dbAPIS: a database of anti-prokaryotic immune system genes.

Anti-prokaryotic immune system (APIS) proteins, typically encoded by phages, prophages, and plasmids, inhibit prokaryotic immune systems (e.g. restriction modification, toxin-antitoxin, CRISPR-Cas). A growing number of APIS genes have been characterized and dispersed in the literature. Here we developed dbAPIS (https://bcb.unl.edu/dbAPIS), as the first literature curated data repository for experimentally verified APIS genes and their associated protein families. The key features of dbAPIS include: (i) experimentally verified APIS genes with their protein sequences, functional annotation, PDB or AlphaFold predicted structures, genomic context, sequence and structural homologs from different microbiome/virome databases; (ii) classification of APIS proteins into sequence-based families and construction of hidden Markov models (HMMs); (iii) user-friendly web interface for data browsing by the inhibited immune system types or by the hosts, and functions for searching and batch downloading of pre-computed data; (iv) Inclusion of all types of APIS proteins (except for anti-CRISPRs) that inhibit a variety of prokaryotic defense systems (e.g. RM, TA, CBASS, Thoeris, Gabija). The current release of dbAPIS contains 41 verified APIS proteins and ∼4400 sequence homologs of 92 families and 38 clans. dbAPIS will facilitate the discovery of novel anti-defense genes and genomic islands in phages, by providing a user-friendly data repository and a web resource for an easy homology search against known APIS proteins.

dbCAN3: automated carbohydrate-active enzyme and substrate annotation.

Carbohydrate active enzymes (CAZymes) are made by various organisms for complex carbohydrate metabolism. Genome mining of CAZymes has become a routine data analysis in (meta-)genome projects, owing to the importance of CAZymes in bioenergy, microbiome, nutrition, agriculture, and global carbon recycling. In 2012, dbCAN was provided as an online web server for automated CAZyme annotation. dbCAN2 (https://bcb.unl.edu/dbCAN2) was further developed in 2018 as a meta server to combine multiple tools for improved CAZyme annotation. dbCAN2 also included CGC-Finder, a tool for identifying CAZyme gene clusters (CGCs) in (meta-)genomes. We have updated the meta server to dbCAN3 with the following new functions and components: (i) dbCAN-sub as a profile Hidden Markov Model database (HMMdb) for substrate prediction at the CAZyme subfamily level; (ii) searching against experimentally characterized polysaccharide utilization loci (PULs) with known glycan substates of the dbCAN-PUL database for substrate prediction at the CGC level; (iii) a majority voting method to consider all CAZymes with substrate predicted from dbCAN-sub for substrate prediction at the CGC level; (iv) improved data browsing and visualization of substrate prediction results on the website. In summary, dbCAN3 not only inherits all the functions of dbCAN2, but also integrates three new methods for glycan substrate prediction.

dbCAN-seq update: CAZyme gene clusters and substrates in microbiomes.

Carbohydrate Active EnZymes (CAZymes) are significantly important for microbial communities to thrive in carbohydrate rich environments such as animal guts, agricultural soils, forest floors, and ocean sediments. Since 2017, microbiome sequencing and assembly have produced numerous metagenome assembled genomes (MAGs). We have updated our dbCAN-seq database (https://bcb.unl.edu/dbCAN_seq) to include the following new data and features: (i) ∼498 000 CAZymes and ∼169 000 CAZyme gene clusters (CGCs) from 9421 MAGs of four ecological (human gut, human oral, cow rumen, and marine) environments; (ii) Glycan substrates for 41 447 (24.54%) CGCs inferred by two novel approaches (dbCAN-PUL homology search and eCAMI subfamily majority voting) (the two approaches agreed on 4183 CGCs for substrate assignments); (iii) A redesigned CGC page to include the graphical display of CGC gene compositions, the alignment of query CGC and subject PUL (polysaccharide utilization loci) of dbCAN-PUL, and the eCAMI subfamily table to support the predicted substrates; (iv) A statistics page to organize all the data for easy CGC access according to substrates and taxonomic phyla; and (v) A batch download page. In summary, this updated dbCAN-seq database highlights glycan substrates predicted for CGCs from microbiomes. Future work will implement the substrate prediction function in our dbCAN2 web server.

Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction.

BACKGROUND: The presence of infarction in patients with unrecognized myocardial infarction (UMI) is a critical feature in predicting adverse cardiac events. This study aimed to compare the detection rate of UMI using conventional and deep learning reconstruction (DLR)-based late gadolinium enhancement (LGEO and LGEDL, respectively) and evaluate optimal quantification parameters to enhance diagnosis and management of suspected patients with UMI. METHODS: This prospective study included 98 patients (68 men; mean age: 55.8 ± 8.1 years) with suspected UMI treated at our hospital from April 2022 to August 2023. LGEO and LGEDL images were obtained using conventional and commercially available inline DLR algorithms. The myocardial signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and percentage of enhanced area (Parea) employing the signal threshold versus reference mean (STRM) approach, which correlates the signal intensity (SI) within areas of interest with the average SI of normal regions, were analyzed. Analysis was performed using the standard deviation (SD) threshold approach (2SD-5SD) and full width at half maximum (FWHM) method. The diagnostic efficacies based on LGEDL and LGEO images were calculated. RESULTS: The SNRDL and CNRDL were two times better than the SNRO and CNRO, respectively (P < 0.05). Parea-DL was elevated compared to Parea-O using the threshold methods (P < 0.05); however, no intergroup difference was found based on the FWHM method (P > 0.05). The Parea-DL and Parea-O also differed except between the 2SD and 3SD and the 4SD/5SD and FWHM methods (P < 0.05). The receiver operating characteristic curve analysis revealed that each SD method exhibited good diagnostic efficacy for detecting UMI, with the Parea-DL having the best diagnostic efficacy based on the 5SD method (P < 0.05). Overall, the LGEDL images had better image quality. Strong diagnostic efficacy for UMI identification was achieved when the STRM was ≥ 4SD and ≥ 3SD for the LGEDL and LGEO, respectively. CONCLUSIONS: STRM selection for LGEDL magnetic resonance images helps improve clinical decision-making in patients with UMI. This study underscored the importance of STRM selection for analyzing LGEDL images to enhance diagnostic accuracy and clinical decision-making for patients with UMI, further providing better cardiovascular care.

Semi-TSGAN: Semi-Supervised Learning for Highlight Removal Based on Teacher-Student Generative Adversarial Network.

Despite recent notable advancements in highlight image restoration techniques, the dearth of annotated data and the lightweight deployment of highlight removal networks pose significant impediments to further advancements in the field. In this paper, to the best of our knowledge, we first propose a semi-supervised learning paradigm for highlight removal, merging the fusion version of a teacher-student model and a generative adversarial network, featuring a lightweight network architecture. Initially, we establish a dependable repository to house optimal predictions as pseudo ground truth through empirical analyses guided by the most reliable No-Reference Image Quality Assessment (NR-IQA) method. This method serves to assess rigorously the quality of model predictions. Subsequently, addressing concerns regarding confirmation bias, we integrate contrastive regularization into the framework to curtail the risk of overfitting on inaccurate labels. Finally, we introduce a comprehensive feature aggregation module and an extensive attention mechanism within the generative network, considering a balance between network performance and computational efficiency. Our experimental evaluations encompass comprehensive assessments on both full-reference and non-reference highlight benchmarks. The results demonstrate conclusively the substantive quantitative and qualitative enhancements achieved by our proposed algorithm in comparison to state-of-the-art methodologies.

Cascade Electrocatalytic and Thermocatalytic Reduction of CO2 to Propionaldehyde.

Electrochemical CO2 reduction can convert CO2 to value-added chemicals, but its selectivity toward C3+ products are very limited. One possible solution is to run the reactions in hybrid processes by coupling electrocatalysis with other catalytic routes. In this contribution, we report the cascade electrocatalytic and thermocatalytic reduction of CO2 to propionaldehyde. Using Cu(OH)2 nanowires as the precatalyst, CO2 /H2 O is reduced to concentrated C2 H4 , CO, and H2 gases in a zero-gap membrane electrode assembly (MEA) reactor. The thermochemical hydroformylation reaction is separately investigated with a series of rhodium-phosphine complexes. The best candidate is identified to be the one with the 1,4-bis(diphenylphosphino)butane diphosphine ligand, which exhibits a propionaldehyde turnover number of 1148 under a mild temperature and close-to-atmospheric pressure. By coupling and optimizing the upstream CO2 electroreduction and downstream hydroformylation reaction, we achieve a propionaldehyde selectivity of ~38 % and a total C3 oxygenate selectivity of 44 % based on reduced CO2 . These values represent a more than seven times improvement over the best prior electrochemical system alone or over two times improvement over other hybrid systems.

Immobilized Tetraalkylammonium Cations Enable Metal-free CO2 Electroreduction in Acid and Pure Water.

Carbon dioxide reduction reaction (CO2 RR) provides an efficient pathway to convert CO2 into desirable products, yet its commercialization is greatly hindered by the huge energy cost due to CO2 loss and regeneration. Performing CO2 RR under acidic conditions containing alkali cations can potentially address the issue, but still causes (bi)carbonate deposition at high current densities, compromising product Faradaic efficiencies (FEs) in present-day acid-fed membrane electrode assemblies. Herein, we present a strategy using a positively charged polyelectrolyte-poly(diallyldimethylammonium) immobilized on graphene oxide via electrostatic interactions to displace alkali cations. This enables a FE of 85 %, a carbon efficiency of 93 %, and an energy efficiency (EE) of 35 % for CO at 100 mA cm-2 on modified Ag catalysts in acid. In a pure-water-fed reactor, we obtained a 78 % CO FE with a 30 % EE at 100 mA cm-2 at 40 °C. All the performance metrics are comparable to or even exceed those attained in the presence of alkali metal cations.

A Review on Human Comfort Factors, Measurements, and Improvements in Human-Robot Collaboration.

As the development of robotics technologies for collaborative robots (COBOTs), the applications of human-robot collaboration (HRC) have been growing in the past decade. Despite the tremendous efforts from both academia and industry, the overall usage and acceptance of COBOTs are still not so high as expected. One of the major affecting factors is the comfort of humans in HRC, which is usually less emphasized in COBOT development; however, it is critical to the user acceptance during HRC. Therefore, this paper gives a review of human comfort in HRC including the influential factors of human comfort, measurement of human comfort in terms of subjective and objective manners, and human comfort improvement approaches in the context of HRC. Discussions on each topic are also conducted based on the review and analysis.

Promising Deep-Ultraviolet Birefringent Materials via Rational Design and Assembly of Planar π-Conjugated [B(OH)3 ] and [B3 O3 (OH)3 ] Functional Species.

Birefringent materials play a significant role in modulating polarized light in optical communication and the laser industry. However, the discovery of deep ultraviolet (DUV, λ<200 nm) birefringent materials still faces a serious challenge. Herein, we propose hydroxylated π-conjugated [B(OH)3 ] and [B3 O3 (OH)3 ] units for designing DUV birefringent materials. Innovatively, four new hydroxyborates have been synthesized under mild synthesis conditions. They present four novel pseudo layers that benefit from the large degree of freedom assembly modes of [B(OH)3 ] and [B3 O3 (OH)3 ] genes and large birefringence (0.057-0.123@532 nm). Moreover, the Cs3 [B(OH)3 ]2 Cl3 crystal features a short DUV cutoff edge (180 nm), which further indicates that the reported compounds are potential DUV birefringent crystals. Free and flexible assembly modes of π-conjugated [B(OH)3 ] and [B3 O3 (OH)3 ] groups endow them a particular advantage as significant genes for exploring promising DUV birefringent materials.

Temporal modulation of host aerobic glycolysis determines the outcome of Mycobacterium marinum infection.

Macrophages are the first-line host defense that the invading Mycobacterium tuberculosis (Mtb) encounters. It has been recently reported that host aerobic glycolysis was elevated post the infection by a couple of virulent mycobacterial species. However, whether this metabolic transition is required for host defense against intracellular pathogens and the underlying mechanisms remain to be further investigated. A pathogenic mycobacterial species, M. marinum, is genetically close to Mtb and was utilized in this study. Through analyzing cellular carbon metabolism of RAW 264.7 (a murine macrophage-like cell line) post M. marinum infection, a strong elevation of glycolysis was observed. Next, three glycolysis inhibitors were examined for their ability to inhibit mycobacterial proliferation inside RAW264.7 macrophages. Among them, a glucose analog, 2-deoxyglucose (2-DG) displayed a protective role against mycobacterial infection. Treatment with 2-DG at concentrations of 0.5 or 1 mM significantly induced autophagy and decreased the phagocytosis of M. marinum by macrophages. Moreover, 2-DG pre-treatment exerted a significantly protective effect on zebrafish larvae by limiting the proliferation of M. marinum, and such effect was correlated to tumor necrosis factor alpha (TNF-α) as the 2-DG pre-treatment increased the expression of TNF-α in both mouse peritoneal macrophages and zebrafish. On the contrary, the 2-DG treatment post infection did not restrain proliferation of M. marinum in WT zebrafish, and even accelerated bacterial replication in TNF-α-/- zebrafish. Together, modulation of glycolysis prior to infection boosts host immunity against M. marinum infection, indicating a potential intervention strategy to control mycobacterial infection.

X-ray-induced acoustic computed tomography and its applications in biomedicine.

Significance: X-ray-induced acoustic computed tomography (XACT) offers a promising approach to biomedical imaging, leveraging X-ray absorption contrast. It overcomes the shortages of traditional X-ray, allowing for more advanced medical imaging. Aim: The review focuses on the significance and draws onto the potential applications of XACT to demonstrate it as an innovative imaging technique. Approach: This review navigates the expanding landscape of XACT imaging within the biomedical sphere. Integral topics addressed encompass the refinement of imaging systems and the advancement in image reconstruction algorithms. The review particularly emphasizes XACT's significant biomedical applications. Results: Key uses, such as breast imaging, bone density maps for osteoporosis, and X-ray molecular imaging, are highlighted to demonstrate the capability of XACT. A unique niche for XACT imaging is its application in in vivo dosimetry during radiotherapy, which has been validated on patients. Conclusions: Because of its unique property, XACT has great potential in biomedicine and non-destructive testing. We conclude by casting light on potential future avenues in this promising domain.

Microvascular permeability and texture analysis of bone marrow in diabetic rabbits with critical limb ischemia based on dynamic contrast-enhanced magnetic resonance imaging.

BACKGROUND: Early on in the development of diabetes, skeletal muscles can exhibit microarchitectural changes that can be detected using texture analysis (TA) based on volume transfer constant (Ktrans) maps. Nevertheless, there have been few studies and thus we evaluated microvascular permeability and the TA of the bone marrow in diabetics with critical limb ischemia (CLI). METHODS: Eighteen male rabbits were randomly assigned equally into an operation group with hindlimb ischemia and diabetes, a sham-operated group with diabetes only, and a control group. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) was performed on all rabbits at predetermined intervals (1, 5, 10, 15, 20, and 25 days post-surgery). The pharmacokinetic model was used to generate the permeability parameters, while the textural parameters were derived from the Ktrans map. Data analysis methods included the independent sample t-test, Mann-Whitney U test, repeated-measures analysis of variance, and Pearson correlation tests. RESULTS: The Ktrans values reached a minimum on day 1 after ischemia induction, then gradually recovered, but remained lower than those of the sham-operated group. The volume fraction only showed a significant difference between the operation group and the sham-operated group on day 5 post-surgery, but not in the extravascular extracellular space volume fraction at all time points. A significantly reduced Ktrans on day 1, a decreased number of bone trabeculae (Tb.N), and the area of bone trabeculae (Tb.Ar), and an increased microvessel density on day 25 in the operation group compared with the sham-operated group were observed. At each time point, there was a discernible difference between the two groups in the mean value, mean of positive pixels, and sumAverage. CONCLUSIONS: The early stages of diabetic bone marrow with CLI can be evaluated by DCE-MRI for microvascular permeability. Texture analysis based on DCE-MRI could act as an imaging discriminator and new radiological analysis tool for critical limb ischemia in diabetes mellitus.

Carbohydrate-active enzyme annotation in microbiomes using dbCAN.

CAZymes or carbohydrate-active enzymes are critically important for human gut health, lignocellulose degradation, global carbon recycling, soil health, and plant disease. We developed dbCAN as a web server in 2012 and actively maintain it for automated CAZyme annotation. Considering data privacy and scalability, we provide run_dbcan as a standalone software package since 2018 to allow users perform more secure and scalable CAZyme annotation on their local servers. Here, we offer a comprehensive computational protocol on automated CAZyme annotation of microbiome sequencing data, covering everything from short read pre-processing to data visualization of CAZyme and glycan substrate occurrence and abundance in multiple samples. Using a real-world metagenomic sequencing dataset, this protocol describes commands for dataset and software preparation, metagenome assembly, gene prediction, CAZyme prediction, CAZyme gene cluster (CGC) prediction, glycan substrate prediction, and data visualization. The expected results include publication-quality plots for the abundance of CAZymes, CGCs, and substrates from multiple CAZyme annotation routes (individual sample assembly, co-assembly, and assembly-free). For the individual sample assembly route, this protocol takes ∼33h on a Linux computer with 40 CPUs, while other routes will be faster. This protocol does not require programming experience from users, but it does assume a familiarity with the Linux command-line interface and the ability to run Python scripts in the terminal. The target audience includes the tens of thousands of microbiome researchers who routinely use our web server. This protocol will encourage them to perform more secure, rapid, and scalable CAZyme annotation on their local computer servers.

Multi-mode adaptive control strategy for a lower limb rehabilitation robot.

Different patients have different rehabilitation requirements. It is essential to ensure the safety and comfort of patients at different recovery stages during rehabilitation training. This study proposes a multi-mode adaptive control method to achieve a safe and compliant rehabilitation training strategy. First, patients' motion intention and motor ability are evaluated based on the average human-robot interaction force per task cycle. Second, three kinds of rehabilitation training modes-robot-dominant, patient-dominant, and safety-stop-are established, and the adaptive controller can dexterously switch between the three training modes. In the robot-dominant mode, based on the motion errors, the patient's motor ability, and motion intention, the controller can adaptively adjust its assistance level and impedance parameters to help patients complete rehabilitation tasks and encourage them to actively participate. In the patient-dominant mode, the controller only adjusts the training speed. When the trajectory error is too large, the controller switches to the safety-stop mode to ensure patient safety. The stabilities of the adaptive controller under three training modes are then proven using Lyapunov theory. Finally, the effectiveness of the multi-mode adaptive controller is verified by simulation results.

The American Cherimoya Genome Reveals Insights into the Intra-Specific Divergence, the Evolution of Magnoliales, and a Putative Gene Cluster for Acetogenin Biosynthesis.

Annona cherimola (cherimoya) is a species renowned for its delectable fruit and medicinal properties. In this study, we developed a chromosome-level genome assembly for the cherimoya 'Booth' cultivar from the United States. The genome assembly has a size of 794 Mb with a N50 = 97.59 Mb. The seven longest scaffolds account for 87.6% of the total genome length, which corresponds to the seven pseudo-chromosomes. A total of 45,272 protein-coding genes (≥30 aa) were predicted with 92.9% gene content completeness. No recent whole genome duplications were identified by an intra-genome collinearity analysis. Phylogenetic analysis supports that eudicots and magnoliids are more closely related to each other than to monocots. Moreover, the Magnoliales was found to be more closely related to the Laurales than the Piperales. Genome comparison revealed that the 'Booth' cultivar has 200 Mb less repeats than the Spanish cultivar 'Fino de Jete', despite their highly similar (>99%) genome sequence identity and collinearity. These two cultivars were diverged during the early Pleistocene (1.93 Mya), which suggests a different origin and domestication of the cherimoya. Terpene/terpenoid metabolism functions were found to be enriched in Magnoliales, while TNL (Toll/Interleukin-1-NBS-LRR) disease resistance gene has been lost in Magnoliales during evolution. We have also identified a gene cluster that is potentially responsible for the biosynthesis of acetogenins, a class of natural products found exclusively in Annonaceae. The cherimoya genome provides an invaluable resource for supporting characterization, conservation, and utilization of Annona genetic resources.

Modeling and Analysis of Human Comfort in Human-Robot Collaboration.

The emergence and recent development of collaborative robots have introduced a safer and more efficient human-robot collaboration (HRC) manufacturing environment. Since the release of COBOTs, a great amount of research efforts have been focused on improving robot working efficiency, user safety, human intention detection, etc., while one significant factor-human comfort-has been frequently ignored. The comfort factor is critical to COBOT users due to its great impact on user acceptance. In previous studies, there is a lack of a mathematical-model-based approach to quantitatively describe and predict human comfort in HRC scenarios. Also, few studies have discussed the cases when multiple comfort factors take effect simultaneously. In this study, a multi-linear-regression-based general human comfort prediction model is proposed under human-robot collaboration scenarios, which is able to accurately predict the comfort levels of humans in multi-factor situations. The proposed method in this paper tackled these two gaps at the same time and also demonstrated the effectiveness of the approach with its high prediction accuracy. The overall average accuracy among all participants is 81.33%, while the overall maximum value is 88.94%, and the overall minimum value is 72.53%. The model uses subjective comfort rating feedback from human subjects as training and testing data. Experiments have been implemented, and the final results proved the effectiveness of the proposed approach in identifying human comfort levels in HRC.

Cellular and Molecular Mechanisms Underly the Combined Treatment of Fasudil and Bone Marrow Derived-Neuronal Stem Cells in a Parkinson's Disease Mouse Model.

Bone marrow-derived neural stem cells (BM-NSCs) have shed light on novel therapeutic approaches for PD with the potential to halt or even reverse disease progression. Various strategies have been developed to promote therapeutic efficacy via optimizing implanted cells and the microenvironment of transplantation in the central nervous system (CNS). This current study further proved that the combination of fasudil, a Rho-kinase inhibitor, and BM-NSCs exhibited a synergetic effect on restoring neuron loss in the MPTP-PD mice model. It simultaneously unveiled cellular mechanisms underlying synergistic neuron-protection effects of fasudil and BM-NSCs, which included promoting the proliferation, and migration of endogenous NSCs, and contributing to microglia shift into the M2 phenotype. Corresponding molecular mechanisms were observed, including the inhibition of inflammatory responses, the elevation of neurotrophic factors, and the induction of WNT/ß-catenin and PI3K/Akt/mTOR signaling pathways. Our study provides evidence for the co-intervention of BM-NSCs and fasudil as a promising therapeutic method with enhanced efficacy in treating neurodegenerative diseases.

Lysosomal dysfunction in carbon black-induced lung disorders.

Carbon black (CB), a component of environmental particulate pollution derived from carbon sources, poses a significant threat to human health, particularly in the context of lung-related disease. This study aimed to investigate the detrimental effects of aggregated CB in the average micron scale on lung tissues and cells in vitro and in vivo. We observed that CB particles induced lung disorders characterized by enhanced expression of inflammation, necrosis, and fibrosis-related factors in vivo. In alveolar epithelial cells, CB exposure resulted in decreased cell viability, induction of cell death, and generation of reactive oxidative species, along with altered expression of proteins associated with lung disorders. Our findings suggested that the damaging effects of CB on the lung involved the targeting of lysosomes. Specifically, CB promoted lysosomal membrane permeabilization, while lysosomal alkalization mitigated the harmfulness of CB on lung cells. Additionally, we explored the protective effects of alkaloids derived from Nelumbinis plumula, with a focus on neferine, against CB-induced lung disorders. In conclusion, these findings contribute to a deeper understanding of the pathophysiological effects of CB particles on the lungs and propose a potential therapeutic approach for pollution-related diseases.

Cell microparticles loaded with tumor antigen and resiquimod reprogram tumor-associated macrophages and promote stem-like CD8+ T cells to boost anti-PD-1 therapy.

The durable response rate to immune checkpoint blockade such as anti-programmed cell death-1 (PD-1) antibody remains relatively low in hepatocellular carcinoma (HCC), mainly depending on an immunosuppressive microenvironment with limited number of CD8+ T cells, especially stem-like CD8+ T cells, in tumor tissues. Here we develop engineered microparticles (MPs) derived from alpha-fetoprotein (AFP)-overexpressing macrophages to load resiquimod (R848@M2pep-MPsAFP) for enhanced anti-PD-1 therapy in HCC. R848@M2pep-MPsAFP target and reprogram immunosuppressive M2-like tumor-associated macrophages (TAMs) into M1-like phenotype. Meanwhile, R848@M2pep-MPsAFP-reprogrammed TAMs act as antigen-presenting cells, not only presenting AFP antigen to activate CD8+ T cell-mediated antitumor immunity, but also providing an intra-tumoral niche to maintain and differentiate stem-like CD8+ T cells. Combination immunotherapy with anti-PD-1 antibody generates strong antitumor immune memory and induces abundant stem-like CD8+ T cell proliferation and differentiation to terminally exhausted CD8+ T cells for long-term immune surveillance in orthotopic and autochthonous HCC preclinical models in male mice. We also show that the R848-loaded engineered MPs derived from macrophages overexpressing a model antigen ovalbumin (OVA) can improve anti-PD-1 therapy in melanoma B16-OVA tumor-bearing mice. Our work presents a facile and generic strategy for personalized cancer immunotherapy to boost anti-PD-1 therapy.

Cu-catalyzed, Mn-mediated propargylation and allenylation of aldehydes with propargyl bromides.

A simple, practical, and high chemo-selective method for the synthesis of propargyl alcohol and allenyl alcohols via Cu-catalyzed, Mn-mediated propargylation and allenylation of aldehydes with propargyl bromides has been established. When 3-bromo-1-propyne was conducted under the standard condition, the aldehydes were transformed to the corresponding propargylation products completely, while when 1-bromo-2-pentyne was used, allenic alcohol was the only product. Variety of homopropargyl alcohols and allenyl alcohols were obtained in high yields and the reaction is compatible with broad substrate scopes. In addition, the large-scale reaction could also be proceeded smoothly indicating the potential synthetic applications of this transformation.