A new chromosome-scale duck genome shows a major histocompatibility complex with several expanded multigene families.

BACKGROUND: The duck (Anas platyrhynchos) is one of the principal natural hosts of influenza A virus (IAV), harbors almost all subtypes of IAVs and resists to many IAVs which cause extreme virulence in chicken and human. However, the response of duck's adaptive immune system to IAV infection is poorly characterized due to lack of a detailed gene map of the major histocompatibility complex (MHC). RESULTS: We herein reported a chromosome-scale Beijing duck assembly by integrating Nanopore, Bionano, and Hi-C data. This new reference genome SKLA1.0 covers 40 chromosomes, improves the contig N50 of the previous duck assembly with highest contiguity (ZJU1.0) of more than a 5.79-fold, surpasses the chicken and zebra finch references in sequence contiguity and contains a complete genomic map of the MHC. Our 3D MHC genomic map demonstrated that gene family arrangement in this region was primordial; however, families such as AnplMHCI, AnplMHCIIß, AnplDMB, NKRL (NK cell receptor-like genes) and BTN underwent gene expansion events making this area complex. These gene families are distributed in two TADs and genes sharing the same TAD may work in a co-regulated model. CONCLUSIONS: These observations supported the hypothesis that duck's adaptive immunity had been optimized with expanded and diversified key immune genes which might help duck to combat influenza virus. This work provided a high-quality Beijing duck genome for biological research and shed light on new strategies for AIV control.

Real-World Effectiveness of Omalizumab in Chronic Urticaria: A Clinical Observational Study.

INTRODUCTION: The purpose of this study was to assess the clinical effectiveness and safety profile of omalizumab as a therapeutic intervention for chronic urticaria (CU). METHODS: From March 1, 2023, to September 30, 2023, data on a cohort comprising 96 patients with CU, who underwent treatment with omalizumab at our medical institution's allergy clinic, were systematically compiled. Subsequent to the administration of omalizumab, the therapeutic efficacy was assessed utilizing the 7-day urticaria activity score and the urticaria control test. RESULTS: Based on the statistical analysis, the mean duration of therapeutic intervention was 2.4 ± 1.3 months, with a corresponding mean cumulative dosage of 765 ± 450 mg. Of the subset of 42 patients with CU who were subjected to a follow-up period exceeding 3 months, it was observed that the treatment led to complete symptom remission, and no instances of recurrence were documented. Notably, there were statistically significant differences in the treatment duration and the cumulative dosage between patients who experienced co-morbid conditions and those who did not (p < 0.01, 95% CI: 0.280-1.326; p < 0.01, 95% CI: 0.597-2.997). Furthermore, there were significant differences in the treatment duration and cumulative dosage between patients in the combined allergic rhinitis group and those in the non-combined allergic rhinitis group (p < 0.01, 95% CI: 0.204-1.305; p = 0.01, 95% CI: 0.326-2.860). CONCLUSION: Omalizumab demonstrates efficacy in the management of CU among Chinese patients by exerting effective symptom control and facilitating the regression of skin lesions. The assessment of its therapeutic efficacy typically requires a 12-week treatment period. Moreover, the co-occurrence of CU with other allergic disorders serves as a pertinent consideration for the adjustment of omalizumab dosing regimens.

Discovery of 4-phenylindolines containing a (5-cyanopyridin-3-yl)methoxy moiety as potent inhibitors of the PD-1/PD-L1 interaction.

With the great success of anti-programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) monoclonal antibodies in clinical applications, blocking the PD-1/PD-L1 pathway has become the most compelling strategy in the field of tumor immunotherapy. In this study, a novel series of 4-phenylindolines containing a (5-cyanopyridin-3-yl)methoxy moiety were developed, and their structure-activity relationships were preliminarily discussed. Among them, compounds M17 and M23 exhibited the most potent ability to disrupt the PD-1/PD-L1 interaction, demonstrating IC50 values of 60.1 nM and 53.2 nM, respectively. The binding mode of M23 was further explored by molecular docking analysis with dimeric PD-L1. Therefore, M17 and M23 are promising lead compounds for developing potent inhibitors of the PD-1/PD-L1 axis.

Development and validation of a novel disulfidptosis-related lncRNAs signature in patients with HPV-negative oral squamous cell carcinoma.

Disulfidptosis is a recently identified mode of regulated cell death. Regulating disulfidptosis in carcinoma is a promising therapeutic approach. Long non-coding RNAs (lncRNAs) have been reported to be related to the occurrence and development of many cancers. Disulfidptosis-related lncRNAs (DRLs) in HPV-negative oral squamous cell carcinoma (OSCC) have not been studied. Based on The Cancer Genome Atlas (TCGA) database, least absolute shrinkage selection operator (LASSO) analysis and Cox regression analysis were used to identify overall survival related DRLs and construct the signature. Kaplan-Meier, time-dependent receiver operating characteristics (ROC) and principal component analyses (PCA) were explored to demonstrate the prediction potential of the signature. Subgroup analysis stratified by different clinicopathological characteristics were conducted. Nomogram was established by DRLs signature and independent clinicopathological characteristics. The calibration plots were performed to reveal the accuracy of nomogram. Immune cell subset infiltration, immunotherapy response, drug sensitivity analysis, and tumor mutation burden (TMB) were conducted. Underlying functions and pathways were explored by Gene Set Enrichment Analysis (GSEA) analysis. Previous lncRNA signatures of OSCC were retrieved from PubMed for further validation. Gene expression omnibus (GEO) datasets (GSE41613 and GSE85446) were merged as an external validation for DRLs signature. Consensus clustering analysis of DRLs signature and experimental validation of DRLs were also explored. This research sheds light on the robust performance of DRLs signature in survival prediction, immune cell infiltration, immune escape, and immunotherapy of HPV-negative OSCC.

Prognostic implications of T stage in different pathological types of colorectal cancer: an observational study using SEER population-based data.

OBJECTIVES: Colorectal cancer (CRC) encompasses a spectrum of pathological types, each exhibiting distinct biological behaviours that challenge the conventional T-staging system's predictive efficiency. Thus, this study aims to explore the prognostic significance of the T stage across various CRC pathological types, seeking to unravel insights that could enhance prognostic assessment in this complex disease. STUDY DESIGN: We performed a retrospective analysis using the Surveillance, Epidemiology, and End Results (SEER) database for primary CRC cases from 2010 to 2017. SETTING: The SEER database, comprising data from various US regional and state cancer registries, identified 39 321 patients with CRC. Our analysis focused on the three most common CRC pathological types: adenocarcinoma (AC), mucinous adenocarcinoma (MC) and signet ring cell carcinoma (SR). PRIMARY OUTCOME MEASURES: The study used Cox regression models to evaluate how different pathological characteristics impact mortality risk in patients with CRC. Time-dependent receiver operating characteristic curves were also applied to assess the prognostic accuracy of various tumour node metastasis (TNM)/non-mucinous (NM) stages. RESULTS: We observed significant associations between T stage and mortality risk for patients with AC and MC. Notably, in comparison to those at T1 stage, patients with AC in the T4 stage demonstrated a 2.01-fold increase in mortality risk (HR=2.01, 95% CI: 1.89 to 2.15), while patients with MC at T4 stage showed a 1.42-fold increase (HR=1.42, 95% CI: 1.03 to 1.97). However, within the SR group, T stages did not independently impact survival, showing no significant distinction (HR=1.07, 95% CI: 0.59 to 1.95). Intriguingly, the traditional TNM staging systems demonstrated limited discriminatory power in predicting prognosis for patients with SR when compared with the more innovative NM staging systems. CONCLUSIONS: This study uncovers important insights about the prognostic significance of the T stage in different types of CRC, highlighting the need for personalised assessments based on specific histological subtypes.

Multiscale 3D genome organization underlies duck fatty liver with no adipose inflammation or serious injury.

Non-alcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease. Little is known about how gene expression and chromatin structure are regulated in NAFLD due to lack of suitable model. Ducks naturally develop fatty liver similar to serious human non-alcoholic fatty liver (NAFL) without adipose inflammation and liver fibrosis, thus serves as a good model for investigating molecular mechanisms of adipose metabolism and anti-inflammation. Here, we constructed a NAFLD model without adipose inflammation and liver fibrosis in ducks. By performing dynamic pathological and transcriptomic analyses, we identified critical genes involving in regulation of the NF-κB and MHCII signaling, which usually lead to adipose inflammation and liver fibrosis. We further generated dynamic three-dimensional chromatin maps during liver fatty formation and recovery. This showed that ducks enlarged hepatocyte cell nuclei to reduce inter-chromosomal interaction, decompress chromatin structure, and alter strength of intra-TAD and loop interactions during fatty liver formation. These changes partially contributed to the tight control the NF-κB and the MHCII signaling. Our analysis uncovers duck chromatin reorganization might be advantageous to maintain liver regenerative capacity and reduce adipose inflammation. These findings shed light on new strategies for NAFLD control.

High-Performance Acceleration of 2-D and 3-D CNNs on FPGAs Using Static Block Floating Point.

Over the past few years, 2-D convolutional neural networks (CNNs) have demonstrated their great success in a wide range of 2-D computer vision applications, such as image classification and object detection. At the same time, 3-D CNNs, as a variant of 2-D CNNs, have shown their excellent ability to analyze 3-D data, such as video and geometric data. However, the heavy algorithmic complexity of 2-D and 3-D CNNs imposes a substantial overhead over the speed of these networks, which limits their deployment in real-life applications. Although various domain-specific accelerators have been proposed to address this challenge, most of them only focus on accelerating 2-D CNNs, without considering their computational efficiency on 3-D CNNs. In this article, we propose a unified hardware architecture to accelerate both 2-D and 3-D CNNs with high hardware efficiency. Our experiments demonstrate that the proposed accelerator can achieve up to 92.4% and 85.2% multiply-accumulate efficiency on 2-D and 3-D CNNs, respectively. To improve the hardware performance, we propose a hardware-friendly quantization approach called static block floating point (BFP), which eliminates the frequent representation conversions required in traditional dynamic BFP arithmetic. Comparing with the integer linear quantization using zero-point, the static BFP quantization can decrease the logic resource consumption of the convolutional kernel design by nearly 50% on a field-programmable gate array (FPGA). Without time-consuming retraining, the proposed static BFP quantization is able to quantize the precision to 8-bit mantissa with negligible accuracy loss. As different CNNs on our reconfigurable system require different hardware and software parameters to achieve optimal hardware performance and accuracy, we also propose an automatic tool for parameter optimization. Based on our hardware design and optimization, we demonstrate that the proposed accelerator can achieve 3.8-5.6 times higher energy efficiency than graphics processing unit (GPU) implementation. Comparing with the state-of-the-art FPGA-based accelerators, our design achieves higher generality and up to 1.4-2.2 times higher resource efficiency on both 2-D and 3-D CNNs.

Novel 4-Arylindolines Containing a Pyrido[3,2-d]pyrimidine Moiety as the Programmed Cell Death-1/Programmed Cell Death-Ligand 1 Interaction Inhibitors for Tumor Immunotherapy.

A series of pyrido[3,2-d]pyrimidine-containing 4-arylindolines were identified as potent inhibitors of the programmed cell death-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) interaction by structural optimization of a 4-arylindoline precursor reported previously. Among them, compound N11 was the most promising inhibitor, showing an IC50 value of 6.3 nM against the PD-1/PD-L1 interaction at the biochemical level. In in vitro T-cell tumor co-culture models, N11 significantly promoted T-cell proliferation, activation, and infiltration into tumor spheres, demonstrating that it possessed excellent immunomodulatory activity. In addition, N11 exhibited favorable in vivo antitumor activity in an LLC/PD-L1 tumor-bearing mouse model. Flow cytometry analysis verified that the in vivo antitumor efficacy of N11 was dependent on the activation of the immune microenvironment. These findings suggest that N11 can serve as a new starting point for the future development of small-molecule antitumor immunomodulators targeting the PD-1/PD-L1 axis.

Inducible Pluripotent Stem Cell-Derived Small Extracellular Vesicles Rejuvenate Senescent Blood-Brain Barrier to Protect against Ischemic Stroke in Aged Mice.

Blood-brain barrier (BBB) breakdown after ischemic stroke exacerbates brain injury and BBB senescence can cause severe neurological deficits in aged ischemic stroke population. Recent evidence reveals that inducible pluripotent stem cell-derived small extracellular vesicles (iPSC-sEVs) possess phenomenal antisenescence capability. However, whether iPSC-sEVs can rejuvenate BBB senescence to improve stroke outcomes in aged mice remains unknown. Here, we showed that long-term treatment with iPSC-sEVs alleviated aging-induced BBB senescence in aged mice. In aged stroke mice, iPSC-sEVs significantly mitigated BBB integrity damage, reduced the following infiltration of peripheral leukocytes, and decreased the release of pro-inflammatory factors from the leukocytes, which ultimately inhibited neuronal death and improved neurofunctional recovery. Mechanism studies showed that iPSC-sEVs could activate the endothelial nitric oxide synthase (eNOS) and up-regulate sirtuin 1 (Sirt1) in senescent endothelial cells. Blocking the activation of eNOS abolished iPSC-sEV-mediated rejuvenation of BBB senescence and the protection of BBB integrity. Proteomics results demonstrated that iPSC-sEVs were enriched with bioactive factors including AKT serine/threonine kinase 1 (AKT1) and calmodulin (CALM) to activate the eNOS-Sirt1 axis. Further investigation showed that AKT1 and CALM inhibitors blocked iPSC-sEV-afforded activation of the eNOS-Sirt1 axis in senescent endothelial cells. Taken together, iPSC-sEVs can protect against ischemic stroke in aged mice by rejuvenating BBB senescence, partially, through delivering AKT1 and CALM to activate eNOS-Sirt1 axis, which indicates that iPSC-sEVs treatment is an effective alternative to treat ischemic stroke in the aged population.

iPSC-sEVs alleviate microglia senescence to protect against ischemic stroke in aged mice.

The polarization of microglia plays an important role in the outcome of ischemic stroke (IS). In the aged population, senescent microglia show a predominant pro-inflammatory phenotype, which leads to worse outcomes in aged ischemic stroke compared to young ischemic stroke. Recent research demonstrated that inducible pluripotent stem cell-derived small extracellular vesicles (iPSC-sEVs) possess the significant anti-ageing ability. We hypothesized that iPSC-sEVs could alleviate microglia senescence to regulate microglia polarization in aged ischemic stroke. In this study, we showed that treatment with iPSC-sEVs significantly alleviated microglia senescence as indicated by the decreased senescence-associated proteins including P16, P21, P53, and γ-H2AX as well as the activity of SA-ß-gal, and inhibited pro-inflammatory activation of microglia both in vivo and in vitro. Furthermore, iPSC-sEVs shifted microglia from pro-inflammatory phenotype to anti-inflammatory phenotype, which reduced the apoptosis of neurons, and improved the outcome of aged stroke mice. Mechanism studies showed that iPSC-sEVs reversed the loss of Rictor and downstream p-AKT (s473) in senescent microglia, which was involved in the senescence and pro-inflammatory phenotype regulation of microglia. Inhibition of Rictor abolished the iPSC-sEVs-afforded phosphorylation of AKT and alleviation of inflammation of senescent microglia. Proteomics results indicated that iPSC-sEVs carried transforming growth factor-ß1 (TGF-ß1) to upregulate Rictor and p-AKT in senescent microglia, which could be hindered by blocking TGF-ß1. Taken together, our work demonstrates iPSC-sEVs reverse the senescent characteristic of microglia in aged brains and therefore improve the outcome after stroke, at least, via delivering TGF-ß1 to upregulate Rictor and p-AKT. Our data suggest that iPSC-sEVs might be a novelty therapeutic method for aged ischemic stroke and other diseases involving senescent microglia.

Toward Full-Stack Acceleration of Deep Convolutional Neural Networks on FPGAs.

Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a growing demand for hardware accelerators that accommodate a variety of CNNs to improve their inference latency and energy efficiency, in order to enable their deployment in real-time applications. Among popular platforms, field-programmable gate arrays (FPGAs) have been widely adopted for CNN acceleration because of their capability to provide superior energy efficiency and low-latency processing, while supporting high reconfigurability, making them favorable for accelerating rapidly evolving CNN algorithms. This article introduces a highly customized streaming hardware architecture that focuses on improving the compute efficiency for streaming applications by providing full-stack acceleration of CNNs on FPGAs. The proposed accelerator maps most computational functions, that is, convolutional and deconvolutional layers into a singular unified module, and implements the residual and concatenative connections between the functions with high efficiency, to support the inference of mainstream CNNs with different topologies. This architecture is further optimized through exploiting different levels of parallelism, layer fusion, and fully leveraging digital signal processing blocks (DSPs). The proposed accelerator has been implemented on Intel's Arria 10 GX1150 hardware and evaluated with a wide range of benchmark models. The results demonstrate a high performance of over 1.3 TOP/s of throughput, up to 97% of compute [multiply-accumulate (MAC)] efficiency, which outperforms the state-of-the-art FPGA accelerators.

Role of the pretreatment neutrophil-to-lymphocyte ratio in the survival of primary parotid cancer patients.

BACKGROUND: To analyze the value of the pretreatment neutrophil-to-lymphocyte ratio (NLR) in the survival of patients with parotid cancer. METHODS: In total, 249 patients were enrolled. Information including age, sex, pretreatment NLR, and pathologic variables such as, tumor stage, intraparotid node (IPN) metastasis, and follow-up findings was extracted and analyzed. RESULTS: IPN metastasis was noted in 45 (18.1%) patients, and the mean NLR was 2.48, with a range from 1.5 to 6.1. The NLR was significantly associated with tumor stage, disease stage, and disease grade. A total of 73 patients died of the disease, and the 10 -year disease-specific survival (DSS) rate was 62%. In patients with an NLR<2.48, the 10 -year DSS rate was 68%; in patients with an NLR≥2.48, the 10 -year DSS rate was 58%, and the difference was significant (P=0.006). Cox model analysis showed that the NLR was an independent prognostic factor for DSS. CONCLUSION: The long-term survival of primary parotid cancer patients is relatively favorable, and the pretreatment NLR is significantly associated with prognosis.

Significance of intraparotid node metastasis in predicting local control in primary parotid cancer.

OBJECTIVE: To analyze the metastasis rate in intraglandular lymph nodes (IGLNs) with a focus on discussing the significance of IGLN metastasis in local control (LC) of parotid gland cancer (PGC). METHODS: A total of 337 patients were enrolled. Information including age; sex; and pathologic variables such as tumor (T) stage, IGLN metastasis, and follow-up findings was extracted and analyzed. RESULTS: IGLN metastasis was noted in 111 (32.9%) patients. Tumor stage, pathologic nodal stage, perineural invasion, resection status, and lymphovascular invasion were significantly related to IGLN metastasis. Local recurrence was noted in 67 (19.9%) patients. IGLN metastasis was an independent predictor of LC. The 10-year LC rate was 94% for patients without IGLN metastasis, 56% for patients with metastasis in no more than two IGLNs, and 22% for patients with metastasis in more than two IGLNs. This difference was significant (P < 0.001). CONCLUSION: The IGLN metastasis rate is relatively high in PGC patients and is significantly associated with disease grade and T stage. IGLN metastasis is associated with poorer local LC, and patients with more than two metastatic nodes have the worst prognosis. LEVEL OF EVIDENCE: 4 Laryngoscope, 129:2309-2312, 2019.

Analysis of anthocyanins and flavonols in petals of 10 Rhododendron species from the Sygera Mountains in Southeast Tibet.

Flower color is one of the major ornamental characteristics of the genus Rhododendron, but few studies on flower color in alpine Rhododendron have been reported. In our study, the flower colors and the pigment constituents of petals from 10 Rhododendron species sampled in the Sygera Mountains of Southeast Tibet were analyzed using high-performance liquid chromatography-diode array detection and mass spectrometry (HPLC-DAD-ESI-MS(2)). The color analysis showed that the 10 Rhododendron species could be divided into five color groupings: yellow, red, red-purple, purple-violet, and purple. A total of 5 anthocyanin compounds and 23 flavonol compounds were tentatively identified and quantified. There were obvious differences in the composition of anthocyanin and flavonol among the petals of the 10 Rhododendron species. The color parameter L* decreased as the TA (total anthocyanin) content increased in the red-purple group. However, there was no obvious correlation between the L* value and the TA content in the other sampled Rhododendron species. In this study, the TA values of most of the Rhododendron species were quite low, but the TF (total flavonol) content was high. These results indicate the existence of copigmentation effects in these 10 Rhododendron species.

On the use of programmable hardware and reduced numerical precision in earth-system modeling.

Programmable hardware, in particular Field Programmable Gate Arrays (FPGAs), promises a significant increase in computational performance for simulations in geophysical fluid dynamics compared with CPUs of similar power consumption. FPGAs allow adjusting the representation of floating-point numbers to specific application needs. We analyze the performance-precision trade-off on FPGA hardware for the two-scale Lorenz '95 model. We scale the size of this toy model to that of a high-performance computing application in order to make meaningful performance tests. We identify the minimal level of precision at which changes in model results are not significant compared with a maximal precision version of the model and find that this level is very similar for cases where the model is integrated for very short or long intervals. It is therefore a useful approach to investigate model errors due to rounding errors for very short simulations (e.g., 50 time steps) to obtain a range for the level of precision that can be used in expensive long-term simulations. We also show that an approach to reduce precision with increasing forecast time, when model errors are already accumulated, is very promising. We show that a speed-up of 1.9 times is possible in comparison to FPGA simulations in single precision if precision is reduced with no strong change in model error. The single-precision FPGA setup shows a speed-up of 2.8 times in comparison to our model implementation on two 6-core CPUs for large model setups.