Major Modules Research Articles

Nested patterns of commensals and endosymbionts in microbial communities of mosquito vectors

BackgroundMosquitoes serve as vectors for numerous pathogens, posing significant health risks to humans and animals. Understanding the complex interactions within mosquito microbiota is crucial for deciphering vector-pathogen dynamics and developing effective disease management strategies. Here, we investigated the nested patterns of Wolbachia endosymbionts and Escherichia-Shigella within the microbiota of laboratory-reared Culex pipiens f. molestus and Culex quinquefasciatus mosquitoes. We hypothesized that Wolbachia would exhibit a structured pattern reflective of its co-evolved relationship with both mosquito species, while Escherichia-Shigella would display a more dynamic pattern influenced by environmental factors.ResultsOur analysis revealed different microbial compositions between the two mosquito species, although some microorganisms were common to both. Network analysis revealed distinct community structures and interaction patterns for these bacteria in the microbiota of each mosquito species. Escherichia-Shigella appeared prominently within major network modules in both mosquito species, particularly in module P4 of Cx. pipiens f. molestus, interacting with 93 nodes, and in module Q3 of Cx. quinquefasciatus, interacting with 161 nodes, sharing 55 nodes across both species. On the other hand, Wolbachia appeared in disparate modules: module P3 in Cx. pipiens f. molestus and a distinct module with a single additional taxon in Cx. quinquefasciatus, showing species-specific interactions and no shared taxa. Through computer simulations, we evaluated how the removal of Wolbachia or Escherichia-Shigella affects network robustness. In Cx. pipiens f. molestus, removal of Wolbachia led to a decrease in network connectivity, while Escherichia-Shigella removal had a minimal impact. Conversely, in Cx. quinquefasciatus, removal of Escherichia-Shigella resulted in decreased network stability, whereas Wolbachia removal had minimal effect.ConclusionsContrary to our hypothesis, the findings indicate that Wolbachia displays a more dynamic pattern of associations within the microbiota of Culex pipiens f. molestus and Culex quinquefasciatus mosquitoes, than Escherichia-Shigella. The differential effects on network robustness upon Wolbachia or Escherichia-Shigella removal suggest that these bacteria play distinct roles in maintaining community stability within the microbiota of the two mosquito species.

Hepcidin Modulators: A Potential Alternative Treatment of Iron Deficiency and Iron Deficiency Anemia in Heart Failure

Heart failure affects roughly 6.7 million Americans and is associated with many comorbidities and complications, including iron deficiency and anemia. Iron homeostasis is integral to optimal body function. Hepcidin, a hepatically produced peptide, is a major modulator in iron homeostasis. Its synthesis is induced in part by inflammatory states. Iron deficiency in heart failure is currently diagnosed with the assessment of serum ferritin and percent of transferrin saturation. Iron deficiency in the setting of hepcidin excess in inflammatory conditions, such as heart failure, is discussed in this literature review. Iron deficiency is associated with worse prognosis and poorer outcomes in heart failure patients. Targeted treatment of iron deficiency and iron deficiency anemia may provide improved quality of life and decrease mortality and morbidity among heart failure patients. Current treatment options for iron deficiency and anemia include intravenous and oral iron supplementation, as well as other erythropoiesis-stimulating agents. However, concerns regarding safety have been raised in regards to these options. An emerging possibility for the treatment of iron deficiency in the setting of hepcidin overload is hepcidin modulators, including hepcidin antagonists or inhibitors. This review has addressed iron deficiency and anemia in heart failure and suggests that hepcidin antagonists/inhibitors may provide a viable alternative to the classic treatment methods.

Pi Class – A Revolutionary Step Forward in Hybrid Class Management System

The COVID-19 pandemic has caused widespread disruptions globally, resulting in a state of health emergency with numerous deaths reported and an overall implementation of quarantine and isolation. Strict lockdowns have been implemented to curb the spread of the virus, requiring social distancing and limiting physical interactions. These measures had far-reaching impacts on all aspects of life, including education. Education was one of the most affected sectors, with difficulty delivering quality education in schools, colleges, and universities. It was hard to provide quality education, a basic need of humanity. In this research paper, we propose an adequate solution to overcome the difficulties of maintaining educational quality during and after the COVID-19 pandemic and its multiple variants. The proposed solution is a hybrid model for an autonomous lecture recording system that facilitates students to attend physical classes and attend lectures virtually. The solution proposed is a cost-effective and convenient way for students to access lectures. The application involves hardware and software components that record and preserve lectures' audio and visual aspects. The system will allow lectures to be delivered directly to the students' devices. The major modules of the project include Python scripting, model training, UI/UX design, and app development.

Exploiting explicit item–item correlations from knowledge graphs for enhanced sequential recommendation

In recent years, the research of employing knowledge graphs (KGs) in sequential recommendation (SR) has received a lot of attention, since the side information extracted from KGs, especially the information of the correlations between items, indeed helps the SR models achieve better performance. However, many previous KG-based SR models tend to introduce some noise information when learning item embeddings, or insufficiently fuse item–item correlations into their sequential modeling, thus limiting their performance improvements. In this paper, we propose a Distance-Aware Knowledge-based Sequential Recommendation model (DAKSR), which exploits the explicit item–item correlations from KGs to achieve enhanced SR. Specifically, as one critical component in our DAKSR, the distance score matrix (DSM) is first obtained to indicate the correlations between items, and then leveraged in the following three major modules of DAKSR. First, in the Item-Set Embedding layer (ISE) all item embeddings are learned based on DSM, in which the noise information is eliminated effectively. Meanwhile, the Knowledge-Infused Transformer (KIT) incorporates DSM into its attention mechanism to improve the feature extraction. Furthermore, the Knowledge Contrastive Learning module (KCL) also leverages the item–item correlations presented in DSM to generate two credible sequence views, which are used to refine sample representations through a contrastive learning strategy, and thus improve the model’s robustness. Our extensive experiments on three SR benchmarks obviously demonstrate our DAKSR’s superior performance over the state-of-the-art (SOTA) KG-based recommendation models. The implementation of our DAKSR is available at https://github.com/Easonsi/DAKSR for reproducing our experiment results conveniently.

A chest imaging diagnosis report generation method based on dual-channel transmodal memory network

Medical imaging is critical for clinical diagnosis. In the past few years, medical imaging data has been steadily growing at an annual rate of 30%, while the annual growth rate in the number of radiologists is only 4. 1%. The traditional manual methods of chest imaging reports result in high information load and workload with low relevance of images and texts. Here we propose a chest CT imaging diagnosis report generation network to effectively integrate image and text feature information, improve the relevance of the cross-modal networks and automatically generate imaging diagnosis reports. The network includes three major modules: a novel ResNetII for information extraction, an image–text dual-channel transmodal memory network (DCTMN), and a dual-channel decoder module combined with LSTM gate. We validate the effectiveness of the proposed network on the IU X-ray and the MIMIC-CXR datasets. Our analysis indicates that the proposed method can strengthen the relationship between chest imaging and text information, ultimately achieving the automatic generation of chest imaging diagnosis reports.

RAB18 regulates extrahepatic siRNA-mediated gene silencing efficacy

Small interfering RNAs (siRNAs) hold considerable therapeutic potential to selectively silence previously “undruggable” disease-associated targets, offering new opportunities to fight human diseases. This therapeutic strategy, however, is limited by the inability of naked siRNAs to passively diffuse across cellular membranes due to their large molecular size and negative charge. Delivery of siRNAs to liver through conjugation of siRNA to N-acetylgalactosamine (GalNAc) has been a success; providing robust and durable gene knockdown, specifically in hepatocytes. Yet, the poor delivery and silencing efficacy of siRNAs in other cell types has hindered their applications outside the liver. We previously reported that a genome-wide pooled knockout screen identified RAB18 as a major modulator of GalNAc-siRNA conjugates. Herein, we demonstrate RAB18 knockout/knockdown efficaciously enhances siRNA-mediated gene silencing in hepatic, extrahepatic cell lines, and in vivo. Our results reveal a mechanism by which retrograde Golgi-ER transport and the intracellular lipid droplets (LDs) positively regulate siRNA-mediated gene silencing.

Influence of competitive padel matches on physical fitness and perceptual responses in high-level players.

Padel is a sport that requires a combination of physical and technical skills. Fatigue is a major modulator of padel players' performance. The aim of this study was to analyze the changes in neuromuscular performance and perceived effort among high-level padel players. Countermovement jump and handgrip strength of 58 participants (men: N.=38 age = 28.3±7.05 years, height = 178.9±8.41 cm; body mass = 75.25±8.2 kg. women: N.=21, age = 23.07±4.6 years, height = 163.86±10.34 cm; body mass = 59.9±6.13 kg) were assessed pre- and post-match in 43 official matches during the Padel Master 2021 of the Andalusian Padel Federation. Additionally, the Rating of Perceived Exertion (RPE) Scale was used to assess players' subjective perception of effort after each match. Players experienced significant increases in postmatch jump height, with no changes in handgrip strength. Moreover, changes in jump height were greater in male players after the match when compared to their female counterparts, but the match duration was not determinant when comparing CMJ or handgrip between 2-sets and 3-sets matches. Finally, players reported a higher RPE after longer matches, but perceived effort was not different between sexes. These findings suggest that the internal load generated during competitive padel matches is insufficient to cause a declination in performance of the studied variables. It is also concluded that players' warm-up routines could be optimized to enhance neuromuscular performance at the start of matches.

Refractory wastewater shapes bacterial assembly and key taxa during long-term acclimatization

Bacterial assembly and key taxa during long-term acclimatization in refractory wastewater treatment systems is of paramount importance for optimizing system performance and improving management strategies. Therefore, this study employed high-throughput sequencing, coupled with machine learning models and statistical analysis approaches, to comprehensively elucidate key features of bacterial communities and assembly processes in pesticide wastewater treatment systems. A nine-month monitoring showed substantial variation in diversity and composition of bacterial community between two interconnected biological treatment units (designated as BA and PA). Dynamics of bacterial communities in both units were similar. Moreover, water quality played crucial roles in regulating the bacterial community structure of activated sludge, which were primarily driven by deterministic patterns. Homogeneous selection contributed to 62.85 % and 64.43 % of the variations in BA and PA samples, respectively. Additionally, network analysis revealed significant modularity in bacterial compositions in both groups. Linear regression analysis identified major bacterial modules associated with metabolism and degradation functions. Notably, Module 2 in PA samples has significant positive correlations with functions related to metabolism of nucleotide, amino acid, and xenobiotics, as well as benzoate degradation. Furthermore, key taxa in ecological modules identified by Random Forest model, such as Pseudomonas, Sphingobium, and PHOS-HE28, were dominant populations with metabolism and degradation functions. Particularly, Sphingobium, appeared to be a potential multifunctional degrading bacterium, related to amino acid and xenobiotics metabolism, as well as fatty acid, valine, leucine, isoleucine, fluorobenzoate, and aminobenzoate degradation. These findings are important for developing operating strategies to maintain stable system performance during refractory wastewater treatment.

Binding to the Other Side: The AT-Hook DNA-Binding Domain Allows Nuclear Factors to Exploit the DNA Minor Groove.

The "AT-hook" is a peculiar DNA-binding domain that interacts with DNA in the minor groove in correspondence to AT-rich sequences. This domain has been first described in the HMGA protein family of architectural factors and later in various transcription factors and chromatin proteins, often in association with major groove DNA-binding domains. In this review, using a literature search, we identified about one hundred AT-hook-containing proteins, mainly chromatin proteins and transcription factors. After considering the prototypes of AT-hook-containing proteins, the HMGA family, we review those that have been studied in more detail and that have been involved in various pathologies with a particular focus on cancer. This review shows that the AT-hook is a domain that gives proteins not only the ability to interact with DNA but also with RNA and proteins. This domain can have enzymatic activity and can influence the activity of the major groove DNA-binding domain and chromatin docking modules when present, and its activity can be modulated by post-translational modifications. Future research on the function of AT-hook-containing proteins will allow us to better decipher their function and contribution to the different pathologies and to eventually uncover their mutual influences.

In vitro evolution driven by epistasis reveals alternative cholesterol-specific binding motifs of perfringolysin O

The crucial molecular factors that shape the interfaces of lipid-binding proteins with their target ligands and surfaces remain unknown due to the complex makeup of biological membranes. Cholesterol, the major modulator of bilayer structure in mammalian cell membranes, is recognized by various proteins, including the well-studied cholesterol-dependent cytolysins. Here, we use invitro evolution to investigate the molecular adaptations that preserve the cholesterol specificity of perfringolysin O, the prototypical cholesterol-dependent cytolysin from Clostridium perfringens. We identify variants with altered membrane-binding interfaces whose cholesterol-specific activity exceeds that of the wild-type perfringolysin O. These novel variants represent alternative evolutionary outcomes and have mutations at conserved positions that can only accumulate when epistatic constraints are alleviated. Our results improve the current understanding of the biochemical malleability of the surface of a lipid-binding protein.

Contrasting alterations in brain chemistry in a crustacean intermediate host of two acanthocephalan parasites

The dynamic properties of neural systems throughout life can be hijacked by so-called manipulative parasites. This study investigated changes in the brain chemistry of the amphipod Gammarus fossarum in response to infection with two trophically-transmitted helminth parasites known to induce distinct behavioral alterations: the bird acanthocephalan Polymorphus minutus and the fish acanthocephalan Pomphorhynchus tereticollis. We quantified brain antioxidant capacity as a common marker of homeostasis and neuroprotection, and brain total protein, on 72 pools of six brains. We analyzed the concentration of serotonin (5HT), dopamine (DA) and tyramine in 52 pools of six brains, by using ultrafast high performance liquid chromatography with electrochemical detection (UHPLC-ECD). Brain total protein concentration scaled hypo-allometrically to dry body weight, and was increased in infected gammarids compared to uninfected ones. The brain of gammarids infected with P. minutus had significantly lower total antioxidant capacity relative to total proteins. Infection with P. tereticollis impacted DA level compared to uninfected ones, and in opposite direction between spring and summer. Brain 5HT level was higher in summer compared to spring independently of infection status, and was decreased by infection after correcting for brain total protein concentration estimated from dry whole-body weight. The potential implication of 5HT/DA balance in parasite manipulation, as a major modulator of the reward-punishment axis, is discussed. Taken together, these findings highlight the need to consider both brain homeostatic and/or structural changes (antioxidant and total protein content) together with neurotransmission balance and flexibility, in studies investigating the impact of parasites on brain and behavior.

Adaptive median filter salt and pepper noise suppression approach for common path coherent dispersion spectrometer

The Common-path Coherent-dispersion Spectrometer (CODES), an exoplanet detection instrument, executes high-precision Radial Velocity (RV) inversions by recording the phase shifts of interference fringes. Salt-and-pepper noise caused by factors such as improper operation of the CCD probe/analog-to-digital converter and strong dark currents may interfere with the phase information of the fringe. This lowers the quality of the interfering fringe image and significantly interferes with the RV’s inversion. In this study, an adaptive median filtering algorithm (CODESmF) based on submaximum and subminimum values is designed to eliminate the interference fringe image's salt-and-pepper noise as well as to reduce RV error. This allows the interference fringe image's phase information to be retained more completely. The algorithm consists of two major modules. Pixel Sub-extreme-based Filtered Noise Monitoring Module: discriminates signal pixels and noise pixels based on the submaximum and subminimum values of the pixels in the filtering window. Adaptive Median Filter Noise Suppression Module: the signal pixel is kept at the original value output, the noise pixel serves as the filtering window's center pixel, and the adaptive median filtering procedure is repeated numerous times with various filtering window sizes. According to the experimental findings, the CODESmF outperforms comparable algorithms and works better at recovering interference fringes. More than 90% of the phase/RV error caused by salt-and-pepper noise is typically eliminated by the CODESmF algorithm, and in certain circumstances, it can even remove roughly 98% of the phase error.

HnRNPA1 Prevents Endothelial-to-mesenchymal Transition-induced VSMC Activation and Neointimal Hyperplasia in Vein Grafts.

Endothelial-to-mesenchymal transition (EndoMT) is associated with neointimal hyperplasia and vein graft failure, and heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) has emerged as a major modulator of EMT. We aimed to investigate the functional consequence of EndoMT in neointimal hyperplasia and the precise role of hnRNPA1 in the regulation of EndoMT and neointimal hyperplasia. We investigated the spatial and temporal distribution characteristics of EndoMT cells in a mouse model of vein graft transplantation. In vitro, we studied the interaction between EndoMT cells and VSMCs, and the underlying mechanism was investigated by cytokine antibody assays. In cultured HUVECs, we studied the effect of hnRNPA1 on EndoMT and the cellular interactions by using siRNA-mediated knockdown and adenovirus-mediated overexpression. We further investigated the role of hnRNPA1 in EndoMT and neointimal hyperplasia in vivo with an AAV-mediated EC-specific hnRNPA1 overexpression murine model. We demonstrated the presence of EndoMT cells during the initial stage of neointimal formation, and that EndoMT cells promoted the proliferation and migration of VSMCs in vitro. Mechanistic studies revealed that EndoMT cells express and secrete a higher level of PDGF-B. Furthermore, we found a regulatory role for hnRNPA1 in EndoMT in vitro and in vivo. Similarly, we found that hnRNPA1 overexpression in ECs reduced the expression and secretion of PDGF-B during EndoMT, effectively inhibiting EndoMT cell-mediated activation of VSMCs in vitro and neointimal formation in vivo. Taken together, these findings indicate that EndoMT cells can activate VSMCs through a paracrine mechanism mediated by hnRNPA1 and lead to neointimal hyperplasia.

Domain adaptation using AdaBN and AdaIN for high-resolution IVD mesh reconstruction from clinical MRI.

Deep learning has firmly established its dominance in medical imaging applications. However, careful consideration must be exercised when transitioning a trained source model to adapt to an entirely distinct environment that deviates significantly from the training set. The majority of the efforts to mitigate this issue have predominantly focused on classification and segmentation tasks. In this work, we perform a domain adaptation of a trained source model to reconstruct high-resolution intervertebral disc meshes from low-resolution MRI. To address the outlined challenges, we use MRI2Mesh as the shape reconstruction network. It incorporates three major modules: image encoder, mesh deformation, and cross-level feature fusion. This feature fusion module is used to encapsulate local and global disc features. We evaluate two major domain adaptation techniques: adaptive batch normalization (AdaBN) and adaptive instance normalization (AdaIN) for the task of shape reconstruction. Experiments conducted on distinct datasets, including data from different populations, machines, and test sites demonstrate the effectiveness of MRI2Mesh for domain adaptation. MRI2Mesh achieved up to a 14% decrease in Hausdorff distance (HD) and a 19% decrease in the point-to-surface (P2S) metric for both AdaBN and AdaIN experiments, indicating improved performance. MRI2Mesh has demonstrated consistent superiority to the state-of-the-art Voxel2Mesh network across a diverse range of datasets, populations, and scanning protocols, highlighting its versatility. Additionally, AdaBN has emerged as a robust method compared to the AdaIN technique. Further experiments show that MRI2Mesh, when combined with AdaBN, holds immense promise for enhancing the precision of anatomical shape reconstruction in domain adaptation.

3D Porous Polymer Scaffold-Conjugated KGF-Mimetic Peptide Promotes Functional Skin Regeneration in Chronic Diabetic Wounds.

The re-epithelialization process gets severely dysregulated in chronic nonhealing diabetic foot ulcers/wounds. Keratinocyte growth factor (KGF or FGF-7) is the major modulator of the re-epithelialization process, which regulates the physiological phenotypes of cutaneous keratinocytes. The existing therapeutic strategies of growth factor administration have several limitations. To overcome these, we have designed a KGF-mimetic peptide (KGFp, 13mer) based on the receptor interaction sites in murine KGF. KGFp enhanced migration and transdifferentiation of mouse bone marrow-derived MSCs toward keratinocyte-like cells (KLCs). A significant increase in the expression of skin-specific markers Bnc1 (28.5-fold), Ck5 (14.6-fold), Ck14 (26.1-fold), Ck10 (187.7-fold), and epithelial markers EpCam (23.3-fold) and Cdh1 (64.2-fold) was associated with the activation of ERK1/2 and STAT3 molecular signaling in the KLCs. Further, to enhance the stability of KGFp in the wound microenvironment, it was conjugated to biocompatible 3D porous polymer scaffolds without compromising its active binding sites followed by chemical characterization using Fourier transform infrared spectroscopy, field-emission scanning electron microscopy, dynamic mechanical analysis, and thermogravimetry. In vitro evaluation of the KGFp-conjugated 3D polymer scaffolds revealed its potential for transdifferentiation of MSCs into KLCs. Transplantation of allogeneic MSCGFP using KGFp-conjugated 3D polymer scaffolds in chronic nonhealing type 2 diabetic wounds (db/db transgenic, 50-52 weeks old male mice) significantly enhanced re-epithelialization-mediated wound closure rate (79.3%) as compared to the control groups (Untransplanted -22.4%, MSCGFP-3D polymer scaffold -38.5%). Thus, KGFp-conjugated 3D porous polymer scaffolds drive the fate of the MSCs toward keratinocytes that may serve as potential stem cell delivery platform technology for tissue engineering and transplantation.

Analysis of the Brand Visual Needs System from the Perspective of Contemporary Market Demands

Brand visual design is not only an essential bridge for companies to convey their ideas and values but also a key factor in shaping the brand image and enhancing market competitiveness. However, a pervasive concern has arisen in society that many recent graduates in brand design and visual design cannot immediately meet the demands of the design industry. Despite attempts by scholars to reform courses and teaching philosophies, there are still significant shortcomings and gaps. Therefore, based on market orientation and supply-demand concepts, this study collected in-depth recruitment demands for brand design from 74 companies and conducted systematic summarization and analysis. It synthesized a demand model consisting of three major modules and 55 content points required by companies for brand design students. Based on these demands, adjustments and plans were made to the curriculum content, aiming to construct a teaching system that not only meets market demands but also enhances students’ comprehensive qualities. The goal is to cultivate more outstanding talents capable of quickly adapting to and excelling in brand design work.

Impacts of multiple environmental factors on soil bacterial community assembly in heavy metal polluted paddy fields

Soil microorganisms play pivotal roles in driving essential biogeochemical processes in terrestrial ecosystems, and they are sensitive to heavy metal pollution. However, our understanding of multiple environmental factors interaction in heavy metal polluted paddy fields to shape microbial community assembly remain limited. In the current study, we used 16S rRNA amplicon sequencing to characterize the microbial community composition in paddy soils collected from a typical industry town in Taihu region, eastern China. The results revealed that Cd and Pb were the major pollutant, and Proteobacteria, Acidobacteria and Chloroflexi were the dominate indigenous bacterial phyla. Linear regression and random forest analysis demonstrated that soil pH was the most important predictor of bacterial diversity. Mantel analysis showed that bacterial community structure was mainly driven by pH, CEC, silt, sand, AK, total Cd and DTPA-Cd. The constructed bacterial co-occurrence network, utilizing a random matrix theory-based approach, exhibited non-random with scale-free and modularity features. The major modules within the networks also showed significant correlations with soil pH. Overall, our study indicated that soil physiochemical properties made predominant contribution to bacterial community diversity, structure and their association in Cd/Pb polluted paddy fields. These findings expand our knowledge of the key environmental drivers and co-occurrence patterns of bacterial community in polluted paddy fields.

The gut-immune axis during hypertension and cardiovascular diseases.

The gut-immune axis is a relatively novel phenomenon that provides mechanistic links between the gut microbiome and the immune system. A growing body of evidence supports it is key in how the gut microbiome contributes to several diseases, including hypertension and cardiovascular diseases (CVDs). Evidence over the past decade supports a causal link of the gut microbiome in hypertension and its complications, including myocardial infarction, atherosclerosis, heart failure, and stroke. Perturbations in gut homeostasis such as dysbiosis (i.e., alterations in gut microbial composition) may trigger immune responses that lead to chronic low-grade inflammation and, ultimately, the development and progression of these conditions. This is unsurprising, as the gut harbors one of the largest numbers of immune cells in the body, yet is a phenomenon not entirely understood in the context of cardiometabolic disorders. In this review, we discuss the role of the gut microbiome, the immune system, and inflammation in the context of hypertension and CVD, and consolidate current evidence of this complex interplay, whilst highlighting gaps in the literature. We focus on diet as one of the major modulators of the gut microbiota, and explain key microbial-derived metabolites (e.g., short-chain fatty acids, trimethylamine N-oxide) as potential mediators of the communication between the gut and peripheral organs such as the heart, arteries, kidneys, and the brain via the immune system. Finally, we explore the dual role of both the gut microbiome and the immune system, and how they work together to not only contribute, but also mitigate hypertension and CVD.

Practice and Reflection on the Transformation of Performance-Oriented Higher Education Financial Digitization Platform

The realization of performance-oriented transformation of financial intelligence in universities is an inevitable choice for universities to achieve high-quality education in the new era. How to achieve deep integration of university finance and intelligence transformation based on performance, and how to optimize the allocation of university resources based on the construction of intelligent platforms, are issues that need to be studied at the current stage. This paper first introduces the background of performance-oriented transformation of financial intelligence in universities, revealing the practical background of this research. Then, it starts with the connotation and significance of performance-oriented financial intelligence transformation, focusing on the system architecture of financial intelligence transformation in universities. It elaborates on the system architecture of financial intelligence transformation from four aspects: budgeting, execution, monitoring, and evaluation. Furthermore, platform design is conducted based on this, specifically introducing the construction of three major modules of the university intelligence platform: financial service system, financial data exchange center, and financial decision system. Finally, this paper puts forward some suggestions for the intelligent transformation of university finance.

Design and application of virtual simulation teaching platform for intelligent manufacturing

Aiming at the practical teaching of intelligent manufacturing majors faced with lack of equipment, tense teachers and other problems such as high equipment investment, high material loss, high teaching risk, difficult to implement internship, difficult to observe production, difficult to reproduce the results, and so on, we take the electrical automation technology, mechatronics technology and industrial robotics technology majors of intelligent manufacturing majors as an example, and design and establish a virtual simulation teaching platform for intelligent manufacturing majors by using the cloud computing platform, edge computing technology, and terminal equipment synergy. The platform includes six major virtual simulation modules, including virtual simulation of electrician electronics and PLC control, virtual and real combination of typical production lines of intelligent manufacturing, dual-axis collaborative robotics workstation, digital twin simulation, virtual disassembly of industrial robots, virtual simulation of magnetic yoke axis flexible production line. The platform covers the virtual simulation teaching content of basic principle experiments, advanced application experiments, and advanced integration experiments in intelligent manufacturing majors. In order to test the effectiveness of this virtual simulation platform for practical teaching in engineering, this paper organizes a teaching practice activity involving 246 students from two parallel classes of three different majors. Through a one-year teaching application, we analyzed the data on the grades of 7 core courses involved in three majors in one academic year, the proportion of participation in competitions and innovative activities, the number of awards and certificates of professional qualifications, and the subjective questionnaires of the testers. The analysis shows that the learners who adopt the virtual simulation teaching platform proposed in this paper for practical teaching are better than the learners under the traditional teaching method in terms of academic performance, proportion of participation in competitions and innovative activities, and proportion of awards and certificates by more than 13%, 37%, 36%, 27% and 22%, respectively. Therefore, the virtual simulation teaching platform of intelligent manufacturing established in this paper has obvious superiority in solving the problem of "three highs and three difficulties" existing in the practical teaching of engineering, and according to the questionnaire feedback from the testers, the platform can effectively alleviate the shortage of practical training equipment, stimulate the interest in learning, and help to broaden and improve the knowledge system of the learners.