Integrative analysis of cancer multimodality data identifying COPS5 as a novel biomarker of diffuse large B-cell lymphoma.

Preventing, diagnosing, and treating diseases requires accurate clinical biomarkers, which remains challenging. Recently, advanced computational approaches have accelerated the discovery of promising biomarkers from high-dimensional multimodal data. Although machine-learning methods have greatly contributed to the research fields, handling data sparseness, which is not unusual in research settings, is still an issue as it leads to limited interpretability and performance in the presence of missing information. Here, we propose a novel pipeline integrating joint non-negative matrix factorization (JNMF), identifying key features within sparse high-dimensional heterogeneous data, and a biological pathway analysis, interpreting the functionality of features by detecting activated signaling pathways. By applying our pipeline to large-scale public cancer datasets, we identified sets of genomic features relevant to specific cancer types as common pattern modules (CPMs) of JNMF. We further detected COPS5 as a potential upstream regulator of pathways associated with diffuse large B-cell lymphoma (DLBCL). COPS5 exhibited co-overexpression with MYC, TP53, and BCL2, known DLBCL marker genes, and its high expression was correlated with a lower survival probability of DLBCL patients. Using the CRISPR-Cas9 system, we confirmed the tumor growth effect of COPS5, which suggests it as a novel prognostic biomarker for DLBCL. Our results highlight that integrating multiple high-dimensional data and effectively decomposing them to interpretable dimensions unravels hidden biological importance, which enhances the discovery of clinical biomarkers.

Fluorine Incorporation for Enhanced Gas Separation Performance in Porous Organic Polymers: Investigating Reaction Pathways and Pore Structure Control.

The precise control of pore structures in porous organic polymer (POP) materials is of paramount importance in addressing a wide range of challenges associated with gas separation processes. In this study, we present a novel approach to optimize the gas separation performance of POPs through the introduction of fluorine groups and figure out an important factor of reaction decision that whether the AlCl3-catalyzed polymerization is Scholl reaction or Friedel-Crafts alkylation. In the chloroform system, the steric hindrance of function groups could make direct coupling between the benzene rings difficult, which would lead to part solvent knitting (Friedel-Crafts alkylation) instead. The fluorinated polymers show enhanced surface area and pore size characteristics. Notably, the fluorinated polymers exhibited significantly improved adsorption and separation performance for SF6, as evidenced by an ideal adsorbed solution theory selectivity (SF6/N2, v: v = 50:50, 273 K) increase of 75.0, 668.8, and 502.8% compared to the nonfluorinated POPs. These findings highlight the potential of fluorination as a strategy for tailoring the properties of POP materials for advanced gas separation applications.

Prediction Model for Lumbar Curve Correction after Selective Thoracic Fusion in Lenke 1 and 2 Adolescent Idiopathic Scoliosis.

STUDY DESIGN: A retrospective study. OBJECTIVE: To identify independent risk factors and construct a prediction model for lumbar curve correction (LCC) after selective thoracic fusion (STF) in patients with Lenke 1 and 2 adolescent idiopathic scoliosis (AIS). SUMMARY OF BACKGROUND DATA: STF has been widely applied in Lenke 1 and 2 AIS patients. However, LCC after STF is still controversial. METHODS: 128 patients undergoing STF with at least 2 years follow-up were included. Cases were divided into high-LCC group and low-LCC group according to a rounded-up median of 65%. 49 variables were taken into account. Logistic regression was applied to identify independent predictive factors. Prediction model was established by backward stepwise regression, and its evaluation was implemented on R. RESULTS: Five parameters showed independent predictive value for low LCC: Right shoulder higher before surgery [right shoulder higher vs. balanced: odds ratio (OR)=0.244, P=0.014], postoperative Cobb angle of lumbar curve (LC) (OR=1.415, P=0.001, cut-off value=11°), lowest instrumented vertebra (LIV) distal to end vertebra (no vs. yes: OR=4.587, P=0.013), postoperative LIV tilt (OR=0.686, P=0.010, cut-off value=6.85°) and postoperative LIV+1 tilt (OR=1.522, P=0.005, cut-off value=6.25°). The prediction model included six variables: lumbar modifier, preoperative shoulder balance, postoperative Cobb angle of LC, LIV position, postoperative LIV tilt and postoperative LIV+1 tilt. Model evaluation demonstrated satisfactory capability and stability [area under curve=0.890, 10-fold cross-validation accuracy=0.782]. CONCLUSION: Preoperative shoulder balance, Cobb angle of LC, LIV position, postoperative LIV and LIV+1 tilt could be used to prognosticate LCC after STF. A model with solid prediction ability was established, which could further our understanding of LCC and assist in making clinical decisions.

An organometallic swap strategy for bottlebrush polymer-protein conjugate synthesis.

Polymer-protein bioconjugation offers a powerful strategy to alter the physical properties of proteins, and various synthetic polymer compositions and architectures have been investigated for this purpose. Nevertheless, conjugation of molecular bottlebrush polymers (BPs) to proteins remains an unsolved challenge due to the large size of BPs and a general lack of methods to transform the chain ends of BPs into functional groups suitable for bioconjugation. Here, we present a strategy to address this challenge in the context of BPs prepared by "graft-through" ring-opening metathesis polymerization (ROMP), one of the most powerful methods for BP synthesis. Quenching ROMP of PEGylated norbornene macromonomers with an activated enyne terminator facilitates the transformation of the BP Ru alkylidene chain ends into Pd oxidative addition complexes (OACs) for facile bioconjugation. This strategy is shown to be effective for the synthesis of two BP-protein conjugates (albumin and ERG), setting the stage for a new class of BP-protein conjugates for future therapeutic and imaging applications.

A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low-Data Regime.

Thermosets present sustainability challenges that could potentially be addressed through the design of deconstructable variants with tunable properties; however, the combinatorial space of possible thermoset molecular building blocks (e.g., monomers, cross-linkers, and additives) and manufacturing conditions is vast, and predictive knowledge for how combinations of these molecular components translate to bulk thermoset properties is lacking. Data science could overcome these problems, but computational methods are difficult to apply to multicomponent, amorphous, statistical copolymer materials for which little data exist. Here, leveraging a data set with 101 examples, we introduce a closed-loop experimental, machine learning (ML), and virtual screening strategy to enable predictions of the glass transition temperature (Tg) of polydicyclopentadiene (pDCPD) thermosets containing cleavable bifunctional silyl ether (BSE) comonomers and/or cross-linkers with varied compositions and loadings. Molecular features and formulation variables are used as model inputs, and uncertainty is quantified through model ensembling, which together with heavy regularization helps to avoid overfitting and ultimately achieves predictions within <15 °C for thermosets with compositionally diverse BSEs. This work offers a path to predicting the properties of thermosets based on their molecular building blocks, which may accelerate the discovery of promising plastics, rubbers, and composites with improved functionality and controlled deconstructability.

Mild acute stress prevents the memory impairment induced by long-term isoflurane anesthesia.

Objectives: Long-term isoflurane anesthesia exposure could result in postoperative cognitive dysfunction (POCD). Preoperative stress is also reported to be a risk factor of POCD. However, it is unknown whether acute stress could impair memory after long-term isoflurane anesthesia. Methods: In this study, we categorized the mice with acute stress into mild (30 min restraint stress), moderate (60 min restraint stress), and severe (120 min restraint stress) stress groups and then we used Open-Field Test (OFT) to detect whether different scales of acute restraint stress successfully induced acute stress in mice. The memory performance of mice was measured using contextual and cued memory test, and the brain-derived neurotrophic factor protein levels of hippocampus was detected by Western blot. Results: We verified that mild stress has pro-cognitive effect, but severe stress has amnestic effect. Moreover, we found that mild and moderate other than severe acute stress could partially attenuate the memory impairment induced by long-term isoflurane anesthesia. Conclusion: Mild and moderate acute stress could partially attenuate the memory impairment induced by long-term isoflurane anesthesia.

iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations.

In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.

Triptolide Suppresses NF-κB-Mediated Inflammatory Responses and Activates Expression of Nrf2-Mediated Antioxidant Genes to Alleviate Caerulein-Induced Acute Pancreatitis.

Triptolide (TP), the main active ingredient of Tripterygium wilfordii Hook.f., displays potent anti-inflammatory, antioxidant, and antiproliferative activities. In the present study, the effect of TP on acute pancreatitis and the underlying mechanisms of the disease were investigated using a caerulein-induced animal model of acute pancreatitis (AP) and an in vitro cell model. In vivo, pretreatment with TP notably ameliorated pancreatic damage, shown as the improvement in serum amylase and lipase levels and pancreatic morphology. Meanwhile, TP modulated the infiltration of neutrophils and macrophages (Ly6G staining and CD68 staining) and decreased the levels of proinflammatory factors (TNF-α and IL-6) through inhibiting the transactivation of nuclear factor-κB (NF-κB) in caerulein-treated mice. Furthermore, TP reverted changes in oxidative stress markers, including pancreatic glutathione (GSH), superoxide dismutase (SOD), and malondialdehyde (MDA), in acute pancreatitis mice. Additionally, TP pretreatment inhibited intracellular reactive oxygen species (ROS) levels via upregulated nuclear factor erythroid 2-related factor 2 (Nrf2) expression and Nrf2-regulated redox genes expression (HO-1, SOD1, GPx1 and NQO1) in vitro. Taken together, our data suggest that TP exert protection against pancreatic inflammation and tissue damage by inhibiting NF-κB transactivation, modulating immune cell responses and activating the Nrf2-mediated antioxidative system, thereby alleviating acute pancreatitis.

Index Networks.

We show that existing upsampling operators in convolutional networks can be unified using the notion of the index function. This notion is inspired by an observation in the decoding process of deep image matting where indices-guided unpooling can often recover boundary details considerably better than other upsampling operators such as bilinear interpolation. By viewing the indices as a function of the feature map, we introduce the concept of 'learning to index', and present a novel index-guided encoder-decoder framework where indices are learned adaptively from data and are used to guide downsampling and upsampling stages, without extra training supervision. At the core of this framework is a new learnable module, termed Index Network (IndexNet), which dynamically generates indices conditioned on the feature map. IndexNet can be used as a plug-in, applicable to almost all convolutional networks that have coupled downsampling and upsampling stages, enabling the networks to dynamically capture variations of local patterns. In particular, we instantiate and investigate five families of IndexNet. We highlight their superiority in delivering spatial information over other upsampling operators with experiments on synthetic data, and demonstrate their effectiveness on four dense prediction tasks, including image matting, image denoising, semantic segmentation, and monocular depth estimation. Code and models are available at https://git.io/IndexNet.

Enhancing the stability of polymer nanostructures via ultrathin oxide coatings for nano-optical device applications.

Polymer nanostructures have drawn tremendous attention due to their wide applications in nanotechnology. However, the morphology of the polymer nanostructures is fragile under harsh conditions such as high-power irradiation and organic-solution environments during the fabrication or the measurement processes, significantly limiting their potential applications. In this work, we propose and demonstrate a simple approach to improve the stability of polymer nanostructures by coating a conformal ultrathin oxide film via atomic-layer deposition. Due to the refractory and dense coating of the oxide layer, the stability of polymer structures is enhanced by the prohibition of deformation occurrences from thermally induced reflow and organic solution. As a proof of concept, poly(methyl methacrylate) (PMMA) nanostructures coated with a sub-10-nm TiO2layer are demonstrated, and the structures exhibit high temperature stability at 180 °C and good resistance to soluble damage from organic solutions. Subsequently, the mechanism of the improved thermal stability is analyzed via mechanical simulations. Such an effective approach is proposed to significantly broaden the application of polymer nanostructures as functional elements for optical structures/devices that require excellent thermal and chemical stability.

A Sponge-Like Double-Layer Wound Dressing with Chitosan and Decellularized Bovine Amniotic Membrane for Promoting Diabetic Wound Healing.

The diabetic wounds do not heal easily in part because they are susceptible to infection due to environmental influences. Wound dressing is crucial to wound healing, as it can basically protect the wound from external damages and provide a suitable microenvironment for tissue regeneration. In this study, a double-layer membrane that consists of chitosan sponge and decellularized bovine amniotic membrane (dBAM) has been developed by freeze-casting method. The results showed that the porous structure of the sponge layer improved the performances of blood coagulation and swelling. The dense dBAM can optimize the mechanical property of wound dressing. In vitro studies revealed that the bilayer membrane had favorable biocompatible, especially for human foreskin fibroblast cells (HFF-1) cell adhesion and proliferation. Moreover, the full-thickness skin defects of diabetic model mice that treated with bilayer membrane showed over 80% closure in 8 days. Our findings imply that the double-layer dressing has great potentials to be used in diabetic patients.