Elucidating the deactivation mechanism of beta zeolite catalyzed linear alkylbenzene production with oxygenated organic compound contaminated feedstocks.

Zeolite catalyzed alkylation of benzene with long-chain α-olefins is a promising method for the detergent industry. Considering the long-chain α-olefins from Fischer-Tropsch synthesis always contain some oxygenated organic compounds, the effect of which on the alkylation of benzene with 1-dodecene was comprehensively investigated over beta zeolite herein. n-heptanol, n-heptaldehyde and n-heptanoic acid were selected as the model oxygenated organic compounds, and it was revealed that an obvious decrease of lifetime occurred when only trace amount of oxygenated organic compounds were added into the feedstocks. The deactivated catalyst was difficult to regenerate by extraction with hot benzene or coke-burning. A series of characterization tests complementary with DFT calculations revealed that the deactivation was mainly caused by the firm adsorption of oxygenated organic compounds on the acid sites. Further, comparison with the open-framework MWW zeolite revealed a similar effect of oxygenated organic compounds and deactivation mechanism for both beta and MWW, but beta is less sensitive to the oxygenated organic compounds. The main reason lies in the three-dimensional framework of beta, wherein the much higher adsorption energy of 1-dodecene makes it difficult to be replaced by oxygenated organic compounds. Additionally, beta could be regenerated more easily by extraction with hot benzene compared with MWW. But coke-burning caused a sharp decrease of its lifetime, which is mainly due to the decreased acid sites after calcination.

Clonal dominance defines metastatic dissemination in pancreatic cancer.

Tumors represent ecosystems where subclones compete during tumor growth. While extensively investigated, a comprehensive picture of the interplay of clonal lineages during dissemination is still lacking. Using patient-derived pancreatic cancer cells, we created orthotopically implanted clonal replica tumors to trace clonal dynamics of unperturbed tumor expansion and dissemination. This model revealed the multifaceted nature of tumor growth, with rapid changes in clonal fitness leading to continuous reshuffling of tumor architecture and alternating clonal dominance as a distinct feature of cancer growth. Regarding dissemination, a large fraction of tumor lineages could be found at secondary sites each having distinctive organ growth patterns as well as numerous undescribed behaviors such as abortive colonization. Paired analysis of primary and secondary sites revealed fitness as major contributor to dissemination. From the analysis of pro- and nonmetastatic isogenic subclones, we identified a transcriptomic signature able to identify metastatic cells in human tumors and predict patients' survival.

SPD Manifold Deep Metric Learning for Image Set Classification.

By characterizing each image set as a nonsingular covariance matrix on the symmetric positive definite (SPD) manifold, the approaches of visual content classification with image sets have made impressive progress. However, the key challenge of unhelpfully large intraclass variability and interclass similarity of representations remains open to date. Although, several recent studies have mitigated the two problems by jointly learning the embedding mapping and the similarity metric on the original SPD manifold, their inherent shallow and linear feature transformation mechanism are not powerful enough to capture useful geometric features, especially in complex scenarios. To this end, this article explores a novel approach, termed SPD manifold deep metric learning (SMDML), for image set classification. Specifically, SMDML first selects a prevailing SPD manifold neural network (SPDNet) as the backbone (encoder) to derive an SPD matrix nonlinear representation. To counteract the degradation of structural information during multistage feature embedding, we construct a Riemannian decoder at the end of the encoder, trained by a reconstruction error term (RT), to induce the generated low-dimensional feature manifold of the hidden layer to capture the pivotal information about the visual data describing the imaged scene. We demonstrate through theory and experiments that it is feasible to replace the Riemannian metric with Euclidean distance in RT. Then, the ReCov layer is introduced into the established Riemannian network to regularize the local statistical information within each input feature matrix, which enhances the effectiveness of the learning process. The theoretical analysis of the activation function used in the ReCov layer in terms of continuity and conditions for generating positive definite matrices is beneficial for network design. Inspired by the fact that the single cross-entropy loss used for training is unable to effectively parse the geometric distribution of the deep representations, we finally endow the suggested model with a novel metric learning regularization term. By explicitly incorporating the encoding and processing of the data variations into the network learning process, this term can not only derive a powerful Riemannian representation but also train an effective classifier. The experimental results show the superiority of the proposed approach on three typical visual classification tasks.

An AFM-Based Model-Fitting-Free Viscoelasticity Characterization Method for Accurate Grading of Primary Prostate Tumor.

Viscoelasticity is a crucial property of cells, which plays an important role in label-free cell characterization. This paper reports a model-fitting-free viscoelasticity calculation method, correcting the effects of frequency, surface adhesion and liquid resistance on AFM force-distance (FD) curves. As demonstrated by quantifying the viscosity and elastic modulus of PC-3 cells, this method shows high self-consistency and little dependence on experimental parameters such as loading frequency, and loading mode (Force-volume vs. PeakForce Tapping). The rapid calculating speed of less than 1ms per curve without the need for a model fitting process is another advantage. Furthermore, this method was utilized to characterize the viscoelastic properties of primary clinical prostate cells from 38 patients. The results demonstrate that the reported characterization method a comparable performance with the Gleason Score system in grading prostate cancer cells, This method achieves a high average accuracy of 97.6% in distinguishing low-risk prostate tumors (BPH and GS6) from higher-risk (GS7-GS10) prostate tumors and a high average accuracy of 93.3% in distinguishing BPH from prostate cancer.

A magnetic multi-layer soft robot for on-demand targeted adhesion.

Magnetic soft robots have shown great potential for biomedical applications due to their high shape reconfigurability, motion agility, and multi-functionality in physiological environments. Magnetic soft robots with multi-layer structures can enhance the loading capacity and function complexity for targeted delivery. However, the interactions between soft entities have yet to be fully investigated, and thus the assembly of magnetic soft robots with on-demand motion modes from multiple film-like layers is still challenging. Herein, we model and tailor the magnetic interaction between soft film-like layers with distinct in-plane structures, and then realize multi-layer soft robots that are capable of performing agile motions and targeted adhesion. Each layer of the robot consists of a soft magnetic substrate and an adhesive film. The mechanical properties and adhesion performance of the adhesive films are systematically characterized. The robot is capable of performing two locomotion modes, i.e., translational motion and tumbling motion, and also the on-demand separation with one side layer adhered to tissues. Simulation results are presented, which have a good qualitative agreement with the experimental results. The feasibility of using the robot to perform multi-target adhesion in a stomach is validated in both ex-vivo and in-vivo experiments.

Zwitterion modified cochlear implants resist postoperative infection and inflammation.

The cochlear implant (CI), an advanced electronic device replacing the entire cochlear function, is the ultimate treatment for over 466 million people with disabling hearing loss. Infection after cochlear implantation is a common and worrisome complication despite the routine administration of the antibiotic. The bacterial biofilms formed on the surface of CI are the main cause of antibiotic failure. To solve this problem, we developed a copper-containing zwitterionic coating consisting of anti-adherent poly sulfobetaine methacrylate (PSB) and steadfast polydopamine (PDA). CuSO4/H2O2. was added to accelerate this co-deposition reaction and enhance the anti-bacterial property. The preparation method was simple, rapid, and suitable for clinical use. In our in vitro and in vivo studies, the PSB/PDA(Cu) coating showed high biocompatibility, and conferred CI implants excellent anti-inflammatory, strong anti-bacterial effects, and great anti-biofilm properties to representative Gram-positive and Gram-negative bacteria. These findings implied that the PSB/PDA(Cu) coating was a unique anti-bacterial strategy for enhancing CI performance.

An Intrinsically Magnetic Epicardial Patch for Rapid Vascular Reconstruction and Drug Delivery.

Myocardial infarction (MI) is a major cause of mortality worldwide. The major limitation of regenerative therapy for MI is poor cardiac retention of therapeutics, which results from an inefficient vascular network and poor targeting ability. In this study, a two-layer intrinsically magnetic epicardial patch (MagPatch) prepared by 3D printing with biocompatible materials like poly (glycerol sebacate) (PGS) is designed, poly (ε-caprolactone) (PCL), and NdFeB. The two-layer structure ensured that the MagPatch multifariously utilized the magnetic force for rapid vascular reconstruction and targeted drug delivery. MagPatch accumulates superparamagnetic iron oxide (SPION)-labelled endothelial cells, instantly forming a ready-implanted organization, and rapidly reconstructs a vascular network anastomosed with the host. In addition, the prefabricated vascular network within the MagPatch allowed for the efficient accumulation of SPION-labelled therapeutics, amplifying the therapeutic effects of cardiac repair. This study defined an extendable therapeutic platform for vascularization-based targeted drug delivery that is expected to assist in the progress of regenerative therapies in clinical applications.

Elaidic acid induced hepatocyte pyroptosis via autophagy-CTSB-NLRP3 pathway.

Elaidic acid (EA, C18:1 trans) is a kind of principal Trans fatty acid (TFA) and is widely found in processed food. Pyroptosis is a form of programmed cell death, distinct from apoptosis and traditional necrosis. Excessive pyroptosis could induce body injury and serious inflammation. However, the effect of EA on pyroptosis has not been reported. In the study, we found that EA exposure caused liver damage and hepatocyte pyroptosis by testing GSDMD-N, Caspase 1, IL-18, and IL-1ß in mice and HepG2 cells. Further exploring the mechanisms, we found that EA-induced pyroptosis depended on Cathepsin B (CTSB)-mediated NLRP3 inflammasome activation. Cell autophagy was closely related to lysosomes. Our study revealed that EA promoted hepatocyte autophagy, and activated autophagy induced lysosomal membrane permeabilization (LMP) and CTSB leakage. Inhibition of autophagy by 3-MA mitigated the CTSB leak, reduced the activation of the NLRP3 inflammasome, and then attenuated the EA-induced pyroptosis. In summary, these results indicated that EA induced hepatocyte pyroptosis via autophagy-CTSB-NLRP3 inflammasome pathway. The study revealed new insights into the toxicity mechanism of EA.

SMARCB1 regulates the hypoxic stress response in sickle cell trait.

Renal medullary carcinoma (RMC) is an aggressive kidney cancer that almost exclusively develops in individuals with sickle cell trait (SCT) and is always characterized by loss of the tumor suppressor SMARCB1. Because renal ischemia induced by red blood cell sickling exacerbates chronic renal medullary hypoxia in vivo, we investigated whether the loss of SMARCB1 confers a survival advantage under the setting of SCT. Hypoxic stress, which naturally occurs within the renal medulla, is elevated under the setting of SCT. Our findings showed that hypoxia-induced SMARCB1 degradation protected renal cells from hypoxic stress. SMARCB1 wild-type renal tumors exhibited lower levels of SMARCB1 and more aggressive growth in mice harboring the SCT mutation in human hemoglobin A (HbA) than in control mice harboring wild-type human HbA. Consistent with established clinical observations, SMARCB1-null renal tumors were refractory to hypoxia-inducing therapeutic inhibition of angiogenesis. Further, reconstitution of SMARCB1 restored renal tumor sensitivity to hypoxic stress in vitro and in vivo. Together, our results demonstrate a physiological role for SMARCB1 degradation in response to hypoxic stress, connect the renal medullary hypoxia induced by SCT with an increased risk of SMARCB1-negative RMC, and shed light into the mechanisms mediating the resistance of SMARCB1-null renal tumors against angiogenesis inhibition therapies.

Bacteria-Targeted Combined with Photothermal/NO Nanoparticles for the Treatment and Diagnosis of MRSA Infection In Vivo.

Currently, undeveloped diagnosis and delayed treatment of bacteria-infected sites in vivo not only expand the risk of tissue infection but are also a major clinical cause of multiple drug-resistant bacterial infections. Herein, an efficient nanoplatform for near-infrared (NIR)-light-controlled release and bacteria-targeted delivery of nitric oxide (NO) combined with photothermal therapy (PTT) is presented. Using maltotriose-decorated mesoporous polydopamine (MPDA-Mal) and BNN6, a smart antibacterial (B@MPDA-Mal) is developed to combine bacterial targeting, gas-controlled release, and PTT. Utilizing bacteria's unique maltodextrin transport system, B@MPDA-Mal can accurately distinguish bacterial infection from sterile inflammation and target the bacteria-infected sites for efficient drug enrichment. Moreover, NIR-light causes MPDA to generate heat, which not only effectively induces BNN6 to produce NO, but also raises the temperature to harm the bacteria further. NO/photothermal combination therapy effectively eliminates biofilm and drug-resistant bacteria. The myositis model of methicillin-resistant Staphylococcus aureus infection is established and indicates that B@MPDA-Mal can successfully eradicate inflammation and abscesses in mice. Meanwhile, magnetic resonance imaging technology is used to monitor the treatment procedure and healing outcomes. Given the aforementioned advantages, the smart antibacterial nanoplatform B@MPDA-Mal can be used as a potential therapeutic tool in the biomedical field against drug-resistant bacterial infections.

Ether phospholipids are required for mitochondrial reactive oxygen species homeostasis.

Mitochondria are hubs where bioenergetics, redox homeostasis, and anabolic metabolism pathways integrate through a tightly coordinated flux of metabolites. The contributions of mitochondrial metabolism to tumor growth and therapy resistance are evident, but drugs targeting mitochondrial metabolism have repeatedly failed in the clinic. Our study in pancreatic ductal adenocarcinoma (PDAC) finds that cellular and mitochondrial lipid composition influence cancer cell sensitivity to pharmacological inhibition of electron transport chain complex I. Profiling of patient-derived PDAC models revealed that monounsaturated fatty acids (MUFAs) and MUFA-linked ether phospholipids play a critical role in maintaining ROS homeostasis. We show that ether phospholipids support mitochondrial supercomplex assembly and ROS production; accordingly, blocking de novo ether phospholipid biosynthesis sensitized PDAC cells to complex I inhibition by inducing mitochondrial ROS and lipid peroxidation. These data identify ether phospholipids as a regulator of mitochondrial redox control that contributes to the sensitivity of PDAC cells to complex I inhibition.

Noncoding translation mitigation.

Translation is pervasive outside of canonical coding regions, occurring in long noncoding RNAs, canonical untranslated regions and introns1-4, especially in ageing4-6, neurodegeneration5,7 and cancer8-10. Notably, the majority of tumour-specific antigens are results of noncoding translation11-13. Although the resulting polypeptides are often nonfunctional, translation of noncoding regions is nonetheless necessary for the birth of new coding sequences14,15. The mechanisms underlying the surveillance of translation in diverse noncoding regions and how escaped polypeptides evolve new functions remain unclear10,16-19. Functional polypeptides derived from annotated noncoding sequences often localize to membranes20,21. Here we integrate massively parallel analyses of more than 10,000 human genomic sequences and millions of random sequences with genome-wide CRISPR screens, accompanied by in-depth genetic and biochemical characterizations. Our results show that the intrinsic nucleotide bias in the noncoding genome and in the genetic code frequently results in polypeptides with a hydrophobic C-terminal tail, which is captured by the ribosome-associated BAG6 membrane protein triage complex for either proteasomal degradation or membrane targeting. By contrast, canonical proteins have evolved to deplete C-terminal hydrophobic residues. Our results reveal a fail-safe mechanism for the surveillance of unwanted translation from diverse noncoding regions and suggest a possible biochemical route for the preferential membrane localization of newly evolved proteins.

Collateral activity of the CRISPR/RfxCas13d system in human cells.

CRISPR/Cas13 systems are increasingly used for programmable targeting of RNAs. While Cas13 nucleases are capable of degrading both target RNAs and bystander RNAs in vitro and in bacteria, initial studies fail to detect collateral degradation of non-target RNAs in eukaryotic cells. Here we show that RfxCas13d, also known as CasRx, a widely used Cas13 system, can cause collateral transcriptome destruction when targeting abundant reporter RNA and endogenous RNAs, resulting in proliferation defect in target cells. While these results call for caution of using RfxCas13d for targeted RNA knockdown, we demonstrated that the collateral activity can be harnessed for selective depletion of a specific cell population defined by a marker RNA in an in vitro setting.

Dynamical Change of Cell Mechanical Properties Accompanying and Modulating Metastasis Process.

The dynamical change of cellular mechanical properties plays an important role in cell metastasis process, while how the cancer cells modulate their mechanical properties during metastasis are still not fully understood. In this report, the cellular detaching and seeding processes, two vital steps of cell metastasis, were simulated in vitro using a self-developed protocol and characterized by the dynamical mechanical properties using AFM. The measured results show that cells decrease their stiffness and increase their surface adhesion force as they are detaching from substrate, while cells present an opposite change in mechanical properties as they seeding. Additionally, the effect of anti-cancer drug (docetaxel) on the detaching and attaching process of cancer cells (PC-3) was also investigated from the aspect of mechanical properties. The results shows that the docetaxel can increase stiffness, decrease surface adhesion force of PC-3 cell, and slow down the change speed of these mechanical properties during PC-3 cell detaching and seeding process. These discoveries demonstrated that a dynamical change of cell mechanical properties is required for cancer cell metastasis, which provide a new drug development strategy for cancer treatment.

Melatonin Prevents against Ethanol-Induced Liver Injury by Mitigating Ferroptosis via Targeting Brain and Muscle ARNT-like 1 in Mice Liver and HepG2 Cells.

The circadian clock acts a pivotal part in human daily physiology and metabolism. Excess alcohol consumption disturbs the circadian rhythm of several metabolism-related genes of the liver. Melatonin is a member of the foremost hormones secreted by the pineal gland with numerous pharmacological properties in quite a number of diseases. However, its potential roles and possible mechanisms in ethanol-induced ferroptosis are still not clear completely. Ethanol feeding studies were performed upon a chronic-plus-binge ethanol feeding protocol in C57BL/6 mice with or without intraperitoneal injection administration of melatonin. HepG2 cells and mice primary hepatocytes were subjected to investigation for ethanol and melatonin. The results showed that melatonin dramatically ameliorated liver injury and decreased ferroptosis makers induced by ethanol. Meanwhile, melatonin effectively reversed the circadian misalignment caused by ethanol. Additionally, melatonin accelerated Nrf2 nuclear translocation and further activated its downstream anti-ferroptosis proteins including FTH, FPN, HO-1, and SLC7A11 in ethanol-changed mice liver tissues and HepG2 cells. However, the impact of melatonin on liver protection and anti-ferroptosis was offset upon brain and muscle ARNT-like 1 (BMAL1) knockdown with the notably blocked Nrf2-ARE pathway. Altogether, this study revealed that melatonin could alleviate ethanol-induced liver injury by impeding ferroptosis via reprogramming the circadian protein BMAL1 and subsequently activating the Nrf2-ARE anti-ferroptosis pathway. The emergence of novel liver protective effects and mechanism of melatonin on ethanol-induced ferroptosis may provide a new dimension for prevention or intervention against liver injury associated with ethanol.

Learning a discriminative SPD manifold neural network for image set classification.

Performing pattern analysis over the symmetric positive definite (SPD) manifold requires specific mathematical computations, characterizing the non-Euclidian property of the involved data points and learning tasks, such as the image set classification problem. Accompanied with the advanced neural networking techniques, several architectures for processing the SPD matrices have recently been studied to obtain fine-grained structured representations. However, existing approaches are challenged by the diversely changing appearance of the data points, begging the question of how to learn invariant representations for improved performance with supportive theories. Therefore, this paper designs two Riemannian operation modules for SPD manifold neural network. Specifically, a Riemannian batch regularization (RBR) layer is firstly proposed for the purpose of training a discriminative manifold-to-manifold transforming network with a novelly-designed metric learning regularization term. The second module realizes the Riemannian pooling operation with geometric computations on the Riemannian manifolds, notably the Riemannian barycenter, metric learning, and Riemannian optimization. Extensive experiments on five benchmarking datasets show the efficacy of the proposed approach.

Fluorescent carbon dots with a high nitric oxide payload for effective antibacterial activity and bacterial imaging.

Multidrug resistance of bacteria has led to the invalidation of traditional therapies using antibiotics and has formed a huge threat to human health. Therefore, promising antibacterial therapies are urgently demanded. Nitric oxide (NO) has attracted much attention in the field of antibacterial agents, and novel antibacterial materials based on NO are being developed rapidly. In this work, we first proposed a carbon dot (CDs)-based and NO-releasing platform for antibacterial application. Here, the chitosan-graft-poly(amidoamine) dendrimer (CPA) was used to synthesize fluorescent CDs via one-step hydrothermal carbonization, and CPA-CDs were successfully prepared, followed by loading NO with the formation of N-diazeniumdiolate (NONOate). The resultant CPA-CDs/NONOate displayed 3.5 times the NO content of the CPA copolymer. Due to their stable photoluminescence, the super-resolution bacterial imaging ability of CPA-CDs/NONOate was observed. Moreover, excellent in vitro and in vivo antibacterial effects were demonstrated against Pseudomonas aeruginosa, where bacterial viability and biofilm were significantly reduced. Further, in vivo assays proved the theranostic activity of CPA-CDs/NONOate in curing rats' wounds with serious bacterial infection. Importantly, these NO-releasing CDs possessed outstanding in vivo and in vitro biocompatibilities. This study provided a multifunctional strategy, providing a foundation for fast bacterial detection and precise antibacterial treatments.

CRISPR Cas13-Based Tools to Track and Manipulate Endogenous Telomeric Repeat-Containing RNAs in Live Cells.

TERRA, TElomeric Repeat-containing RNA, is a long non-coding RNA transcribed from telomeres. Emerging evidence indicates that TERRA regulates telomere maintenance and chromosome end protection in normal and cancerous cells. However, the mechanism of how TERRA contributes to telomere functions is still unclear, partially owing to the shortage of approaches to track and manipulate endogenous TERRA molecules in live cells. Here, we developed a method to visualize TERRA in live cells via a combination of CRISPR Cas13 RNA labeling and SunTag technology. Single-particle tracking reveals that TERRA foci undergo anomalous diffusion in a manner that depends on the timescale and telomeric localization. Furthermore, we used a chemically-induced protein dimerization system to manipulate TERRA subcellular localization in live cells. Overall, our approaches to monitor and control TERRA locations in live cells provide powerful tools to better understand its roles in telomere maintenance and genomic integrity.

CLUE: A Fast Parallel Clustering Algorithm for High Granularity Calorimeters in High-Energy Physics.

One of the challenges of high granularity calorimeters, such as that to be built to cover the endcap region in the CMS Phase-2 Upgrade for HL-LHC, is that the large number of channels causes a surge in the computing load when clustering numerous digitized energy deposits (hits) in the reconstruction stage. In this article, we propose a fast and fully parallelizable density-based clustering algorithm, optimized for high-occupancy scenarios, where the number of clusters is much larger than the average number of hits in a cluster. The algorithm uses a grid spatial index for fast querying of neighbors and its timing scales linearly with the number of hits within the range considered. We also show a comparison of the performance on CPU and GPU implementations, demonstrating the power of algorithmic parallelization in the coming era of heterogeneous computing in high-energy physics.

Syndecan 1 is a critical mediator of macropinocytosis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains recalcitrant to all forms of cancer treatment and carries a five-year survival rate of only 8%1. Inhibition of oncogenic KRAS (hereafter KRAS*), the earliest lesion in disease development that is present in more than 90% of PDACs, and its signalling surrogates has yielded encouraging preclinical results with experimental agents2-4. However, KRAS*-independent disease recurrence following genetic extinction of Kras* in mouse models anticipates the need for co-extinction strategies5,6. Multiple oncogenic processes are initiated at the cell surface, where KRAS* physically and functionally interacts to direct signalling that is essential for malignant transformation and tumour maintenance. Insights into the complexity of the functional cell-surface-protein repertoire (surfaceome) have been technologically limited until recently and-in the case of PDAC-the genetic control of the function and composition of the PDAC surfaceome in the context of KRAS* signalling remains largely unknown. Here we develop an unbiased, functional target-discovery platform to query KRAS*-dependent changes of the PDAC surfaceome, which reveals syndecan 1 (SDC1, also known as CD138) as a protein that is upregulated at the cell surface by KRAS*. Localization of SDC1 at the cell surface-where it regulates macropinocytosis, an essential metabolic pathway that fuels PDAC cell growth-is essential for disease maintenance and progression. Thus, our study forges a mechanistic link between KRAS* signalling and a targetable molecule driving nutrient salvage pathways in PDAC and validates oncogene-driven surfaceome annotation as a strategy to identify cancer-specific vulnerabilities.