GroupMorph: Medical Image Registration via Grouping Network with Contextual Fusion.

Pyramid-based deformation decomposition is a promising registration framework, which gradually decomposes the deformation field into multi-resolution subfields for precise registration. However, most pyramid-based methods directly produce one subfield per resolution level, which does not fully depict the spatial deformation. In this paper, we propose a novel registration model, called GroupMorph. Different from typical pyramid-based methods, we adopt the grouping-combination strategy to predict deformation field at each resolution. Specifically, we perform group-wise correlation calculation to measure the similarities of grouped features. After that, n groups of deformation subfields with different receptive fields are predicted in parallel. By composing these subfields, a deformation field with multi-receptive field ranges is formed, which can effectively identify both large and small deformations. Meanwhile, a contextual fusion module is designed to fuse the contextual features and provide the inter-group information for the field estimator of the next level. By leveraging the inter-group correspondence, the synergy among deformation subfields is enhanced. Extensive experiments on four public datasets demonstrate the effectiveness of GroupMorph. Code is available at https://github.com/TVayne/GroupMorph.

A low-cost and eco-friendly recombinant protein expression system using copper-containing industrial wastewater.

The development of innovative methods for highly efficient production of recombinant proteins remains a prominent focus of research in the biotechnology field, primarily due to the fact that current commercial protein expression systems rely on expensive chemical inducers, such as isopropyl ß-D-thiogalactoside (IPTG). In our study, we designed a novel approach for protein expression by creating a plasmid that responds to copper. This specialized plasmid was engineered through the fusion of a copper-sensing element with an optimized multiple cloning site (MCS) sequence. This MCS sequence can be easily customized by inserting the coding sequences of target recombinant proteins. Once the plasmid was generated, it was introduced into an engineered Escherichia coli strain lacking copA and cueO. With this modified E. coli strain, we demonstrated that the presence of copper ions can efficiently trigger the induction of recombinant protein expression, resulting in the production of active proteins. Most importantly, this expression system can directly utilize copper-containing industrial wastewater as an inducer for protein expression while simultaneously removing copper from the wastewater. Thus, this study provides a low-cost and eco-friendly strategy for the large-scale recombinant protein production. To the best of our knowledge, this is the first report on the induction of recombinant proteins using industrial wastewater.

An engineered influenza virus to deliver antigens for lung cancer vaccination.

The development of cancer neoantigen vaccines that prime the anti-tumor immune responses has been hindered in part by challenges in delivery of neoantigens to the tumor. Here, using the model antigen ovalbumin (OVA) in a melanoma model, we demonstrate a chimeric antigenic peptide influenza virus (CAP-Flu) system for delivery of antigenic peptides bound to influenza A virus (IAV) to the lung. We conjugated attenuated IAVs with the innate immunostimulatory agent CpG and, after intranasal administration to the mouse lung, observed increased immune cell infiltration to the tumor. OVA was then covalently displayed on IAV-CPG using click chemistry. Vaccination with this construct yielded robust antigen uptake by dendritic cells, a specific immune cell response and a significant increase in tumor-infiltrating lymphocytes compared to peptides alone. Lastly, we engineered the IAV to express anti-PD1-L1 nanobodies that further enhanced regression of lung metastases and prolonged mouse survival after rechallenge. Engineered IAVs can be equipped with any tumor neoantigen of interest to generate lung cancer vaccines.

Novel mono- and multivalent N-acetylneuraminic acid glycoclusters as potential broad-spectrum entry inhibitors for influenza and coronavirus infection.

N-acetylneuraminic acid (Neu5Ac) is a glycan receptor of viruses spread in many eukaryotic cells. The present work aimed to design, synthesis and biological evaluation of a panel of Neu5Ac derivatives based on a cyclodextrin (CD) scaffold for targeting influenza and coronavirus membrane proteins. The multivalent Neu5Ac glycoclusters efficiently inhibited chicken erythrocyte agglutination induced by intact influenza virus in a Neu5Ac density-dependent fashion. Compared with inhibition by Neu5Ac, the multivalent inhibitor with 21 Neu5Ac residues on the primary face of the ß-CD scaffold afforded 1788-fold higher binding affinity inhibition for influenza virus hemagglutinin with a dissociation constant (KD) of 3.87 × 10-7 M. It showed moderate binding affinity to influenza virus neuraminidase, but with only about one-thirtieth the potency of that with the HA protein. It also exhibited strong binding affinity to the spike protein of three human coronaviruses (severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus, and severe acute respiratory syndrome coronavirus 2), with KD values in the low micromolar range, which is about 10-time weaker than that of HA. Therefore, these multivalent sialylated CD derivatives have possible therapeutic application as broad-spectrum antiviral entry inhibitors for many viruses by targeting the Neu5Ac of host cells.

Referring Image Segmentation With Fine-Grained Semantic Funneling Infusion.

Recently, referring image segmentation has attracted wide attention given its huge potential in human-robot interaction. Networks to identify the referred region must have a deep understanding of both the image and language semantics. To do so, existing works tend to design various mechanisms to achieve cross-modality fusion, for example, tile and concatenation and vanilla nonlocal manipulation. However, the plain fusion usually is either coarse or constrained by the exorbitant computation overhead, finally causing not enough understanding of the referent. In this work, we propose a fine-grained semantic funneling infusion (FSFI) mechanism to solve the problem. The FSFI introduces a constant spatial constraint on the querying entities from different encoding stages and dynamically infuses the gleaned language semantic into the vision branch. Moreover, it decomposes the features from different modalities into more delicate components, allowing the fusion to happen in multiple low-dimensional spaces. The fusion is more effective than the one only happening in one high-dimensional space, given its ability to sink more representative information along the channel dimension. Another problem haunting the task is that the instilling of high-abstract semantic will blur the details of the referent. Targetedly, we propose a multiscale attention-enhanced decoder (MAED) to alleviate the problem. We design a detail enhancement operator (DeEh) and apply it in a multiscale and progressive way. Features from the higher level are used to generate attention guidance to enlighten the lower-level features to more attend to the detail regions. Extensive results on the challenging benchmarks show that our network performs favorably against the state-of-the-arts (SOTAs).

CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection.

Most of the existing bi-modal (RGB-D and RGB-T) salient object detection methods utilize the convolution operation and construct complex interweave fusion structures to achieve cross-modal information integration. The inherent local connectivity of the convolution operation constrains the performance of the convolution-based methods to a ceiling. In this work, we rethink these tasks from the perspective of global information alignment and transformation. Specifically, the proposed cross-modal view-mixed transformer (CAVER) cascades several cross-modal integration units to construct a top-down transformer-based information propagation path. CAVER treats the multi-scale and multi-modal feature integration as a sequence-to-sequence context propagation and update process built on a novel view-mixed attention mechanism. Besides, considering the quadratic complexity w.r.t. the number of input tokens, we design a parameter-free patch-wise token re-embedding strategy to simplify operations. Extensive experimental results on RGB-D and RGB-T SOD datasets demonstrate that such a simple two-stream encoder-decoder framework can surpass recent state-of-the-art methods when it is equipped with the proposed components.

The structure-based optimization of 3-substituted indolin-2-one derivatives as potent and isoform-selective c-Jun N-terminal kinase 3 (JNK3) inhibitors and biological evaluation.

An indolin-2-(4-thiazolidinone) scaffold was previously shown to be a novel chemotype for JNK3 inhibition. However, more in vivo applications were limited due to the unconfirmed configuration and poor physicochemical properties. Here, the indolin-2-(4-thiazolidinone) scaffold validated the absolute configuration; substituents on the scaffold were optimized. Extensive structure activity relationship (SAR) studies were performed using kinase activity assays, thus leading to potent and highly selective JNK3 inhibitors with neuroprotective activity and good oral bioavailability. One lead compound, A53, was a potent and selective JNK3 inhibitor (IC50 = 78 nM) that had significant inhibition (>80% at 1 µM) to only JNK3 in a 398-kinase panel. A53 had low inhibition against JNK3 and high stability (t1/2(α) = 0.98 h, t1/2(ß) = 2.74 h) during oral administration. A modeling study of A53 in human JNK3 showed that the indolin-2-(4-thiazolidinone)-based JNK3 inhibitor with a 5-position-substituted hydrophilic group offered improved kinase inhibition.

Proximity-enabled covalent binding of IL-2 to IL-2Rα selectively activates regulatory T cells and suppresses autoimmunity.

Interleukin-2 (IL-2) is a pleiotropic cytokine that orchestrates bidirectional immune responses via regulatory T cells (Tregs) and effector cells, leading to paradoxical consequences. Here, we report a strategy that exploited genetic code expansion-guided incorporation of the latent bioreactive artificial amino acid fluorosulfate-L-tyrosine (FSY) into IL-2 for proximity-enabled covalent binding to IL-2Rα to selectively promote Treg activation. We found that FSY-bearing IL-2 variants, such as L72-FSY, covalently bound to IL-2Rα via sulfur-fluoride exchange when in proximity, resulting in persistent recycling of IL-2 and selectively promoting the expansion of Tregs but not effector cells. Further assessment of L72-FSY-expanded Tregs demonstrated that L72-FSY maintained Tregs in a central memory phenotype without driving terminal differentiation, as demonstrated by simultaneously attenuated expression of lymphocyte activation gene-3 (LAG-3) and enhanced expression of programmed cell death protein-1 (PD-1). Subcutaneous administration of L72-FSY in murine models of pristane-induced lupus and graft-versus-host disease (GvHD) resulted in enhanced and sustained therapeutic efficacy compared with wild-type IL-2 treatment. The efficacy of L72-FSY was further improved by N-terminal PEGylation, which increased its circulatory retention for preferential and sustained effects. This proximity-enabled covalent binding strategy may accelerate the development of pleiotropic cytokines as a new class of immunomodulatory therapies.

Referring Segmentation via Encoder-Fused Cross-Modal Attention Network.

This paper focuses on referring segmentation, which aims to selectively segment the corresponding visual region in an image (or video) according to the referring expression. However, the existing methods usually consider the interaction between multi-modal features at the decoding end of the network. Specifically, they interact the visual features of each scale with language respectively, thus ignoring the correlation between multi-scale features. In this work, we present an encoder fusion network (EFN), which transfers the multi-modal feature learning process from the decoding end to the encoding end and realizes the gradual refinement of multi-modal features by the language. In EFN, we also adopt a co-attention mechanism to promote the mutual alignment of language and visual information in feature space. In the decoding stage, a boundary enhancement module (BEM) is proposed to enhance the network's attention to the details of the target. For video data, we introduce an asymmetric cross-frame attention module (ACFM) to effectively capture the temporal information from the video frames by computing the relationship between each pixel of the current frame and each pooled sub-region of the reference frames. Extensive experiments on referring image/video segmentation datasets show that our method outperforms the state-of-the-art performance.

Freeze-resistant, rapidly polymerizable, ionic conductive hydrogel induced by Deep Eutectic Solvent (DES) after lignocellulose pretreatment for flexible sensors.

In this paper, ternary DES (choline chloride, glycerol, Lewis acid) was used to pretreat lignocellulose, and the DES solution with dissolved lignin was utilized as the medium of hydrogel to prepare DES-based polyacrylic acid/polyvinyl alcohol (PAA/PVA) double network hydrogels with great mechanical properties, self-adhesion, and high electrochemical sensitivity. The entanglement of PAA with PVA chains, the covalent linkage between Al3+ and PAA chains and the metal phenol network (MPN) formed by Al3+ and lignin improved the mechanical properties of the hydrogels, enabling the prepared hydrogels to achieve a tensile strain of 400 % and an elongation at break of 150 kPa. Secondly, the introduction of DES solution endowed the hydrogel with excellent electrical sensing ability and anti-freezing property, so that the hydrogel still maintains good flexibility and ionic conductivity at -20 °C. It was also found that the above hydrogel can achieve a high gauge factor of 4.19 as a flexible sensor, which provides scientific ideas for the application of the pretreated DES solution in the field of flexible wearable.

Bidirectional Relationship Inferring Network for Referring Image Localization and Segmentation.

Recently, referring image localization and segmentation has aroused widespread interest. However, the existing methods lack a clear description of the interdependence between language and vision. To this end, we present a bidirectional relationship inferring network (BRINet) to effectively address the challenging tasks. Specifically, we first employ a vision-guided linguistic attention module to perceive the keywords corresponding to each image region. Then, language-guided visual attention adopts the learned adaptive language to guide the update of the visual features. Together, they form a bidirectional cross-modal attention module (BCAM) to achieve the mutual guidance between language and vision. They can help the network align the cross-modal features better. Based on the vanilla language-guided visual attention, we further design an asymmetric language-guided visual attention, which significantly reduces the computational cost by modeling the relationship between each pixel and each pooled subregion. In addition, a segmentation-guided bottom-up augmentation module (SBAM) is utilized to selectively combine multilevel information flow for object localization. Experiments show that our method outperforms other state-of-the-art methods on three referring image localization datasets and four referring image segmentation datasets.

Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction.

Benefiting from color independence, illumination invariance and location discrimination attributed by the depth map, it can provide important supplemental information for extracting salient objects in complex environments. However, high-quality depth sensors are expensive and can not be widely applied. While general depth sensors produce the noisy and sparse depth information, which brings the depth-based networks with irreversible interference. In this paper, we propose a novel multi-task and multi-modal filtered transformer (MMFT) network for RGB-D salient object detection (SOD). Specifically, we unify three complementary tasks: depth estimation, salient object detection and contour estimation. The multi-task mechanism promotes the model to learn the task-aware features from the auxiliary tasks. In this way, the depth information can be completed and purified. Moreover, we introduce a multi-modal filtered transformer (MFT) module, which equips with three modality-specific filters to generate the transformer-enhanced feature for each modality. The proposed model works in a depth-free style during the testing phase. Experiments show that it not only significantly surpasses the depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts a high-quality depth map and salient contour at the same time. And, the resulted depth map can help existing RGB-D SOD methods obtain significant performance gain.

Learning From Box Annotations for Referring Image Segmentation.

Referring image segmentation (RIS) has obtained an impressive achievement by fully convolutional networks (FCNs). However, previous RIS methods require a large number of pixel-level annotations. In this article, we present a weakly supervised RIS method by using bounding box (BB) annotations. In the first stage, we introduce an adversarial boundary loss to extract the object contour from the BB, which is then used to select appropriate region proposals for pseudoground-truth (PGT) generation. In the second stage, we design a co-training (Co-T) strategy to purify the pseudolabels. Specifically, we train two networks and interactively guide them to pick clean labels for each other's networks, which can weaken the effect of noisy labels on model training. Experiment results on four benchmark datasets demonstrate that the proposed method can produce high-quality masks with a speed of 63 frames/s.

Measurement of the Cobb angle by 3D ultrasound: a valuable additional method for the prenatal evaluation of congenital scoliosis.

Background: This study aimed to measure the Cobb angle of the fetal spine using three-dimensional ultrasound (3D-US) and to assess the relationship between the Cobb angle and the prognosis of congenital scoliosis. Methods: From March 2015 to June 2019, 77 pregnant women whose fetuses had suspected spinal skeletal dysplasia consented to undergo 3D-US examinations, and 54 fetuses were selected for the analysis group. The study protocol was approved by the review board of the Institutional Ethics Committee for Fetal Medicine. 3D-US was used to show the structure of the fetal spine in 3 planes, and the Cobb angle was measured on the coronal plane. The diagnostic efficacy of 3D-US was compared to that of X-ray for 33 fetuses. Results: In the diagnosis of congenital scoliosis, the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 3D-US were 91.7%, 90.0%, 90.7%, 88.0%, and 93.1%, respectively. The area under the receiver operating characteristic (ROC) curve with 3D-US was 0.908. The Spearman correlation coefficient between the Cobb angle measurement on an X-ray image and on the coronal plane image acquired by 3D-US was 0.84, which showed a significant correlation (P<0.05). Conclusions: 3D-US was successful in the diagnosis of congenital scoliosis. It is feasible to measure the Cobb angle on the coronal plane of the fetal spine by using 3D-US. The Cobb angle has the potential to become an auxiliary index for evaluating the prognosis of congenital scoliosis.

Novel ß-Cyclodextrin-Based Heptavalent Glycyrrhetinic Acid Conjugates: Synthesis, Characterization, and Anti-Influenza Activity.

In our continuing efforts toward the design of novel pentacyclic triterpene derivatives as potential anti-influenza virus entry inhibitors, a series of homogeneous heptavalent glycyrrhetinic acid derivatives based on ß-cyclodextrin scaffold were designed and synthesized by click chemistry. The structure was unambiguously characterized by NMR, IR, and MALDI-TOF-MS measurements. Seven conjugates showed sufficient inhibitory activity against influenza virus infection based on the cytopathic effect reduction assay with IC50 values in the micromolar range. The interactions of conjugate 37, the most potent compound (IC50 = 2.86 µM, CC50 > 100 µM), with the influenza virus were investigated using the hemagglutination inhibition assay. Moreover, the surface plasmon resonance assay further confirmed that compound 37 bound to the influenza HA protein specifically with a dissociation constant of 5.15 × 10-7 M. Our results suggest the promising role of ß-cyclodextrin as a scaffold for preparing a variety of multivalent compounds as influenza entry inhibitors.

Discovery of Pentacyclic Triterpenoid PROTACs as a Class of Effective Hemagglutinin Protein Degraders.

Influenza hemagglutinin that drives viral entry into cells via the membrane fusion process is an up-and-coming antiviral drug target. Herein, we described for the first time the design, synthesis, and biological characteristics of a new class of pentacyclic triterpenoid-based proteolysis targeting chimeras (PROTACs) to enhance the degradation of hemagglutinin target. Among these PROTACs, V3 showed the best degradation effect on the hemagglutinin with a median degradation concentration of 1.44 µM in a ubiquitin and proteasome-dependent manner and broad-spectrum anti-influenza A virus activity but not affected the entry of influenza virus. Moreover, intravenous injection of V3 protected mice against influenza A virus-induced toxic effects. Further diazirine-containing photo-crosslinking mass spectrometric analysis of hemagglutinin complexes indicated crosslinking to Asn15, Thr31, and Asn27, a novel target of hemagglutinin. Taken together, our data revealed that oleanolic acid-based PROTACs could degrade hemagglutinin protein, providing a new direction toward the discovery of potential anti-influenza drugs.

Discovery of novel ataxia telangiectasia mutated (ATM) kinase modulators: Computational simulation, biological evaluation and cancer combinational chemotherapy study.

Ataxia-telangiectasia mutated (ATM) kinase is a serine/threonine protein kinase and plays a key role in DNA double-strand breaks repair. Thus, ATM is considered a promising target for radiotherapy and chemotherapy sensitizing. Herein, we report the discovery of ATM agonist A22 and inhibitor A41 by computational methods and further biological evaluation. Among them, A22 exhibited low cytotoxicity in vitro and might serve as a useful tool for ATM research. Moreover, we firstly proved that ATM inhibitors could sensitize Irinotecan and Etoposide in a time-dependent manner on MCF-7 and SW480 cells, antagonism in a short period treatment while synergy at a long-term treatment and ATM agonist worked in an opposite way of ATM inhibitors. Further mechanism study demonstrated that the antagonism effect of ATM inhibitors with chemotherapeutic agents in a short period was resulting from inhibiting the p53/p21 axis to accelerate G1/S phase cell-cycle transition and promote cell survival. Additionally, A41 displayed antitumor effects combined with a chemotherapeutic drug in the SW480 xenograft model, indicating that A41 is a promising ATM inhibitor, which could increase the antitumor effect of chemotherapeutic drugs in vivo. All in all, these findings will guide the combination of ATM inhibitors with chemotherapeutic agents in further preclinical and clinical studies.

Utilization of lignin upon successive fractionation and esterification in polylactic acid (PLA)/lignin biocomposite.

The study presents the preparation of novel biocomposites based on different lignin fractions and polylactic acid (PLA). Based on the improvement of PLA mechanical properties, we proposed a process to assemble a series of lignin samples and PLA matrix using melt blending to investigate the effects on PLA properties from the perspective of the structure and molecular weight for lignin. The lignin was extracted from pine residue using deep eutectic solvent (DES) and was subjected to fractionation with ethanol and acetone as well as esterification modification with succinic anhydride (SAn). The treated lignin samples were used as additives, which not only improved the mechanical properties of PLA on the basis of retaining its thermal stability, but also granted excellent antimicrobial and biocompatibility properties. The results showed that the molecular weight of lignin was negatively correlated with the phenolic hydroxyl content, and the mechanical properties of the composites were also significantly affected by different molecular weights of lignin, indicating that the inhomogeneity of lignin affects its value-added utilization. The enhanced mechanical properties, antibacterial effect, and steady biocompatibility provide potential possibilities for lignin-based composites in biomedical applications.

LncSNHG3 promotes hepatocellular carcinoma epithelial mesenchymal transition progression through the miR-152-3p/JAK1 pathway.

BACKGROUND: The dysregulation of LncRNAs is related to the malignant progression of many cancers. OBJECTIVE: The study aimed to investigate the expression and the biological role of LncSNHG3 in hepatocellular carcinoma (HCC). METHODS: The TCGA data of the LncSNHG3 in HCC were analyzed. The expression in HCC cell lines was detected by qRT-PCR. Proliferation, migration, and invasion of HepG2 and Huh7 were examined by cell counting kit-8, colony formation, transwell assays, and wound healing assays. At the same time, the interactions among LncSNHG3, miR-152-3p, and JAK1 were confirmed by dual-luciferase reporter assay, RNA immunoprecipitation, subcellular distribution. Xenograft tumor-bearing mice models were used to measure the effect of LncSNHG3 on the growth of HCC in vivo. The apoptosis and epithelial mesenchymal transition (EMT)-associated proteins were checked by WB and IHC. RESULTS: LncSNHG3 was overexpressed in HCC tissues and cell lines. In addition, it is correlated with the tumor stage and survival time of HCC patients. Down-regulated LncSNHG3 could significantly suppress the EMT progression of HCC in vivo and in vitro. LncSNHG3 could promote the JAK1 expression by sponging miR-152-3p. CONCLUSIONS: LncSNHG3 acted as an oncogene and promoted the EMT procession in HCC by binding miR-152-3p and promoting JAK1 expression. Predictably, LncSNHG3 was used as a potential marker and will be used as a novel therapy target for HCC in the future.