FROM AUTHOR]

The article titled "Programmable and Reversible Integrin-Mediated Cell Adhesion Reveals Hysteresis in Actin Kinetics that Alters Subsequent Mechanotransduction" published in Advanced Science discusses how cyclical adhesion can impact stem cell perception of the stiffness of their surrounding environment, influencing their mechanosensing abilities and fate determination. The study, conducted by Min Lin and colleagues, combines modeling and experimental approaches to demonstrate that cyclical adhesion can enhance cytoskeletal kinetics and amplify nuclear mechanosensing. The findings have significant implications for stem cell therapies and the study of development. [Extracted from the article]

Keywords: adverse outcome; D-dimer; prediction; pregnancy; reference interval EN adverse outcome D-dimer prediction pregnancy reference interval e99 e101 3 02/06/21 20210301 NES 210301 To the Editor, Pregnancy is a complicated physiological process, during which the balance of the hemostatic systems is tipped toward a hypercoagulable state [[1]]. Unlike postpartum hemorrhage and preeclampsia, much fewer reports were focused on the associations between D-dimer and preterm birth and fetal distress. Further, the elevation of D-dimer was found to be associated with increased risks of preeclampsia, fetal distress, postpartum hemorrhage and a decreased risk of preterm delivery in our retrospective study. [Extracted from the article]

In this paper, we propose YoTube—a novel deep learning framework for generating action proposals in untrimmed videos, where each action proposal corresponds to a spatial-temporal tube that potentially locates one human action. Most of the existing works generate proposals by clustering low-level features or linking image proposals, which ignore the interplay between long-term temporal context and short-term cues. Different from these works, our method considers the interplay by designing a new recurrent YoTube detector and static YoTube detector. The recurrent YoTube detector sequentially regresses candidate bounding boxes using Recurrent Neural Network learned long-term temporal contexts. The static YoTube detector produces bounding boxes using rich appearance cues in every single frame. To fully exploit the complementary appearance, motion, and temporal context, we train the recurrent and static detector using RGB (Color) and flow information. Moreover, we fuse the corresponding outputs of the detectors to produce accurate and robust proposal boxes and obtain the final action proposals by linking the proposal boxes using dynamic programming with a novel path trimming method. Benefiting from the pipeline of our method, the untrimmed video could be effectively and efficiently handled. Extensive experiments on the challenging UCF-101, UCF-Sports, and JHMDB datasets show superior performance of the proposed method compared with the state of the arts. [ABSTRACT FROM AUTHOR]

Capsaicin is a principal component of hot red peppers and chili peppers. Previous studies have reported that capsaicin exhibits antitumor functions in a variety of tumor models. Although various mechanisms underlying the capsaicin‑mediated inhibition of tumor growth have been demonstrated, the impact of capsaicin on tumor metabolism has rarely been reported. The present study demonstrated that capsaicin exhibited an inhibitory effect on tumor glycolysis in esophageal squamous cell carcinoma (ESCC) cells. Following treatment with capsaicin, glucose consumption and lactate production in ESCC cells was decreased. Capsaicin resulted in a decrease of hexokinase‑2 (HK‑2) expression, which is known for its important role in tumor glycolysis. Further investigations demonstrated that phosphatase and tensin homolog (PTEN) expression was increased in ESCC cells treated with capsaicin, and that the RAC‑α serine threonine‑protein kinase signaling pathway was downregulated. In PTEN‑knockdown KYSE150 cells, the decrease in HK‑2 and inhibition of glycolysis caused by capsaicin was attenuated, which suggested that the impact of capsaicin on tumor metabolism was associated with its effect on PTEN. [ABSTRACT FROM AUTHOR]

Localizing, identifying, and extracting humans with consistent appearance jointly from a personal photo stream is an important problem and has wide applications. The strong variations in foreground and background and irregularly occurring foreground humans make this realistic problem challenging. Inspired by advancements in object detection, scene understanding, and image cosegmentation, we explore explicit constraints to label and segment human objects rather than other nonhuman objects and “stuff.” We refer to such a problem as multiple human identification and cosegmentation (MHIC). To identify specific human subjects, we propose an efficient human instance detector by combining an extended color line model with a poselet-based human detector. Moreover, to capture high-level human shape information, a novel soft shape cue is proposed. It is initialized by the human detector, then further enhanced through a generalized geodesic distance transform, and finally refined with a joint bilateral filter. We also propose to capture the rich feature context around each pixel by using an adaptive cross-region data structure, which gives a higher discriminative power than a single pixel-based estimation. The high-level object cues from the detector and the shape are then integrated with the low-level pixel cues and midlevel contour cues into a principled conditional random field (CRF) framework, which can be efficiently solved by using fast graph cut algorithms. We evaluate our method over a newly created NTU-MHIC human dataset, which contains 351 images with manually annotated groundtruth segmentation. Both visual and quantitative results demonstrate that our method achieves state-of-the-art performance for the MHIC task. [ABSTRACT FROM PUBLISHER]

The operational knowledge of skilled technicians gained from years of experience is invaluable for an enterprise. Possession of such knowledge will facilitate an enterprise sharing technician’s know-how and training of new employees effectively. However, until now there is rare efficient quantitative method to obtain this kind of tacit knowledge. In this paper we propose a concept of engineering-oriented operational empirical knowledge (OEK) to describe this kind of knowledge and design a framework to acquire OEK from skilled technician’s operations. The framework integrates motion analysis, motion elicitation, and intent analysis. The modular arrangement of predetermined time standards (MODAPTS) is used to divide the technician’s operational process into basic motion elements; and the variable precision rough set (VPRS) algorithm is used to extract the technician’s OEK content, which combined with the technician’s intent elicited via interview; the completed OEK is obtained. At the end of our study, an engineering case is used to validate the feasibility of the proposed method, which shows that satisfactory results have been reached for the study. [ABSTRACT FROM AUTHOR]

Genetically identical cells exposed to homogeneous environment can show remarkable phenotypic difference. To predict how phenotype is shaped, understanding of how each factor contributes is required. During gene expression processes, noise could arise either intrinsically in biochemical processes of gene expression or extrinsically from other cellular processes such as cell growth. In this work, important noise sources in gene expression of phage λ lysogen are quantified using models described by stochastic differential equations (SDEs). Results show that DNA looping has sophisticated impacts on gene expression noise: When DNA looping provides autorepression, like in wild type, it reduces noise in the system; When the autorepression is defected as it is in certain mutants, DNA looping increases expression noise. We also study how each gene operator affects the expression noise by changing the binding affinity between the gene and the transcription factor systematically. We find that the system shows extraordinarily large noise when the binding affinity is in certain range, which changes the system from monostable to bistable. In addition, we find that cell growth causes non-negligible noise, which increases with gene expression level. Quantification of noise and identification of new noise sources will provide deeper understanding on how stochasticity impacts phenotype. [ABSTRACT FROM AUTHOR]