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Lyophyllum consists of rare edible and medicinal mushrooms. Considering this group’s economic and ecological significance, understanding its biodiversity could be strategically important. Our analysis involved an extensive examination of publicly available ITS sequences from NCBI-GenBank and fungal amplicon sequencing data obtained from NCBI-SRA. At a 98% sequence similarity level, we deliminated 88 Lyophyllum OTUs, 49 of which were not categorized as ‘known species’. The diversity of Lyophyllum is predominantly concentrated in the Northern Hemisphere and tends to display endemic distribution, and Europe is particularly notable for its high diversity of Lyophyllum. Given the escalating volume of data produced by amplicon sequencing, employing the amplicon dataset can facilitate an accurate survey of species diversity in Lyophyllum taxonomy. By using data from amplicon datasets, the fieldwork time and research funding for taxonomists can be saved, thereby significantly advancing the progress of the entire field of biodiversity research.

A new myxomycete species, Didymium arenosum, was described based on morphological evidence and phylogenetic analyses. The species was discovered in the arid region at the confluence of the Badain Jaran desert and Tengger desert on the leaves of Betula platyphylla and was cultivated in a moist chamber culture. Morphologically, the species is distinguished by the greenish-yellow calcium carbonate crystals on the surface and the spores covered with small warts, some of which are connected into a short line. A phylogenetic analysis of D. arenosum strongly supports its classification as a separate clade. The spore to spore agar culture of D. arenosum requires 23 days, and this study provides a detailed description of its life cycle.

Abstract Background Myxomycetes are a group of eukaryotes belonging to Amoebozoa, which are characterized by a distinctive life cycle, including the plasmodium stage and fruit body stage. Plasmodia are all found to be associated with bacteria. However, the information about bacteria diversity and composition in different plasmodia was limited. Therefore, this study aimed to investigate the bacterial diversity of plasmodia from different myxomycetes species and reveal the potential function of plasmodia-associated bacterial communities. Results The bacterial communities associated with the plasmodia of six myxomycetes (Didymium iridis, Didymium squamulosum, Diderma hemisphaericum, Lepidoderma tigrinum, Fuligo leviderma, and Physarum melleum) were identified by 16S rRNA amplicon sequencing. The six plasmodia harbored 38 to 52 bacterial operational taxonomic units (OTUs) that belonged to 7 phyla, 16 classes, 23 orders, 40 families, and 53 genera. The dominant phyla were Bacteroidetes, Firmicutes, and Proteobacteria. Most OTUs were shared among the six myxomycetes, while unique bacteria in each species only accounted for a tiny proportion of the total OTUs. Conclusions Although each of the six myxomycetes plasmodia had different bacterial community compositions, a high similarity was observed in the plasmodia-associated bacterial communities' functional composition. The high enrichment for gram-negative (> 90%) and aerobic (> 99%) bacteria in plasmodia suggest that myxomycetes may positively recruit certain kinds of bacteria from the surrounding environment.

Electromagnetic coil launch is an important branch of electromagnetic launch (EML) technology, which is suitable for launching anti-torpedo torpedo (ATT). This paper focuses on the EML parameters and the interception probability of the EML two ATTs salvo. Based on the launching model of a multi-stage coil launcher, the trajectory model of the ATT and the attacking torpedo, a calculation method for the EML two ATTs salvo parameters is proposed, with the conditions of capture and interception given reasonably. An adaptive particle swarm optimization (APSO) algorithm is proposed to calculate the optimal launching parameters, by designing the adaptive inertia weight and time-varying study factors. According to the analysis of the simulation with Monte Carlo method, EML will improve the interception probability effectively, and the interception probability is affected by the launching range. The results demonstrate good performance of the proposed APSO in calculating EML parameters for the two ATTs salvo in certain combat situation. Implications of these results are particular regarding the command and decision in the anti-torpedo combat.

Background: Histone deacetylase 3 (HDAC3) restores chromatin nucleosomes to a transcriptional repression state, thereby inhibiting gene expression. Studies have found that HDAC3 expression is upregulated in a variety of pathological states of the central nervous system and related to its neurotoxicity. However, the role of HDAC3 in surgical brain injury (SBI) has not been thoroughly explored. Objective: To observe the role of HDAC3 in SBI and the outcome of SBI after its suppression. Methods: Rat SBI model was used, and intraperitoneal injection of RGFP966 (HDAC3 specific inhibitor) was used to detect the changes of HDAC3 expression and neuronal apoptosis indexes in the surrounding cortex of SBI rats, and the cerebral edema and neurological outcome of rats were observed. Results: The expression of HDAC3 in the peripheral cortex of SBI rats was increased, and RGFP966 inhibited the upregulation of HDAC3 and saved the nerve cells around the damaged area. In addition, RGFP966 increased the expression of anti-oxidative stress proteins such as heme oxygenase-1 (HO-1) and superoxide dismutase 2 (SOD2). At the same time, the expression of apoptotic marker protein cleaved-caspase-3 (cle-caspase-3) was decreased, while the expression level of apoptotic protective marker protein B-cell lymphoma 2 (Bcl-2) was increased. In addition, this research demonstrated that in the RGFP966 rat SBI model, the expression level of antioxidant modifier nuclear factor-erythroid 2-related factor 2 (Nrf2) was increased. Conclusion: RGFP966 might activate HDAC3/Nrf2 signaling pathway by inhibiting HDAC3, regulated oxidative stress and nerve cell apoptosis induced by SBI in rat SBI model, reduced brain edema, and had a protective effect on nerve injury. It might be a potential target of SBI pathology.

With the deep integration of computer technology into social life,more and more short text messages are spreaded all over the web platform.Aiming at the problem of data sparsity of short texts,a robust heterogeneous information network framework(HTE) for modeling short texts,which can integrate any type of additional information and capture the relationship between them to solve the data sparsity problem,is constructed.Based on this framework,six short text expansion methods are designed using different external knowledge,and the short text features are enriched by introducing entity information such as entities,entity categories,inter-entity relationships and textual information such as text topics from Wikipedia and Freebase knowledge bases.Finally,the similarity measurement result is used to verify the experimental effect.By comparing the six text expansion me-thods with the traditional three similarity measures on two short text datasets and the current mainstream short text matching algorithms,the results of the proposed six text expansion methods are improved.Compared with BERT,the similarity measurement results of the best method improves by 5.97%.The proposed framework is robust and can include any type of external know-ledge,and the proposed method can overcome the data sparsity problem of short texts and can perform similarity metrics on short texts with high accuracy in an unsupervised manner.

The transportation sector is under increasing pressure to reduce greenhouse gas emissions by decarbonizing its operations. One prominent solution that has emerged is the adoption of electric vehicles (EVs). As the electric vehicles market experiences rapid growth, the utilization of lithium-ion batteries (LiB) has become the predominant choice for energy storage. However, it is important to note that lithium-ion battery technology is sensitive to factors, like excessive voltage and temperature. Therefore, the development of an accurate battery model and a reliable state of charge (SOC) estimator is crucial to safeguard against the overcharging and over-discharging of the battery. Numerous studies have been conducted to address lithium-ion battery cell modeling and SOC estimations. These studies have explored variations in the number of RC networks within the model and different estimation methods. However, it is worth mentioning that the capacity of a single lithium-ion battery cell is relatively low and cannot be directly employed in electric vehicles. To meet the total capacity and voltage requirements for electric vehicles, multiple cells are typically connected in series or parallel configurations to form a battery pack. Surprisingly, this aspect has often been overlooked in previous research. To tackle this overlooked challenge, our study introduces a comprehensive battery pack model and an advanced Battery Management System (BMS). We then integrate these components into an electric vehicle model. Subsequently, we simulate the integrated EV-BMS model under the conditions of four different urban driving scenarios to replicate real-world driving conditions. The BMS that we have developed includes an Extended Kalman Filter (EKF)-based SOC estimation system, a mechanism for controlling coolant flow, and a passive cell-balancing algorithm. These components work together to ensure the safe and efficient operation of the battery pack within the electric vehicles.

Background: Esophageal squamous cell carcinoma (ESCC) is a highly lethal tumor type, but studies on the ESCC tumor microenvironment are limited. We found that cystatin SN (CST1) plays an important role in the ESCC tumor microenvironment. CST1 has been reported to act as an oncogene in multiple human cancers, but its clinical significance and underlying mechanism in ESCC remain elusive. Methods: We performed ESCC gene expression profiling with data from RNA-sequencing and public databases and found CST1 upregulation in ESCC. Then, we assessed CST1 expression in ESCC by RT‒qPCR and Western blot analysis. In addition, immunohistochemistry (IHC) and enzyme-linked immunosorbent assay (ELISA) were used to estimate the expression of CST1 in ESCC tissue and serum. Moreover, further functional experiments were conducted to verify that the gain and loss of CST1 in ESCC cell lines significantly influenced the proliferation and metastasis of ESCC. Mass spectrometry, coimmunoprecipitation, and gelatin zymography experiments were used to validate the interaction between CST1 and matrix metalloproteinase 2 (MMP2) and the mechanism of CST1 influence on metastasis in ESCC. Results: Here, we found that CST1 expression was significantly elevated in ESCC tissues and serum. Moreover, compared with patients with low CST1 expression, patients with high CST1 expression had a worse prognosis. Overall survival (OS) and disease-free survival (DFS) were significantly unfavorable in the high CST1 expression subgroup. Likewise, the CST1 level was significantly increased in ESCC serum compared with healthy control serum, indicating that CST1 may be a potential serum biomarker for diagnosis, with an area under the curve (AUC) = 0.9702 and p < 0.0001 by receiver operating curve (ROC) analysis. Furthermore, upregulated CST1 can promote the motility and metastatic capacity of ESCC in vitro and in vivo by influencing epithelial mesenchymal transition (EMT) and interacting with MMP2 in the tumor microenvironment (TME). Conclusions: Collectively, the results of this study indicated that high CST1 expression mediated by SPI1 in ESCC may serve as a potentially prognostic and diagnostic predictor and as an oncogene to promote motility and metastatic capacity of ESCC by influencing EMT and interacting with MMP2 in the TME.

With the unique characteristics, electromagnetic launch technology is applicable to launch shipborne anti-torpedo torpedo(ATT). This paper aims to establish an analytic model to pre-evaluate the capture probability of the electromagnetic launched ATT. The mathematics model of the multi-stage coilgun and the trajectory of the ATT is established for analysis. The influence factors of the capture probability are analyzed respectively, including the entry point dispersion of the ATT and the position dispersion of the incoming torpedo. Adopting the advanced angle interception mode, the ATT search model is obtained according to the positional relationship, and the course error is synthetically calculated according to the differentiation of implicit function. A geometric method to calculate the integral boundaries of the probability density function is proposed, based on the relative motion of the ATT and the incoming target. To verify the proposed integral model, the digital simulation and comparison is conducted. The results reveal that the variation trends and the calculation value of the proposed analytic model are coincident with the statistic results from Monte Carlo method. And implications of the results regarding the analytic model are discussed.

Starting from August 24, 2020, the daily stock price limits in China's ChiNext market have been adjusted from 10% to 20%. We use this reform to study the effectiveness of price limits in China's stock market. We test four hypotheses about price limits: delayed price discovery, volatility spillover, trading interference, and magnet effect. Using the event study method, we examine the differences in the behavior of stock price, trading volume, and volatility before and after the reform. We confirm the delayed price discovery, volatility spillover and trading interference hypothesis of price limits, and find that these negative effects of price limits are more serious when lower limits are hit. In addition, we examine the distribution of large price movements before and after the reform and find no evidence of the magnet effect of price limits. The present research has important implications for policymakers and investors in China's stock market.

The marine biological sonar system evolved in the struggle of nature is far superior to the current artificial sonar. Therefore, the development of bionic underwater concealed detection is of great strategic significance to the military and economy. In this paper, a generative adversarial network (GAN) is trained based on the dolphin vocal sound dataset we constructed, which can achieve unsupervised generation of dolphin vocal sounds with global consistency. Through the analysis of the generated audio samples and the real audio samples in the time domain and the frequency domain, it can be proven that the generated audio samples are close to the real audio samples, which meets the requirements of bionic underwater concealed detection.