Ideal Model Research Articles

Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

<div>Cloud computing (CC) is a rapidly developing IT approach with intrusion detection system being a crucial tool for safeguarding virtual networks and machines from potential threats, thereby mitigating security concerns in the cloud environment. The intrusion detection system (IDS) system demands significant improvements, primarily based on optimizing performance and bolstering security measures. This research aims to implement an IDS in cloud computing utilizing deep learning (DL) method. The DL model is a promising technique and is widely used to detect intrusions. The implemented hierarchical long short-term memory (HLSTM) method’s performance is evaluated for feature selection through variance threshold-based regression (VTR) on two IDS network datasets: Bot-IoT and network security lab-knowledge discovery and data mining (NSL-KDD). This paper concludes the use of an intrusion detection network resulting in high security and performance. Moreover, the implemented method on the NSL-KDD and Bot-IoT datasets obtains respective accuracies of 99.50% and 0.995. It is compared with the existing methods namely, ensemble ID model for CC utilizing DL, LeNet, fuzzy deep neural network with a Honey Bader algorithm for privacy-preserving ID, and improved metaheuristics with a fuzzy logic-based IDS for cloud security, and beluga whale-tasmanian devil optimization based on deep convolutional neural network (CNN) with TL, chronological slap swarm algorithm-based deep belief network (DBN), and dragonfly improved invasive weed optimization-based Shepard CNN.</div>

Investigation of The Effect of Phase Angle on The Aerodynamic Performance of Three-Bladed Helical Savonius Wind Turbines Using Computational Fluid Dynamics Method

In this study, the effect of the phase angle of the semicircular blades between the stages on the aerodynamic performance of the three-blade, double-stage helical Savonius wind turbines (HSWT), which are vertical axis wind turbines, was examined by the computational fluid dynamics (CFD) method. In the Savonius wind turbine system, the drag force effect provides the most significant contribution to aerodynamic performance. Performance improvements that can affect drag force can provide significant advantages. For this purpose, three-bladed double-stage helical Savonius rotors with eccentricity L/H=1/2 and phase angles of the semicircular blades between the stages Ɵ=0°, 45° and 90° were designed. Solidworks R2018 is used for designs and ANSYS-Fluent 18.1 programs are used for analysis. The turbine with L/H=1/2 and Ɵ=90° was produced on a 3D printer and tested experimentally. Experiments were carried out in the T-490 air tunnel. The results obtained were used as a reference for numerical analysis and the ideal turbine model was tried to be determined. 10 different air velocities ranging between 3.83-20.35 m/s were used in the numerical analysis. As a result, an 11.64% increase in the drag force was observed by changing the phase angle from 0° to 45° in HSWT 1/2s. By changing the phase angle from 0° to 45° in HSWT 1/2s, a 10.77% rise in the drag coefficient was observed. It has been evaluated that the HSWT efficiency improved with the increment in drag force.

A comparison between centralized and asynchronous federated learning approaches for survival outcome prediction using clinical and PET data from non-small cell lung cancer patients

Background and Objective: Survival analysis plays an essential role in the medical field for optimal treatment decision-making. Recently, survival analysis based on the deep learning (DL) approach has been proposed and is demonstrating promising results. However, developing an ideal prediction model requires integrating large datasets across multiple institutions, which poses challenges concerning medical data privacy.Methods: In this paper, we propose FedSurv, an asynchronous federated learning (FL) framework designed to predict survival time using clinical information and positron emission tomography (PET)-based features. This study used two datasets: a public radiogenic dataset of non-small cell lung cancer (NSCLC) from the Cancer Imaging Archive (RNSCLC), and an in-house dataset from the Chonnam National University Hwasun Hospital (CNUHH) in South Korea, consisting of clinical risk factors and F-18 fluorodeoxyglucose (FDG) PET images in NSCLC patients. Initially, each dataset was divided into multiple clients according to histological attributes, and each client was trained using the proposed DL model to predict individual survival time. The FL framework collected weights and parameters from the clients, which were then incorporated into the global model. Finally, the global model aggregated all weights and parameters and redistributed the updated model weights to each client. We evaluated different frameworks including single-client-based approach, centralized learning and FL.Results: We evaluated our method on two independent datasets. First, on the RNSCLC dataset, the mean absolute error (MAE) was 490.80±22.95 d and the C-Index was 0.69±0.01. Second, on the CNUHH dataset, the MAE was 494.25±40.16 d and the C-Index was 0.71±0.01. The FL approach achieved centralized method performance in PET-based survival time prediction and outperformed single-client-based approaches.Conclusions: Our results demonstrated the feasibility and effectiveness of employing FL for individual survival prediction in NSCLC patients, using clinical information and PET-based features.

Magnetically driven capsules with multimodal response and multifunctionality for biomedical applications

Untethered capsules hold clinical potential for the diagnosis and treatment of gastrointestinal diseases. Although considerable progress has been achieved recently in this field, the constraints imposed by the narrow spatial structure of the capsule and complex gastrointestinal tract environment cause many open-ended problems, such as poor active motion and limited medical functions. In this work, we describe the development of small-scale magnetically driven capsules with a distinct magnetic soft valve made of dual-layer ferromagnetic soft composite films. A core technological advancement achieved is the flexible opening and closing of the magnetic soft valve by using the competitive interactions between magnetic gradient force and magnetic torque, laying the foundation for the functional integration of both drug release and sampling. Meanwhile, we propose a magnetic actuation strategy based on multi-frequency response control and demonstrate that it can achieve effective decoupled regulation of the capsule’s global motion and local responses. Finally, through a comprehensive approach encompassing ideal models, animal ex vivo models, and in vivo assessment, we demonstrate the versatility of the developed magnetic capsules and their multiple potential applications in the biomedical field, such as targeted drug delivery and sampling, selective dual-drug release, and light/thermal-assisted therapy.

DNA tetrahedron as a carrier of doxorubicin for metastatic breast cancer treatment

AbstractMetastatic breast cancer is a significant clinical challenge calling for novel and efficient therapeutic approaches. DNA tetrahedron, a highly programmable nanocage, offers some promising attributes including biocompatibility, stability, and functionalization making it an attractive candidate for drug delivery. In this study, we have explored the potential of DNA tetrahedron as a carrier of doxorubicin, a DNA and RNA synthesis‐inhibiting chemotherapy drug. We have encapsulated doxorubicin in DNA tetrahedron (TD : Dox) and subsequently focused on metastatic breast cancer cells for the effect of the same. We showed that TD : Dox has the potential to inhibit the migration of cancerous cells in the 2D model and inhibit the invasion of tumor cells in the 3D model as well. This system also can be uptaken in in vivo zebrafish model as well. Overall, this study promises the TD : Dox system as an ideal drug delivery model and a viable approach for metastatic breast cancer treatment.

Ion transfer mediated by TEMPO in ionophore-doped thin films for multi-ion sensing by cyclic voltammetry

We report here on the development of thin-layer ion-selective membranes containing lipophilic TEMPO as a phase-transfer redox mediator for the simultaneous detection of non-redoxactive ions. This redox probe was recently introduced by our group and provides ideal ion-transfer waves when the membrane is interrogated by cyclic voltammetry. To perform multianalyte detection in the same sensing film, plasticized PVC-based membranes were doped with lithium and potassium ionophores in addition to a lipophilic cation-exchanger. The ionophores allow for ion discrimination owing to the different ionophore-cation complexation constants and the oxidation of TEMPO to the oxoammonium form results in the selective transfer of lithium and potassium at different potentials. The resulting voltammograms have half-peak widths of 100 and 102 mV, and the peak separation between anodic and cathodic scans is 8 and 9 mV for lithium and potassium, respectively, close to theoretical expectations. High peak resolution was observed, and the ion-transfer waves are still distinguishable when the ion activities differ by three orders of magnitude. These parameters are remarkably better than those obtained with other redox probes, which is important for multianalyte detection in the same voltammetric scan. Optimized membranes showed independent Nernstian shifts (slopes of 59.23 mV and 54.8 mV for K+ and Li+, respectively) of the peak position for increasing ion concentrations. An idealized model for two ionophore-based membranes combining redox and phase-boundary potentials was applied to the proposed system with excellent correlation. Potassium and lithium ions were simultaneously detected in undiluted human serum samples with good accuracy and precision.

Dynamics of Chromatin Opening across Larval Development in the Urochordate Ascidian Ciona savignyi.

Ascidian larvae undergo tail elongation and notochord lumenogenesis, making them an ideal model for investigating tissue morphogenesis in embryogenesis. The cellular and mechanical mechanisms of these processes have been studied; however, the underlying molecular regulatory mechanism remains to be elucidated. In this study, assays for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA sequencing (RNA-seq) were applied to investigate potential regulators of the development of ascidian Ciona savignyi larvae. Our results revealed 351 and 138 differentially accessible region genes through comparisons of ATAC-seq data between stages 21 and 24 and between stages 24 and 25, respectively. A joint analysis of RNA-seq and ATAC-seq data revealed a correlation between chromatin accessibility and gene transcription. We further verified the tissue expression patterns of 12 different genes. Among them, Cs-matrix metalloproteinase 24 (MMP24) and Cs-krüppel-like factor 5 (KLF5) were highly expressed in notochord cells. Functional assay results demonstrated that both genes are necessary for notochord lumen formation and expansion. Finally, we performed motif enrichment analysis of the differentially accessible regions in different tailbud stages and summarized the potential roles of these motif-bearing transcription factors in larval development. Overall, our study found a correlation between gene expression and chromatin accessibility and provided a vital resource for understanding the mechanisms of the development of ascidian embryos.

Influence of the Presence of Sulfur and Oxygen Atoms on Molecular, Thermodynamic and Transport Properties in Hydrocarbon Mixtures

Introduction: Crude oil is a complex blend of various hydrocarbon families, with compositions that vary depending on the source well and exploitation duration. To categorize its constituents, SARA analysis divides them into saturated, aromatic, resins, and asphaltenes. Heavy asphaltene- rich crude oils can present challenges like viscosity and pipeline blockages, which are often addressed with viscosity-reducing additives. However, a theoretical framework explaining how these additives affect crude oil is lacking, relying primarily on empirical observations. To optimize these additives, it is crucial to understand the underlying chemical and physical processes. This study hypothesizes that asphaltenic crude oils influence viscosity through colloidal properties linked to molecular interactions. Methods: The research aimed to analyze the impact of sulfur in asphaltenes and oxygen in flow improvers on the transport properties of an idealized crude oil, with the goal of predicting additive feasibility. A methodology that combined computational quantum chemistry and statistical thermodynamics was used. An idealized model of crude oil was created, consisting of non-polar alkanes and polar asphaltenes with sulfur atoms. A flow improver was simulated with an aromatic-aliphatic structure containing oxygen and hydroxyl groups, and viscosity was calculated. Results: This study assessed the transport properties of the mixture using principles of statistical thermodynamics. The theoretical insights revealed that reducing viscosity in asphaltene-rich crude oils with additives depends on several critical factors, including the formation of the dispersed phase, the reduced viscosity of the additive, and the effects of dilution. The research identified a strong link between the enhanced effectiveness of these additives and their structural and molecular properties. Conclusion: The theoretical results suggest that additives that act as viscosity reducers in asphalt crudes achieve optimal performance when they possess both higher polarity and reduced viscosity.

Intrusion Detection System based on Chaotic Opposition for IoT Network

The rapid advancement of network technologies and protocols has fueled the widespread endorsement of the Internet of Things (IoT) in numerous domains, including everyday life, healthcare, industries, agriculture, and more. However, this rapid growth has also given rise to numerous security concerns within IoT systems. Consequently, privacy and security have become paramount issues in the IoT framework. Due to the heterogeneous data produced by smart IoT devices, traditional intrusion detection system doesn't work well with IoT system. The massive volume of heterogeneous data has several irrelevant, redundant, and unnecessary features which lead to high computation time and low accuracy of IDS. Therefore, to tackle these challenges, this paper presents a novel metaheuristic-based IDS model for the IoT systems. The chaotic opposition-based Harris Hawk optimization (CO-IHHO) algorithm is used to perform the feature selection of data traffic. The chosen features are subsequently inputted into a machine learning (ML) classifier to detect network traffic intrusions. The performance of the CO-IHHO based IDS model is verified against the BoT-IoT dataset. Experimental findings reveal that CO-IHHO-DT achieves the maximal accuracy of 99.65% for multiclass classification and 100% for binary classification, and minimal computation time of 31.34 sec for multiclass classification and 133.54 sec for binary classification.

Establishment and characterization of turtle liver organoids provides a potential model to decode their unique adaptations

Painted turtles are remarkable for their freeze tolerance and supercooling ability along with their associated resilience to hypoxia/anoxia and oxidative stress, rendering them an ideal biomedical model for hypoxia-induced injuries (including strokes), tissue cooling during surgeries, and organ cryopreservation. Yet, such research is hindered by their seasonal reproduction and slow maturation. Here we developed and characterized adult stem cell-derived turtle liver organoids (3D self-assembled in vitro structures) from painted, snapping, and spiny softshell turtles spanning ~175My of evolution, with a subset cryopreserved. This development is, to the best of our knowledge, a first for this vertebrate Order, and complements the only other non-avian reptile organoids from snake venom glands. Preliminary characterization, including morphological, transcriptomic, and proteomic analyses, revealed organoids enriched in cholangiocytes. Deriving organoids from distant turtles and life stages demonstrates that our techniques are broadly applicable to chelonians, permitting the development of functional genomic tools currently lacking in herpetological research. Such platform could potentially support studies including genome-to-phenome mapping, gene function, genome architecture, and adaptive responses to climate change, with implications for ecological, evolutionary, and biomedical research.

Characterization of Firmiana danxiaensis plastomes and comparative analysis of Firmiana: insight into its phylogeny and evolution

BackgroundFirmiana danxiaensis is a critically endangered and ecologically important tree currently only found in four locations in Danxia or Karst habitats in northern Guangdong Province, China. The specialized habitat preference makes it an ideal model species for study of adaptive evolution. Meanwhile, the phylogenetic relationships of F. danxiaensis in four locations under two landforms are unclear. Therefore, we sequenced its complete chloroplast (cp.) genomes and conducted comprehensive interspecific and intrageneric plastome studies.ResultsThe F. danxiaensis plastomes in four locations showed a typical quadripartite and circular structure that ranged from 160,832 to 161,206 bp in size, with 112 unique genes encoded. Comparative genomics showed that the plastomes of F. danxiaensis were relatively conserved with high similarity of genome organization, gene number, GC content and SSRs. While the genomes revealed higher biased codon preferences in Karst habitat than those in Danxia habitats. Eighteen and 11 divergent hotpots were identified at interspecific and intrageneric levels for species identification and further phylogenetic studies. Seven genes (clpP, accD, ccsA, ndhH, rpl20, rpoC2, and rps4) were under positive selection and may be related to adaptation. Phylogenetic analysis revealed that F. danxiaensis is sister to F. major and F. simplex. However, the interspecific relationships are not consistent with the habitat types.ConclusionsThe characteristics and interspecific relationship of F. danxiaensis plastomes provide new insights into further integration of geographical factors, environmental factors, and genetic variations on the genomic study of F. danxiaensis. Together, our study will contribute to the study of species identification, population genetics, and conservation biology of F. danxiaensis.

β-catenin inhibition disrupts the homeostasis of osteogenic/adipogenic differentiation leading to the development of glucocorticoid-induced osteonecrosis of the femoral head

Glucocorticoid-induced osteonecrosis of the femoral head (GONFH) is a common refractory joint disease characterized by bone damage and the collapse of femoral head structure. However, the exact pathological mechanisms of GONFH remain unknown. Here, we observed abnormal osteogenesis and adipogenesis associated with decreased β-catenin in the necrotic femoral head of GONFH patients. In vivo and in vitro studies further revealed that glucocorticoid exposure disrupted osteogenic/adipogenic differentiation of bone marrow mesenchymal cells (BMSCs) by inhibiting β-catenin signaling in glucocorticoid-induced GONFH rats. Col2+ lineage largely contributes to BMSCs and was found an osteogenic commitment in the femoral head through 9 mo of lineage trace. Specific deletion of β-catenin gene (Ctnnb1) in Col2+ cells shifted their commitment from osteoblasts to adipocytes, leading to a full spectrum of disease phenotype of GONFH in adult mice. Overall, we uncover that β-catenin inhibition disrupting the homeostasis of osteogenic/adipogenic differentiation contributes to the development of GONFH and identify an ideal genetic-modified mouse model of GONFH.

Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

BackgroundDomesticated pigs serve as an ideal animal model for biomedical research and also provide the majority of meat for human consumption in China. Porcine intramuscular fat content associates with human health and diseases and is essential in pork quality. The molecular mechanisms controlling lipid metabolism and intramuscular fat accretion across tissues in pigs, and how these changes in response to pig breeds, remain largely unknown.ResultsWe surveyed the tissue-resident cell types of the porcine jejunum, colon, liver, and longissimus dorsi muscle between Lantang and Landrace breeds by single-cell RNA sequencing. Combining lipidomics and metagenomics approaches, we also characterized gene signatures and determined key discriminating markers of lipid digestibility, absorption, conversion, and deposition across tissues in two pig breeds. In Landrace, lean-meat swine mainly exhibited breed-specific advantages in lipid absorption and oxidation for energy supply in small and large intestinal epitheliums, nascent high-density lipoprotein synthesis for reverse cholesterol transport in enterocytes and hepatocytes, bile acid formation, and secretion for fat emulsification in hepatocytes, as well as intestinal-microbiota gene expression involved in lipid accumulation product. In Lantang, obese-meat swine showed a higher synthesis capacity of chylomicrons responsible for high serum triacylglycerol levels in small intestinal epitheliums, the predominant characteristics of lipid absorption in muscle tissue, and greater intramuscular adipcytogenesis potentials from muscular fibro-adipogenic progenitor subpopulation.ConclusionsThe findings enhanced our understanding of the cellular biology of lipid metabolism and opened new avenues to improve animal production and human diseases.D7xkEMpXE5aNHjWuhXhcDpVideo

Can we design food taxes to reduce agricultural greenhouse gas emissions in China? A perspective from animal protein intake of low-income populations

PurposeStudies on developed economies showed that imposing taxes on animal-based foods could effectively reduce agricultural greenhouse gas emissions (AGHGEs), while this taxation may not be appropriate in developing countries due to the complex nutritional status across income classes. Hence, this study aims to explore optimal tax rate levels considering both emission reduction and nutrient intake, and examine the heterogenous effects of taxation across various income classes in urban and rural China.Design/methodology/approachThe authors estimated the Quadratic Almost Ideal Demand System model to calculate the price elasticities for eight food groups, and performed three simulations to explore the relative optimal tax regions via the relationships between effective animal protein intake loss and AGHGE reduction by taxes.FindingsThe results showed that the optimal tax rate bands can be found, depending on the reference levels of animal protein intake. Designing taxes on beef, mutton and pork could be a preliminary option for reducing AGHGEs in China, but subsidy policy should be designed for low-income populations at the same time. Generally, urban residents have more potential to reduce AGHGEs than rural residents, and higher income classes reduce more AGHGEs than lower income classes.Originality/valueThis study fills the gap in the literature by developing the methods to design taxes on animal-based foods from the perspectives of both nutrient intake and emission reduction. This methodology can also be applied to analyze food taxes and GHGE issues in other developing countries.

Application of Caenorhabditis elegans in Neurotoxicology

Neurotoxicological studies usually use mammals similar to humans as model animals for research. Although the high similarity with humans makes it a good choice, the complicated nervous system and the difficulty of feeding and operation have become unfavorable conditions in the research. And the longer life span greatly extends the research cycle to study long-term toxicity. The problem can be solved using Caenorhabditis elegans (C.elegans)as experimental animals. C.elegans have a fast growth rate, short research period, and easy culture and preservation compared with mammals used to study Neurotoxicology. It has the advantages of strong genetic stability, gene conservation, small individual differences, simple structure, and easy observation and operation. So, C. elegans is an ideal model for long-term toxicity study.

Changes in the structure of household expenditure in urban and rural China

ABSTRACT In the last two decades, China has witnessed a remarkable change in household expenditure structure following the rapid growth of household income. Using provincial-level panel data from 2001 to 2019 and the Quadratic Almost Ideal Demand System model, we derive the expenditure elasticities, as well as uncompensated and compensated price elasticities, for urban and rural households. Our results suggest that food and clothing are ‘necessity’ goods, whereas residence, education, and transportation are ‘luxury’ goods for both urban and rural households. Household facilities and medical services are necessities for urban households, while they are luxuries for rural households. All own-price elasticity estimates are negative, indicating that no Giffen goods exist. We also observe temporal changes in the household expenditure structure over China’s Five-Year Plan cycles. Finally, we find significant differences in the own-price elasticity across the eastern, central, and western regions with varying levels of economic development.

Development of a non-programmable sperm cryopreservation technique in dwarf surfclams Mulinia lateralis–a potential model species for bivalve research

Sperm cryopreservation technique has been published in many farmed bivalve species. One of the key factors preventing its application in aquaculture and/or cryobanking is the knowledge gap on the performance of resultant progeny at late developmental stages and subsequent generations. An effective strategy to overcome these challenges is to use a model species with a short generation interval, such as the dwarf surfclam Mulinia lateralis (three months). This study evaluated the parameters key to the development of a non-programmable sperm cryopreservation technique in this species, with a D-stage larval rate similar to control being achieved when the sperm were cryopreserved under the conditions (cryoprotectant agent: 8% dimethyl sulfoxide; equilibration period: 10 min; rack height: 4 cm; thawing temperature: 60°C and sperm to egg ratio: 1100:1) optimized. This technique is the most common method applied in bivalve and the results from this study were all within the ranges published for other bivalve species, indicating this species would be an ideal bivalve model species for addressing cryopreservation evaluation issues that need a long extended time to collect data and/or challenging field experiments.

Analysis of the correlation mechanism between geometric parameters and the thermal environment of Xi'an's summer outdoor commercial pedestrian streets.

Intensive urban development has resulted in the degradation of the urban thermal environment in most regions. There is a growing consensus on the need to enhance urban thermal comfort through well-designed forms, especially in open spaces like urban canyons. To address this, our study focuses on Xi'an's commercial pedestrian streets, employing K-means clustering analysis to create 32 representative models based on actual scenes, capturing their textural characteristics. Simultaneously, 11 geometric indicators (2D/3D) were chosen to quantify the canyon's geometric form. We assessed the spatial and temporal distribution differences in the thermal environment across these models using Envi-met simulation. Finally, Spearman correlation analysis was employed to examine the correlation and significance of the two sets of indicators, culminating in formulating an ideal model. The findings reveal that (1) wind conditions are predominantly influenced by the canyon's geometric form, followed by solar radiation and temperature, with the lowest relative humidity change amplitude among the assessed thermal parameters. (2) Among the 11 geometric form indicators, 3D indicators correlate more significantly with thermal environment parameters than 2D indicators. Specifically, street orientation significantly impacts the thermal environment, Build-To-Line Rat holds greater significance than interface density, and both building shape coefficient and block surface ratio are significantly correlated with air temperature and wind speed, with a weaker correlation to solar radiation. (3) In the Xi'an region, courtyards oriented north-south demonstrate a more favorable trend in the thermal environment.

Distinct regulatory networks control toxin gene expression in elapid and viperid snakes

BackgroundVenom systems are ideal models to study genetic regulatory mechanisms that underpin evolutionary novelty. Snake venom glands are thought to share a common origin, but there are major distinctions between venom toxins from the medically significant snake families Elapidae and Viperidae, and toxin gene regulatory investigations in elapid snakes have been limited. Here, we used high-throughput RNA-sequencing to profile gene expression and microRNAs between active (milked) and resting (unmilked) venom glands in an elapid (Eastern Brown Snake, Pseudonaja textilis), in addition to comparative genomics, to identify cis- and trans-acting regulation of venom production in an elapid in comparison to viperids (Crotalus viridis and C. tigris).ResultsAlthough there is conservation in high-level mechanistic pathways regulating venom production (unfolded protein response, Notch signaling and cholesterol homeostasis), there are differences in the regulation of histone methylation enzymes, transcription factors, and microRNAs in venom glands from these two snake families. Histone methyltransferases and transcription factor (TF) specificity protein 1 (Sp1) were highly upregulated in the milked elapid venom gland in comparison to the viperids, whereas nuclear factor I (NFI) TFs were upregulated after viperid venom milking. Sp1 and NFI cis-regulatory elements were common to toxin gene promoter regions, but many unique elements were also present between elapid and viperid toxins. The presence of Sp1 binding sites across multiple elapid toxin gene promoter regions that have been experimentally determined to regulate expression, in addition to upregulation of Sp1 after venom milking, suggests this transcription factor is involved in elapid toxin expression. microRNA profiles were distinctive between milked and unmilked venom glands for both snake families, and microRNAs were predicted to target a diversity of toxin transcripts in the elapid P. textilis venom gland, but only snake venom metalloproteinase transcripts in the viperid C. viridis venom gland. These results suggest differences in toxin gene posttranscriptional regulation between the elapid P. textilis and viperid C. viridis.ConclusionsOur comparative transcriptomic and genomic analyses between toxin genes and isoforms in elapid and viperid snakes suggests independent toxin regulation between these two snake families, demonstrating multiple different regulatory mechanisms underpin a venomous phenotype.

Enhancing Additive Restoration of Damaged Polymer Curved Surfaces through Compensated Support Beam Utilization

As additive manufacturing advances, it offers a cost-effective avenue for structurally repairing components. However, a challenge arises in the additive repair of suspended damaged surfaces, primarily due to gravitational forces. This can result in excessive deformation during the repair process, rendering the formation of proper repair impractical and leading to potential failure. In light of this rationale, conventional repair techniques are impractical for extensively damaged surfaces. Thus, this article proposes a novel repair methodology that is tailored to address large-area damage. Moreover, and departing from conventional practices involving the addition and subsequent subtraction of materials for precision machining, the proposed process endeavors to achieve more precise repair outcomes in a single operation. This paper introduces an innovative repair approach employing fused deposition modeling (FDM) to address the complexities associated with the repair of damaged polymer material parts. To mitigate geometric errors in the repaired structural components, beams with minimal deformation are printed using a compensation method. These beams then serve as supports for overlay printing. The paper outlines a methodology by which to determine the distribution of these supporting beams based on the shape of the damaged surface. A beam deformation model is established, and the printing trajectory of the compensated beam is calculated according to this model. Using the deformation model, the calculated deformation trajectories exhibit excellent fitting with the experimentally collected data, with an average difference between the two of less than 0.3 mm, validating the accuracy of the suspended beam deformation model. Based on the statistical findings, the maximum average deformation of the uncompensated sample is approximately 5.20 mm, whereas the maximum deformation of the sampled point after compensation measures around 0.15 mm. Consequently, the maximum deformation of the printed sample post-compensation is mitigated to roughly 3% of its pre-compensation magnitude. The proposed method in this paper was applied to the repair experiment of damaged curved surface components. A comparison was made between the point cloud data of the repaired surface and the ideal model of the component, with the average distance between them serving as the repair error metric. The mean distance between the point clouds of the repaired parts using the proposed repair strategy is 0.197 mm and the intact model surface is noticeably less than the mean distance corresponding to direct repair, at 0.830 mm. The repair error with compensatory support beams was found to be 76% lower than that without compensatory support beams. The surface without compensatory support beams exhibited gaps, while the surface with compensatory support beams appeared dense and complete. Experimental results demonstrate the effectiveness of the proposed method in significantly reducing the geometric errors in the repaired structural parts. The outcomes of the FDM repair method are validated through these experiments, affirming its practical efficacy. It is noteworthy that, although only PLA material was used in this study, the proposed method is general and effective for other polymer materials. This holds the potential to significantly reduce costs for the remanufacturing of widely used polymers.