Preliminary species diversity and community phylogenetics of wood-inhabiting basidiomycetous fungi in the Dabie Mountains, Central China reveal unexpected richness.

Wood-inhabiting fungi have important economic values as well as playing a major ecological role in forest ecosystem cycles. The Dabie Mountains, at the junction of Henan, Hubei, and Anhui Provinces, Central China, provide an ideal climate and favorable niches for the speciation and diversification of various forms of life including fungi. We studied the species diversity and community phylogenetics of wood-inhabiting basidiomycetous fungi that revealed 175 wood-inhabiting basidiomycetous species, of which 20 represented unidentified species, based on morphological and phylogenetic analyses of 575 specimens collected from ten sampling sites. These species belonged to two classes, 11 orders, 42 families, and 106 genera of Basidiomycota, and included 12 edible species, 28 medicinal species, four poisonous species, and seven forest pathogens. Four types of fungal distribution pattern at the genus level were recognized for 65 genera, while another 41 genera could not be placed in any known distribution pattern. The five sampling sites in the eastern part of the Dabie Mountains had significantly higher species diversity and phylogenetic diversity of wood-inhabiting basidiomycetous fungi than those in the western part, and thus deserve priority in terms of conservation. The community of wood-inhabiting basidiomycetous fungi in the Dabie Mountains is generally affected by a combination of habitat filtering and competitive exclusion. This study provides a basis on which to build actions for the comprehensive recognition, utilization, and conservation of wood-inhabiting basidiomycetous fungi in the region.

Xenasmatellales ord. nov. and Xenasmatellaceae fam. nov. for Xenasmatella (Agaricomycetes, Basidiomycota).

In the era of molecular phylogeny as dominant evidence in fungal taxonomy, the taxonomic framework of fungi adopted from morphological characteristics has been largely updated. Compared with other fungal groups, macrofungi underwent fewer updates at the order and higher level. In this study, the taxonomic placement of a poorly known macro-basidiomycetous genus Xenasmatella is studied. Phylogenetic and molecular clock analyses inferred from a seven-locus dataset support that the genus represents an order rank lineage. Accordingly, a monotypic order Xenasmatellales and a monotypic family Xenasmatellaceae are newly introduced for Xenasmatella within Agaricomycetes. The species diversity and relationships of Xenasmatella are further clarified with the aid of the phylogenetic analysis inferred from a four-locus dataset. In association with morphological characteristics, a new species Xenasmatella hjortstamii is described. Moreover, the distribution of Xenasmatella ailaoshanensis, X. gossypina, and X. wuliangshanensis previously known only from type localities in Yunnan Province, China are expanded. In addition, two unnamed single-specimen lineages of Xenasmatella from Victoria State, Australia and Sichuan, China are revealed, likely representing two potential new species of this genus. In summary, the current study updates the taxonomic framework of Agaricomycetes and provides a crucial supplement for comprehensively understanding the evolutionary history of this fungal class.

Nomograms for postsurgical extrahepatic recurrence prediction of hepatocellular carcinoma based on presurgical circulating tumor cell status and clinicopathological factors.

BACKGROUND AND AIMS: Extrahepatic recurrence (EHR) is one of the major reasons for the poor prognosis of hepatocellular carcinoma (HCC). The present study aimed to develop and assess the performance of predictive models by using a combination of presurgical circulating tumor cell (CTCs) data and clinicopathological features to screen patients at high risk of EHR to achieve precise decision-making. PATIENTS AND METHODS: A total of 227 patients with recurrent HCC and preoperative CTC data from January 2014 to August 2019 were enrolled. All patients were randomly assigned to one of two cohorts: development or validation. Two preoperative and postoperative nomogram models for EHR prediction were developed and multi-dimensionally validated. RESULTS: Patients with EHR had generally lower recurrence-free survival (p < 0.001), and overall survival (p < 0.001), and significantly higher CTC counts (epithelial CTCs, epithelial/mesenchymal hybrid CTCs, and mesenchymal CTCs count, all p < 0.05) than those without EHR. Univariate and multivariate analyses revealed that EHR was associated with four risk factors in the development cohort: total CTC count (p = 0.014), tumor size (p = 0.028), node number (p = 0.045), and microvascular invasion (p = 0.035). These factors were incorporated into two nomogram models (preoperative and postoperative), which reliably predicted EHR through multidimensional verification (e.g., calibration plot, receiver operating characteristic analysis, decision curve analysis, and clinical impact curve analysis) in the development and validation cohorts, respectively. With threshold of scores of 100.3 and 176.8 before and after surgery respectively, both nomograms were able to stratify patients into two distinct prognostic subgroups (all p < 0.05). CONCLUSION: The present study proposed two nomogram models integrating presurgical CTC counts and clinicopathological risks and showed relatively good predictive performance of EHR, which may be beneficial to the clinical practice of HCC recurrence. Further multicenter studies are needed to assess its general applicability.

Prognostic factors and an innovative nomogram model for patients with hepatocellular carcinoma treated with postoperative adjuvant transarterial chemoembolization.

PURPOSE: The purpose of this study was to estimate the clinical efficacy and identify the best beneficiaries of postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE) in hepatocellular carcinoma (HCC). PATIENTS AND METHODS: A total of 749 HCC patients who underwent surgical resection (380 underwent PA-TACE, 369 had resection only) with a high risk of recurrence were reviewed retrospectively. Patients receiving PA-TACE were randomly split into development and validation cohorts. Univariate and multivariate analyses were performed in the development cohort. A novel model for PA-TACE-insensitivity prediction was built based on univariate and multivariate analysis and was multi-dimensionally validated in the validation set and all samples. RESULTS: After propensity score matching (PSM), in the early-recurrence group, no significant improvement in RFS was achieved with PA-TACE compared to radical hepatic resection alone. PA-TACE insensitive patients were considered as the PA-TACE non-benefit population and were associated with six clinicopathological factors: AFP, node number, tumor capsule, Ki-67 index, MVI, and complications in the development cohort. These factors were incorporated into a nomogram model, which reliably predicted PA-TACE insensitivity, with concordance indices of 0.874 and 0.897 for the development and validation cohort, respectively. In the overall sample, PA-TACE did not significantly improve patients' RFS and OS in the high-score group, while the low-score group had statistical significance. Recurrence pattern diversity was also found to be a factor leading to PA-TACE insensitivity. CONCLUSION: We constructed a new PA-TACE-insensitivity prediction model with potential clinical value. The good predictive performance and availability would allow this model to effectively screen PA-TACE beneficiaries.KEY MESSAGESThe independent influencing factors of PA-TACE insensitivity in patients who received PA-TACE were analyzed to construct a predictive model and its clinical application performance was verified with multi-dimensional methods.PA-TACE treatment should be avoided for patients with high scores according to this model, while it should be cautiously recommended for patients with low scores after multiple considerations.Compared with other related models, this model has obvious advantages in versatility and effectiveness. It can effectively screen the best benefit population of PA-TACE and provide a reliable reference for the selection of precise treatment plans for patients after radical resection of hepatocellular carcinoma.

Down-regulation of ALDOB during metabolic reprogramming mediates malignant behavior in hepatocellular carcinoma and insensitivity to postoperative adjuvant transarterial chemoembolization.

BACKGROUND: Postoperative transarterial chemoembolization (PA-TACE) is an effective adjuvant therapy for preventing early postoperative recurrence of hepatocellular carcinoma (HCC); however, many patients are insensitive to it. Therefore, the present study aimed to explore the in-depth reasons for PA-TACE resistance and provide a reliable basis for selecting patients who will benefit the most from PA-TACE. METHODS: The unique gene expression profiles of primary tumors from PA-TACE-sensitive or -insensitive patients were analyzed using microarray data. Combined differential expression analysis, gene set enrichment analysis (GSEA), and weighted correlation network analysis (WGCNA) were used to screen for potential drivers of PA-TACE insensitivity. The expression of ALDOB was silenced or overexpressed in hepatoma cell lines, and changes in glycolytic activity, cycle, apoptosis, and malignant biological phenotypes were observed under normoxia and hypoxia. Finally, an animal model was constructed to verify the effects of ALDOB dysregulation on the tumorigenic ability of HCC cells in vivo. RESULTS: The inhibition of ALDOB promoted the up-regulation of Ki67 expression, and glycolytic activity was significantly enhanced. Moreover, the proliferation, invasion, and migration capabilities were increased in HCC cells and even worse in hypoxia. This advantage of malignant behavior was also validated using in vivo models. CONCLUSION: Down-regulation of ALDOB may underlie the metabolic reprogramming observed in HCC by promoting the malignant behavior of HCC cells. Hypoxia and ALDOB down-regulation acted additively, which was closely related to PA-TACE insensitivity. The use of ALDOB and Ki67 as a combined marker has the potential to identify the 'PA-TACE beneficiary population'.

Real-Time LiDAR Point-Cloud Moving Object Segmentation for Autonomous Driving.

The key to autonomous navigation in unmanned systems is the ability to recognize static and moving objects in the environment and to support the task of predicting the future state of the environment, avoiding collisions, and planning. However, because the existing 3D LiDAR point-cloud moving object segmentation (MOS) convolutional neural network (CNN) models are very complex and have large computation burden, it is difficult to perform real-time processing on embedded platforms. In this paper, we propose a lightweight MOS network structure based on LiDAR point-cloud sequence range images with only 2.3 M parameters, which is 66% less than the state-of-the-art network. When running on RTX 3090 GPU, the processing time is 35.82 ms per frame and it achieves an intersection-over-union(IoU) score of 51.3% on the SemanticKITTI dataset. In addition, the proposed CNN successfully runs the FPGA platform using an NVDLA-like hardware architecture, and the system achieves efficient and accurate moving-object segmentation of LiDAR point clouds at a speed of 32 fps, meeting the real-time requirements of autonomous vehicles.

PD-1/PD-L1 inhibitors plus anti-angiogenic agents with or without chemotherapy versus PD-1/PD-L1 inhibitors plus chemotherapy as second or later-line treatment for patients with advanced non-small cell lung cancer: A real-world retrospective cohort study.

Background: The aim of this study was to assessment the efficacy and safety of Programmed cell death protein 1 (PD-1)/Programmed cell death-Ligand protein 1 (PD-L1) inhibitors plus anti-angiogenic agents with or without chemotherapy versus PD-1/PD-L1 inhibitors plus chemotherapy as second or later-line treatment for patients with advanced non-small cell lung cancer. Methods: In this study, pre-treatment clinical and laboratory indicators from 73 patients with advanced non-small cell lung cancer were retrieved for retrospective analysis. According to the therapy regimes they received, the patients were separated into groups, PD-1/PD-L1 inhibitors plus chemotherapy group (PC group), PD-1/PD-L1 inhibitors plus anti-angiogenic agents' group (PA group), PD-1/PD-L1 inhibitors plus anti-angiogenic agents plus chemotherapy group (PAC group). Cox's proportional hazards regression model and Kaplan-Meier (KM) curves were used to assess the connection between treatment regimens and progression free survival (PFS) and overall survival (OS). In addition, the association of treatment regimens with the risk of disease progression and death was evaluated by subgroup analysis. Results: The average age of the enrolled patients was 58.2 ± 10.2 years and 75.3% were male. Multivariate analyses showed that patients in PA group (Disease progression: HR 0.4, P=0.005. Death: HR 0.4, P=0.024) and PAC group (Disease progression: HR 0.3, P=0.012. Death: HR 0.3, P=0.045) had a statistically significant lower hazard ratio (HR) for disease progression and death compared to patients in PC group. Kaplan-Meier analysis showed that patients in PA group (mPFS:7.5 vs.3.5, P=0.00052. mOS:33.1 vs.21.8, P=0.093) and PAC group (mPFS:5.1 vs.3.5, P=0.075. mOS:37.3 vs.21.8, P=0.14) had a longer PFS and OS compared to patients in PC group. In all the pre-defined subgroups, patients in PA and PAC groups showed a decreasing trend in the risk of disease progression and death in most subgroups. The patients in PA group (DCR:96.3% vs.58.3%, P=0.001) and PAC group (DCR:100% vs.58.3%, P=0.019) had a better disease control rate (DCR) than patients in PC group. Conclusion: PD-1/PD-L1 inhibitors plus anti-angiogenic agents with or without chemotherapy were superior to PD-1/PD-L1 inhibitors plus chemotherapy as second or later-line treatment in patients with advanced non-small cell lung cancer.

Proximity Detection During Epidemics: Direct UWB TOA Versus Machine Learning Based RSSI.

In this paper, we compare the direct TOA-based UWB technology with the RSSI-based BLE technology using machine learning algorithms for proximity detection during epidemics in terms of complexity of implementation, availability in existing smart phones, and precision of the results. We establish the theoretical limits on the precision and confidence of proximity estimation for both technologies using the Cramer Rao Lower Bound (CRLB) and validate the theoretical foundations using empirical data gathered in diverse practical operating scenarios. We perform our empirical experiments at eight distances in three flat environments and one non-flat environment encompassing both Line of Sight (LOS) and Obstructed-LOS (OLOS) situations. We also analyze the effects of various postures (eight angles) of the person carrying the sensor, and four on-body locations of the sensor. To estimate the range with BLE RSSI, we use 14 features for training the Gradient Boosted Machines (GBM) learning algorithm and we compare the precision of results with those obtained from memoryless UWB TOA ranging algorithm. We show that the memoryless UWB TOA algorithm achieves 93.60% confidence, slightly outperforming the 92.85% confidence of the BLE RSSI with more complex GBM machine learning (ML) algorithm and the need for substantial training. The training process for the RSSI-based BLE social distance measurements involved 3000 measurements to create a training dataset for each scenario and post-processing of data to extract 14 features of RSSI, and the ML classification algorithm consumed 200 s of computational time. The memoryless UWB ranging algorithm achieves more robust results without any need for training in less than 0.5 s of computation time.

Clinical implications and biological features of a novel postoperative recurrent HCC classification: A multi-centre study.

BACKGROUND & AIMS: The multiplicity of hepatocellular carcinoma (HCC) recurrence patterns is the most important determinant of patients' postsurgical survival. A systematic HCC recurrence classification is needed to help prevent and treat postoperative HCC recurrence in the era of precision medicine. METHODS: A total of 1319 patients with recurrent HCC from four hospitals were enrolled and divided into a development cohort (n = 916), internal validation cohort (n = 225) and external validation cohort (n = 178). A comprehensive study of patients' clinicopathological factors and biological features was conducted. RESULTS: Four subtypes of recurrence were identified, which integrated recurrence features, survival, effects on systemic and liver function and potential therapeutics after recurrence: type I (solitary-intrahepatic oligorecurrence); type II (multi-intrahepatic oligorecurrence); type III (progression recurrence) and type IV (hyper-progression recurrence). Type III~IV recurrence indicated exceptionally poor prognosis. Subsequently, two nomogram models were established for type III~IV recurrence prediction, and both demonstrated excellent predictive performance and applicability of pre and postoperative strategy formulation. Multiple biological analyses revealed that HCC cases with type III~IV recurrence were characterized by enrichment in p53 mutations, CCND1 amplification, high proliferation/metastasis potential, inactive metabolism and immune exhaustion features. Over-expression of high mobility group protein 2 (HMGA2) enhanced the highly malignant behaviour of HCC through multiple molecular pathways, making it a potential prognostic predictor and therapeutic target. CONCLUSIONS: This 'recurrent HCC classification' has important potential value in identifying patients with surgical benefit, predicting postsurgical survival and guiding treatment strategies. Multidimensional biological insights also increased knowledge of factors associated with HCC recurrence.