Common Module Research Articles

Design and Implementation of Vocational Education Management System Based on Discrete Fourier Transform

In some remote areas and even some vocational colleges, the information management of students is still stagnant on paper. Such traditional methods have obviously lagged behind the development and needs of society. The traditional manual management is a waste of both manpower and material resources. In today’s rapidly developing information age, it is very necessary to study an efficient vocational education management system. The vocational education management system designed in this paper includes Web server, DB server, college management terminal, and load balancing switch. In this system, CONN.ASP is a common module of all ASP programs in the whole system. It is mainly used to realize the connection between ASP program and database and the calling of common function submodules. In order to reduce the power consumption of the vocational education management system, the Discrete Fourier Transform (DFT) theory is proposed in this paper. This algorithm can effectively save the size of the storage unit and the number and frequency of reading the twiddle factor, so as to achieve the purpose of improving the operation speed. This paper randomly selected the scores of 10 classes and use the system for data processing test. A total of 50% of teachers were particularly satisfied with the tables generated by the discrete Fourier transform-based vocational education management system. They believed that the vocational education management system based on discrete Fourier transform is very fast in data processing, and the processing speed was within 0.1 s. The system designed in this paper can meet various demands in vocational education management and continuously enhance the function of educational administration management.

RNA-As-Graphs Motif Atlas-Dual Graph Library of RNA Modules and Viral Frameshifting-Element Applications.

RNA motif classification is important for understanding structure/function connections and building phylogenetic relationships. Using our coarse-grained RNA-As-Graphs (RAG) representations, we identify recurrent dual graph motifs in experimentally solved RNA structures based on an improved search algorithm that finds and ranks independent RNA substructures. Our expanded list of 183 existing dual graph motifs reveals five common motifs found in transfer RNA, riboswitch, and ribosomal 5S RNA components. Moreover, we identify three motifs for available viral frameshifting RNA elements, suggesting a correlation between viral structural complexity and frameshifting efficiency. We further partition the RNA substructures into 1844 distinct submotifs, with pseudoknots and junctions retained intact. Common modules are internal loops and three-way junctions, and three submotifs are associated with riboswitches that bind nucleotides, ions, and signaling molecules. Together, our library of existing RNA motifs and submotifs adds to the growing universe of RNA modules, and provides a resource of structures and substructures for novel RNA design.

The scINSIGHT Package for Integrating Single-Cell RNA-Seq Data from Different Biological Conditions.

Data integration is a critical step in the analysis of multiple single-cell RNA sequencing samples to account for heterogeneity due to both biological and technical variability. scINSIGHT is a new integration method for single-cell gene expression data, and can effectively use the information of biological condition to improve the integration of multiple single-cell samples. scINSIGHT is based on a novel non-negative matrix factorization model that learns common and condition-specific gene modules in samples from different biological or experimental conditions. Using these gene modules, scINSIGHT can further identify cellular identities and active biological processes in different cell types or conditions. Here we introduce the installation and main functionality of the scINSIGHT R package, including how to preprocess the data, apply the scINSIGHT algorithm, and analyze the output.

Multiple parasitoid species enhance top-down control, but parasitoid performance is context dependent.

Ecological communities are composed of many species, forming complex networks of interactions. Current environmental changes are altering the structure and species composition of ecological networks, which could modify interactions, either directly or indirectly. To predict changes in the functioning of communities, we need to understand whether species interactions are primarily driven by network structure (i.e. topology) or the specific identities of species (i.e. nodes). Yet, this partitioning of effects is challenging and thus rarely explored. Here we disentangled the influence of network structure and the identities of species on the outcome of consumer-resource interactions using a host-parasitoid system. We used four common community modules in host-parasitoid communities to represent network structure (i.e. host-parasitoid, exploitative competition, alternative host and a combination of exploitative competition and alternative host). We assembled nine different species combinations per community module in a laboratory experiment using a pool of three Drosophila hosts and three larval parasitoid species (Leptopilina sp., Ganaspis sp. and Asobara sp.). We compared host suppression and parasitoid performance across community modules and species assemblages to identify general effects linked to network structure and specific effects due to species community composition. We found that multiple parasitoid species enhanced host suppression due to sampling effect, weaker interspecific than intraspecific competition between parasitoids, and synergism. However, the effects of network structure on parasitoid performance were species specific and dependent on the identity of co-occurring species. Consequently, multiple parasitoid species generally strengthen top-down control, but the performance of the parasitoids depends on the identity of either the co-occurring parasitoid species, the alternative host species or both. Our results highlight the importance of preserving parasitoid diversity for ecosystem functioning and show that other effects depend on species community composition, and may therefore be altered by ongoing environmental changes.

Automated synthesis of [68Ga]Ga-FAPI-46 without pre-purification of the generator eluate on three common synthesis modules and two generator types

BackgroundThe recent development of quinoline-based radiotracers, which act as fibroblast activation protein inhibitors (FAPIs), has shown promising preclinical and clinical advantages. [68Ga]Ga-FAPI-46 is a new radiotracer for in vivo detection of the fibroblast activation protein by positron emission tomography (PET). Recently, the automated synthesis of [68Ga]Ga-FAPI-46 was reported based on pre-concentration and purification of the generator eluate by using a cation exchange-cartridge.Our aim was to simplify the synthesis and shorten the automated synthesis of [68Ga]Ga-FAPI-46 to make it accessible and thus even more attractive to a broader clinical and scientific community.ResultsWe developed and evaluated the GMP compliant automatic synthesis of [68Ga]Ga-FAPI-46 using two different 68Ge/68Ga generators (an Eckert & Ziegler, GalliaPharm generator, 1.85 GBq/50 mCi and an iThemba generator, 1.85 GBq/50 mCi) Somerset West, South Africa) and three different commercial and customized systems: the EasyOne module from Trasis; the GaSy module from Synthra with a customized synthesis template and a customized single use cassette. Additionally, the automatic synthesis of [68Ga]Ga-FAPI-46 was established on a GallElut synthesis module from Scintomics with fixed tubing.ConclusionsIndependent of the synthesis modules or the generators employed we were able to complete the synthesis of [68Ga]Ga-FAPI-46 in 12 min including the process of purification and formulation. In all cases, the final products showed more than 99.5% chemical purity and the radiochemical yield reached around 92.5% (decay corrected). All quality control parameters (e.g. sterility, stability and radiochemical purity) were conform to the European Pharmacopoeia.

Effect of Yoga on Quality of Life of Oral Healthcare Professionals during Covid- 19 Pandemic

Yoga is a holistic approach to mental and physical health and is classified by the national institutes of health as a form of complementary and alternative medicine for human life. COVID 19 pandemic has impacted the quality of life of oral health care professionals. Our aim was to assess the effect of yoga on the quality of life of these professionals . Thirty participants (12 male/18 female) with a mean age 40.5 ± 2.5 years were prepared utilizing a 45-minute common yoga protocol module .All members went to 24 sessions over a 4-week regimen. A standard WHOQOL-BREF survey was utilized to evaluate the QOL in 4 areas (physical, psychological, social and environmental). The responses in terms of scores from the survey were investigated utilizing independent student ‘t’ test. The change in the QOL following a 4-week yoga session was our studied outcome. After 4 weeks there was a significant change in physical domain (p=0.001), psychological domain (p=0.045) and in the total mean WHOQOL score (p=0.028).It was found that integrating yoga in daily curriculum does enhance the quality of life of oral healthcare professionals in the current COVID 19 Pandemic .

Test Bench Concept for Testing of Gripper Properties in a Robotic Palletizing Process

Palletizing and depalletizing processes require specialized equipment, such as grippers dedicated to the type and dimensions of the goods to be handled. The traditional approach used in the robotisation of palletizing workstations is therefore mainly based on the development of specialized grippers, whose design limits their use in other applications. The article presents a concept of a modular gripper system enabling palletization of goods in collective packaging, including cartons, shrink-wrap packs, and bags. The concept developed involves the construction of a gripper system based on common modules that provide for extensive parameterization of device operation. The authors also propose a test bench to test the properties of grippers in a robotic palletizing process.

A Closed Form Model for Molecular Ratchet-Type Chemically Induced Dimerization Modules.

Chemical-induced dimerization (CID) modules enable users to implement ligand-controlled cellular and biochemical functions for a number of problems in basic and applied biology. A special class of CID modules occur naturally in plants and involve a hormone receptor that binds a hormone, triggering a conformational change in the receptor that enables recognition by a second binding protein. Two recent reports show that such hormone receptors can be engineered to sense dozens of structurally diverse compounds. As a closed form model for molecular ratchets would be of immense utility in forward engineering of biological systems, here we have developed a closed form model for these distinct CID modules. These modules, which we call molecular ratchets, are distinct from more common CID modules called molecular glues in that they engage in saturable binding kinetics and are characterized well by a Hill equation. A defining characteristic of molecular ratchets is that the sensitivity of the response can be tuned by increasing the molar ratio of the hormone receptor to the binding protein. Thus, the same molecular ratchet can have a pico- or micromolar EC50 depending on the concentration of the different receptor and binding proteins. Closed form models are derived for a base elementary reaction rate model, for ligand-independent complexation of the receptor and binding protein, and for homodimerization of the hormone receptor. Useful governing equations for a variety of in vitro and in vivo applications are derived, including enzyme-linked immunosorbent assay-like microplate assays, transcriptional activation in prokaryotes and eukaryotes, and ligand-induced split protein complementation.

Network subgraph-based approach for analyzing and comparing molecular networks.

Molecular networks are built up from genetic elements that exhibit feedback interactions. Here, we studied the problem of measuring the similarity of directed networks by proposing a novel alignment-free approach: the network subgraph-based approach. Our approach does not make use of randomized networks to determine modular patterns embedded in a network, and this method differs from the network motif and graphlet methods. Network similarity was quantified by gauging the difference between the subgraph frequency distributions of two networks using Jensen–Shannon entropy. We applied the subgraph approach to study three types of molecular networks, i.e., cancer networks, signal transduction networks, and cellular process networks, which exhibit diverse molecular functions. We compared the performance of our subgraph detection algorithm with other algorithms, and the results were consistent, but other algorithms could not address the issue of subgraphs/motifs embedded within a subgraph/motif. To evaluate the effectiveness of the subgraph-based method, we applied the method along with the Jensen–Shannon entropy to classify six network models, and it achieves a 100% accuracy of classification. The proposed information-theoretic approach allows us to determine the structural similarity of two networks regardless of node identity and network size. We demonstrated the effectiveness of the subgraph approach to cluster molecular networks that exhibit similar regulatory interaction topologies. As an illustration, our method can identify (i) common subgraph-mediated signal transduction and/or cellular processes in AML and pancreatic cancer, and (ii) scaffold proteins in gastric cancer and hepatocellular carcinoma; thus, the results suggested that there are common regulation modules for cancer formation. We also found that the underlying substructures of the molecular networks are dominated by irreducible subgraphs; this feature is valid for the three classes of molecular networks we studied. The subgraph-based approach provides a systematic scenario for analyzing, compare and classifying molecular networks with diverse functionalities.

Expression of the GFP-mammalian pleckstrin homology (PH) domain of the phospholipase C δ1 in Saccharomyces cerevisiae BY4741.

Pleckstrin homology (PH) domains are common modules of ∼120 amino acids found in proteins involved in signalling, cytoskeletal organization, membrane transport, and modification of phospholipids. Previous live cell studies have involved the use of the green-fluorescent protein (GFP) labelling of PH-domain of phospholipase C δ1 (PLC δ1) to study the interactions of molecules at the membrane interface. For this study, the aim was to construct and express the GFP-PH domain of PLC δ1 in the Saccharomyces cerevisiae BY4741. The transformants expressing GFP-PH domain of PLC δ1 displayed localised fluorescence to the cell periphery (plasma membrane) while the negative control expressed GFP within the cytoplasm only. No GFP was observed in the non-transformed yeast cells. Thus, this technique could be useful in future molecular interactions studies targeted specifically at the yeast cell membrane interface in live yeast cells.

Prototype hardware design and testing of the global common module for the global trigger subsystem of the ATLAS phase-II upgrade

The High-Luminosity Large Hadron Collider (HL-LHC) will deliver more than ten times the integrated luminosity of the previous runs combined. Meeting its stricter throughput requirements poses new challenges to the Trigger and Data Acquisition (TDAQ) systems of the LHC experiments. Introduced in the framework of the ATLAS experiment’s HL upgrade, the Global Trigger (GT) is a new subsystem which will perform offline-like algorithms on full-granularity calorimeter data. The implementation of the GT’s functionality is firmware-focused and is composed of three layers: multiplexing (or data aggregating), global event processing, and demultiplexing interface to the central trigger processor. Each layer will be composed of several, similar nodes, hosted on replicas of identical hardware, the Global Common Module (GCM), an ATCA front board which is designed to be adopted throughout the entire GT subsystem. This article proceeds from the TWEPP 2021 conference and presents the GCM hardware design, performed in 2020, and focuses on some key results of its extensive testing performed in 2021.

Investigation of interfacial crystallization fouling behaviors and membrane re-functionalization based on a long-distance membrane distillation module

Membrane distillation technique can be applicable for the treatment of highly concentrated aqueous solutions. Crystallization and fouling on the membrane surface are important issues during a membrane distillation process, which significantly affect its separation performance. Crystallization precipitation is a gradual process along the normal direction of the membrane and the feed flow direction. It is not easy to observe the gradual behavior in a relatively small module with a short flow distance in common lab scale modules. In this work, a long-distance membrane distillation module (~2 m in length) was designed and set up to investigate the gradual evolutionary process of crystallization fouling on the porous membrane. A practical model was proposed to estimate the approximate position of crystallization fouling in a long-distance membrane distillation process based on multiple factors including operating parameters and intrinsic membrane properties. A set of proof-of-principle experiments were proceeded to observe and characterize the crystallization fouling behaviors on the membrane surface and inside the pores under various conditions. Furthermore, to re-functionalize the membrane distillation system with the membrane fully covered by salt crystals, flushing the system with pure water was applied. The performance of the re-functionalized system was also investigated.

Transcriptomic, Hormonomic and Metabolomic Analyses Highlighted the Common Modules Related to Photosynthesis, Sugar Metabolism and Cell Division in Parthenocarpic Tomato Fruits during Early Fruit Set

Parthenocarpy, the pollination-independent fruit set, can raise the productivity of the fruit set even under adverse factors during the reproductive phase. The application of plant hormones stimulates parthenocarpy, but artificial hormones incur extra financial and labour costs to farmers and can induce the formation of deformed fruit. This study examines the performance of parthenocarpic mutants having no transcription factors of SlIAA9 and SlTAP3 and sldella that do not have the protein-coding gene, SlDELLA, in tomato (cv. Micro-Tom). At 0 day after the flowering (DAF) stage and DAFs after pollination, the sliaa9 mutant demonstrated increased pistil development compared to the other two mutants and wild type (WT). In contrast to WT and the other mutants, the sliaa9 mutant with pollination efficiently stimulated the build-up of auxin and GAs after flowering. Alterations in both transcript and metabolite profiles existed for WT with and without pollination, while the three mutants without pollination demonstrated the comparable metabolomic status of pollinated WT. Network analysis showed key modules linked to photosynthesis, sugar metabolism and cell proliferation. Equivalent modules were noticed in the famous parthenocarpic cultivars ‘Severianin’, particularly for emasculated samples. Our discovery indicates that controlling the genes and metabolites proffers future breeding policies for tomatoes.

Identifying common driver modules by equilibrating coverage and mutual exclusivity across pan-cancer data

It is of significance to identify common driver modules from pan-cancer data to interpret heterogeneity of cancer, and the accumulated omic data have made it into reality. In this paper, the pan-cancer common driver module identification problem is formulated, which takes the frequency difference among various cancers into account. For solving this problem, a K-nearest neighbors based imputation algorithm KNNImp is firstly devised to infer the variation values for some potential significant missing genes. Secondly, a harmonic mean of coverage and mutual exclusivity based random walk algorithm HMCEwalk is proposed. It weights the integrated PPI network with the harmonic mean of gene coverage scores and mutual exclusion scores among various cancer types, and extracts modules through a random walk process. Experiments were implemented on both simulated data and real biological data. The experimental results on simulated data indicate that given two types of cancers, the HMCEwalk algorithm has a stronger tendency to identify a set of modules which not only mutate in a large proportion of samples of these cancers, but have close proportion of mutated samples for each cancer. The experimental results on biological data show that the presented imputation algorithm does play roles in regaining some important cancer related genes. In comparison with two state-of-the-art identification methods MEXCOwalk and DriveWays, the presented one exhibits competitive performance in most instances in terms of revealing the known cancer genes, producing modules having satisfied coverage and mutual exclusivity for each cancer. Many detected modules engage in the known cancer-related biological pathways. In addition, the presented method does recognize many cancer-associated genes omitted by methods MEXCOwalk and DriveWays.

No more DoS? An empirical study on defense techniques for web server Denial of Service mitigation

Denial-of-Service (DoS) attacks are becoming increasingly common and undermine the availability of widely used web servers. Even if DoS attacks cannot be rendered completely harmless, ready-to-use defense modules and solutions to mitigate their effect are highly beneficial for site administrators. Unfortunately, there is a lack of measurement studies that explore the pros and cons of common DoS web server defense modules in order to understand their limitations and to drive practitioners’ choices.This paper presents an empirical study of the ubiquitous Apache web server, with an assessment of two well-known pluggable defense modules and an enlargement technique that provides the server with additional resources. Measurements are based on a mixture of flooding and slow DoS attacks. The experimentation shows that, in spite of the large availability of pluggable security modules that can be usefully deployed in practice, there is not a bulletproof defense solution to mitigate the DoS attacks in hand. The findings of our analysis can be useful to support the deployment of proper defense mechanisms, as well as the development of robust and effective solutions for DoS protection.

Characterization and Validation of ceRNA-Mediated Pathway-Pathway Crosstalk Networks Across Eight Major Cardiovascular Diseases.

Pathway analysis is considered as an important strategy to reveal the underlying mechanisms of diseases. Pathways that are involved in crosstalk can regulate each other and co-regulate downstream biological processes. Furthermore, some genes in the pathways can function with other genes via the relationship of the competing endogenous RNA (ceRNA) mechanism, which has also been demonstrated to play key roles in cellular biology. However, the comprehensive analysis of ceRNA-mediated pathway crosstalk is lacking. Here, we constructed the landscape of the ceRNA-mediated pathway–pathway crosstalk of eight major cardiovascular diseases (CVDs) based on sequencing data from ∼2,800 samples. Some common features shared by numerous CVDs were uncovered. A fraction of the pathway–pathway crosstalk was conserved in multiple CVDs and a core pathway–pathway crosstalk network was identified, suggesting the similarity of pathway–pathway crosstalk among CVDs. Experimental evidence also demonstrated that the pathway crosstalk was functioned in CVDs. We split all hub pathways of each pathway–pathway crosstalk network into three categories, namely, common hubs, differential hubs, and specific hubs, which could highlight the common or specific biological mechanisms. Importantly, after a comparison analysis of the hub pathways of networks, ∼480 hub pathway-induced common modules were identified to exert functions in CVDs broadly. Moreover, we performed a random walk algorithm on the hub pathway-induced sub-network and identified 23 potentially novel CVD-related pathways. In summary, our study revealed the potential molecular regulatory mechanisms of ceRNA crosstalk in pathway–pathway crosstalk levels and provided a novel routine to investigate the pathway–pathway crosstalk in cardiology. All CVD pathway–pathway crosstalks are provided in http://www.licpathway.net/cepathway/index.html.

Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding

Global and local relational reasoning enable scene understanding models to perform human-like scene analysis and understanding. Scene understanding enables better semantic segmentation and object-to-object interaction detection. In the medical domain, a robust surgical scene understanding model allows the automation of surgical skill evaluation, real-time monitoring of surgeon's performance and post-surgical analysis. This paper introduces a globally-reasoned multi-task surgical scene understanding model capable of performing instrument segmentation and tool-tissue interaction detection. Here, we incorporate global relational reasoning in the latent interaction space and introduce multi-scale local (neighborhood) reasoning in the coordinate space to improve segmentation. Utilizing the multi-task model setup, the performance of the visual-semantic graph attention network in interaction detection is further enhanced through global reasoning. The global interaction space features from the segmentation module are introduced into the graph network, allowing it to detect interactions based on both node-to-node and global interaction reasoning. Our model reduces the computation cost compared to running two independent single-task models by sharing common modules, which is indispensable for practical applications. Using a sequential optimization technique, the proposed multi-task model outperforms other state-of-the-art single-task models on the MICCAI endoscopic vision challenge 2018 dataset. Additionally, we also observe the performance of the multi-task model when trained using the knowledge distillation technique. The official code implementation is made available in GitHub.

Heterogeneous Ground and Air Platforms, Homogeneous Sensing: Team CSIRO Data61’s Approach to the DARPA Subterranean Challenge

Heterogeneous teams of robots, leveraging a balance between autonomy and human interaction, bring powerful capabilities to the problem of exploring dangerous, unstructured subterranean environments. Here we describe the solution developed by Team CSIRO Data61, consisting of CSIRO, Emesent, and Georgia Tech, during the DARPA Subterranean Challenge. These presented systems were fielded in the Tunnel Circuit in August 2019, the Urban Circuit in February 2020, and in our own Cave event, conducted in September 2020. A unique capability of the fielded team is the homogeneous sensing of the platforms utilized, which is used to obtain a decentralized multi-agent SLAM solution on each platform (both ground agents and UAVs) using peer-to-peer communications. This approach enabled a shift in focus from constructing a pervasive communications network to relying on multi-agent autonomy, motivated by experiences in early circuit events. These experiences also showed the surprising capability of rugged tracked platforms for challenging terrain, which in turn led to the heterogeneous team structure based on a BIA5 OzBot Titan ground robot and an Emesent Hovermap UAV, supplemented by smaller tracked or legged ground robots. The ground agents use a common CatPack perception module, which allowed reuse of the perception and autonomy stack across all ground agents with minimal adaptation.

Dual Modality Collaborative Learning for Cross-Source Remote Sensing Retrieval

Content-based remote sensing (RS) image retrieval (CBRSIR) is a critical way to organize high-resolution RS (HRRS) images in the current big data era. The increasing volume of HRRS images from different satellites and sensors leads to more attention to the cross-source CSRSIR (CS-CBRSIR) problem. Due to the data drift, one crucial problem in CS-CBRSIR is the modality discrepancy. Most existing methods focus on finding a common feature space for various HRRS images to address this issue. In this space, their similarity relations can be measured directly to obtain the cross-source retrieval results straight. This way is feasible and reasonable, however, the specific information corresponding to HRRS images from different sources is always ignored, limiting retrieval performance. To overcome this limitation, we develop a new model for CS-CBRSIR in this paper named dual modality collaborative learning (DMCL). To fully explore the specific information from diverse HRRS images, DMCL first introduces ResNet50 as the feature extractor. Then, a common space mutual learning module is developed to map the specific features into a common space. Here, the modality discrepancy is reduced from the aspects of features and their distributions. Finally, to supplement the specific knowledge to the common features, we develop modality transformation and the dual-modality feature learning modules. Their function is to transmit the specific knowledge from different sources mutually and fuse the specific and common features adaptively. The comprehensive experiments are conducted on a public dataset. Compared with many existing methods, the behavior of our DMCL is stronger. These encouraging results for a public dataset indicate that the proposed DMCL is useful in CS-CBRSIR tasks.

A Comprehensive Study of Module Layouts for Silicon Solar Cells Under Partial Shading

Integrated applications for solar energy production becomes increasingly important. The electrification of car bodies and building facades are only two prominent examples. In such applications shading becomes a challenging problem, since the classic serial interconnection of solar cells in terms of power output is highly vulnerable to partial shading. In this article, we investigate the three most common module layouts in the market (conventional, butterfly, and shingle string) and add a fourth layout (shingle matrix) to be introduced to the market in the future. We discuss an approach to cluster shadings occurring in urban surroundings into basic shapes like “rectangular” and “random”. Choosing a Monte Carlo technique in combination with latin hypercube sampling (LHS), we consider more than 3000 scenarios in total. For the evaluation of the scenarios, we conduct circuit simulations using LTspice. Furthermore, we define a normalization base, which considers only partial shading as a quantitative baseline for comparison. Our results show, that already for 200–400 scenarios the obtained output values stabilize. Among the investigated module layouts, the shingle matrix interconnection achieves the highest score, followed by a shingle string, half-cell butterfly and the conventional full-cell layout.