Research on distribution strategy of logistics enterprise alliance based on three-party evolution game.

Currently, collaborative distribution models have not reached the optimal state of carbon emissions. The cost of additional low-carbon expenditures and the problem of carbon data verification have led to the lack of motivation for reducing emissions among collaborative distribution enterprises. Therefore, how to incentivize them to adopt the low-carbon model is crucial for achieving low-carbon goal. By relying on a government-led digital platform, this paper designs a dual low-carbon incentive strategy to encourage enterprise-alliance to adopt a low-carbon distribution model. In this paper, we first construct an evolutionary game model of the government, enterprise-alliance and end-users; then we explore the conditions of the three-party equilibrium evolution strategy by solving the model and analyzing the stability; and finally, we conduct simulation validation and results analysis with the help of MATLAB. In summary, we found that government punishment is more effective at regulating enterprise-alliance than reward. End-users' behavior is affected by the costs they need to bear, and they no longer support enterprise-alliance to carry out collaborative low-carbon distribution above a certain threshold.

Sub-lobar resection versus lobectomy for challenging intraoperative frozen sections in lung adenocarcinoma within 3 cm.

OBJECTIVES: Intraoperative frozen section (FS) analysis is pivotal in guiding surgical interventions for early-stage lung adenocarcinoma. However, the challenge arises when distinguishing between Minimally Invasive Adenocarcinoma (MIA) and Invasive Adenocarcinoma (IAC) poses diagnostic difficulties. This study investigates the prognosis and clinicopathological characteristics of patients encountering this diagnostic challenge. METHODS: We conducted a retrospective analysis of 7082 intraoperative FSs from early-stage lung adenocarcinoma cases. The cases with pulmonary nodules within 3 cm and diagnosed as indeterminate FSs were included. We analyzed baseline data, computed tomography (CT) findings, and pathological characteristics. Prognostic data were obtained from patients with confirmed IAC diagnoses through final pathological examination. RESULTS: Out of 7082 FSs, 551 cases presented challenges in distinguishing between MIA and IAC. Upon final pathological examination, 233 cases were identified as IAC, while 314 were classified as MIA. The median invasive pathological size in the IAC group was larger than that in the MIA group (0.6 cm vs 0.3 cm). 131 cases (56.2 %) with IAC underwent lobectomy, while 102 cases (43.8 %) underwent sub-lobar resection. Among the MIA cases, 220 cases (69.8 %) underwent sub-lobar resection, while 95 cases (30.2 %) underwent lobectomy. No recurrence and disease specific death was observed during the follow-up period, regardless of surgical strategy. CONCLUSIONS: Indeterminate intraoperative FSs, posing diagnostic challenges in distinguishing between MIA and IAC. Sub-lobar resection presented the same long term survival benefit compared with the lobectomy for indeterminate lung adenocarcinoma within 3 cm during intraoperative FSs.

Effects of paclobutrazol seed priming on seedling quality, photosynthesis, and physiological characteristics of fragrant rice.

BACKGROUND: Paclobutrazol is widely used in the agricultural field. This study investigated the effects of seed priming with different concentrations of paclobutrazol on seedling quality, 2-acetyl-1-pyrroline (2-AP, a key aroma component of fragrant rice) biosynthesis, and related physiological and biochemical indicators in fragrant rice seedlings. RESULTS: The experiment is being conducted at the College of Agriculture, South China Agricultural University. In the experiment, three concentrations of paclobutrazol (Pac 1: 20 mg·L-1; Pac 2: 40 mg·L-1; Pac 3: 80 mg·L-1) were used to initiate the treatment of fragrant rice seeds, while water treatment was used as a control (CK). The results showed that compared with CK, paclobutrazol treatment reduced plant height, increased stem diameter, and increased fresh and dry weight of aromatic rice seedlings. Moreover, paclobutrazol treatment also increased the seedlings' photosynthetic pigment content and net photosynthetic rate. CONCLUSIONS: This study demonstrates that paclobutrazol primarily increases the content of proline by reducing the content of glutamate and down-regulating the expression of P5CS2, thereby promoting the conversion of proline to the aromatic substance 2-AP. Under the appropriate concentration of paclobutrazol (40 mg·L-1~80 mg·L-1), the seedling quality, stress resistance, and aroma of fragrant rice can be improved.

A trade-off between proliferation and defense in the fungal pathogen Cryptococcus at alkaline pH is controlled by the transcription factor GAT201.

Cryptococcus is a fungal pathogen whose virulence relies on proliferation in and dissemination to host sites, and on synthesis of a defensive yet metabolically costly polysaccharide capsule. Regulatory pathways required for Cryptococcus virulence include a GATA-like transcription factor, Gat201, that regulates Cryptococcal virulence in both capsule-dependent and capsule-independent ways. Here we show that Gat201 is part of a negative regulatory pathway that limits fungal survival. RNA-seq analysis found strong induction of GAT201 expression within minutes of transfer to host-like media at alkaline pH. Microscopy, growth curves, and colony forming units to test viability show that in host-like media at alkaline pH wild-type Cryptococcus neoformans yeast cells produce capsule but do not bud or maintain viability, while gat201Δ cells make buds and maintain viability, yet fail to produce capsule. GAT201 is required for transcriptional upregulation of a specific set of genes in host-like media, the majority of which are direct Gat201 targets. Evolutionary analysis shows that Gat201 is conserved within pathogenic fungi but lost in model yeasts. This work identifies the Gat201 pathway as controlling a trade-off between proliferation, which we showed is repressed by GAT201, and production of defensive capsule. The assays established here will allow characterisation of the mechanisms of action of the Gat201 pathway. Together, our findings urge improved understanding of the regulation of proliferation as a driver of fungal pathogenesis.

Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database.

This study explores the analysis and modeling of energy consumption in the context of database workloads, aiming to develop an eco-friendly database management system (DBMS). It leverages vibration energy harvesting systems with self-sustaining wireless vibration sensors (WVSs) in combination with the least square support vector machine algorithm to establish an energy consumption model (ECM) for relational database workloads. Through experiments, the performance of self-sustaining WVS in providing power is validated, and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements is evaluated. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% during SQL statement execution. Furthermore, the ECM developed for relational databases closely approximates actual energy consumption for query operations, with errors ranging from 1 to 4%. In most cases, the predictions are conservative, falling below the actual values. This finding underscores the high predictive accuracy of the ECM in anticipating relational database workloads and their associated energy consumption. Additionally, this paper delves into prediction accuracy under different types of operations and reveals that ECM excels in single-block read operations, outperforming multi-block read operations. ECM exhibits substantial accuracy in predicting energy consumption for SQL statements in sequential and random read modes, especially in specialized database management system environments, where the error rate for the sequential read model is lower. In comparison to alternative models, the proposed ECM offers superior precision. Furthermore, a noticeable correlation between model error and the volume of data processed by SQL statements is observed. In summary, the relational database ECM introduced in this paper provides accurate predictions of workload and database energy consumption, offering a theoretical foundation and practical guidance for the development of eco-friendly DBMS.

[Analysis of Clinicopathological Features on Spread Through Air Spaces
of Lung Adenocarcinoma].

BACKGROUND: The biological and molecular characteristics of spread through air spaces (STAS), a newly recognized invasive mode of lung cancer, remain controversial. The aim of this study was to investigate the clinicopathological features and molecular characteristics of STAS in patients with pulmonary adenocarcinoma. METHODS: A total of 694 resected invasive non-mucinous lung adenocarcinomas diagnosed by clinicopathology from July 2019 to March 2021 in the First Affiliated Hospital of Guangzhou Medical University were collected, and the relationship between STAS and clinicopathological factors was analyzed. The state of protein expression of anaplastic lymphoma kinase (ALK) was detected by immunohistochemical method. Epidermal growth factor receptor (EGFR) was detected by amplification refractory mutation system-polymerase chain reaction (ARMS-PCR). ROS proto-oncogene 1-receptor (ROS1) was detected by reverse transcription-PCR (RT-PCR). RESULTS: A total of 344 STAS positive cases and 350 STAS negative cases were collected. By univariate analysis, STAS positivity was statistically associated with tumor maximum diameter (P<0.001), pleural invasion (P<0.001), lymphovascular invasion (P<0.001), nerve invasion (P=0.013), lymph node metastasis (P<0.001), clinical stage (P<0.001) and histological type (P<0.001). There was a statistical correlation between STAS and ALK protein expression (P=0.001). Multivariate analysis showed that STAS positive was correlated with pleural invasion (P=0.001), vascular invasion (P<0.001), lymph node metastasis (P=0.005)and ALK protein expression (P=0.032). CONCLUSIONS: STAS is associated with highly aggressive biological behavior of lung adenocarcinoma, suggesting a poor prognosis.

A Cardinality Estimator in Complex Database Systems Based on TreeLSTM.

Cardinality estimation is critical for database management systems (DBMSs) to execute query optimization tasks, which can guide the query optimizer in choosing the best execution plan. However, traditional cardinality estimation methods cannot provide accurate estimates because they cannot accurately capture the correlation between multiple tables. Several recent studies have revealed that learning-based cardinality estimation methods can address the shortcomings of traditional methods and provide more accurate estimates. However, the learning-based cardinality estimation methods still have large errors when an SQL query involves multiple tables or is very complex. To address this problem, we propose a sampling-based tree long short-term memory (TreeLSTM) neural network to model queries. The proposed model addresses the weakness of traditional methods when no sampled tuples match the predicates and considers the join relationship between multiple tables and the conjunction and disjunction operations between predicates. We construct subexpressions as trees using operator types between predicates and improve the performance and accuracy of cardinality estimation by capturing the join-crossing correlations between tables and the order dependencies between predicates. In addition, we construct a new loss function to overcome the drawback that Q-error cannot distinguish between large and small cardinalities. Extensive experimental results from real-world datasets show that our proposed model improves the estimation quality and outperforms traditional cardinality estimation methods and the other compared deep learning methods in three evaluation metrics: Q-error, MAE, and SMAPE.

Fluctuation-based outlier detection.

Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects. As a result of these two properties, we show that outliers are susceptible to a mechanism called fluctuation. This article proposes a method called fluctuation-based outlier detection (FBOD) that achieves a low linear time complexity and detects outliers purely based on the concept of fluctuation without employing any distance, density or isolation measure. Fundamentally different from all existing methods. FBOD first converts the Euclidean structure datasets into graphs by using random links, then propagates the feature value according to the connection of the graph. Finally, by comparing the difference between the fluctuation of an object and its neighbors, FBOD determines the object with a larger difference as an outlier. The results of experiments comparing FBOD with eight state-of-the-art algorithms on eight real-worlds tabular datasets and three video datasets show that FBOD outperforms its competitors in the majority of cases and that FBOD has only 5% of the execution time of the fastest algorithm. The experiment codes are available at: https://github.com/FluctuationOD/Fluctuation-based-Outlier-Detection .

Trans-Zeatin induce regulation the biosynthesis of 2-acetyl-1-pyrroline in fragrant rice (Oryza sativa L.) seedlings.

BACKGROUND: In plants, cytokinin is activated into trans-zeatin to fight abiotic stresses. However, the mechanism of the effect of trans-zeatin on 2-acetyl-1-pyrroline (2-AP) biosynthesis in fragrant rice has yet to be studied. The present study was conducted to explore the effects of exogenous trans-zeatin on enzymes activities, genes expression, and precursors involved in 2-AP biosynthesis and 2-AP contents as well as the seedling quality of a fragrant rice cultivar viz., Meixiangzhan2. Four concentrations of trans-zeatin solutions at 20, 40, and 80 µmol L- 1 (ZT1, ZT2, and ZT3) were sprayed onto rice seedlings. RESULTS: Compared to the control, trans-zeatin treatments showed significantly higher 2-AP contents of fragrant rice seedlings. Increased plant height and stem width were observed due to trans-zeatin treatments. The trans-zeatin application increased 1-pyrroline, methylglyoxal, proline, and P5C contents, enhanced P5CS and OAT activities, and reduced glutamic acid contents. In addition, expressions of ProDH, P5CS2, and DAO4 were comparatively higher under trans-zeatin treatments than CK in fragrant rice seedlings. CONCLUSIONS: Overall, up-regulation of P5C, 1-pyrroline, and proline and down-regulation of glutamic acid under appropriate trans-zeatin concentrations (20 and 40 µmol L- 1) resulted in enhanced 2-AP biosynthesis in fragrant rice seedlings and 20-40 µmol L- 1 was considered as the suggested concentrations of trans-zeatin application in fragrant rice seedling.

Energy saving strategy of cloud data computing based on convolutional neural network and policy gradient algorithm.

Cloud Data Computing (CDC) is conducive to precise energy-saving management of user data centers based on the real-time energy consumption monitoring of Information Technology equipment. This work aims to obtain the most suitable energy-saving strategies to achieve safe, intelligent, and visualized energy management. First, the theory of Convolutional Neural Network (CNN) is discussed. Besides, an intelligent energy-saving model based on CNN is designed to ameliorate the variable energy consumption, load, and power consumption of the CDC data center. Then, the core idea of the policy gradient (PG) algorithm is introduced. In addition, a CDC task scheduling model is designed based on the PG algorithm, aiming at the uncertainty and volatility of the CDC scheduling tasks. Finally, the performance of different neural network models in the training process is analyzed from the perspective of total energy consumption and load optimization of the CDC center. At the same time, simulation is performed on the CDC task scheduling model based on the PG algorithm to analyze the task scheduling demand. The results demonstrate that the energy consumption of the CNN algorithm in the CDC energy-saving model is better than that of the Elman algorithm and the ecoCloud algorithm. Besides, the CNN algorithm reduces the number of virtual machine migrations in the CDC energy-saving model by 9.30% compared with the Elman algorithm. The Deep Deterministic Policy Gradient (DDPG) algorithm performs the best in task scheduling of the cloud data center, and the average response time of the DDPG algorithm is 141. In contrast, the Deep Q Network algorithm performs poorly. This paper proves that Deep Reinforcement Learning (DRL) and neural networks can reduce the energy consumption of CDC and improve the completion time of CDC tasks, offering a research reference for CDC resource scheduling.

Host-Endosymbiont Relationship Impacts the Retention of Bacteria-Containing Amoeba Spores in Porous Media.

Amoebae are protists that are commonly found in water, soil, and other habitats around the world and have complex interactions with other microorganisms. In this work, we investigated how host-endosymbiont interactions between amoebae and bacteria impacted the retention behavior of amoeba spores in porous media. A model amoeba species, Dictyostelium discoideum, and a representative bacterium, Burkholderia agricolaris B1qs70, were used to prepare amoeba spores that carried bacteria. After interacting with B. agricolaris, the retention of D. discoideum spores was enhanced compared to noninfected spores. Diverse proteins, especially proteins contributing to the looser exosporium structure and cell adhesion functionality, are secreted in higher quantities on the exosporium surface of infected spores compared to that of noninfected ones. Comprehensive examinations using a quartz crystal microbalance with dissipation (QCM-D), a parallel plate chamber, and a single-cell force microscope present coherent evidence that changes in the exosporium of D. discoideum spores due to infection by B. agricolaris enhance the connections between spores in the suspension and the spores that were previously deposited on the collector surface, thus resulting in more retention compared to the uninfected ones in porous media. This work provides novel insight into the retention of amoeba spores after bacterial infection in porous media and suggests that the host-endosymbiont relationship regulates the fate of biocolloids in drinking water systems, groundwater, and other porous environments.

Soil Amoebae Affect Iron and Chromium Reduction through Preferential Predation between Two Metal-Reducing Bacteria.

Soil protists are essential but often overlooked in soil and could impact microbially driven element cycling in natural ecosystems. However, how protists influence heavy metal cycling in soil remains poorly understood. In this study, we used a model protist, Dictyostelium discoideum, to explore the effect of interactions between soil amoeba and metal-reducing bacteria on the reduction of soil Fe(III) and Cr(VI). We found that D. discoideum could preferentially prey on the Fe(III)-reducing bacterium Shewanella decolorationis S12 and significantly decrease its biomass. Surprisingly, this predation pressure also stimulated the activity of a single S. decolorationis S12 bacterium to reduce Fe(III) by enhancing the content of electron-transfer protein cyt c, intracellular ATP synthesis, and reactive oxygen species (e.g., H2O2). We also found that D. discoideum could not prey on the Cr(VI)-reducing bacterium Brevibacillus laterosporus. In contrast, B. laterosporus became edible to amoebae in the presence of S. decolorationis S12, and their Cr(VI) reduction ability decreased under amoeba predation pressure. This study provides direct evidence that protists can affect the Cr and Fe cycling via the elective predation pressure on the metal-reducing bacteria, broadening our horizons of predation of protists on soil metal cycling.

Symbiont-Induced Phagosome Changes Rather than Extracellular Discrimination Contribute to the Formation of Social Amoeba Farming Symbiosis.

Symbiont recognition is essential in many symbiotic relationships, especially for horizontally transferred symbionts. Therefore, how to find the right partner is a crucial challenge in these symbiotic relationships. Previous studies have demonstrated that both animals and plants have evolved various mechanisms to recognize their symbionts. However, studies about the mechanistic basis of establishing protist-bacterium symbioses are scarce. This study investigated this question using a social amoeba Dictyostelium discoideum and their Burkholderia symbionts. We found no evidence that D. discoideum hosts could distinguish different Burkholderia extracellularly in chemotaxis assays. Instead, symbiont-induced phagosome biogenesis contributed to the formation of social amoeba symbiosis, and D. discoideum hosts have a higher phagosome pH when carrying symbiotic Burkholderia than nonsymbiotic Burkholderia. In conclusion, the establishment of social amoeba symbiosis is not linked with extracellular discrimination but related to symbiont-induced phagosome biogenesis, which provides new insights into the mechanisms of endosymbiosis formation between protists and their symbionts. IMPORTANCE Protists are single-celled, extremely diverse eukaryotic microbes. Like animals and plants, they live with bacterial symbionts and have complex relationships. In protist-bacterium symbiosis, while some symbionts are strictly vertically transmitted, others need to reestablish and acquire symbionts from the environment frequently. However, the mechanistic basis of establishing protist-bacterium symbioses is mostly unclear. This study uses a novel amoeba-symbiont system to show that the establishment of this symbiosis is not linked with extracellular discrimination. Instead, symbiont-induced phagosome biogenesis contributes to the formation of social amoeba-bacterium symbiosis. This study increases our understanding of the mechanistic basis of establishing protist-bacterium symbioses.

Complex Bilateral Interactions Determine the Fate of Polystyrene Micro- and Nanoplastics and Soil Protists: Implications from a Soil Amoeba.

Nano- and microplastics have become a serious global concern, threatening our living environments. Previous studies have shown that many organisms, including bacteria, animals, and plants, can be affected by microplastics. However, little is known about one ecologically important group of soil organisms, the protists. In this study, we investigated how polystyrene micro- and nanoplastics interacted with a soil amoeba Dictyostelium discoideum. The results showed that environmental concentrations of nano- and microplastics could negatively affect the soil amoeba's fitness and development. D. discoideum ingested both nano- and microplastics through phagocytosis but packed and excreted them during slug migration, which also promoted their biodegradation. Fourier transform infrared spectroscopy analyses revealed the formation of new oxygen-containing functional groups and the sign of possible oxidation of polystyrene. Also, nano- and microplastic exposure disrupted the nutrient and energy metabolisms of D. discoideum and affected the expression of key genes (e.g., cf45-1, dcsA, aprA, dymB, and gefB) related to morphogenesis and phagocytosis. In conclusion, our results show that nano- and microplastics have complex bilateral interactions with the soil amoeba, affecting each other's fate in the soil environment. This study provides new insights into how soil protists interact with nano- and microplastics in the soil ecosystem.

Disrupted Functional Connectivity of the Amygdala Predicts the Efficacy of Non-steroidal Anti-inflammatory Drugs in Migraineurs Without Aura.

Machine learning (ML) has been largely applied for predicting migraine classification. However, the prediction of efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in migraine is still in the early stages. This study aims to evaluate whether the combination of machine learning and amygdala-related functional features could help predict the efficacy of NSAIDs in patients with migraine without aura (MwoA). A total of 70 MwoA patients were enrolled for the study, including patients with an effective response to NSAIDs (M-eNSAIDs, n = 35) and MwoA patients with ineffective response to NSAIDs (M-ieNSAIDs, n = 35). Furthermore, 33 healthy controls (HCs) were matched for age, sex, and education level. The study participants were subjected to resting-state functional magnetic resonance imaging (fMRI) scanning. Disrupted functional connectivity (FC) patterns from amygdala-based FC analysis and clinical characteristics were considered features that could promote classification through multivariable logistic regression (MLR) and support vector machine (SVM) for predicting the efficacy of NSAIDs. Further, receiver operating characteristic (ROC) curves were drawn to evaluate the predictive ability of the models. The M-eNSAIDs group exhibited enhanced FC with ipsilateral calcarine sulcus (CAL), superior parietal gyrus (SPG), paracentral lobule (PCL), and contralateral superior frontal gyrus (SFG) in the left amygdala. However, the M-eNSAIDs group showed decreased FC with ipsilateral caudate nucleus (CAU), compared to the M-ieNSAIDs group. Moreover, the M-eNSAIDs group showed higher FC with left pre-central gyrus (PreCG) and post-central gyrus (PoCG) compared to HCs. In contrast, the M-ieNSAIDs group showed lower FC with the left anterior cingulate cortex (ACC) and right SFG. Furthermore, the MwoA patients showed increased FC with the left middle frontal gyrus (MFG) in the right amygdala compared to HCs. The disrupted left amygdala-related FC patterns exhibited significant correlations with migraine characteristics in the M-ieNSAIDs group. The MLR and SVM models discriminated clinical efficacy of NSAIDs with an area under the curve (AUC) of 0.891 and 0.896, sensitivity of 0.971 and 0.833, and specificity of 0.629 and 0.875, respectively. These findings suggest that the efficacy of NSAIDs in migraine could be predicted using ML algorithm. Furthermore, this study highlights the role of amygdala-related neural function in revealing underlying migraine-related neuroimaging mechanisms.

Comparative Study between ZOOMit and Conventional Intravoxel Incoherent Motion MRI for Assessing Parotid Gland Abnormalities in Patients with Early- or Mid-Stage Sjögren's Syndrome.

OBJECTIVE: To compare the reproducibility and performance of quantitative metrics between ZOOMit and conventional intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) in the diagnosis of early- and mid-stage Sjögren's syndrome (SS). MATERIALS AND METHODS: Twenty-two patients (mean age ± standard deviation, 52.0 ± 10.8 years; male:female, 2:20) with early- or mid-stage SS and 20 healthy controls (46.9 ± 14.6 years; male:female, 7:13) were prospectively enrolled in our study. ZOOMit IVIM and conventional IVIM MRI were performed simultaneously in all individuals using a 3T scanner. Quantitative IVIM parameters - including tissue diffusivity (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) - inter- and intra-observer reproducibility in measuring these parameters, and their ability to distinguish patients with SS from healthy individuals were assessed and compared between ZOOMit IVIM and conventional IVIM methods, appropriately. MR gland nodular grade (MRG) was also examined. RESULTS: Inter- and intra-observer reproducibility was better with ZOOMit imaging than with conventional IVIM imaging (ZOOMit vs. conventional, intraclass correlation coefficient of 0.897-0.941 vs. 0.667-0.782 for inter-observer reproducibility and 0.891-0.968 vs. 0.814-0.853 for intra-observer reproducibility). Significant differences in ZOOMit f, ZOOMit D*, conventional D*, and MRG between patients with SS and healthy individuals (all p < 0.05) were observed. ZOOMit D* outperformed conventional D* in diagnosing early- and mid-stage SS (area under receiver operating curve, 0.867 and 0.658, respectively; p = 0.002). The combination of ZOOMit D*, MRG, and ZOOMit f as a new diagnostic index for SS, increased diagnostic area under the curve to 0.961, which was higher than that of any single parameter (all p < 0.01). CONCLUSION: Considering its better reproducibility and performance, ZOOMit IVIM may be preferred over conventional IVIM MRI, and may subsequently improve the ability to diagnose early- and mid-stage SS.

Identifying Learners' Interaction Patterns in an Online Learning Community.

The interactions among all members of an online learning community significantly impact collaborative reflection (co-reflection). Although the relationship between learners' roles and co-reflection levels has been explored by previous researchers, it remains unclear when and with whom learners at different co-reflection levels tend to interact. This study adopted multiple methods to examine the interaction patterns of diverse roles among learners with different co-reflection levels based on 11,912 posts. First, the deep learning technique was applied to assess learners' co-reflection levels. Then, a social network analysis (SNA) was conducted to identify the emergent roles of learners. Furthermore, a lag sequence analysis (LSA) was employed to reveal the interaction patterns of the emergent roles among learners with different co-reflection levels. The results showed that most learners in an online learning community reached an upper-middle co-reflection level while playing an inactive role in the co-reflection process. Moreover, higher-level learners were superior in dialog with various roles and were more involved in self-rethinking during the co-reflection process. In particular, they habitually began communication with peers and then with the teacher. Based on these findings, some implications for facilitating online co-reflection from the perspective of roles is also discussed.

Efficient inactivation of intracellular bacteria in dormant amoeba spores by FeP.

Waterborne pathogens and related diseases are a severe public health threat worldwide. Recent studies suggest that microbial interactions among infectious agents can significantly disrupt the disinfection processes, and current disinfection methods cannot inactivate intracellular pathogens effectively, posing an emerging threat to the safety of drinking water. This study developed a novel strategy, the FeP/persulfate (PS) system, to effectively inactivate intracellular bacteria within the amoeba spore. We found that the sulfate radical (SO4•-) produced by the FeP/PS system can be quickly converted into hydroxyl radicals (•OH), and •OH can penetrate the amoeba spores and inactivate the bacteria hidden inside amoeba spores. Therefore, this study proposes a novel technique to overcome the protective effects of microbial interactions and provides a new direction to inactivate intracellular pathogens efficiently.

PAX5-activated lncRNA ARRDC1-AS1 accelerates the autophagy and progression of DLBCL through sponging miR-2355-5p to regulate ATG5.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) has high cancer-related mortality. Studies have supported that lncRNAs can regulate cancer progression by affecting autophagy of cells. ARRDC1 antisense RNA 1 (ARRDC1-AS1) was found to be upregulated in DLBCL tissues in GEPIA, but it has never been detected in DLBCL. AIM: In this study, we aimed to explore the regulatory mechanism of ARRDC1-AS1 in DLBCL cells. MAIN METHODS: RT-qPCR was taken to measure the expression of ARRDC1-AS1, microRNA-2355-5p (miR-2355-5p) and autophagy-related gene 5 (ATG5) in DLBCL cells. Western blot was conducted to detect protein levels. The malignant behaviors of DLBCL cells were estimated through functional assays. The molecular interactions were detected by Chromatin immunoprecipitation (ChIP), RNA pull-down, RNA immunoprecipitation (RIP) and luciferase reporter assays. RESULTS: We found that ARRDC1-AS1 was upregulated in DLBCL tissues and cell lines. ARRDC1-AS1 was activated by transcription factor PAX5. Knockdown of ARRDC1-AS1 suppressed DLBCL autophagy to aggravate proliferation, repress apoptosis, and facilitate invasion and migration. Furthermore, ARRDC1-AS1 sponged miR-2355-5p to upregulate ATG5. CONCLUSION: Present study first showed that PAX5-activated ARRDC1-AS1 accelerates the autophagy and progression of DLBCL via sponging miR-2355-5p to regulate ATG5, revealing a novel molecular mechanism of ARRDC1-AS1 in DLBCL and suggested ARRDC1-AS1 as a potential target in DLBCL.

lncRNA XIST knockdown suppresses hypoxia/reoxygenation (H/R)-induced apoptosis of H9C2 cells by regulating miR-545-3p/G3BP2.

This study was aimed at determining the roles and functions of lncRNA XIST/miR-545-3p/G3BP2 axis during hypoxia/reoxygenation (H/R)-induced H9C2 cell apoptosis. H9C2 cells were distributed into two groups, the H/R injury and control groups. High-throughput lncRNA sequencing was applied in the determination of differentially expressed lncRNAs between H/R-induced H9C2 cells and normal H9C2 cells. Real-time polymerase chain reactions (RT-PCR) were used to confirm the expression levels of lncRNA XIST in H/R-induced H9C2 cells. H9C2 cells were then transfected with lncRNA XIST recombinant plasmid (lncRNA XIST), sh-LINC XIST, agomiR-545-3p, antagomiR-545-3p, pcDNA-G3BP2, sh-G3BP2, and a corresponding negative control (NC). Bioinformatic analyses revealed that MiR-545-3p was a target for lncRNA XIST. This finding was confirmed by dual-luciferase reporter assay. The degree of cell apoptosis was evaluated by a flow cytometer. RT-PCR and western blot were performed to assess the apoptotic-related proteins in each group. A total of 859 differentially expressed lncRNAs (up-regulated = 502, down-regulated = 357) were identified. LncRNA XIST was found to be down-regulated in H/R-induced H9C2 cells while miR-545-3p was distinctly up-regulated. miR-545-3p was established to be a direct target for LncRNA XIST. LncRNA XIST significantly enhanced the apoptotic rate, while its inhibition suppressed the apoptotic rate. AgomiR-545-3p partially blocked the lncRNA XIST and enhanced the apoptosis of H/R-induced H9C2 cells. Moreover, miR-545-3p was shown to be a direct target for G3BP2. The overexpression of G3BP2 partially reversed the apoptotic effects of miR-545-3p on H/R-induced H9C2 cells. lncRNA XIST/miR-545-3p/GBP2 was found to be an apoptotic regulator in H/R-induced H9C2 cells.